Control and Mechatronics

The Industrial Electronics HandbookS E c o n d E d I T I o n

control and mechatronIcs

Edited by

Bogdan M. WilamowskiJ. david Irwin

2011 by Taylor and Francis Group, LLC

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Control and mechatronics / editors, Bogdan M. Wilamowski and J. David Irwin.p. cm.

A CRC title.Includes bibliographical references and index.ISBN 978-1-4398-0287-8 (alk. paper)1. Mechatronics. 2. Electronic control. 3. Servomechanisms. I. Wilamowski, Bogdan M. II. Irwin,

J. David. III. Title.

TJ163.12.C67 2010629.8043--dc22 2010020062

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vii

Contents

Preface....................................................................................................................... xiAcknowledgments................................................................................................... xiiiEditorial.Board..........................................................................................................xvEditors..................................................................................................................... xviiContributors............................................................................................................ xxi

Part I Control System analysis

. 1. Nonlinear.Dynamics........................................................................................1-1Istvn Nagy and Zoltn Sto

. 2. Basic.Feedback.Concept.................................................................................. 2-1Tong Heng Lee, Kok Zuea Tang, and Kok Kiong Tan

. 3. Stability.Analysis............................................................................................. 3-1Naresh K. Sinha

. 4. Frequency-Domain.Analysis.of.Relay.Feedback.Systems.............................. 4-1Igor M. Boiko

. 5. Linear.Matrix.Inequalities.in.Automatic.Control......................................... 5-1Miguel Bernal and Thierry Marie Guerra

. 6. Motion.Control.Issues..................................................................................... 6-1Roberto Oboe, Makoto Iwasaki, Toshiyuki Murakami, and Seta Bogosyan

. 7. New.Methodology.for.Chatter.Stability.Analysis.in.Simultaneous.Machining........................................................................................................7-1Nejat Olgac and Rifat Sipahi

Part II Control System Design

. 8. Internal.Model.Control................................................................................... 8-1James C. Hung

. 9. Dynamic.Matrix.Control................................................................................ 9-1James C. Hung


viii Contents

.10. PID.Control....................................................................................................10-1James C. Hung and Joel David Hewlett

.11. Nyquist.Criterion........................................................................................... 11-1James R. Rowland

.12. Root.Locus.Method........................................................................................12-1Robert J. Veillette and J. Alexis De Abreu Garcia

.13. Variable.Structure.Control.Techniques.........................................................13-1Asif abanovic and Nadira abanovic-Behlilovic

.14. Digital.Control...............................................................................................14-1Timothy N. Chang and John Y. Hung

.15. Phase-Lock-Loop-Based.Control...................................................................15-1Guan-Chyun Hsieh

.16. Optimal.Control.............................................................................................16-1Victor M. Becerra

.17. Time-Delay.Systems....................................................................................... 17-1Emilia Fridman

.18. AC.Servo.Systems...........................................................................................18-1Yong Feng, Liuping Wang, and Xinghuo Yu

.19. Predictive.Repetitive.Control.with.Constraints...........................................19-1Liuping Wang, Shan Chai, and Eric Rogers

.20. Backstepping.Control.....................................................................................20-1Jing Zhou and Changyun Wen

.21. Sensors............................................................................................................ 21-1Tiantian Xie and Bogdan M. Wilamowski

.22. Soft.Computing.Methodologies.in.Sliding.Mode.Control............................22-1Xinghuo Yu and Okyay Kaynak

Part III Estimation, Observation, and Identification

.23. Adaptive.Estimation.......................................................................................23-1Seta Bogosyan, Metin Gokasan, and Fuat Gurleyen

.24. Observers.in.Dynamic.Engineering.Systems................................................24-1Christopher Edwards and Chee Pin Tan

.25. Disturbance.ObservationCancellation.Technique......................................25-1Kouhei Ohnishi

.26. Ultrasonic.Sensors.........................................................................................26-1Lindsay Kleeman

.27. Robust.Exact.Observation.and.Identification.via.High-Order.Sliding.Modes............................................................................................................. 27-1Leonid Fridman, Arie Levant, and Jorge Angel Davila Montoya


Contents ix

Part IV Modeling and Control

.28. Modeling.for.System.Control.........................................................................28-1A. John Boye

.29. Intelligent.Mechatronics.and.Robotics..........................................................29-1Satoshi Suzuki and Fumio Harashima

.30. State-Space.Approach.to.Simulating.Dynamic.Systems.in.SPICE................30-1Joel David Hewlett and Bogdan M. Wilamowski

.31. Iterative.Learning.Control.for.Torque.Ripple.Minimization.of.Switched.Reluctance.Motor.Drive................................................................................. 31-1Sanjib Kumar Sahoo, Sanjib Kumar Panda, and Jian-Xin Xu

.32. Precise.Position.Control.of.Piezo.Actuator...................................................32-1Jian-Xin Xu and Sanjib Kumar Panda

.33. Hardware-in-the-Loop.Simulation................................................................33-1Alain Bouscayrol

Part V Mechatronics and robotics

.34. Introduction.to.Mechatronic.Systems...........................................................34-1Ren C. Luo and Chin F. Lin

.35. Actuators.in.Robotics.andAutomation.Systems...........................................35-1Choon-Seng Yee and Marcelo H. Ang Jr.

.36. Robot.Qualities..............................................................................................36-1Raymond Jarvis

.37. Robot.Vision................................................................................................... 37-1Raymond Jarvis

.38. Robot.Path.Planning......................................................................................38-1Raymond Jarvis

.39. Mobile.Robots.................................................................................................39-1Miguel A. Salichs, Ramn Barber, and Mara Malfaz

Index.................................................................................................................. Index-1


xi

Preface

The.field.of.industrial.electronics.covers.a.plethora.of.problems.that.must.be.solved.in.industrial.prac-tice..Electronic.systems.control.many.processes.that.begin.with.the.control.of.relatively.simple.devices.like.electric.motors,.through.more.complicated.devices.such.as.robots,.to.the.control.of.entire.fabrica-tion.processes..An.industrial.electronics.engineer.deals.with.many.physical.phenomena.as.well.as.the.sensors.that.are.used.to.measure.them..Thus,.the.knowledge.required.by.this.type.of.engineer.is.not.only.traditional.electronics.but.also.specialized.electronics,.for.example,.that.required.for.high-power.appli-cations..The.importance.of.electronic.circuits.extends.well.beyond.their.use.as.a.final.product.in.that.they.are.also.important.building.blocks.in.large.systems,.and.thus.the.industrial.electronics.engineer.must.also.possess.knowledge.of.the.areas.of.control.and.mechatronics..Since.most.fabrication.processes.are.relatively.complex,.there.is.an.inherent.requirement.for.the.use.of.communication.systems.that.not.only.link.the.various.elements.of.the.industrial.process.but.are.also.tailor-made.for.the.specific.indus-trial.environment..Finally,.the.efficient.control.and.supervision.of.factories.require.the.application.of.intelligent.systems.in.a.hierarchical.structure.to.address.the.needs.of.all.components.employed.in.the.production.process..This.need. is.accomplished. through. the.use.of. intelligent.systems.such.as.neural.networks,.fuzzy.systems,.and.evolutionary.methods..The.Industrial.Electronics.Handbook.addresses.all.these.issues.and.does.so.in.five.books.outlined.as.follows:

. 1.. Fundamentals of Industrial Electronics

. 2.. Power Electronics and Motor Drives

. 3.. Control and Mechatronics

. 4.. Industrial Communication Systems

. 5.. Intelligent Systems

The.editors.have.gone.to.great.lengths.to.ensure.that.this.handbook.is.as.current.and.up.to.date.as.pos-sible..Thus,.this.book.closely.follows.the.current.research.and.trends.in.applications.that.can.be.found.in.IEEE Transactions on Industrial Electronics..This.journal.is.not.only.one.of.the.largest.engineering.pub-lications.of.its.type.in.the.world.but.also.one.of.the.most.respected..In.all.technical.categories.in.which.this.journal.is.evaluated,.its.worldwide.ranking.is.either.number.1.or.number.2..As.a.result,.we.believe.that.this.handbook,.which.is.written.by.the.worlds.leading.researchers.in.the.field,.presents.the.global.trends.in.the.ubiquitous.area.commonly.known.as.industrial.electronics.

The.successful.construction.of.industrial.systems.requires.an.understanding.of.the.various.aspects.of.control.theory..This.area.of.engineering,.like.that.of.power.electronics,.is.also.seldom.covered.in.depth.in.engineering.curricula.at.the.undergraduate.level..In.addition,.the.fact.that.much.of.the.research.in.control.theory.focuses.more.on.the.mathematical.aspects.of.control.than.on.its.practical.applications.makes.matters.worse..Therefore,.the.goal.of.Control and Mechatronics.is.to.present.many.of.the.concepts.of.control.theory.in.a.manner.that.facilitates.its.understanding.by.practicing.engineers.or.students.who.would.like.to.learn.about.the.applications.of.control.systems..Control and Mechatronics.is.divided.into.several.parts..Part.I.is.devoted.to.control.system.analysis.while.Part.II.deals.with.control.system.design..


xii Preface

Various.techniques.used.for.the.analysis.and.design.of.control.systems.are.described.and.compared.in.these.two.parts..Part.III.deals.with.estimation,.observation,.and.identification.and.is.dedicated.to.the.identification.of.the.objects.to.be.controlled..The.importance.of.this.part.stems.from.the.fact.that. in.order.to.efficiently.control.a.system,.it.must.first.be.clearly.identified..In.an.industrial.environment,.it.is.difficult.to.experiment.with.production.lines..As.a.result,.it.is.imperative.that.good.models.be.developed.to.represent.these.systems..This.modeling.aspect.of.control.is.covered.in.Part.IV..Many.modern.factories.have.more.robots.than.humans..Therefore,.the.importance.of.mechatronics.and.robotics.can.never.be.overemphasized..The.various.aspects.of.robotics.and.mechatronics.are.described.in.Part.V..In.all.the.material.that.has.been.presented,.the.underlying.central.theme.has.been.to.consciously.avoid.the.typical.theorems.and.proofs.and.use.plain.English.and.examples.instead,.which.can.be.easily.understood.by.students.and.practicing.engineers.alike.

For.MATLAB.and.Simulink.product.information,.please.contact

The.MathWorks,.Inc.3.Apple.Hill.DriveNatick,.MA,.01760-2098.USATel:.508-647-7000Fax:.508-647-7001E-mail:[email protected]:.www.mathworks.com


I-1

IControl System Analysis 1 Nonlinear Dynamics Istvn Nagy and Zoltn Sto...........................................................1-1

Introduction. . Basics. . Equilibrium.Points. . Limit.Cycle. . Quasi-Periodic.and.Frequency-Locked.State. . Dynamical.Systems.Described.by.Discrete-Time.Variables:.Maps. . Invariant.Manifolds:.Homoclinic.and.Heteroclinic.Orbits. . Transitions.to.Chaos. . Chaotic.State. . Examples.from.Power.Electronics. . Acknowledgments. . References

2 Basic Feedback Concept Tong Heng Lee, Kok Zuea Tang, and Kok Kiong Tan.............2-1Basic.Feedback.Concept. . Bibliography

3 Stability Analysis Naresh K. Sinha.......................................................................................3-1Introduction. . States.of.Equilibrium. . Stability.of.Linear.Time-Invariant.Systems. . Stability.of.Linear.Discrete-Time.Systems. . Stability.of.Nonlinear.Systems. . References

4 Frequency-Domain Analysis of Relay Feedback Systems Igor M. Boiko......................4-1RelayFeedback.Systems. . Locus.of.a.Perturbed.Relay.System.Theory. . Design.ofCompensating.Filters.in.Relay.Feedback.Systems. . References

5 Linear Matrix Inequalities in Automatic Control Miguel Bernal and Thierry Marie Guerra................................................................................................................................5-1What.Are.LMIs?. . What.Are.LMIs.Good.For?. . References

6 Motion Control Issues Roberto Oboe, Makoto Iwasaki, Toshiyuki Murakami, and Seta Bogosyan........................................................................................................................6-1Introduction. . High-Accuracy.Motion.Control. . Motion.Control.and.Interaction.withthe.Environment. . Remote.Motion.Control. . Conclusions. . References

7 New Methodology for Chatter Stability Analysis in Simultaneous Machining Nejat Olgac and Rifat Sipahi..............................................................................7-1Introduction.and.a.Review.of.Single.Tool.Chatter. . Regenerative.Chatter.in.Simultaneous.Machining. . CTCR.Methodology. . Example.Case.Studies. . Optimization.of.the.Process. . Conclusion. . Acknowledgments. . References


1-1

1.1 Introduction

A.new.class.of.phenomena.has.recently.been.discovered.three.centuries.after.the.publication.of.Newtons Principia.(1687).in.nonlinear.dynamics..New.concepts.and.terms.have.entered.the.vocabulary.to.replace.time. functions. and. frequency. spectra. in. describing. their. behavior,. e.g.,. chaos,. bifurcation,. fractal,.Lyapunov.exponent,.period.doubling,.Poincar.map,.and.strange.attractor.

Until.recently,.chaos.and.order.have.been.viewed.as.mutually.exclusive..Maxwells.equations.gov-ern.the.electromagnetic.phenomena;.Newtons.laws.describe.the.processes.in.classical.mechanics,.etc..

1Nonlinear Dynamics

1.1. Introduction....................................................................................... 1-11.2. Basics................................................................................................... 1-2

Classification. . Restrictions. . Mathematical.Description1.3. Equilibrium.Points............................................................................ 1-5

Introduction. . Basin.of.Attraction. . Linearizing.around.theEP. . Stability. . Classification.of.EPs,.Three-Dimensional.StateSpace.(N.=.3). . No-Intersection.Theorem

1.4. Limit.Cycle.......................................................................................... 1-9Introduction. . Poincar.Map.Function.(PMF). . Stability

1.5. Quasi-Periodic.and.Frequency-Locked.State.............................. 1-12Introduction. . Nonlinear.Systems.with.Two.Frequencies. . .Geometrical.Interpretation. . N-Frequency.Quasi-Periodicity

1.6. Dynamical.Systems.Described.by.Discrete-Time.Variables:Maps.................................................................................1-14Introduction. . Fixed.Points. . MathematicalApproach. . .Graphical.Approach. . Study.of.Logistic.Map. .Stability.of.Cycles

1.7. Invariant.Manifolds:.Homoclinic.and.Heteroclinic.Orbits...... 1-21Introduction. . Invariant.Manifolds,.CTM. . Invariant.Manifolds,.DTM. . Homoclinic.and.Heteroclinic.Orbits,.CTM

1.8. Transitions.to.Chaos....................................................................... 1-24Introduction. . Period-Doubling.Bifurcation. . Period-Doubling.Scenario.in.General

1.9. Chaotic.State..................................................................................... 1-26Introduction. . Lyapunov.Exponent

1.10. Examples.from.Power.Electronics................................................ 1-27Introduction. . High-Frequency.Time-Sharing.Inverter. . Dual.Channel.Resonant.DCDC.Converter. . Hysteresis.Current-Controlled.Three-Phase.VSC. . Space.Vector.Modulated.VSC.withDiscrete-Time.Current.Control. . Direct.Torque.Control

Acknowledgments....................................................................................... 1-41References..................................................................................................... 1-41

Istvn NagyBudapestUniversityofTechnologyandEconomics

Zoltn StoBudapestUniversityofTechnologyandEconomics


1-2 ControlandMechatronics

They.represent.the.world.of.order,.which.is.predictable..Processes.were.called.chaotic.when.they.failed.to.obey.laws.and.they.were.unpredictable..Although.chaos.and.order.have.been.believed.to.be.quite.distinct.faces.of.our.world,.there.were.tricky.questions.to.be.answered..For.example,.knowing.all.the.laws.governing.our.global.weather,.we.are.unable.to.predict.it,.or.a.fluid.system.can.turn.easily.from.order.to.chaos,.from.laminar.flow.into.turbulent.flow.

It.came.as.an.unexpected.discovery.that.deterministic.systems.obeying.simple.laws.belonging.undoubt-edly.to.the.world.of.order.and.believed.to.be.completely.predictable.can.turn.chaotic..In.mathematics,.the.study.of.the.quadratic.iterator.(logistic.equation.or.population.growth.model).[xn+1.=.axn(1..xn),.n.=.0,.1,.2,.].revealed.the.close.link.between.chaos.and.order.[5]..Another.very.early.example.came.from.the.atmospheric.science.in.1963;.Lorenzs.three.differential.equations.derived.from.the.NavierStokes.equa-tions.of.fluid.mechanics.describing.the.thermally.induced.fluid.convection.in.the.atmosphere,.Peitgen.etal..[9]..They.can.be.viewed.as.the.two.principal.paradigms.of.the.theory.of.chaos..One.of.the.first.cha-otic.processes.discovered.in.electronics.can.be.shown.in.diode.resonator.consisting.of.a.series.connection.of.a.pn.junction.diode.and.a.10100.mH.inductor.driven.by.a.sine.wave.generator.of.50100.kHz.

The.chaos.theory,.although.admittedly.still.young,.has.spread.like.wild.fire.into.all.branches.of.sci-ence..In.physics,.it.has.overturned.the.classic.view.held.since.Newton.and.Laplace,.stating.that.our.uni-verse.is.predictable,.governed.by.simple.laws..This.illusion.has.been.fueled.by.the.breathtaking.advances.in.computers,.promising.ever-increasing.computing.power.in.information.processing..Instead,.just.the.opposite.has.happened..Researchers.on.the.frontier.of.natural.science.have.recently.proclaimed.that.this.hope. is.unjustified.because.a. large.number.of.phenomena.in.nature.governed.by.known.simple. laws.are.or.can.be.chaotic..One.of.their.principle.properties.is.their.sensitive.dependence.on.initial.condi-tions..Although.the.most.precise.measurement.indicates.that.two.paths.have.been.launched.from.the.same.initial.condition,.there.are.always.some.tiny,.impossible-to-measure.discrepancies.that.shift.the.paths.along.very.different.trajectories..The.uncertainty. in.the. initial.measurements.will.be.amplified.and.become.overwhelming.after.a.short.time..Therefore,.our.ability.to.predict.accurately.future.develop-ments.is.unreasonable..The.irony.of.fate.is.that.without.the.aid.of.computers,.the.modern.theory.of.chaos.and.its.geometry,.the.fractals,.could.have.never.been.developed.

The.theory.of.nonlinear.dynamics.is.strongly.associated.with.the.bifurcation.theory..Modifying.the.parameters.of.a.nonlinear.system,.the.location.and.the.number.of.equilibrium.points.can.change..The.study.of.these.problems.is.the.subject.of.bifurcation.theory.

The.existence.of.well-defined.routes.leading.from.order.to.chaos.was.the.second.great.discovery.and.again.a.big.surprise.like.the.first.one.showing.that.a.deterministic.system.can.be.chaotic.

The.overview.of.nonlinear.dynamics.here.has. two.parts..The.main.objective. in. the.first.part. is. to.summarize. the. state. of. the. art. in. the. advanced. theory. of. nonlinear. dynamical. systems.. Within. the.overview,.five.basic.states.or.scenario.of.nonlinear.systems.are.treated:.equilibrium.point,.limit.cycle,.quasi-periodic. (frequency-locked).state,. routes. to.chaos,.and.chaotic.state..There.will.be.some.words.about.the.connection.between.the.chaotic.state.and.fractal.geometry.

In.the.second.part,.the.application.of.the.theory.is.illustrated.in.five.examples.from.the.field.of.power.elec-tronics..They.are.as.follows:.high-frequency.time-sharing.inverter,.voltage.control.of.a.dual-channel.resonant.DCDC.converter,.and.three.different.control.methods.of.the.three-phase.full.bridge.voltage.source.DCAC/ACDC.converter,.a.sophisticated.hysteresis.current.control,.a.discrete-time.current.control.equipped.with.space.vector.modulation.(SVM).and.the.direct.torque.control.(DTC).applied.widely.in.AC.drives.

1.2 Basics

1.2.1 Classification

The. nonlinear. dynamical. systems. have. two. broad. classes:. (1). autonomous systems. and. (2). non-autonomous systems..Both.are.described.by.a.set.of.first-order.nonlinear.differential.equations.and.can.be.represented.in.state.(phase).space..The.number.of.differential.equations.equals.the.degree of freedom.


NonlinearDynamics 1-3

(ordimension).of.the.system,.which.is.the.number.of.independent.state.variables.needed.to.determine.uniquely.the.dynamical.state.of.the.system.

1.2.1.1 autonomous Systems

There.are.no.external.input.or.forcing.functions.applied.to.the.system..The.set.of.nonlinear.differential.equations.describing.the.system.is

.ddtx v f x= = ,( )m

.(1.1)

wherexvT

NT

N

x x xv v v

=

=

[ , , , ][ , , ]

1 2

1 2

is the state vectoris the velocity vvectoris the nonlinear vector functionf T N

Tf f f=

=

[ , , ][ ,

1 2

1 2

m ,, ]NTt

is the parameter vectordenotes the transpose of a vectoris tthe timeis the dimension of the systemN

The.time.t.does.not.appear.explicitly.

1.2.1.2 Non-autonomous Systems

Time-dependent.external.inputs.or.forcing.functions.u(t).are.applied.to.the.system..It.is.a.set.of.nonlin-ear.differential.equations:

.ddt

tx v f x u= = , ,( ( ) )m.

(1.2)

Time.t.explicitly.appears.in.u(t)..(1.1).and.(1.2).can.be.solved.analytically.or.numerically.for.a.given.ini-tial.condition.x0.and.parameter.vector...The.solution.describes.the.state.of.the.system.as.a.function.of.time..The.solution.can.be.visualized.in.a.reference.frame.where.the.state.variables.are.the.coordinates..It.is.called.the.state space.or.phase space..At.any.instant,.a.point.in.the.state.space.represents.the.state.of.the.system..As.the.time.evolves,.the.state.point.is.moving.along.a.path.called.trajectory.or.orbit.starting.from.the.initial.condition.

1.2.2 restrictions

The.non-autonomous.system.can.always.be.transformed.to.autonomous.systems.by.introducing.a.new.state.variable.xN+1.=.t..Now.the.last.differential.equation.in.(1.1).is

.dxdt

dtdt

N += =

1 1.

(1.3)

The.number.of.dimensions.of.the.state.space.was.enlarged.by.one.by.including.the.time.as.a.state.vari-able..From.now.on,.we.confine.our.consideration.to.autonomous.systems.unless.it.is.stated.otherwise..By.this.restriction,.there.is.no.loss.of.generality.



The.discussion.is.confined.to.real,.dissipative.systems..As.time.evolves,.the.state.variables.will.head.for.some.final.point,.curve,.area,.or.whatever.geometric.object.in.the.state.space..They.are.called.the.attractor.for.the.particular.system.since.certain.dedicated.trajectories.will.be.attracted.to.these.objects..We.focus.our.considerations.on.the.long-term.behavior.of.the.system.rather.than.analyzing.the.start-up.and.transient.processes.

The.trajectories.are.assumed.to.be.bounded.as.most.physical.systems.are..They.cannot.go.to.infinity.

1.2.3 Mathematical Description

Basically,.two.different.concepts.applying.different.approaches.are.used..The.first.concept.considers.all.the.state.variables.as.continuous.quantities.applying.continuous-time.model.(CTM)..As.time.evolves,.the.system.behavior.is.described.by.a.moving.point. in.state.space.resulting.in.a.trajectory.(or.flow).obtained.by.the.solution.of.the.set.of.differential.equations.(1.1).[or.(1.2)]..Figure.1.1.shows.the.continu-ous.trajectory.of.function.(x0,.t,.).for.three-dimensional.system,.where.x0.is.the.initial.point..The.second.concept.takes.samples.from.the.continuously.changing.state.variables.and.describes.the.system.behavior.by.discrete.vector.function.applying.the.Poincar concept..Figure.1.1.shows.the.way.how.the.samples.are.taken.for.a.three-dimensional.autonomous.system..A.so-called.Poincar plane,.in.general.Poincar.section,.is.chosen.and.the.intersection.points.cut.by.the.trajectory.are.recorded.as.samples..The.selection.of.the.Poincar.plane.is.not.crucial.as.long.as.the.trajectory.cuts.the.surface.transversely..The.relation.between.xn.and.xn+1,.i.e.,.between.subsequent.intersection.points.generated.always.from.the.same.direction.are.described.by.the.so-called.Poincar.map.function.(PMF)

. x P xn n+ =1 ( ) . (1.4)

Pay.attention,.the.subscript.of.x.denotes.the.time.instant,.not.a.component.of.vector.x..The.Poincar.sec-tion.is.a.hyperplane.for.systems.with.dimension.higher.than.three,.while.it.is.a.point.and.a.straight.line.for.a.one-.and.a.two-dimensional.system,.respectively.

For.a.non-autonomous.system.having.a.periodic.forcing.function,.the.samples.are.taken.at.a.definite.phase.of.the.forcing.function,.e.g.,.at.the.beginning.of.the.period..It.is.a.stroboscopic.sampling,.the.state.variables.for.a.mechanical.system.are.recorded.with.a.flash.lamp.fired.once.in.every.period.of.the.forc-ing.function.[8]..Again,.the.PMF.describes.the.relation.between.sampled.values.of.the.state.variables.

Knowing.that.the.trajectories.are.the.solution.of.differential.equation.system,.which.are.unique.and.deterministic,.it.implies.the.existence.of.a.mathematical.relation.between.xn.and.xn+1,.i.e.,.the.existence.

x3

x2

xn

x0 x1

xn+1=P(xn)

Poincarplane

Trajectory (x0, t, )

FIGURE 1.1 Trajectory.described.by.(x0,. t,.)..xn,.xn+1. are. the. intersection.points.of. the. trajectory.with. the.Poincar.plane.



of.PMF..However,.the.discrete-time.equation.(1.4).can.be.solved.analytically.or.numerically.indepen-dent.of.the.differential.equation..In.the.second.concept,.the.discrete-time.model.(DTM).is.used.

The.Poincar.section.reduces.the.dimensionality.of.the.system.by.one.and.describes.it.by.an.iterative,.finite-size.time.step.function.rather.than.a.differential,.infinitesimal.time.step..On.the.other.hand,.PMF.retains.the.essential.information.of.the.system.dynamics.

Even.though.the.state.variables.are.changing.continuously.in.many.systems.like.in.power.electron-ics,.they.can.advantageously.be.modeled.by.discrete-iteration.function.(1.4)..In.some.other.cases,.the.system.is.inherently.discrete,.their.state.variables.are.not.changing.continuously.like.in.digital.systems.or.models.describing.the.evolution.of.population.of.species.

1.3 Equilibrium Points

1.3.1 Introduction

The.nonlinear.world.is.much.more.colorful.than.the.linear.one..The.nonlinear.systems.can.be.in.various.states,.one.of.them.is.the.equilibrium point.(EP)..It.is.a.point.in.the.state.space.approached.by.the.trajec-tory.of.a.continuous,.nonlinear.dynamical.system.as.its.transients.decay..The.velocity.of.state.variables.v.=.x.is.zero.in.the.EP:

.ddtx v f x= = , =( )m 0

.(1.5)

The. solution. of. the. nonlinear. algebraic. function. (1.5). can. result. in. more. than. one. EPs.. They. are.x x x x1 2* * * *, , , , k n, ..The.stable.EPs.are.attractors.

1.3.2 Basin of attraction

The.natural.consequence.of.the.existence.of.multiple.attractors.is.the.partition.of.state.space.into.dif-ferent. regions. called. basins of attractions.. Any. of. the. initial. conditions. within. a. basin. of. attraction.launches.a.trajectory.that.is.finally.attracted.by.the.particular.EP.belonging.to.the.basin.of.attraction.(Figure.1.2)..The.border.between.two.neighboring.basins.of.attraction.is.called.separatrix..They.organize.the.state.space.in.the.sense.that.a.trajectory.born.in.a.basin.of.attraction.will.never.leave.it.

x2

x1

x3

x*2

x*1

x*i

x*2 (0)

x*k (0)

x*k

Basins of attractions

Linearizedregion byJacobianmatrix Jk

FIGURE 1.2 Basins. of. attraction. and. their. EP. (N. =. 3).. x1,. x2,. and. x3. are. coordinates. of. the. state. space..vectorx..xk*.is.a.particular.value.of.state.space.vector.x,.xk*.denotes.an.EP,.and.xk(0).is.an.initial.condition.in.the.basin.of.attraction.of.xk*.



1.3.3 Linearizing around the EP

Introducing. the.small.perturbation. = x x xk*,. (1.1).can.be. linearized. in. the.close.neighborhood.of.the.EP.xk* ..Now. f x x f x J x( * ) ( * )k k k+ = + +, ,m m ..Neglecting.the.terms.of.higher.order.than.x.and.substituting.it.back.to.(1.1):

.ddt k

= x v J x.

(1.6)

where

.

Jk

N

N

N N

fx

fx

fx

fx

fx

fx

fx

fx

f

=

1

1

1

2

1

2

1

2

2

2

1 2

NN

Nx

.

(1.7)

is.the.Jacobian.matrix..The.partial.derivatives.have.to.be.evaluated.at. xk* ..f(x*,.).=.0.was.observed.in.(1.6)..Jk.is.a.real,.time-independent.N..N.matrix..Seeking.the.solution.of.(1.7).in.the.form

. =x erte . (1.8)

and.substituting.it.back.to.(1.6):

. J e ek r r= . (1.9)

Its.nontrivial.solution.for..must.satisfy.the.Nth.order.polynomial.equation

. det( )J Ik = 0 . (1.10)

where.I.is.the.N..N.identity.matrix..From.(1.6),.(1.8),.and.(1.9),

. = =v J e ek rt

rte e . (1.11)

Selecting.the.direction.of.vector.x.in.the.special.way.given.by.(1.8),.i.e.,.its.change.in.time.depends.only.on.one.constant.,.it.has.two.important.consequences:

. 1.. The.product.Jker.(=.er).only.results.in.the.expansion.or.contraction.of.er.by...The.direction.of.er.is.not.changed.

. 2.. The.direction.of.the.perturbation.of.the.velocity.vector.v.will.be.the.same.as.that.of.vector.er .

The.direction.of.er.is.called.characteristic.direction.and.er.is.the.right-hand.side.eigenvector.of.Jk.as.Jk.is.multiplied.from.the.right.by.er...is.the.eigenvalue.(or.characteristic.exponent).of.Jk.

We.confine.our.consideration.of.N.distinct.roots.of.(1.10).(multiple.roots.are.excluded)..They.are.1,.2,.m,,.N..Correspondingly,.we.have.N.distinct.eigenvectors.as.well..The.general.solution.of.(1.6):

. = =

= =

x e( ) ( )t e tm

N

mt

m

N

mm

1 1

.

(1.12)



Here.we.assumed.that.the.initial.condition.was. = ==

x e( )t mN m0 1 ..The.roots.of.(1.10).are.either.real.or.complex.conjugate.ones.since.the.coefficients.are.real.in.(1.10).

When. we. have. complex. conjugate. pairs. of. eigenvalues. m. =. m+1. =. m. . jm,. the. corresponding.eigenvectors.are.em.=.m+1.=.em,R.+.jem,I,.where.the..denotes.complex.conjugate.and.where.m,.m,.and.em,R,.em,I.are.all.real.and.real-valued.vectors,.respectively..From.the.two.complex.solutions.m(t).=.em.exp(mt).and.m+1(t).=.em+1.exp(m+1t),.two.linearly.independent.real.solutions.sm(t).and.sm+1(t).can.be.composed:

.s e em m m t m R m m I mt t t e t tm( ) ( ) ( ) cos sin= + = + + , ,12 1

.

(1.13)

.s e em m m t m I m m R mt j

t t e t tm+ + , ,= = 1 112( ) ( ) ( ) cos sin

.(1.14)

When.the.eigenvalue.is.real.m.=.m.and.the.solution.belonging.to.m,

. s em m mtt t e m( ) ( )= = . (1.15)

Note.that.the.time.function.belonging.to.an.eigenvalue.or.a.pair.of.eigenvalues.is.the.same.for.all.state.variables.

1.3.4 Stability

The.EP.is.stable.if.and.only.if.the.real.part.m.of.all.eigenvalues.belonging.to.the.EP.is.negative..Otherwise,.one.or.more.solutions.sm(t).goes.to.infinity..m.=.0.is.considered.as.unstable.case..When.m.0

Unstablem>0

em

em,I

em,RP

PStablem


1.3.5 Classification of EPs, three-Dimensional State Space (N = 3)

Depending.on.the.location.of.the.three.eigenvalues.in.the.complex.plane,.eight.types.of.the.EPs.are.distin-guished.(Figure.1.4)..Three.eigenvectors.are.used.for.reference.frame..The.origin.is.the.EP..Eigenvectors.e1,.e2,.and.e3.are.used.when.all.eigenvalues.are.real.(Figure.1.4a,c,e,g).and.e1,.e2,R,.and.e2,I.are.used.when.we.have.a.pair.of.conjugate.complex.eigenvalues.(Figure.1.4b,d,f,h)..The.orbits.(trajectories).are.moving.exponentially.in.time.along.the.eigenvectors.e1,.e2,.or.e3.when.the.initial.condition.is.on.them..The.orbits.are.spiraling.in.the.plane.spanned.by.e2,R.and.e2,I.with.initial.condition.in.the.plane..The.operation.points.are.attracted.(repelled).by.stable.(unstable).EP.

All.three.eigenvalues.are.on.the.left-hand.side.of.the.complex.plane.for.node.and.spiral node.(Figure.1.4a.and.b)..The.spiral.node.is.also.called.attracting focus..All.three.eigenvalues.are.on.the.right-hand.side.for.repeller.and.spiral repeller.(Figure.1.4c.and.d)..The.spiral.repeller.is.also.called.repelling focus..For.saddle points,.either.one.(Index.1).(Figure.1.4e.and.f).or.two.(Index.2).(Figure.1.4g.and.h).eigenvalues.are.on.the.right-hand.side.

Saddle.points.play.very.important.role.in.organizing.the.trajectories.in.state.space..A.trajectory.asso-ciated. to.an.eigenvector.or.a.pair.of.eigenvectors.can.be.stable.when. its.eigenvalue(s). is. (are).on. the.left-hand.side.of.the.complex.plane.(Figure.1.4a.and.b).or.unstable.when.they.are.on.the.right-hand.side.(Figure.1.4c.through.f)..Trajectories.heading.directly.to.and.directly.away.from.a.saddle.point.are.called.

e2

e2

e1

e1

Im

Im

Re

Re

e2,R

e2,R

e2,I

e2,I

e1

e1

Im

Im

Re

Re

Hopfbifurcation

e3

e3

(a) (b)

(c) (d)

e2

e2

e1

e1

Im

Im

Re

Re

e2,R

e2,R

e2,I

e2,I

e1

e1

Im

Im

Re

Re

e3

e3

(e) (f )

(g) (h)

Stablemanifold

Stablemanifold

Unstablemanifold

Unstablemanifold

FIGURE 1.4 Classifications.of.EPs. (N.=.3).. (a).Node,. (b). spiral.node,. (c). repeller,. (d). spiral. repeller,. (e). saddle.pointindex.1,.(f).spiral.saddle.pointindex.1,.(g).saddle.point2,.and.(h).spiral.saddle.pointindex.2.



stable.and.unstable invariant manifold.or.shortly.manifold..Sometimes,.the.stable.(unstable).manifolds.are.called.insets.(outsets)..The.operation.point.either.on.the.stable.or.on.the.unstable.manifold.cannot.leave. the.manifold..The.manifolds.of. a. saddle.point. in. its.neighborhood. in.a. two-dimensional. state.space.divide.it.into.four.regions..A.trajectory.born.in.a.region.is.confined.to.the.region..The.manifolds.are.part.of.the.separatrices.separating.the.basins.of.attractions..In.this.sense,.the.manifolds.organize.the.state.space.

Finally,.when.the.real.part.m.is.zero.in.the.pair.of.the.conjugate.complex.eigenvalue.and.the.dimen-sion.is.N.=.2,.the.EP.is.called.center..The.trajectories.in.the.reference.frame.eReI.are.circles.(Figure.1.5)..Their.radius.is.determined.by.the.initial.condition.

1.3.6 No-Intersection theorem

Trajectories. in.state.space.cannot. intersect.each.other..The.theorem.is. the.direct.consequence.of. the.deterministic.system..The.state.of.the.system.is.unambiguously.determined.by.the.location.of.its.opera-tion.point.in.the.state.space..As.the.system.is.determined.by.(1.1),.all.of.the.derivatives.are.determined.by.the.instantaneous.values.of.the.state.variables..Consequently,.there.is.only.one.possible.direction.for.a.trajectory.to.continue.its.journey.

1.4 Limit Cycle

1.4.1 Introduction

Two-.or.higher-dimensional.nonlinear.systems.can.exhibit.periodic.(cyclic).motion.without.external.periodical.excitation..This.behavior.is.represented.by.closed-loop.trajectory.called.Limit Cycle.(LC).in.the.state.space..There.are.stable.(attracting).and.unstable.(repelling).LC..The.basic.difference.between.the.stable.LC.and.the.center.(see.Figure.1.5).having.closed.trajectory.is.that.the.trajectories.starting.from.nearby.initial.points.are.attracted.by.stable.limit.cycle.and.sooner.or.later.they.end.up.in.the.LC,.while.the.trajectories.starting.from.different.initial.conditions.will.stay.forever.in.different.tracks.determined.by.the.initial.conditions.in.the.case.of.center.

Figure.1.6.shows.a.stable.LC.together.with.the.Poincar.plane..Here,.the.dimension.is.3..The.LC.inter-sects.the.Poincar.plane.at.point.Pk.called.Fixed Point.(FP)..It.plays.a.crucial.role.in.nonlinear.dynamics..Instead.of.investigating.the.behavior.of.the.LC,.the.FP.is.studied.

1.4.2 Poincar Map Function (PMF)

After.moving.the.trajectory.from.the.LC.by.a.small.deviation,.the.discrete.Poinear.Map.Function.(PMF).relates.the.coordinates.of.intersection.point.Pn.to.those.of.the.previous.point.Pn1..All.points.are. the. intersection.points. in. the.Poincar.plane.generated.by. the. trajectory..The.PMF.in.a. three-dimensional.state.space.is.(Figure.1.6)

Initialconditions

e2

e1

Im

Re

FIGURE 1.5 Center..Eigenvalues.are.complex.conjugate,.N.=.2.



.

u P u v

v P u v

n n n

n n n

= ,( )= ,( )

1 1 1

2 1 1 .

(1.16)

whereP1.and.P2.are.the.PMFun.and.vn.are.the.coordinates.of.the.intersection.point.Pn.in.the.Poincar.plane

Introducing.vector.zn.by

. znT

n nu v= , . (1.17)

where.zn.points.to.intersection.Pn.from.the.origin,.the.PMF.is

. z P zn n= ( )1 . (1.18)

At.fixed.point.Pk

. z P zk k= ( ) . (1.19)

The.first.benefit.of.applying.the.PMF.is.the.reduction.of.the.dimension.by.one.as.the.stability.of.FP.Pk.is.studied.now.in.the.two-dimensional.state.space.rather.than.studying.the.stability.of.the.LC.in.the.three-dimensional.state.space,.and.the.second.one.is.the.substitution.of.the.differential.equation.by.difference.equation.

1.4.3 Stability

The.stability.can.be.investigated.on.the.basis.of.the.PMF.(1.18)..First,.the.nonlinear.function.P(zn1).has.to.be.linearized.by.its.Jacobian.matrix.Jk.evaluated.at.its.FP.zk..Knowing.Jk,.(1.18).can.be.rewritten.for.small.perturbation.around.the.FP.zk.as

Poincarplane

Trajectory

Attracting limit cyclex2

x1

x3

Pk is fixed point

znPk Pn

Pn1

v

u

FIGURE 1.6 Stable.limit.cycle.and.the.Poincar.plane.(N.=.3).



. = = z J J zn k n knz 1 0 . (1.20)

where.z0.is.the.initial.small.deviation.from.FP.Pk.Substituting.Jk.by.its.eigenvalues.k.and.right.emr.and.left.eml.eigenvectors,

. =

=

z e e zn mN

mn

mr mlT

1

1

0.

(1.21)

Due.to.(1.21),.the.LC.is.stable.if.and.only.if.all.eigenvalues.are.within.the.circle.with.unit.radius.in.the.complex.plane.

In.the.stability.analysis,.both.in.continuous-time.model.(CTM).and.in.discrete-time.model.(DTM),.the.Jacobian.matrix.is.operated.on.the.small.perturbation.of.the.state.vector.[see.(1.6).and.(1.20)]..The.essential. difference. is. that. it. determines. the. velocity. vector. for. CTM. and. the. next. iterate. for. DTM,.respectively.

Assume. that. the. very. first. point. at. the. beginning. of. iteration. is. placed. on. eigenvector. em. of. the.Jacobian. matrix.. Figure. 1.7. shows. four. different. iteration. processes. corresponding. to. the. particular.value.of.eigenvalue.m.associated.to.em..In.Figure.1.7a.and.b,.m.is.real,.but.its.value.is.0.


1.5 Quasi-Periodic and Frequency-Locked State

1.5.1 Introduction

Beside.the.EP.and.LC,.another.possible.state.or.motion.is.the.quasi-periodic.(Qu-P).motion.in.CTM..In.Qu-P.state,.the.motionin.theorynever.exactly.repeats.itself..Other.terms.used.in.literature.are.conditionally periodic.or.almost periodic..Qu-P.state.is.possible.in.inherently.discrete.systems.as.well..The.frequency-locked.(F-L).state.is.a.special.case.of.Qu-P.state..Qu-P.state.is.not.possible.in.N.=.1.or.2.dimensional.systems,.i.e.,.N.must.be.N..3..Similarly.to.chaos,.Qu-P.state.is.aperiodic..In.chaotic.state,.two.points.in.state.space,.which.are.arbitrarily.close,.will.diverge..In.other.words,.the.chaotic.system.is.extremely.sensitive.to.initial.conditions.and.to.changes.in.control.parameters..In.contrast.to.chaotic.state,.two.points.that.are.initially.close.will.remain.close.over.time.in.Qu-P.state.

The. Qu-P. motion. is. a. mixture. of. periodic. motions. of. several. different. angular. frequencies. 1,.2,,.m..Depending.on.the.value.of.their.linear.combination.L,

. L k k km m= + + +1 1 2 2 . (1.22)

the.motion.can.be.Qu-P,.i.e.,.aperiodic.when.the.sum.L..0.or.it.can.be.F-L,.i.e.,.periodic.state.when.the.sum.L.=.0..Here.k1,.k2,,.km.are.any.positive.(or.negative).integer.(k1.=.k2.=..=.km.=.0.is.excluded).

The.EP,.LC,.Qu-P,.and.F-L.states.are.regular attractors.while.in.chaotic.state,.the.system.has.strange attractor. (see. later. in.Section.1.9)..The.Qu-P.motion.plays.central. role. in.Hamiltonian. systems,. e.g.,.in. mechanical. systems. modeled. without. friction,. which. are. non-dissipative. ones.. They. do. not. have.attractors.

1.5.2 Nonlinear Systems with two Frequencies

Qu-P.and.F-L.motions.occur.frequently.in.practice.in.systems.having.a.natural.oscillation.frequency.and.a.different.external.forcing.frequency.or.two.different.natural.oscillation.frequencies..Because.of.nonlinearity,.the.superposition.of.the.independent.frequencies.is.not.valid.

Starting.from.(1.22),.assume.that

.

1

2

2

1= =TT

pq .

(1.23)

where.T1.=.2/1.and.T2.=.2/2.are.the.periods.of.respective.harmonic.oscillations.and.p.and.q.are.positive.integers..Here.we.assume.that.any.common.factors.in.the.frequency.ratio.have.been.removed,.e.g.,.if.f1/f2.=.2/6,.the.common.factor.of.2.will.be.removed.and.f1/f2.=.p/q.=.1/3.can.be.written..When.the.fre-quency.ratio.is.the.ratio.of.two.integers,.then.the.ratio.is.called.rational.in.mathematical.sense,.i.e.,.the.two.frequencies.are.commensurate,.the.behavior.of.the.system.is.periodic..It.is.in.F-L.state.

On.the.other.hand,.when.the.frequency.ratio.is.irrational,.the.two.frequencies.are.incommensurate,.and.the.behavior.is.Qu-P..The.last.two.statements.can.easily.be.understood.in.the.geometrical.interpre-tation.of.the.system.trajectory.

1.5.3 Geometrical Interpretation

A.two-frequency.Qu-P.trajectory.on.a.toroidal.surface.in.the.three-dimensional.state.space.is.shown.in.Figure.1.8..Introducing.two.angles.r.=.rt.=.1t.and.R.=.Rt.=.2t,.they.determine.point.P.of.the.trajectory.on.the.surface.of.the.torus.provided.that.the.initial.condition.point.P0.belonging.to.t.=.0.is.known..The.center.of.the.torus.is.at.the.origin..R.is.the.large.radius.of.the.torus.whose.cross-sectional.radius.is.r..The.two.angles.R.and.r.are.increasing.as.time.evolves.and.therefore.point.P.is.moving.on.



the.surface.of.the.torus.tracking.the.trajectory.of.state.vector.xT.=.[x1,.x2,.x3]..The.three.components.of.the.state.vector.x.are.given.by.the.equations.as.follows:

.

x R r

x r

x R r

r R

r

r R

1

2

3

=

=

=

( )cos

sin

( )sin

+ cos

+ cos

.

(1.24)

The.trajectory. is.winding.on.the. torus.around.the.cross.section.with.minor.radius.r,.making.R/2.rotations.per.unit.time..As.r/R.=.p/q.[see.(1.23)].and.assuming.that.p.and.q.are.integers,.the.number.of.rotations.around.circle.r.and.circle.R.in.per.unit.is.p.and.q,.respectively..For.example,.if.p.=.1.and.q=3,.point.P.makes.three.rotations.around.circle.R.as.long.as.it.makes.only.one.rotation.around.circle.r..Figure.1.9.shows.the.torus.and.the.Poincar.plane.intersecting.the.torus.together.with.the.trajectory.on.the.surface.of.the.torus..The.Poincar.plane.illustrates.its.intersection.points.with.the.trajectory.

When.the.frequency.ratio.p/q.=.1/3,.it.is.rational..As.long.as.point.P.rotates.once.around.circle.R,.it.rotates.120.around.circle.r..Starting.from.point.0.on.the.Poincar.plane.(Figure.1.9a).after.123.rotations.around.circle.R,.point.P.intersects.the.Ponicar.plane.successively.at.point.123..Point.3.

Torus

x2

x1

x3

P0

R

r

P

R

2r

FIGURE 1.8 Two-frequency.trajectory.on.a.toroidal.surface.in.state.space.(N.=.3).

r/R=p/q=1/3

Poincarplane(a)

1

230

reeintersection

points

r/R=p/q=2pi

(b)Poincarplane

After long time thenumber of intersection

points approaches innity

FIGURE 1.9 Example.for.(a).frequency-locked.and.(b).quasi-periodic.state.



coincides.with.the.starting.point.0.as.three.times.120.is.360..The.process.is.periodic,.the.trajectory.closes.on.itself,.it.is.the.F-L.state.

On.the.other.hand,.when.the.frequency.ratio.is.irrational,.e.g.,.p/q.=.2.as.long.as.the.point.P.makes.one.rotation.around.circle.R,.it.completes.2.rotations.around.circle.r..The.phase.shift.of.the.first.inter-section.point.on.the.Poincar.plane.from.the.initial.point.P0.is.given.by.an.irrational.angle.=.360..2(mod.1).where.(mod.1).is.the.modulus.operator.that.takes.the.fraction.of.a.number.(e.g.,.6.28(mod.1).=.0.28)..After.any.further.full.rotations.around.circle.R,.the.phase.shifts.of.the.intersection.points.from.P0.on.the.Poincar.plane.remain.irrational;.therefore,.they.will.never.coincide.with.P0,.and.the.trajectory.will.never.close.on.itself..All.intersection.points.will.be.different..As.t..,.the.number.of.intersection.points.will.be.infinite.and.a.circle.of.radius.r.will.be.visible.on.the.Poincar.plane.consisting.of.infinite.number.of.distinct.points.(Figure.1.9b)..The.system.is.in.Qu-P.state.

1.5.4 N-Frequency Quasi-Periodicity

We.have.treated.up.to.now.the.two-frequency.quasi-periodicity.a.little.bit.in.detail..It.has.to.be.stressed.that.in.mathematical.sense,.the.N-frequency.quasi-periodicity.is.essentially.the.same..The.N.frequen-cies.define.N.angles.1.=.1t,.2.=.2t,.N.=.Nt.determining.uniquely.the.position.and.movement.of.the.operation.point.P.on.the.surface.of.the.N-dimensional.torus..Now.again,.the.trajectory.fills.up.the.surface.of.the.N-dimensional.torus.in.the.state.space.

1.6 Dynamical Systems Described by Discrete-time Variables: Maps

1.6.1 Introduction

The.dynamical.systems.can.be.described.by.difference.equation.systems.with.discrete-time.variables..The.relation.in.vector.form.is

. x f xn n+ =1 ( ) . (1.25)

where.xn.is.K-dimensional.state.variable.xnT n n nKx x x= , ,[ ]( ) ( ) ( )1 2 ,.f.is.nonlinear.vector.function..State.vec-tor.xn.is.obtained.at.discrete.time.n.=.1.by.x1.=.f(x0),.where.x0.is.the.initial.condition..From.x1,.the.value.x2.=.f(x1).can.be.calculated,.etc..Knowing.x0,.the.orbit.of.discrete-time.system.x0,.x1,.x2,.is.generated.

We.can.consider.that.vector.function.f.maps.xn.into.xn+1..In.this.sense,.f.is.a.map function..The.number.of.state.variables.determines.the.dimension.of.the.map..

Examples:One-dimensional.map.(K.=.1):

. Logistic.map:. xn+1.=.axn(1..xn).

Tentmap: =

ifif

xax x

a x xnn n

n n+

>

10 5

1 0 5.

( ) ..

where.a.is.constant.Two-dimensional.map.(K.=.2):

.Henonmap:

x ax bxx cx

n n n

n n

+

+

= +

=

11 2 1

12 1

12( ) ( ) ( )

( ) ( )

.

where.a,.b,.and.c.are.constants.



K-dimensional.map:

Poincar.map.of.an.N.=.K.+.1.dimensional.state.space.

Maps.can.give.useful.insight.for.the.behavior.of.complex.dynamic.systems.

The.map.function.(1.25).can.be.invertible.or.non-invertible..It.is.invertible.when.the.discrete.function

. x f xn n=

+1

1( ) . (1.26)

can. be. solved. uniquely. for. xn.. f 1. is. the. inverse. of. f.. Two. examples. are. given. next.. First,. the. invert-ible.Henon.map.and.after,.the.non-invertible.quadratic.(logistic).map.are.discussed..Henon map.is.fre-quently.cited.example.in.nonlinear.systems..It.has.two.dimensions.and.maps.the.point.with.coordinate.xn( )1 .and.xn( )2 . in. the.plane.to.a.new.point. xn+11( ) .and. xn+12( ) .. It. is. invertible,.because. xn+11( ) .and. xn+12( ) .uniquely.determine.the.value.xn( )1 .and.xn( )2 ,.since

.

x xc

x x bxa

nn

nn n

( )( )

( )( ) ( )

1 12

2 11 2

121

=

=

+

+

+ +

Here,.a..0.and.c..0.must.hold..(For.some.values.of.a,.b,.and.c,.the.Henon.map.can.exhibit.chaotic.behavior.)

Turning.now. to. the.non-invertible.maps,. the.quadratic or logistic map. is. taken.as. example.. It.was.developed.originally.as.a.demography.model..It.is.a.very.simple.system,.but.its.response.can.be.surpris-ingly.colorful.. It. is.non-invertible,.as.Figure.1.10.shows..We.cannot.uniquely.determine.xn. from.xn+1..Aswe.see.later,.an.invertible.map.can.be.chaotic.only.if.its.dimension.is.two.or.more.(Henon.map)..On.the.other.hand,.the.non-invertible.map.can.be.chaotic.even.in.one-dimensional.cases,.e.g.,.the.logistic.map.

1.6.2 Fixed Points

The.concept.of.FP.was.introduced.earlier.(see.Figure.1.6)..The.system.stays.in.steady.state.at.FP.where.xn+1.=.xn.=.x*..When.the.discrete.function.(1.25).is.nonlinear,.it.can.have.more.than.one.FP.

1.6.2.1 One-Dimensional Iterated Maps

For.the.sake.of.simplicity,.the.one-dimensional.maps.are.discussed.from.now.on..The.one-dimensional.iterated.maps.can.describe.the.dynamics.of.large.number.of.systems.of.higher.dimension..To.throw.light.

xn+1

xn

FIGURE 1.10 The.quadratic.or.logistic.iterated.map.



to.the.statement,.consider.a.three-dimensional.dynamical.system.with.PMF. z PnT n n n n nu v u v+ + += , = ,1 1 1[ ] ( ) .[see. (1.18)].. In. certain. cases,. we. have. a. relation. between. the. two. coordinates. un. and. vn:. vn. =. Fv(un)..Substituting.it.into.the.map.function.un+1.=.Pu(un,.vn),.we.end.up.with

. u P u F u f un u n v n n+ = =1 ( ( )) ( ), . (1.27)

one-dimensional.map.function.More.arguments.can.be.found.in.the.literature.(see.chapter.5.2.in.Ref..[4].and.page.66.of.Ref..[5]).

on.the.wide.scope.of.applications.on.the.one-dimensional.discrete.map.functions..If.the.dissipation.in.the.system.is.high.enough,.then.even.systems.with.dimension.more.than.three.can.be.analyzed.by.one-dimensional.map.

1.6.2.2 return Map or Cobweb

The.iteration.in.the.one-dimensional.discrete.map.function.or.difference.equation

. x f xn n+ =1 ( ) . (1.28)

can.be.done.with.numerical.or.graphical.method..The.return.map.or.cobweb.is.a.graphical.method..To.illustrate.the.return.map.method,.we.take.as.example.the.equation

.x ax

bxnn

nc+ = +

1 1 ( ) .(1.29)

where.a,.b,.and.c.are.constants..The.iteration.has.to.be.performed.as.follows.(Figure.1.11):

. 1.. Plot.the.function.xn+1(xn).in.plane.xn+1.versus.xn.

. 2.. Select.x0.as.initial.condition.

. 3.. Draw.a.straight.line.starting.from.origin.with.slope.1.called.mirror line.or.diagonal.

. 4.. Read.the.value.x1.from.the.graph.and.draw.a.line.in.parallel.with.the.horizontal.axis.from.x1.to.the.mirror.line.(line.11).

. 5.. Read.the.value.x2.by.vertical.line.12.

. 6.. Repeat.the.graphical.process.by.drawing.the.horizontal.line.22.and.finally.determining.x3.

In.order.to.find.the.FP.x*,.we.have.to.repeat.the.graphical.process.

FP

xn+1

x2

x1

x1 x3 x2 x0

xnx*

2

1

2

1

FIGURE 1.11 Iteration.in.return.map.



1.6.2.3 kth return Map

Periodic.steady.state.with.period.T.is.represented.by.a.single.FP.x*.in.the.mapping,.i.e.,.x*.=.f(x*)..kth-order.subharmonic. solutions. with. period. kT. correspond. to. FPs. { * *}x xk1 ,. where. x f x xn2 1 1* ( * ) *= =+f x x f xn k( *) * ( *) 1 = ..The.kth.iterate.of.f(x).is.defined.as.the.function.that.results.from.applying.f k.times,.its.notation.is.f (k)(x).=.f(f(f(x))),.and.its.mapping.is.called.kth.return.map.and.the.process.is.called.period-k.

1.6.2.4 Stability of FP in One-Dimensional Map

The.FP.is.locally stable.if.subsequent.iterates.starting.from.a.sufficiently.near.initial.point.to.the.FP.are.eventually.getting.closer.and.closer.the.FP..The.expressions.attracting.FP.or.asymptotically.stable.FP.are.also.used..On.the.other.hand,.if.the.subsequent.iterates.move.away.from.x*,.then.the.name.unstable.or.repelling.FP.is.used.

1.6.3 Mathematical approach

By.knowing.the.nonlinear.function.f(x).and.one.of.its.FPs.x*,.we.can.express.the.first.iterate.x1.by.apply-ing.a.Taylor.series.expansion.near.x*:

.x f x f x df

dxx f x df

dxx

x x1 0 0 0= = + + + ( ) ( *) ( *)

* *

.

(1.30)

where.x0.=.x0..x*.and.the.initial.condition.x0.is.sufficiently.near.x*..The.derivative..=.df/dx.has.to.be.evaluated.at.x*...is.the.eigenvalue.of.f(x).at.x*..The.nth.iterate.is

. = = =

x x x x dfdx

xn n nn

x

( *)*

0 0 0

.(1.31)

It.is.obvious.that.x*.is.stable.FP.if. | / | df dx x* 1..In.general,.when.the.dimension.is.more.than.one,..must.be.within.the.unit.circle.drawn.around.the.origin.of.the.complex.plane.for.stable.FP.

The.nonlinear.f(x).has.multiple.FPs..The.initial.conditions.leading.to.a.particular.x*.constitute.the.basin of attraction.of.x*..As.there.are.more.basins.of.attraction,.none.of.FPs.can.be.globally stable..They.can.be.only.locally.stable.

1.6.4 Graphical approach

Graphical approach.is.explained.in.Figure.1.12..Figure.1.12a,c,e,.and.g.presents.the.return.map.with.FP.x*.and.with.the.initial.condition.(IC)..The.thick.straight.line.with.slope..at.x*.approximates.the.function.f(x).at.x*..Figure.1.12b,d,f,.and.h.depicts.the.discrete-time.evolution.xn(n)..||.1..The.subsequent.iterates.explode,.FP.is.unstable..Note.that.the.cobweb.and.time.evolution.is.oscillating.when..


In.general,.it.has.two.FPs:. x a1 1 1* = / .and.x2 0* = ..The.respective.eigenvalues.are.1.=.2..a.and.2.=.a..Figure.1.13.shows.the.return.map.(left).and.the.time.evolution.(right).at.different.value.a.changing.in.range.0.


stability.of.cycles)..Increasing.a.over.a.=.3.570,.we.enter.the.chaotic.range.(Figure.1.13k.and.l),.the.itera-tion.is.a.periodic.with.narrow.ranges.of.a.producing.periodic.solutions.

As.a.is.increased,.first.we.have.period-1.in.steady.state,.later.period-2,.then.period-4.emerge,.etc..The.scenario.is.called.period doubling cascade.

1.6.6 Stability of Cycles

We.have.already.introduced.the.notation.f (k)(x).=.f(f(f(x)).for.the.kth.iterate.that.results.from.apply-ing.f k-times..If.we.start.at.x1*.and.after.applying.f k-times.we.end.up.with.x xk* *= 1 ,.then.we.say.we.have.period.or.cycle-k.with.k.separate.FPs:.x x f x x f xk k1 2 1 1* * ( * ) , * ( * ), ,= = .

In.the.simplest.case.of.period-2,.the.two.FPs.are:.x f x2 1* ( *)= .and.x f x f f x1 2 1* ( * ) ( ( *))= = ..Referring.to.(1.30),.we.know.that.the.stability.of.FP.x1*.depends.on.the.value.of.the.derivative

.( ) ( ( ))

*2

1

=

df f xdx x .

(1.33)

(a)

1

1

xn+1

xn+1

0

0

0 1

1

IC

0 IC

xn

xn

xn

xna

a4 1

00

1

00

3

2

1

0

4

3

2

1

0

n

n

1xn+1

010 IC

xn

xna 1

00

4

3

2

1

0 n

(b)

(c) (d)

(e) (f )

FIGURE 1.13 Return.map.(left).and.time.evolution.(right).of.the.logistic.map.(continued)



Using.the.chain.rule.for.derivatives,

.

( )

( )

( )

*

( ( ))* * * *

2

1 1 1 1 2

df xdx

df f xdx

dfdx

dfdx

dfdx

dfd

x x f x x x

= = =

xx x1* .(1.34)

Consequently,

. x x

d f xdx

d f xdx

1 2

2 2

*

( ) ( )

*

( ) ( )

=

.

(1.35)

(1.35). states. that. the.derivatives.or.eigenvalues.of. the.second. iterate.of. f(x).are. the.same.at.both.FPs.belonging.to.period-2.

As. an. example,. Figure. 1.14. shows. the. return. map. for. the. first. (Figure. 1.14a). and. for. the. second.(Figure. 1.14b). iterate. when. a. =. 3.2.. Both. FPs. in. the. first. iterate. map. are. unstable. as. we. have. just.

1

1

xn+1

xn+1

0

0

0 1

1

IC

0 IC

xn

xn

xn

xna

a4 1

00

1

00

3

2

1

0

4

3

2

1

0

n

n

1xn+1

010 IC

xn

xna 1

00

4

3

2

1

0n

(g) (h)

(i) (j)

(k) (l)

FIGURE 1.13 (continued)



discussed..We.have.four.FPs.in.the.second.iterate.map..Two.of.them,. x1*.and.x2*,.are.stable.FPs.(point.S1.and.S2).and.the.other.two.(zero.and.x*).are.unstable.(point.U1.and.U2)..The.FP.of.the.first.iterate.must.be.the.FP.of.the.second.iterate.as.well:.x*.=.f(x*).and.x*.=.f( f(x*)).

1.7 Invariant Manifolds: Homoclinic and Heteroclinic Orbits

1.7.1 Introduction

To. obtain. complete. understanding. of. the. global. dynamics. of.nonlinear.systems,.the.knowledge.of.invariant.manifolds.is.abso-lutely. crucial.. The. invariant. manifolds. or. briefly. the. manifolds.are.borders.in.state.space.separating.regions..A.trajectory.born.in.one.region.must.remain.in.the.same.region.as.time.evolves..The..manifolds. organize. the. state. space.. There. are. stable. and. unsta-ble. manifolds.. They. originate. from. saddle. points.. If. the. .initial..condition.is.on.the.manifold.or.subspace,.the.trajectory.stays.on.the. manifold.. Homoclinic. orbit. is. established. when. stable. and.unstable.manifolds.of.a.saddle.point.intersect..Heteroclinic.orbit.is.established.when.stable.and.unstable.manifolds.from.different.saddle.points.intersect.

1.7.2 Invariant Manifolds, CtM

The.CTM.is.applied.for.describing.the.system..Invariant.mani-fold. is. a. curve. (trajectory). in. plane. (N. =. 2). (Figure. 1.15),. or.curve.or.surface. in.space.(N.=.3).(Figure.1.16),.or. in.general.a.subspace.(hypersurface).of.the.state.space.(N.>.3)..The.manifolds.are.always.associated.with.saddle.point.denoted.here.by.x*..Any.initial.condition.in.the.manifold.results.in.movement.of.the.operation.point. in. the. manifold. under. the. action. of. the. relevant. differential. equations.. There. are. two. kinds.

Unstablemanifold

Stablemanifold

W u(x *)

W s(x*)x*

es

eu

t

t

FIGURE 1.15 Stable.Ws.and.unstable.Wu.manifold.(N.=.2)..The.CTM.is.used..es.and.eu.are.eigenvectors.at.saddle.point.x*.belonging.to.Ws.and.Wu,.respectively.

(a)

xn+1

xn+2

xnU1

S1

S2U2

x*1 x*2x*

xnx*

(b)

FIGURE 1.14 Cycle. of. period-2. in.the.logistic.map.for.a.=.3.3..(a).Return.map.for.first.iterate..(b).Return.map.for.second.iterate.xn+2.versus.xn.



of. manifolds:. stable. manifold. denoted. by. Ws. and. unstable. manifold. denoted. by. Wu.. If. the. initial.points.are.on.Ws.or.on.Wu,.the.operation.points.remain.on.Ws.or.Wu.forever,.but.the.points.on.Ws.are.attracted.by.x*.and.the.points.on.Wu.are.repelled.from.x*..By.considering.t ,.every.movement.along.the.manifolds.is.reversed.

If.the.initial.conditions.(points.P1,.P2,.P3,.P4.in.Figure.1.17).are.not.on.the.manifolds,.their.trajectories.will.not.cross.any.of.the.manifolds,.they.remain.in.the.space.bounded.by.the.manifolds..The.trajecto-ries.are.repelled.from.Ws(x*).and.attracted.by.Wu(x*)..Any.of.these.trajectories.(orbits).must.remain.in.the.space.where.it.was.born..Wu(x*).(or.Ws(x*)).are.boundaries..Consequently,.the.invariant.manifolds.organize.the.state.space.

1.7.3 Invariant Manifolds, DtM

Applying. DTM,. i.e.,. difference. equations. describe. the. system,. then. mostly. the. PMF. is. used.. The.fixed.point.x*.must.be.a.saddle.point.of.PMF.to.have.manifolds..Figure.1.18.presents.the.Ponicar.surface.or.plane.with.the.stable.Ws(x*).and.unstable.Wu(x*).manifold..s0.and.u0.is.the.intersection.point.of.the.trajectory.with.the.Poincar.surface,.respectively..The.next.intersection.point.of.the.same.

Stablemanifold W s(x*)

Unstablemanifold W u(x*)

eu

Es

x*t

t

t

FIGURE 1.16 Stable.Ws.and.unstable.Wu.manifold.(N.=.3)..The.CTM.is.used..Es.=.span[es1,.es2].stable.subspace.is.tangent.of.Ws(x*).at.x*..eu.is.an.unstable.eigenvector.at.x*..x*.is.saddle.point.

W u(x*)P4

P3

P2P1

x*

W s(x*)

FIGURE 1.17 Initial.conditions.(P1,.P2,.P3,.P4).are.not.placed.on.any.of.the.invariant.manifolds..The.trajectories.are.repelled.from.Ws(x*).and.attracted.by.Wu(x*)..Any.of.the.trajectories.(orbits).must.remain.in.the.region.where.it.was.born..Wu(x*).(or.Ws(x*)).are.boundaries.



trajectory.with.the.Poincar.surface.is.s1.and.u1.the.following.s2.and.u2,.etc..If.Ws,.Wu.are.manifolds.and.s0,.u0.are.on.the.mani-folds,.all. subsequent. intersection.point.will.be.on. the.respective.manifold.. Starting. from. infinitely. large. number. of. initial. point.s0(u0). on. Ws. (or. on. Wu),. infinitely. large. number. of. intersection.points.are.obtained.along.W s.(or.Wu)..Curve.Ws(Wu).is.determined.by.using.infinitely.large.number.of.intersection.points.

1.7.4 Homoclinic and Heteroclinic Orbits, CtM

In.homoclinic connection,.the.stable.Ws.and.unstable.Wu.manifold.of.the.same.saddle.point.x*.intersect.each.other.(Figure.1.19)..The.two.manifolds,.Ws.and.Wu,.constitute.a.homolinic.orbit..The.opera-tion.point.on.the.homoclinic.orbits.approach.x*.both. in. forward.and.in.backward.time.under.the.action.of.the.relevant.differential.equation..In.heteroclinic connection,.the.stable.manifold.W xs( *)1 .of.saddle.point.x1*.is.connected.to.the.unstable.manifold.W xu( *)2 .of.saddle.point.x2*,.and.vice.versa.(Figure.1.20)..The.two.manifolds.W xs( *)1 .and.W xu( *)2 .[similarly.W xs( *)2 .and.W xu( *)1 ].constitute.a.heteroclinic.orbit.

Poincarsurface

eu

s0s1

s2 s3 s4 u0x*

u1u2

u3u4

es

W s (x*)

W u (x*)

Points denoted by s (by u) approach(diverge from) x* as n

FIGURE 1.18 Stable.Ws.and.unstable.Wu.manifold..DTM.is.used..es.and.eu.are.eigenvectors.at.saddle.point.x*.belonging.to.Ws.and.Wu,.respectively.

W u (x*)

W s (x*)

Homoclinic connection

Homoclinicorbit

x*

FIGURE 1.19 Homoclinic. con-nection.and.orbit.(CTM).

Wu (x1*)

W s (x2*)

W s (x1*)

Wu (x1*)

x11*x2*Heteroclinic

orbit

Heteroclinic connection

FIGURE 1.20 Heteroclinic.connection.and.orbit.(CTM).



1.8 transitions to Chaos

1.8.1 Introduction

One.of. the.great.achievements.of. the. theory.of. chaos. is. the.discovery.of. several. typical. routes. from.regular.states.to.chaos..Quite.different.systems.in.their.physical.appearance.exhibit.the.same.route..The.main.thing.is.the.universality..There.are.two.broad.classes.of.transitions.to.chaos:.the local and global bifurcations..In.the.first.case,.for.example,.one.EP.or.one.LC.loses.its.stability.as.a.system.parameter.is.changed..The.local.bifurcation.has.three.subclasses:.period.doubling,.quasi-periodicity,.and.intermit-tency..The.most.frequent.route.is.the.period.doubling.

In.the.second.case,. the.global.bifurcation.involves. larger.scale.behavior. in.state.space,.more.EPs,.and/or.more.LCs.lose.their.stability..It.has.two.subclasses,.the.chaotic.transient.and.the.crisis..In.this.section,.only.the.local.bifurcations.and.the.period-doubling.route.is.treated..Only.one.short.comment.is.made.both.on.the.quasi-periodic.route.and.on.the.intermittency.

In.quasi-periodic route,.as.a.result.of.alteration.in.a.parameter,.the.system.state.changes.first.from.EP.to.LC.through.bifurcation..Later,.in.addition,.another.frequency.develops.by.a.new.bifurcation.and.the.system.exhibits.quasi-periodic.state..In.other.words,.there.are.two.complex.conjugate.eigenvalues.within.the.unit.circle.in.this.state..By.changing.further.the.parameter,.eventually.the.chaotic.state.is.reached.from.the.quasi-periodic.one.

In.the. intermittency route. to.chaos,.apparently.periodic.and.chaotic.states.alternately.develop..The.system.state.seems.to.be.periodic.in.certain.intervals.and.suddenly.it.turns.into.a.burst.of.chaotic.state..The.irregular.motion.calms.down.and.everything.starts.again..Changing.the.system.parameter.further,.the.length.of.chaotic.states.becomes.longer.and.finally.the.periodic.states.are.not.restored.

1.8.2 Period-Doubling Bifurcation

Considering.now.the.period-doubling.route,.let.us.assume.a.LC.as.starting.state.in.a.three-dimensional.system.(Figure.1.21a)..The.trajectory.crosses.the.Poincar.plane.at.point.P..As.a.result.of.changing.one.system.parameter,.the.eigenvalue.1.of.the.Jacobian.matrix.of.PMF.belonging.to.FP.P.is.moving.along.the.negative.real.axis.within.the.unit.circle.toward.point.1.and.crosses.it.as.1.becomes.1..The.eigenvalue.1.moves.outside.the.unit.circle..FP.P.belonging.to.the.first.iterate.loses.its.stability..Simultaneously,.two.new.stable.FPs.P1.and.P2.are.born.in.the.second.iterate.process.having.two.eigenvalues.2.=.3.(Figure.1.21b)..Their.value.is.equal.+1.at.the.bifurcation.point.and.it.is.getting.smaller.than.one.as.the.system.parameter.is.changing.further.in.the.same.direction..2.=.3.are.moving.along.the.positive.real.axis.toward.the.origin.

Continuing.the.parameter.change,.new.bifurcation.occurs.following.the.same.pattern.just.described.and.the.trajectory.will.cross.the.Poincar.plane.four.times.(2..2).in.one.period.instead.of.twice.from.the.new.bifurcation.point..The.period-doubling.process.keeps.going.on.but.the.difference.between.two.consecutive.parameter.values.belonging.to.bifurcations.is.getting.smaller.and.smaller.

1.8.3 Period-Doubling Scenario in General

The.period-doubling.scenario.is.shown.in.Figure.1.22.where.. is.the.system.parameter,.the.so-called.bifurcation parameter,.x.is.one.of.the.system.state.variables.belonging.to.the.FP,.or.more.generally.to.the.intersection.points.of.the.trajectory.with.the.Poincar.plane..The.intersection.points.belonging.to.the.transient.process.are.excluded..For.this.reason,.the.diagram.is.called.sometime.final state diagram..The.name.bifurcation.diagram.refers.to.the.bifurcation.points.shown.in.the.diagram.and.it.is.more.generally.used.

Feigenbaum.[7].has.shown.that.the.ratio.of.the.distances.between.successive.bifurcation.points.mea-sured.along.the.parameter.axis..approaches.to.a.constant.number.as.the.order.of.bifurcation,.labeled.by.k,.approaches.infinity:



.

= = .

+

limk

k

k 14 6693

.(1.36)

where..is.the.so-called.Feigenbaum constant..It.is.found.that.the.ratio.of.the.distances.measured.in.the.axis.x.at.the.bifurcation.points.approaches.another.constant.number,.the.so-called.Feigenbaum.

.

= = .

+

limk

k

k 12 5029

.(1.37)

.is.universal.constant.in.the.theory.of.chaos.like.other.fundamental.numbers,.for.example,.e.=.2.718,.,.and.the.golden.mean.ratio.( )5 1 2 / .

(a)

(b)

Limitcycle

Newlimitcycle

Before bifurcation

After bifurcation

Parameterchange

Poincarplane

Poincarplane

P

P1

P2

P

FIGURE 1.21 Period-doubling.bifurcation..(a).Period-1.state.before,.and.(b).period-2.state.after.the.bifurcation.

x Final state ofstate variable

1 2 3S

Parameter

2

1

3

FIGURE 1.22 Final.state.or.bifurcation.diagram..S.=.Feigenbaum.point.



1.9 Chaotic State

1.9.1 Introduction

The.recently.discovered.new.state.is.the.chaotic.state..It.can.evolve.only.in.nonlinear.systems..The.condi-tions.required.for.chaotic.state.are.as.follows:.at.least.three.or.higher.dimensions.in.autonomous.systems.and.with.at.least.two.or.higher.dimensions.in.non-autonomous.systems.provided.that.they.are.described.by.CTM..On.the.other.hand,.when.DTM.is.used,.two.or.more.dimensions.suffice.for.invertible.iteration.functions.and.only.one.dimension.(e.g.,.logistic.map).or.more.for.non-invertible.iteration.functions.are.needed.for.developing.chaotic.state..In.addition.to.that,.some.other.universal.qualitative.features.com-mon.to.nonlinear.chaotic.systems.are.summarized.as.follows:

. The.systems.are.deterministic,.the.equations.describing.them.are.completely.known.. They.have.extreme.sensitive.dependence.on.initial.conditions.. Exponential.divergence.of.nearby.trajectories.is.one.of.the.signatures.of.chaos.. Even.though.the.systems.are.deterministic,.their.behavior.is.unpredictable.on.the.long.run.. The.trajectories. in.chaotic.state.are.non-periodic,.bounded,.cannot.be.reproduced,.and.do.not.

intersect.each.other.. The.motion.of.the.trajectories.is.random-like.with.underlying.order.and.structure.

As. it. was. mentioned,. the. trajectories. setting. off. from. initial. conditions. approach. after. the. transient.process.either.FPs.or.LC.or.Qu-P.curves.in.dissipative.systems..All.of.them.are.called.attractors.or.clas-sical attractors.since.the.system.is.attracted.to.one.of.the.above.three.states..When.a.chaotic.state.evolves.in.a.system,.its.trajectory.approaches.and.sooner.or.later.reaches.an.attractor.too,.the.so-called.strange attractor.

In.three.dimensions,.the.classical.attractors.are.associated.with.some.geometric.form,.the.station-ary.state.with.point,.the.LC.with.a.closed.curve,.and.the.quasi-periodic.state.with.surface..The.strange.attractor.is.associated.with.a.new.kind.of.geometric.object..It.is.called.a.fractal structure.

1.9.2 Lyapunov Exponent

Conceptually,.the.Lyapunov.exponent.is.a.quantitative.test.of.the.sensitive.dependence.on.initial.condi-tions.of.the.system..It.was.stated.earlier.that.one.of.the.properties.of.chaotic.systems.is.the.exponential.divergence.of.nearby.trajectories..The.calculation.methods.usually.apply.this.property.to.determine.the.Lyapunov.exponent..

After.the.transient.process,.the.trajectories.always.find.their.attractor.belonging.to.the.special.initial.condition.in.dissipative.systems..In.general,.the.attractor.as.reference.trajectory.is.used.to.calculate...Starting.two.trajectories.from.two.nearby.initial.points.placed.from.each.other.by.small.distance.d0,.the.distance.d.between.the.trajectories.is.given.by

. d t d et( ) = 0 . (1.38)

where..is.the.Lyapunov.exponent..One.of.the.initial.points.is.on.the.attractor..The.equation.can.hold.true.only.locally.because.the.chaotic.systems.are.bounded,.i.e.,.d(t).cannot.increase.to.infinity..The.value..may.depend.on.the.initial.point.on.the.attractor..To.characterize.the.attractor.by.a.Lyapunov.expo-nent,.the.calculation.described.above.has.to.be.repeated.for.a.large.number.of.n.of.initial.points.distrib-uted.along.the.attractor..Eventually,.the.average.Lyapunov.exponent..calculated.from.the.individual.n.values.will.characterize.the.state.of.the.system..The.criterion.for.chaos.is..>.0..When...0,.the.system.is.in.regular.state.(Figure.1.23).



1.10 Examples from Power Electronics

1.10.1 Introduction

Power.electronic.systems.have.wide.applications.both.in.industry.and.at.home..The.structure.of.these.systems. keeps. changing. due. to. consecutive. turn-on. and. turn-off. processes. of. electronic. switches..They. are. variable structure,. piece-wise. linear,. nonlinear. dynamic. controlled. systems.. The. primary.source.of.nonlinearity.in.these.systems.is.that.the.switching.instants.depend.on.the.state.variables.[1]..Furthermore,.the.frequent.nonlinearities.can.also.be.found.in.the.systems..In.order.to.show.the.applica-tions.of.the.theory.of.nonlinear.dynamics.summarized.in.the.previous.sections,.five.examples.have.been.selected.from.the.field.of.power.electronics.[15,16]..Various.system.states.and.bifurcations.will.be.pre-sented.without.the.claim.of.completeness.in.this.section..Our.main.objective.is.to.direct.the.attention.of.the.readers,.engineers.to.the.possible.strange.phenomenon.that.can.be.encountered.in.power.electronics.and.explained.by.the.theory.of.nonlinear.dynamics.

1.10.2 High-Frequency time-Sharing Inverter

1.10.2.1 the System

It. is.applied. for. induction.heating.where.high-frequency.power.supply. is. required.with. frequency.of.several.thousand.hertz.Figure.1.24.shows.the.inverter.configuration.in.its.simplest.form..I+.and.I,.the.so-called.positive.and.negative.sub-inverters,.are.encircled.by.dotted.lines.

The. parallel. oscillatory. circuit. LpCpRp. represents. the. load.. The. supply. is. provided. by. a. center-tapped.DC.voltage.source..In.order.to.explain.the.mode.of.action.of.the.inverter,.ideal.components.are.assumed..The.basic.operation.of.the.inverter.can.be.understood.by.the.time.functions.of.Figure.1.25..In.Figure.1.25a,.the.high.frequency.approximately.sinusoidal.output.voltage.v0,.the.inverter.output.current.pulses.i0,.and.the.condenser.voltage.vc.can.be.seen..Thyristors.T1.and.T2.are.alternately.fired.at.instants.located.at.every.sixth.zero.crossings.on.the.positive.and.on.the.negative.slope.of.the.output.voltage,.respectively..After.firing.a.thyristor,.an.output.current.pulse.i0.is.flowing.into.the.load,.which.changes.the.polarity.of.the.series.condenser.voltage.vc,.for.instance,.from.Vcm.to.Vcm..The.thyristor.turn-off.time.can.be.a.little.bit.longer.than.two.and.half.cycles.of.the.output.voltage.(Figure.1.25b)..Figure.1.26.presents.a.configuration.with.three.positive.and.three.negative.sub-inverters..Here,.the.respective.input.and. output. terminals. of. the. sub-inverters. are. paralleled.. The. numbering. of. the. sub-inverters. corre-sponds.to.the.firing.order..Each.sub-inverter.pair.works.in.the.same.way.as.it.was.previously.described.

x2

x3

x1

d0

d(t) =d0et

>0Chaotic

x2

x3

x1

d0

d(t) =d0et


1.10.2.2 results

One.of.the.most.interesting.results.is.that.by.using.an.approximate.model.assuming.sinusoidal.output.voltage,.no.steady-state.solution.can.be.found.for.the.current.pulse.and.other.variables.in.certain.opera-tion.region.and.its. theoretical.explanation.can.be.found.in.Ref..[6]..The.laboratory.tests.verified.this.theoretical.conclusion..To.describe.the.phenomena.in.the.region.in.quantitative.form,.a.more.accurate.model.was.used..The.independent.energy.storage.elements.are.six.Ls.series.inductances,.three.Cs.series.capacitances,.one.Lp.inductance,.and.one.Cp.capacitance,.altogether.11.elements,.with.11.state.variables..The.accurate.analysis.was.performed.by.simulation.in.MATLAB.environment.for.open-.and.for.closed-loop.control.

Ls

Ls

vL

vT1Cs/2

Cs/2vT2

vC

I+

I

Loadiip

iin

io

vi

+

vivo

iCp

iLp

Cp

Lp

RpT1

T2

FIGURE 1.24 The.basic.configuration.of.the.inverter.

io vc

vT1

Vom

t

(a)

(b)

t

vo

Vcm

vi

vcritic=(vi+Vom) Vcm

t1 Vcm+ viVom

Vcm> (vi+Vom)

Vcm1 2 1

2

To

FIGURE 1.25 Time.functions.of.(a).output.voltage.vo,.output.current.io, condenser.voltage.vc, and.(b).thyristor.voltage.vT1.in.basic.operation.



1.10.2.3 Open-Loop Control

Bifurcation.diagram.was.generated.here,.the.peak.values.of.the.output.voltage.Vom.were.sampled,.stored,.and.plotted.as.a.function.of.the.control.parameter.To,.where.To.is.the.period.between.two.consecutive.fir-ing.pulses.in.the.positive.or.in.the.negative.sub-inverters.(Figure.1.27)..Having.just.one.single.value.Vom.for.a.given.To,.the.output.voltage.vo.repeats.itself.in.each.period.To..This.state.is.called.period-1..Similarly,.period-5.state.develops,.for.example,.at.firing.period.To.=.277.s..Now,.there.are.five.consecutive.distinct.Vom.values..vo.is.still.periodic,.it.repeats.itself.after.5To.has.elapsed.(Figure.1.28).

1.10.2.4 Closed-Loop Control

A.self-control.structure.is.obtained.by.applying.a.feedback.control.loop..Now,.the.approximately.sinu-soidal.output.voltage.vo.is.compared.with.a.DC.control.voltage.VDC.and.the.thyristors.are.alternatively.fired.at.the.crossing.points.of.the.two.curves..The.study.is.concerned.with.the.effect.of.the.variation.of.the.DC.control.voltage.level.VDC.on.the.behavior.of.the.feedback-controlled.inverter..Again,.the.bifur-cation.diagram.is.used.for.the.presentation.of.the.results.(Figure.1.29)..The.peak.values.of.the.output.

Ls

T1 T3 T5 Csio iL

iC

Lp

Rp

Cp

T4 T6 T2

vT1

Ls Ls

Ls Ls Ls

iip

iin

vovC1 vi

vi

0

+

io5+ io2

io3+ io6

io1+ io4

FIGURE 1.26 The.power.circuit.of.the.time-sharing.inverter..(Reprinted.from.Bajnok,.M..et.al.,.Surprises.stem-ming.from.using.linear.models.for.nonlinear.systems:.Review..In.Proceedings of the 29th Annual Conference on Industrial Electronics, Control and Instrumentation (IECON03),.Roanoke,.VA,.vol..I,.pp..961971,.November.26,.2003..CD.Rom.ISBN:0-7803-7907-1...[2003].IEEE..With.permission.)

500

450

400

V om [V

]

350

300265 270 275 280

Period-5

To [s]285 290

FIGURE 1.27 Bifurcation.diagram.of.inverter.with.open-loop.control.



voltage.Vom.were.sampled,.stored,.and.plotted.as.a.function.of.the.control.parameter.VDC..The.results.are.basically.similar.to.those.obtained.for.open-loop.control..As.in.the.previous.study,.the.feedback-controlled.inverter.generates.subharmonics.as.the.DC.voltage.level.is.varied.

1.10.3 Dual Channel resonant DCDC Converter

1.10.3.1 the System

The.dual.channel.resonant.DCDC.converter.family.was.introduced.earlier.[3]..The.family.has.12.members..A.common.feature.of.the.different.entities.is.that.they.transmit.power.from.input.to.output.through.two.channels,.the.so-called.positive.and.negative.ones,.coupled.by.a.resonating.capacitor..The.converter.can.operate.both.in.symmetrical.and.in.asymmetrical.mode..The.respective.variables.in.the.two.channels.

800

600

400

200

0v o [V

], i o1

[A]

200

400

0.092 0.094 0.096 0.098

3T5

To

voio1T5= 5To

t [s]0.1

FIGURE 1.28 vo(t).and.io1(t)..To.=.277.s..Period-5.state..(Reprinted.from.Bajnok,.M..et.al.,.Surprises.stemming.from.using.linear.models.for.nonlinear.systems:.Review..In.Proceedings of the 29th Annual Conference on Industrial Electronics, Control and Instrumentation (IECON03),.Roanoke,.VA,.vol..I,.pp..961971,.November.26,.2003..CD.Rom.ISBN:0-7803-7907-1...[2003].IEEE..With.permission.)

500

450

400

350

30020 0 20

VDC [V]

V om [V

]

40 60 80

FIGURE 1.29 Bifurcation. diagram. of. inverter. with. feedback. control. loop.. (Reprinted. from. Bajnok,. M.. et. al.,.Surprises.stemming.from.using. linear.models. for.nonlinear.systems:.Review..In.Proceedings of the 29th Annual Conference on Industrial Electronics, Control and Instrumentation (IECON03),.Roanoke,.VA,.vol..I,.pp..961971,.November.26,.2003..CD.Rom.ISBN:0-7803-7907-1...[2003].IEEE..With.permission.)



vary.symmetrically.or.asymmetrically.in.the.two.modes..The.energy.change.between.the.two.channels.is.accomplished.by.a.capacitor. in.asymmetrical.operation..There. is.no.energy.exchange.between. the.positive.and.the.negative.channels.in.the.symmetrical.case..Our.description.is.restricted.to.the.buck.configuration.(Figure.1.30).in.symmetrical.operation..Suffix.i.and.o.refer.to.input.and.output.while.suf-fix.p.and.n.refer.to.positive.and.negative.channel,.respectively..Two.different.converter.versions.can.be.derived.from.the.configuration..The.first.version.contains.diodes.in.place.of.clamping.switches.Scp.and.Scn..The.second.one.applies.controlled.switches.conducting.current.in.the.direction.of.arrow..To.sim-plify,.the.configuration.capacitance.C.is.replaced.by.short.circuit.and.Scp,.Scn.are.applied..The.controlled.switches.within.one.channel.are.always.in.complementary.states.(i.e.,.when.Sp.is.on,.Scp.is.off,.and.vice.versa)..By.turning.on.switch.Sp,.a.sinusoidal.current.pulse.iip.is.developed.from.t.=.0.to.p.( = /1 LC .in.circuitSp,.L,.vop,.C,.and.vip.(Figure.1.31))..The.currents.are.iip.=.iop.=.icp.in.interval.0.


current..In.DCM,.the.current.is.zero.between.ep.and.Ts..In.the.continuous.current-conduction.mode.(CCM).of.operation,.the.inductor.current.flows.continuously.(iop.>.0)..The.inductor.current.iop.decreases.in.both.cases.in.a.linear.fashion..After.turning.on.Scp,.the.capacitor.voltage.vc.stops.changing..It.keeps.its.value.Vcp.

The.same.process.takes.place.at.the.negative.side,.resulting.in.a.negative.condenser.current.pulse.and.voltage.swing.after.turning.on.Sn.

1.10.3.2 the PWM Control

For.controlling.the.output.voltage.vo.=.vop.+.von.by.PWM.switching,.a.feedback.control. loop.is.applied.(Figure.1.32a)..The.control.signal.vcon.is.compared.to.the.repetitive.sawtooth.waveform.(Figure.1.32b)..The.control.voltage.signal.vcon. is.obtained.by.amplifying.the.error,.the.difference.between.the.actual.output.voltage.vo,.and.its.desired.value.Vref..When.the.amplified.error.signal.vcon.is.greater.than.the.sawtooth.wave-form,.the.switch.control.signal.(Figure.1.32c).becomes.high.and.the.selected.switch.turns.on..Otherwise,.the.switch.is.off..The.controlled.switches.are.Sp.and.Sn.(the.switches.within.one.channel,.e.g.,.Sp.and.Scp.are.in.complementary.states),.and.they.are.controlled.alternatively,.i.e.,.the.switch.control.signal.is.generated.for.the.switch.in.one.channel.in.one.period.of.the.sawtooth.wave.and.in.the.next.period,.the.signal.is.gener-ated.for.the.switch.in.the.other.channel..The.switching.frequency.of.these.switches.is.half.of.the.frequency.of.the.sawtooth.wave.

Vref

(a)

Kvvcon

vramp

voComparator ConverterSwitchcontrol

VU

(b)

VL T t

vconvramp

(c)

t

Switch controlHigh

Low

FIGURE 1.32 PWM.control:.(a).Block.diagram,.(b).input.and.(c).output.signals.of.the.comparator.

49.8

49.7

49.673.4 73.6 73.8

Kv

v ok (

V)

74 74.2

FIGURE 1.33 Enlarged.part.of.the.bifurcation.diagram.



1.10.3.3 results

The. objective. is. the. calculation. of. the. controlled. variable. vo. in. steady. state. in.order.to.discover.the.various.possible.states.of.this.nonlinear.variable.structure.dynamical. system.. The. analysis. was. performed. by. simulation. in. MATLAB.environment..The.effect.of.variation.Kv.was.studied..A.representative.bifurcation.diagram.is.shown.in.Figure

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