1 Selv-optimaliserende regulering Anvendelser mot prosessindustrien, biologi og maratonløping...

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Selv-optimaliserende reguleringAnvendelser mot prosessindustrien, biologi og maratonløping

Sigurd Skogestad

Institutt for kjemisk prosessteknologi, NTNU, Trondheim

HC, 31. januar 2012

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Self-optimizing controlFrom key performance indicators to control of biological systems

Sigurd Skogestad

Department of Chemical Engineering

Norwegian University of Science and Technology (NTNU)

Trondheim

PSE 2003, Kunming, 05-10 Jan. 2004

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Outline• Optimal operation

• Implememtation of optimal operation: Self-optimizing control

• What should we control?

• Applications– Marathon runner

– KPI’s

– Biology

– ...

• Optimal measurement combination

• Optimal blending example

Focus: Not optimization (optimal decision making)But rather: How to implement decision in an uncertain world

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Optimal operation of systems

• Theory:– Model of overall system– Estimate present state– Optimize all degrees of freedom

• Problems: – Model not available and optimization complex– Not robust (difficult to handle uncertainty)

• Practice– Hierarchical system– Each level: Follow order (”setpoints”) given from level above– Goal: Self-optimizing

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Process operation: Hierarchical structure

PID

RTO

MPC

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Engineering systems

• Most (all?) large-scale engineering systems are controlled using hierarchies of quite simple single-loop controllers – Large-scale chemical plant (refinery)

– Commercial aircraft

• 1000’s of loops

• Simple components: on-off + P-control + PI-control + nonlinear fixes + some feedforward

Same in biological systems

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What should we control?

y1 = c ? (economics)

y2 = ? (stabilization)

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Self-optimizing Control

Self-optimizing control is when acceptable operation can be achieved using constant set points (c

s)

for the controlled variables c

(without re-optimizing when disturbances occur).

c=cs

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Optimal operation (economics)

• Define scalar cost function J(u0,d)

– u0: degrees of freedom

– d: disturbances

• Optimal operation for given d:

minu0 J(u0,d)subject to:

f(u0,d) = 0

g(u0,d) < 0

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Implementation of optimal operation

• Idea: Replace optimization by setpoint control

• Optimal solution is usually at constraints, that is, most of the degrees of freedom u0 are used to satisfy “active constraints”, g(u0,d) = 0

• CONTROL ACTIVE CONSTRAINTS!– Implementation of active constraints is usually simple.

• WHAT MORE SHOULD WE CONTROL?– Find variables c for remaining

unconstrained degrees of freedom u.

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Unconstrained variables

Cost J

Selected controlled variable (remaining unconstrained)

ccoptopt

JJoptopt

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Implementation of unconstrained variables is not trivial: How do we deal with uncertainty?

• 1. Disturbances d

• 2. Implementation error n

cs = copt(d*) – nominal optimization

nc = cs + n

d

Cost J Jopt(d)

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Problem no. 1: Disturbance d

Cost J

Controlled variable ccoptopt(d(d**))

JJoptopt

dd**

d d ≠≠ dd**

Loss with constant value for c

) Want copt independent of d

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Problem no. 2: Implementation error n

Cost J

ccss=c=coptopt(d(d**))

JJoptopt

dd** Loss due to implementation error for c

c = cs + n

) Want n small and ”flat” optimum

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Optimizer

Controller thatadjusts u to keep

cm = cs

Plant

cs

cm=c+n

u

c

n

d

u

c

J

cs=copt

uopt

n

) Want c sensitive to u (”large gain”)

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Which variable c to control?

• Define optimal operation: Minimize cost function J

• Each candidate variable c:

With constant setpoints cs compute loss L for expected disturbances d and implementation errors n

• Select variable c with smallest loss

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Constant setpoint policy:Loss for disturbances (“problem 1”)

Acceptable loss ) self-optimizing control

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Good candidate controlled variables c (for self-optimizing control)

Requirements:

• The optimal value of c should be insensitive to disturbances (avoid problem 1)

• c should be easy to measure and control (rest: avoid problem 2)

• The value of c should be sensitive to changes in the degrees of freedom

(Equivalently, J as a function of c should be flat)

• For cases with more than one unconstrained degrees of freedom, the selected controlled variables should be independent.

Singular value rule (Skogestad and Postlethwaite, 1996):Look for variables that maximize the minimum singular value of the appropriately scaled steady-state gain matrix G from u to c

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Examples self-optimizing control

• Marathon runner

• Central bank

• Cake baking

• Business systems (KPIs)

• Investment portifolio

• Biology

• Chemical process plants: Optimal blending of gasoline

Define optimal operation (J) and look for ”magic” variable (c) which when kept constant gives acceptable loss (self-optimizing control)

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Self-optimizing Control – Marathon

• Optimal operation of Marathon runner, J=T– Any self-optimizing variable c (to control at constant

setpoint)?

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Self-optimizing Control – Marathon

• Optimal operation of Marathon runner, J=T– Any self-optimizing variable c (to control at constant

setpoint)?• c1 = distance to leader of race

• c2 = speed

• c3 = heart rate

• c4 = level of lactate in muscles

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Further examples

• Central bank. J = welfare. c=inflation rate (2.5%)• Cake baking. J = nice taste, c = Temperature (200C)• Business, J = profit. c = ”Key performance indicator (KPI), e.g.

– Response time to order– Energy consumption pr. kg or unit– Number of employees– Research spendingOptimal values obtained by ”benchmarking”

• Investment (portofolio management). J = profit. c = Fraction of investment in shares (50%)

• Biological systems:– ”Self-optimizing” controlled variables c have been found by natural

selection– Need to do ”reverse engineering” :

• Find the controlled variables used in nature• From this identify what overall objective J the biological system has been

attempting to optimize

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Looking for “magic” variables to keep at constant setpoints.

How can we find them?

• Consider available measurements y, and evaluate loss when they are kept constant (“brute force”):

• More general: Find optimal linear combination (matrix H):

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Optimal measurement combination (Alstad)

• Basis: Want optimal value of c independent of disturbances ) copt = 0 ¢ d

• Find optimal solution as a function of d: uopt(d), yopt(d)

• Linearize this relationship: yopt = F d • F – sensitivity matrix

• Want:

• To achieve this for all values of d:

• Always possible if

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cs = y1s

MPC

PID

y2s

RTO

u (valves)

Follow path (+ look after other variables)

CV=y1 (+ u); MV=y2s

Stabilize + avoid drift CV=y2; MV=u

Min J (economics); MV=y1s

OBJECTIVE

Dealing with complexity

Main simplification: Hierarchical decomposition

Process control The controlled variables (CVs)interconnect the layers

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Outline

• Control structure design (plantwide control)

• A procedure for control structure designI Top Down

• Step 1: Define operational objective (cost) and constraints

• Step 2: Identify degrees of freedom and optimizate for disturbances

• Step 3: What to control ? (primary CV’s) (self-optimizing control)

• Step 4: Where set the production rate? (Inventory control)

II Bottom Up • Step 5: Regulatory control: What more to control (secondary CV’s) ?

• Step 6: Supervisory control

• Step 7: Real-time optimization

• Case studies

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Main message

• 1. Control for economics (Top-down steady-state arguments)– Primary controlled variables c = y1 :

• Control active constraints

• For remaining unconstrained degrees of freedom: Look for “self-optimizing” variables

• 2. Control for stabilization (Bottom-up; regulatory PID control)– Secondary controlled variables y2 (“inner cascade loops”)

• Control variables which otherwise may “drift”

• Both cases: Control “sensitive” variables (with a large gain)!

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Process control:

“Plantwide control” = “Control structure design for complete chemical plant”

• Large systems

• Each plant usually different – modeling expensive

• Slow processes – no problem with computation time

• Structural issues important– What to control? Extra measurements, Pairing of loops

Previous work on plantwide control: •Page Buckley (1964) - Chapter on “Overall process control” (still industrial practice)•Greg Shinskey (1967) – process control systems•Alan Foss (1973) - control system structure•Bill Luyben et al. (1975- ) – case studies ; “snowball effect”•George Stephanopoulos and Manfred Morari (1980) – synthesis of control structures for chemical processes•Ruel Shinnar (1981- ) - “dominant variables”•Jim Downs (1991) - Tennessee Eastman challenge problem•Larsson and Skogestad (2000): Review of plantwide control

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• Control structure selection issues are identified as important also in other industries.

Professor Gary Balas (Minnesota) at ECC’03 about flight control at Boeing:

The most important control issue has always been to select the right controlled variables --- no systematic tools used!

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Main objectives control system

1. Stabilization2. Implementation of acceptable (near-optimal) operation

ARE THESE OBJECTIVES CONFLICTING?

• Usually NOT – Different time scales

• Stabilization fast time scale

– Stabilization doesn’t “use up” any degrees of freedom• Reference value (setpoint) available for layer above• But it “uses up” part of the time window (frequency range)

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cs = y1s

MPC

PID

y2s

RTO

u (valves)

Follow path (+ look after other variables)

CV=y1 (+ u); MV=y2s

Stabilize + avoid drift CV=y2; MV=u

Min J (economics); MV=y1s

OBJECTIVE

Dealing with complexity

Main simplification: Hierarchical decomposition

Process control The controlled variables (CVs)interconnect the layers

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Example: Bicycle ridingNote: design starts from the bottom

• Regulatory control: – First need to learn to stabilize the bicycle

• CV = y2 = tilt of bike• MV = body position

• Supervisory control: – Then need to follow the road.

• CV = y1 = distance from right hand side• MV=y2s

– Usually a constant setpoint policy is OK, e.g. y1s=0.5 m

• Optimization: – Which road should you follow? – Temporary (discrete) changes in y1s

Hierarchical decomposition

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Summary: The three layers

• Optimization layer (RTO; steady-state nonlinear model): • Identifies active constraints and computes optimal setpoints for primary controlled variables (y1).

• Supervisory control (MPC; linear model with constraints): • Follow setpoints for y1 (usually constant) by adjusting setpoints for secondary variables (MV=y2s) • Look after other variables (e.g., avoid saturation for MV’s used in regulatory layer)

• Regulatory control (PID): • Stabilizes the plant and avoids drift, in addition to following setpoints for y2. MV=valves (u).

Problem definition and overall control objectives (y1, y2) starts from the top.Design starts from the bottom.

A good example is bicycle riding:

• Regulatory control: • First you need to learn how to stabilize the bicycle (y2)

• Supervisory control: • Then you need to follow the road. Usually a constant setpoint policy is OK, for example, stay

y1s=0.5 m from the right hand side of the road (in this case the "magic" self-optimizing variable self-optimizing variable is y1=distance to right hand side of road)

• Optimization: • Which road (route) should you follow?

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Control structure design procedure

I Top Down • Step 1: Define operational objectives (optimal operation)

– Cost function J (to be minimized)

– Operational constraints

• Step 2: Identify degrees of freedom (MVs) and optimize for expected disturbances

– Identify regions of active constraints

• Step 3: Select primary controlled variables c=y1 (CVs)

• Step 4: Where set the production rate? (Inventory control)

II Bottom Up

• Step 5: Regulatory / stabilizing control (PID layer)

– What more to control (y2; local CVs)?

– Pairing of inputs and outputs

• Step 6: Supervisory control (MPC layer)

• Step 7: Real-time optimization (Do we need it?)

Understanding and using this procedure is the most important part of this course!!!!

y1

y2

Process

MVs

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Step 3. What should we control (c)? (primary controlled variables y1=c)

1. CONTROL ACTIVE CONSTRAINTS, c=cconstraint

2. REMAINING UNCONSTRAINED, c=?

What should we control?c = Hy

H

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Step 1. Define optimal operation (economics)

• What are we going to use our degrees of freedom u (MVs) for?• Define scalar cost function J(u,x,d)

– u: degrees of freedom (usually steady-state)– d: disturbances– x: states (internal variables)Typical cost function:

• Optimize operation with respect to u for given d (usually steady-state):

minu J(u,x,d)subject to:

Model equations: f(u,x,d) = 0Operational constraints: g(u,x,d) < 0

J = cost feed + cost energy – value products

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Optimal operation distillation column

• Distillation at steady state with given p and F: N=2 DOFs, e.g. L and V

• Cost to be minimized (economics)

J = - P where P= pD D + pB B – pF F – pV V

• Constraints

Purity D: For example xD, impurity · max

Purity B: For example, xB, impurity · max

Flow constraints: min · D, B, L etc. · max

Column capacity (flooding): V · Vmax, etc.

Pressure: 1) p given, 2) p free: pmin · p · pmax

Feed: 1) F given 2) F free: F · Fmax

• Optimal operation: Minimize J with respect to steady-state DOFs

value products

cost energy (heating+ cooling)

cost feed

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Optimal operation

1. Given feedAmount of products is then usually indirectly given and J = cost energy.

Optimal operation is then usually unconstrained:

2. Feed free Products usually much more valuable than feed + energy costs small.

Optimal operation is then usually constrained:

minimize J = cost feed + cost energy – value products

“maximize efficiency (energy)”

“maximize production”

Two main cases (modes) depending on marked conditions:

Control: Operate at bottleneck (“obvious what to control”)

Control: Operate at optimal trade-off (not obvious what to control to achieve this)

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Solution I (“obvious”): Optimal feedforward

Problem: UNREALISTIC!

1. Lack of measurements of d2. Sensitive to model error

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Solution II (”obvious”): Optimizing control

Estimate d from measurements y and recompute uopt(d)

Problem:COMPLICATED! Requires detailed model and description of uncertainty

y

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Solution III (in practice): FEEDBACK with hierarchical decomposition

When disturbance d:Degrees of freedom (u) are updated indirectly to keep CVs at setpoints

y

CVs: link optimization and control layers

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How does self-optimizing control (solution III) work?

• When disturbances d occur, controlled variable c deviates from setpoint cs

• Feedback controller changes degree of freedom u to uFB(d) to keep c at cs

• Near-optimal operation / acceptable loss (self-optimizing control) is achieved if– uFB(d) ≈ uopt(d)– or more generally, J(uFB(d)) ≈ J(uopt(d))

• Of course, variation of uFB(d) is different for different CVs c.

• We need to look for variables, for which J(uFB(d)) ≈ J(uopt(d)) or Loss = J(uFB(d)) - J(uopt(d)) is small

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Remarks “self-optimizing control”

1. Old idea (Morari et al., 1980):

“We want to find a function c of the process variables which when held constant, leads automatically to the optimal adjustments of the manipulated variables, and with it, the optimal operating conditions.”

2. “Self-optimizing control” = acceptable steady-state behavior (loss) with constant CVs.

is similar to

“Self-regulation” = acceptable dynamic behavior with constant MVs.

3. The ideal self-optimizing variable c is the gradient (c = J/ u = Ju)

– Keep gradient at zero for all disturbances (c = Ju=0)

– Problem: no measurement of gradient

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Step 3. What should we control (c)?Simple examples

What should we control?

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– Cost to be minimized, J=T

– One degree of freedom (u=power)

– What should we control?

Optimal operation - Runner

Optimal operation of runner

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Self-optimizing control: Sprinter (100m)

• 1. Optimal operation of Sprinter, J=T– Active constraint control:

• Maximum speed (”no thinking required”)

Optimal operation - Runner

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• 2. Optimal operation of Marathon runner, J=T

Optimal operation - Runner

Self-optimizing control: Marathon (40 km)

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Solution 1 Marathon: Optimizing control

• Even getting a reasonable model requires > 10 PhD’s … and the model has to be fitted to each individual….

• Clearly impractical!

Optimal operation - Runner

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Solution 2 Marathon – Feedback(Self-optimizing control)

– What should we control?

Optimal operation - Runner

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• Optimal operation of Marathon runner, J=T• Any self-optimizing variable c (to control at

constant setpoint)?• c1 = distance to leader of race

• c2 = speed

• c3 = heart rate

• c4 = level of lactate in muscles

Optimal operation - Runner

Self-optimizing control: Marathon (40 km)

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Conclusion Marathon runner

c = heart rate

select one measurement

• Simple and robust implementation• Disturbances are indirectly handled by keeping a constant heart rate• May have infrequent adjustment of setpoint (heart rate)

Optimal operation - Runner

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Example: Cake Baking

• Objective: Nice tasting cake with good texture

u1 = Heat input

u2 = Final time

d1 = oven specifications

d2 = oven door opening

d3 = ambient temperature

d4 = initial temperature

y1 = oven temperature

y2 = cake temperature

y3 = cake color

Measurements Disturbances

Degrees of Freedom

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Further examples self-optimizing control

• Marathon runner

• Central bank

• Cake baking

• Business systems (KPIs)

• Investment portifolio

• Biology

• Chemical process plants: Optimal blending of gasoline

Define optimal operation (J) and look for ”magic” variable (c) which when kept constant gives acceptable loss (self-optimizing control)

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More on further examples

• Central bank. J = welfare. u = interest rate. c=inflation rate (2.5%)• Cake baking. J = nice taste, u = heat input. c = Temperature (200C)• Business, J = profit. c = ”Key performance indicator (KPI), e.g.

– Response time to order– Energy consumption pr. kg or unit– Number of employees– Research spendingOptimal values obtained by ”benchmarking”

• Investment (portofolio management). J = profit. c = Fraction of investment in shares (50%)

• Biological systems:– ”Self-optimizing” controlled variables c have been found by natural

selection– Need to do ”reverse engineering” :

• Find the controlled variables used in nature• From this possibly identify what overall objective J the biological system has

been attempting to optimize