Active Force Control

Professor Dr Musa Mailah

Intelligent Active Force Control (IAFC) Research GroupDepartment of Applied Mechanics, Faculty of Mechanical Engineering

Universiti Teknologi Malaysia81310 Skudai, Johor

Outline

• Introduction• AFC algorithm• PD and AFC• AFC applied to a robot arm• Performance evaluation• Other applications of AFC• Current research and future directions• Conclusions

IntroductionTrend and emphasis in control - Robust Control

Active Force Control (AFC):

• A disturbance cancellation control scheme, also known as disturbance rejection control, disturbance observer, robust control

• Proposed in ‘complete’ form by Hewit and Burdess (1981); initiated by Johnson (1971) and Davison (1976)

• Based on principle of invariance and Newton second law of motion

• Relies on measurement and estimation of parameters

• Use a very simple control algorithm, reduced computational load and readily implemented in real-time

AFC Algorithm

AFC algorithm: f = W(s) Q’ = W(s) (F’ – M’a’)

Where W(s): Weighting function

Q’: Computed estimated disturbance

F’: Measured force

a’: Measured acceleration

M’: Estimated mass

Ga(s) G(s)

M'W(s)

Q

+F

Forcesensor

Accelerometer

+ -

Q'

+

+

a 1 s2

x+

F'

Disturbances

Dynamic systemActuator

Actualposition

Estimated mass

Measured force

Measuredacceleration

a'

Outer loop control

Computed estimated disturbance

M' estimation methods:

Crude Approximation

On-line Neural Network

Iterative Learning Algorithm

Adaptive Fuzzy Logic

Knowledge-based

Genetic Algorithm

Particle Swarm Optimisation

f

PD and AFC

PDcontroller

Dynamic system

AFCcontroller

Sensor

Commanded trajectory

Actualtrajectory+

-

+

+

Actuator

Disturbance

PD and AFC (cont…)

Overall control algorithm: [PD+AFC] = Kpe(s) + Kd s e(s) + Q’ W(s)

Ks = 0 PD; Ks = 1 AFC

Ga(s) G(s)

M'

W(s)

Q

+F

Forcesensor

Accelerometer

+ -

Q'

+

+

a 1 s2

x+

F'

Disturbances

Dynamic systemActuator Actualposition

Estimated mass

Measured force

Measuredacceleration

a'

Gc(s)

PD controller

H(s)

+

-

xde

Desired position

Position sensor

Estimated disturbance

KsSwitch

AFC Applied to A Robot Arm

IN/Kt Kt

1/Kt

1 / H 1/s 1/s

INk

ref

++

Ic Tq

Td

Td* -+

++coordinate

transformation

Kd

Kp

coordinate transformation

coordinate transformation

It

Ia

x refxbar

xbar

xbar

+

+

-

-

++

++

x

x

++(t)d/dt

(t)

+

TEk

INk+1

.

.. .. .. ..

.

.

AFC with RMAC and Iterative Learning Control Scheme

AFC Applied to A Robot Arm (cont…)

Dynamic model :

Tq = H() + h(, ) + G() + Td

.. .

link 1

link 2

L1

L2

(x, y)

AFC Applied to A Robot Arm (cont…)

AFC: f = (1/Kt) Td*

Td* = Tq - IN RMAC-PD:

ILA:

Disturbances: h = 30 N, 100N, 1 rad/s; k = 300 N/m

Trajectory: Circular, r = 0.1 m

End-point velocity: Vcut = 0.2 m/s

.. .. . .

( ) ( )ref bar barp bar dx x K x x K x x

)()()()(1 tTEtTEtINtIN kkkk

..

AFC Applied to A Robot Arm (cont…)

AFC Loop

Tq2

Tq1

t

Clock

Robot Arm Model

thdd2

thdd1

th1, th2, thd1, thd2

+---

Sum1

+---

Sum

Disturbance Models

TrajectoryPlanner

RMAC-PD

IterativeLearning

Performance Evaluation

0 0.1 0.2 0.3 0.40

0.1

0.2

0.3

0.4

0.5

X Axis

Y Ax

is

X Y Plot

0 5 10 15 200

0.002

0.004

0.006

0.008

0.01

X Axis

Y A

xis

X Y Plot

0 0.1 0.2 0.3 0.40

0.1

0.2

0.3

0.4

0.5

X Axis

Y A

xis

X Y Plot

0 5 10 15 200

0.002

0.004

0.006

0.008

0.01

X Axis

Y A

xis

X Y Plot

AFC: h = 30 N, = 1 rad/s; k = 300 N/m

PD: h = 30 N, = 1 rad/s; k = 300 N/m

Performance Evaluation (cont…)

0 5 10 15 200

0.002

0.004

0.006

0.008

0.01

X Axis

Y A

xis

X Y Plot

0 0.1 0.2 0.3 0.40

0.1

0.2

0.3

0.4

0.5

X Axis

Y A

xis

X Y Plot

0 0.1 0.2 0.3 0.40

0.1

0.2

0.3

0.4

0.5

X Axis

Y Ax

is

X Y PlotPD: h = 100 N, = 1 rad/s

0 5 10 15 200

0.002

0.004

0.006

0.008

0.01

X Axis

Y A

xis

X Y Plot

AFC: h = 100 N, = 1 rad/s

Performance Evaluation (cont…)

Other Applications of AFC

• Nonholonomic Wheeled Mobile Robot

• Mobile Manipulator

• Vehicle Suspension System

• Antilock Brake System

• Active Vibration Control

• Gantry Crane

• Motion Control

Nonholonomic Wheeled Mobile Robot

Mobile Manipulator

Mobile Platform

Gripper

Manipulator system

castor

Front wheels (nonholonomics)

PC Pentium III (with ISA slot)

2 units DAS1602 (inside)

Parallel cables (8 16lines)

MMH852.EXE is executed here.

Mobile Manipulator system

Vehicle Suspension System

D/A

D/A

Suspension Test Rig

PC-based control

MATLAB/CST/Simulink/RTW

DAS1602I/O card

to pneumatic actuator

from sensors (LVDTs, pressure sensor

& accelerometers)

PID, PI, skyhook, AFC, NN algorithms

Programmable Logic

Controller (PLC)

Disturbances

Actuator (Ga)

NN2

Suspensionsystem

NN1

1/s 1/s+

- +

++

+ -

Forcesensor

Accelero-meter

Disturbances

Zs

Q'

PIDZs des +

Active Force Control (AFC)

Zs..

Estimated Mass

Ga-1

Skyhook

-

Active Vibration Control

Pentium III PC

DAS-1602 card

Mechanical Suspension

System

DC motor with driver

Sensor (position sensor, current sensor and accelerometer)

Gantry Crane

Current Research and Future Directions

• Intelligent active vibration control of human-like arm• Development of smart glove for active tremor control• Active vibration suppression of machines / equipments• Hybrid active vibration control of thin plates and

structures• Robust control of satellite system• Microsystems: micro-robotics, micro-machining, micro-

actuation, sensing & control• Embedded AFC systems

Biomechanics Application

0

0.01

0.02

0.03

0.04

0.05

0.06

0 0.2 0.4 0.6 0.8 1 1.2

Time (s)

Tra

ck E

rro

r (m

)

PD-AFC

PD

Conclusions

• AFC is very robust compared to PID control• The algorithm is simple, not computationally

intensive and can be practically implemented in real-time

• Simulation results are very promising• Problems of selecting the appropriate actuators

(and drivers) and noises in sensors need to be addressed and solved

• Micro and embedded AFC systems

Thank You

Q & A