active force control
DESCRIPTION
Introduction to a very robust control scheme with real-time potential applicationsTRANSCRIPT
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