Neuro-Fuzzy Control of Industrial Systems

Neural networks and fuzzy systems are model free control design approaches that represent an advantage over classical control when dealing with complicated nonlinear actuator dynamics. Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities brings neural networks and fuzzy logic together with dynamical control systems. Each chapter presents powerful control approaches for the design of intelligent controllers to compensate for actuator nonlinearities such as time delay, friction, deadzone, and backlash that can be found in all industrial motion systems, plus a thorough development, rigorous stability proofs, and simulation examples for each design. In the final chapter, the authors develop a framework to implement intelligent control schemes on actual systems. Rigorous stability proofs are further verified by computer simulations, and appendices contain the computer code needed to build intelligent controllers for real-time applications.

Preface

xiii

Background on Neural Networks and Fuzzy Logic Systems

1

(12)

Neural networks

1

(7)

Two-layer neural network topology and tuning

1

(3)

Single-layer neural networks

4

(2)

Universal approximation property of neural networks

6

(2)

Fuzzy logic systems

8

(5)

Membership functions and defuzzification

9

(1)

Universal approximation property and tuning of fuzzy logic networks

10

(3)

Background on Dynamical Systems and Industrial Actuators

13

(16)

Dynamical systems

13

(8)

Continuous and discrete dynamical systems

13

(2)

Mathematics and system properties

15

(3)

Lyapunov stability analysis

18

(3)

Industrial actuators

21

(1)

Friction

22

(2)

Deadzone

24

(2)

Backlash

26

(3)

Neurocontrol of Systems with Friction

29

(24)

Neural network approximation of functions with discontinuities

29

(10)

Neural network approximation of continuous functions

30

(1)

Neural network approximation of functions with jumps

30

(5)

Augmented multilayer neural networks for jump function approximation

35

(1)

Simulation of the augmented multilayer neural network

36

(3)

Neurocontroller for friction compensation

39

(11)

Robot arm dynamics

40

(2)

Neural network controller

42

(6)

Simulation of a neural network controller

48

(2)

Conclusions

50

(3)

Neural and Fuzzy Control of Systems with Deadzones

53

(38)

Introduction to deadzone control

53

(1)

Position tracking controller with neural network deadzone compensation

54

(16)

Neural network deadzone precompensator

54

(6)

Robot arm dynamics

60

(1)

Design of tracking controller with neural network deadzone compensation

61

(6)

Simulation of neural network deadzone compensator for robotic systems

67

(3)

Fuzzy logic discrete-time deadzone precompensation

70

(13)

Fuzzy logic discrete-time deadzone precompensator

73

(4)

Discrete-time fuzzy logic controller with deadzone compensation

77

(4)

Simulation results

81

(2)

Appendix: Proof of Theorem 4.3.1

83

(8)

Neural Control of Systems with Backlash

91

(34)

Introduction to backlash control

91

(1)

Continuous-time neural network backlash compensation

92

(13)

Backlash inverse

93

(1)

Dynamics and control of nonlinear motion systems

94

(2)

Neural network backlash compensation using dynamic inversion

96

(7)

Simulation of neural network backlash compensator

103

(2)

Discrete-time neural network backlash compensation

105

(10)

Discrete-time backlash nonlinearity and backlash inverse

107

(1)

Dynamics and control of a nonlinear discrete-time system

108

(2)

Discrete-time neural network backlash compensation using dynamic inversion

110

(3)

Simulation results

113

(2)

Appendix: Proof of Theorem 5.3.2

115

(10)

Fuzzy Logic Control of Vehicle Active Suspension

125

(14)

Introduction

125

(2)

System model and dynamics

127

(3)

Backstepping-based fuzzy logic controller

130

(6)

Outer loop design for ideal force input

130

(3)

Inner backstepping loop design

133

(3)

Simulation results

136

(3)

Neurocontrol Using the Adaptive Critic Architecture

139

(12)

Introduction

139

(2)

Dynamics of a nonlinear system

141

(1)

Adaptive critic feedback controller

142

(3)

Adaptive critic neurocontrol architecture

142

(3)

Simulation results

145

(2)

Conclusions

147

(2)

Appendix: Proof of Theorem 7.3.1

149

(2)

Neurocontrol of Telerobotic Systems with Time Delays

151

(20)

Introduction

151

(2)

Background and problem statement

153

(2)

Identification of time-delay-free systems using recurrent neural networks

155

(2)

Recurrent neural network predictive control strategy for compensating time delays

157

(3)

Review of the Smith predictor

158

(1)

Control structure

159

(1)

Recurrent neural network predictive control and stability analysis

160

(3)

Simulation example

163

(3)

Conclusion

166

(1)

Appendix: Proof of Theorem 8.3.1

167

(1)

Appendix: Proof of Theorem 8.5.1

168

(3)

Implementation of Neural Network Control Systems

171

(16)

PC-PC real-time digital control system

171

(7)

Hardware description

172

(2)

Software description

174

(2)

State-variable data management

176

(2)

ATB1000 U.S. Army tank gun barrel testbed

178

(1)

Derivation of neural net control system for flexible systems

179

(4)

Flexible-link robot dynamics

180

(1)

Singular perturbation approach

181

(1)

Neural network control algorithm

182

(1)

Implementation of neural network controller on ATB1000 testbed

183

(3)

Proportional-plus-derivative control

185

(1)

Proportional-plus-derivative control plus neural network

185

(1)

Conclusions

186

(1)

Appendix A C Code for Neural Network Friction Controller

187

(14)

Appendix B C Code for Continuous-Time Neural Network Deadzone Controller

201

(18)

Appendix C C Code for Discrete-Time Neural Network Backlash Controller

219

(10)

Appendix D Versatile Real-Time Executive Code for Implementation of Neural Network Backstepping Controller on ATB1000 Tank Gun Barrel

229

(4)

References

233

(8)

Index

241

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Neuro-Fuzzy Control of Industrial Systems With Actuator Nonlinearities

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