PID Control with Intelligent Compensation for Exoskeleton Robots

Author： Yu   Wen  

Publisher： Elsevier Science‎

Publication year： 2018

E-ISBN: 9780128134641

P-ISBN(Paperback): 9780128133804

Subject： TP242 Robot

Keyword： 机器人技术

Language： ENG

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Description

PID Control with Intelligent Compensation for Exoskeleton Robots explains how to use neural PD and PID controls to reduce integration gain, and provides explicit conditions on how to select linear PID gains using proof of semi-global asymptotic stability and local asymptotic stability with a velocity observer. These conditions are applied in both task and joint spaces, with PID controllers compensated by neural networks. This is a great resource on how to combine traditional PD/PID control techniques with intelligent control. Dr. Wen Yu presents several leading-edge methods for designing neural and fuzzy compensators with high-gain velocity observers for PD control using Lyapunov stability.

Proportional-integral-derivative (PID) control is widely used in biomedical and industrial robot manipulators. An integrator in a PID controller reduces the bandwidth of the closed-loop system, leads to less-effective transient performance and may even destroy stability. Many robotic manipulators use proportional-derivative (PD) control with gravity and friction compensations, but improved gravity and friction models are needed. The introduction of intelligent control in these systems has dramatically changed the face of biomedical and industrial control engineering.

  • Discusses novel PD and PID controllers for biomedical and industrial robotic applications, demonstrating how PD and PID with intelligent compensation is more effective than other model-based compe

Chapter

Front Cover

PID Control with Intelligent Compensation for Exoskeleton Robots

Copyright

Contents

About the Author

Preface

Introduction

1 Preliminaries

1.1 Exoskeleton robots

1.2 Control of exoskeleton robots

1.3 Neural network and fuzzy systems

1.4 PD and PID control

1.4.1 PID parameters tuning

1.4.2 PID control in task space

1.4.3 PID control with velocity observer

1.5 PD and PID control with compensations

1.6 Robot admittance control

1.7 Trajectory generation of exoskeleton robots

2 Stable PID Control and Systematic Tuning of PID Gains

2.1 Stable PD and PID control for exoskeleton robots

2.1.1 Stable PD control

2.1.2 Stable PID control

2.2 PID parameters tuning in closed-loop

2.2.1 Linearization of the closed-loop system

2.2.2 PD/PID tuning

2.2.3 Refine PID gains

2.2.4 Stability conditions for PID gains

2.3 Application to an exoskeleton

2.4 Conclusions

3 PID Control in Task Space

3.1 Linear PID control in task space

3.2 Linear PID control with velocity observers

3.3 Experimental results

3.4 Conclusions

4 PD Control with Neural Compensation

4.1 PD control with high gain observer

4.1.1 Singular perturbation method

4.1.2 Lyapunov method

4.2 PD control with neural compensator

4.2.1 PD control with single layer neural compensation

4.2.2 PD control with a multilayer feedforward neural compensator

4.3 PD control with velocity estimation and neural compensator

4.4 Simulation

4.5 Conclusions

5 PID Control with Neural Compensation

5.1 Stable neural PID control

5.2 Neural PID control with unmeasurable velocities

5.3 Neural PID tracking control

5.4 Experimental results of the neural PID

5.5 Conclusions

6 PD Control with Fuzzy Compensation

6.1 PD control with fuzzy compensation

6.2 Membership functions learning and stability analysis

6.3 Experimental comparisons

6.4 Conclusion

7 PD Control with Sliding Mode Compensation

7.1 PD control with parallel neural networks and sliding mode

7.2 PD control with serial neural networks and sliding mode

7.3 Simulation

7.4 Conclusions

8 PID Admittance Control in Task Space

8.1 Human-robot cooperation via admittance control

8.2 PID admittance control in task space

8.3 PID admittance control in task space with neural compensation

8.4 Admittance PD control with Jacobian approximation

8.5 Admittance control with adaptive compensations

8.6 Experimental results

8.6.1 Pan and tilt robot

8.6.2 4-DoF robot

8.7 Conclusions

9 PID Admittance Control in Joint Space

9.1 PD admittance control

9.2 PD admittance control with adaptive compensations

9.3 PD admittance control with sliding mode compensations

9.4 PID admittance control

9.5 Experimental results

9.5.1 Pan and tilt robot

9.5.2 4-DoF exoskeleton

9.6 Conclusion

10 Robot Trajectory Generation in Joint Space

10.1 Codebook and key-points generation

10.2 Joint space trajectory generation with a modified hidden Markov model

10.3 Experiments of learning trajectory

10.3.1 Two-link planar elbow manipulator

10.3.2 4-DoF upper limb exoskeleton

10.4 Conclusions

A Design of Upper Limb Exoskeletons

A.1 Heavy duty exoskeleton robot

A.2 Portable exoskeleton robot

Bibliography

Index

Back Cover

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