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Book 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 compensations
  • Presents a stability analysis of the book for industrial linear PID
  • Includes practical applications of robotic PD/PID control, such as serial sliding mode, explicit conditions for linear PID and high gain observers for neural PD control
  • Includes applied exoskeleton applications and MATLAB code for simulations and applications

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Dedication
  6. About the Author
  7. Preface
  8. Introduction
  9. Chapter 1: Preliminaries
    1. Abstract
    2. 1.1. Exoskeleton robots
    3. 1.2. Control of exoskeleton robots
    4. 1.3. Neural network and fuzzy systems
    5. 1.4. PD and PID control
    6. 1.5. PD and PID control with compensations
    7. 1.6. Robot admittance control
    8. 1.7. Trajectory generation of exoskeleton robots
    9. Bibliography
  10. Chapter 2: Stable PID Control and Systematic Tuning of PID Gains
    1. Abstract
    2. 2.1. Stable PD and PID control for exoskeleton robots
    3. 2.2. PID parameters tuning in closed-loop
    4. 2.3. Application to an exoskeleton
    5. 2.4. Conclusions
    6. Bibliography
  11. Chapter 3: PID Control in Task Space
    1. Abstract
    2. 3.1. Linear PID control in task space
    3. 3.2. Linear PID control with velocity observers
    4. 3.3. Experimental results
    5. 3.4. Conclusions
    6. Bibliography
  12. Chapter 4: PD Control with Neural Compensation
    1. Abstract
    2. 4.1. PD control with high gain observer
    3. 4.2. PD control with neural compensator
    4. 4.3. PD control with velocity estimation and neural compensator
    5. 4.4. Simulation
    6. 4.5. Conclusions
    7. Bibliography
  13. Chapter 5: PID Control with Neural Compensation
    1. Abstract
    2. 5.1. Stable neural PID control
    3. 5.2. Neural PID control with unmeasurable velocities
    4. 5.3. Neural PID tracking control
    5. 5.4. Experimental results of the neural PID
    6. 5.5. Conclusions
    7. Bibliography
  14. Chapter 6: PD Control with Fuzzy Compensation
    1. Abstract
    2. 6.1. PD control with fuzzy compensation
    3. 6.2. Membership functions learning and stability analysis
    4. 6.3. Experimental comparisons
    5. 6.4. Conclusion
    6. Bibliography
  15. Chapter 7: PD Control with Sliding Mode Compensation
    1. Abstract
    2. 7.1. PD control with parallel neural networks and sliding mode
    3. 7.2. PD control with serial neural networks and sliding mode
    4. 7.3. Simulation
    5. 7.4. Conclusions
    6. Bibliography
  16. Chapter 8: PID Admittance Control in Task Space
    1. Abstract
    2. 8.1. Human–robot cooperation via admittance control
    3. 8.2. PID admittance control in task space
    4. 8.3. PID admittance control in task space with neural compensation
    5. 8.4. Admittance PD control with Jacobian approximation
    6. 8.5. Admittance control with adaptive compensations
    7. 8.6. Experimental results
    8. 8.7. Conclusions
    9. Bibliography
  17. Chapter 9: PID Admittance Control in Joint Space
    1. Abstract
    2. 9.1. PD admittance control
    3. 9.2. PD admittance control with adaptive compensations
    4. 9.3. PD admittance control with sliding mode compensations
    5. 9.4. PID admittance control
    6. 9.5. Experimental results
    7. 9.6. Conclusion
    8. Bibliography
  18. Chapter 10: Robot Trajectory Generation in Joint Space
    1. Abstract
    2. 10.1. Codebook and key-points generation
    3. 10.2. Joint space trajectory generation with a modified hidden Markov model
    4. 10.3. Experiments of learning trajectory
    5. 10.4. Conclusions
    6. Bibliography
  19. Appendix A: Design of Upper Limb Exoskeletons
    1. A.1. Heavy duty exoskeleton robot
    2. A.2. Portable exoskeleton robot
    3. Bibliography
  20. Bibliography
  21. Index
44.192.75.131