Pid Control With Intelligent Compensation For Exoskeleton Robots by Wen Yu

Pid Control With Intelligent Compensation For Exoskeleton Robots

byWen Yu

Paperback | January 1, 2018

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PID Control with Intelligent Compensation for Exoskeleton Robotsshows readers how to use neural PD and neural PID controls to reduce integration gain, providing 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. Users will find this to be a great resource on how to combine traditional PD/PID control techniques with intelligent control.

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. 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.



  • 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

About The Author

Wen Yu received the B.S. degree from Tsinghua University, Beijing, China in 1990 and the M.S. and Ph.D. degrees, both in Electrical Engineering, from Northeastern University, Shenyang, China, in 1992 and 1995, respectively. Since 1996, he has been with theNational Polytechnic Institute (CINVESTAV-IPN), Mexico City, Mexico, where he is ...
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Details & Specs

Title:Pid Control With Intelligent Compensation For Exoskeleton RobotsFormat:PaperbackDimensions:234 pages, 8.75 × 6.35 × 0.68 inPublished:January 1, 2018Publisher:Academic PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0128133805

ISBN - 13:9780128133804

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Table of Contents

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.PID Control with Intelligent Compensation for Exoskeleton Robotsshows readers 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. PD/PID using intelligent compensation has proven much more effective than other model-based compensations.PID Control with Intelligent Compensation for Exoskeleton Robotsshows readers how to use neural PD and neural PID controls to reduce integration gain, providing explicit conditions on how to select linear PID gains by 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.