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E-raamat: PID Control with Intelligent Compensation for Exoskeleton Robots

(Professor and Department Chair, Automatic Control Department, National Polytechnic Institute (CINVESTAV-IPN), Mexico City, Mexico)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 10-Jan-2018
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128134641
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PID Control with Intelligent Compensation for Exoskeleton RobotsSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 10-Jan-2018
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128134641
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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

Muu info

Proposes novel PD/PID controllers for upper and lower limb exoskeleton robots
About the Author xi
Preface xiii
Introduction xv
1 Preliminaries
1(12)
1.1 Exoskeleton robots
1(2)
1.2 Control of exoskeleton robots
3(1)
1.3 Neural network and fuzzy systems
4(1)
1.4 PD and PID control
5(2)
1.4.1 PID parameters tuning
5(1)
1.4.2 PID control in task space
6(1)
1.4.3 PID control with velocity observer
7(1)
1.5 PD and PID control with compensations
7(2)
1.6 Robot admittance control
9(1)
1.7 Trajectory generation of exoskeleton robots
10(3)
2 Stable PID Control and Systematic Tuning of PID Gains
13(22)
2.1 Stable PD and PID control for exoskeleton robots
13(9)
2.1.1 Stable PD control
14(3)
2.1.2 Stable PID control
17(5)
2.2 PID parameters tuning in closed-loop
22(7)
2.2.1 Linearization of the closed-loop system
25(1)
2.2.2 PD/PID tuning
26(2)
2.2.3 Refine PID gains
28(1)
2.2.4 Stability conditions for PID gains
28(1)
2.3 Application to an exoskeleton
29(4)
2.4 Conclusions
33(2)
3 PID Control in Task Space
35(20)
3.1 Linear PID control in task space
35(9)
3.2 Linear PID control with velocity observers
44(4)
3.3 Experimental results
48(5)
3.4 Conclusions
53(2)
4 PD Control with Neural Compensation
55(26)
4.1 PD control with high gain observer
55(10)
4.1.1 Singular perturbation method
56(7)
4.1.2 Lyapunov method
63(2)
4.2 PD control with neural compensator
65(6)
4.2.1 PD control with single layer neural compensation
65(1)
4.2.2 PD control with a multilayer feedforward neural compensator
66(5)
4.3 PD control with velocity estimation and neural compensator
71(4)
4.4 Simulation
75(5)
4.5 Conclusions
80(1)
5 PID Control with Neural Compensation
81(28)
5.1 Stable neural PID control
81(10)
5.2 Neural PID control with unmeasurable velocities
91(5)
5.3 Neural PID tracking control
96(5)
5.4 Experimental results of the neural PID
101(5)
5.5 Conclusions
106(3)
6 PD Control with Fuzzy Compensation
109(16)
6.1 PD control with fuzzy compensation
109(5)
6.2 Membership functions learning and stability analysis
114(6)
6.3 Experimental comparisons
120(4)
6.4 Conclusion
124(1)
7 PD Control with Sliding Mode Compensation
125(14)
7.1 PD control with parallel neural networks and sliding mode
125(4)
7.2 PD control with serial neural networks and sliding mode
129(4)
7.3 Simulation
133(5)
7.4 Conclusions
138(1)
8 PID Admittance Control in Task Space
139(20)
8.1 Human-robot cooperation via admittance control
139(2)
8.2 PID admittance control in task space
141(4)
8.3 PID admittance control in task space with neural compensation
145(4)
8.4 Admittance PD control with Jacobian approximation
149(5)
8.5 Admittance control with adaptive compensations
154(2)
8.6 Experimental results
156(2)
8.6.1 Pan and tilt robot
156(1)
8.6.2 4-DoF robot
156(2)
8.7 Conclusions
158(1)
9 PID Admittance Control in Joint Space
159(16)
9.1 PD admittance control
159(5)
9.2 PD admittance control with adaptive compensations
164(3)
9.3 PD admittance control with sliding mode compensations
167(1)
9.4 PID admittance control
168(2)
9.5 Experimental results
170(4)
9.5.1 Pan and tilt robot
170(1)
9.5.2 4-DoF exoskeleton
170(4)
9.6 Conclusion
174(1)
10 Robot Trajectory Generation in Joint Space
175(20)
10.1 Codebook and key-points generation
175(4)
10.2 Joint space trajectory generation with a modified hidden Markov model
179(6)
10.3 Experiments of learning trajectory
185(7)
10.3.1 Two-link planar elbow manipulator
186(3)
10.3.2 4-DoF upper limb exoskeleton
189(3)
10.4 Conclusions
192(3)
A Design of Upper Limb Exoskeletons
195(10)
A.1 Heavy duty exoskeleton robot
195(5)
A.2 Portable exoskeleton robot
200(5)
Bibliography 205(8)
Index 213
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 the National Polytechnic Institute (CINVESTAV-IPN), Mexico City, Mexico, where he is currently a professor and department chair of the Automatic Control Department. From 2002 to 2003, he held research positions with the Mexican Institute of Petroleum. He was a Senior Visiting Research Fellow with Queens University Belfast, Belfast, U.K., from 2006 to 2007, and a Visiting Associate Professor with the University of California, Santa Cruz, from 2009 to 2010. He gas published more than 100 research papers in reputed journals. His Google Scholar h-index is 33, the citation number is 4100. He serves as associate editors of IEEE Transactions on Cybernetics, Neurocomputing, and Journal of Intelligent and Fuzzy Systems. He is a member of the Mexican Academy of Sciences.
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