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E-raamat: Adaptive Identification and Control of Uncertain Systems with Non-smooth Dynamics

(Professor, Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China), (Professor, Deputy Dean of the Graduate School, and Director of the Provincial Experimental Teaching Center for),
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Adaptive Identification and Control of Uncertain Systems with Non-smooth DynamicsSuurem pilt
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Adaptive Identification and Control of Uncertain Systems with Nonsmooth Dynamics reports some of the latest research on modeling, identification and adaptive control for systems with nonsmooth dynamics (e.g., backlash, dead zone, friction, saturation, etc). The authors present recent research results for the modelling and control designs of uncertain systems with nonsmooth dynamics, such as friction, dead-zone, saturation and hysteresis, etc., with particular applications in servo systems. The book is organized into 19 chapters, distributed in five parts concerning the four types of nonsmooth characteristics, namely friction, dead-zone, saturation and hysteresis, respectively. Practical experiments are also included to validate and exemplify the proposed approaches.

This valuable resource can help both researchers and practitioners to learn and understand nonlinear adaptive control designs. Academics, engineers and graduate students in the fields of electrical engineering, control systems, mechanical engineering, applied mathematics and computer science can benefit from the book. It can be also used as a reference book on adaptive control for servo systems for students with some background in control engineering.

  • Explains the latest research outputs on modeling, identification and adaptive control for systems with nonsmooth dynamics
  • Provides practical application and experimental results for robotic systems, and servo motors

Arvustused

"This book is interesting for both researchers and practitioners which can learn and understand nonlinear adaptive control designs. It is also useful for academics, engineers and graduate students in the fields of electrical engineering, control systems, mechanical engineering, applied mathematics and computer science. It may be used as a reference book on adaptive control for servo systems for students with some background in control engineering." --zbMATH

About the Authors xv
Preface xvii
Acknowledgment xix
Part 1 Introduction
1.1 Prologue
1(2)
1.2 Objective of the Book
3(1)
1.3 Book Outline
4(7)
Part 2 Modeling and Control of Uncertain Systems With Friction
1 Friction Dynamics and Modeling
11(8)
1.1 Introduction
11(1)
1.2 Friction Dynamics and Models
12(4)
1.2.1 Friction Dynamics
12(1)
1.2.2 Classical Friction Models
13(1)
1.2.3 Continuously Differentiable Friction Model
14(1)
1.2.4 Discontinuous Piecewise Parametric Friction Model
15(1)
1.3 Conclusion
16(3)
References
17(2)
2 Adaptive Sliding Mode Control of Non-linear Servo Systems With LuGre Friction Model
19(18)
2.1 Introduction
19(1)
2.2 System Description and Problem Formulation
20(1)
2.3 Offline Friction Identification
21(5)
2.3.1 Glowworm Swarm Optimization
21(1)
2.3.2 Static Parameters Identification
22(3)
2.3.3 Dynamic Parameters Identification
25(1)
2.4 Controller Design and Stability Analysis
26(5)
2.4.1 Adaptive Non-linear Sliding Mode Control Design
26(2)
2.4.2 Finite-Time Parameter Estimation
28(1)
2.4.3 Stability Analysis
29(2)
2.5 Simulations
31(1)
2.6 Conclusion
32(5)
References
34(3)
3 Adaptive Dynamic Surface Control of Two-Inertia Systems With LuGre Friction Model
37(20)
3.1 Introduction
37(1)
3.2 Problem Formulation and Preliminaries
38(6)
3.2.1 Modeling of Two-Inertia System
38(2)
3.2.2 Echo State Network (ESN)
40(1)
3.2.3 Prescribed Performance Function
41(2)
3.2.4 High-Gain Tracking Differentiator
43(1)
3.3 Controller Design and Stability Analysis
44(6)
3.3.1 Luenberger Observer
44(1)
3.3.2 Error Constraint Dynamic Surface Control Design
45(2)
3.3.3 Friction Compensation With ESN
47(2)
3.3.4 Stability Analysis
49(1)
3.4 Simulation and Experiment
50(4)
3.4.1 Simulation Results
50(1)
3.4.2 Experiment Results
51(3)
3.5 Conclusion
54(3)
References
54(3)
4 Adaptive Prescribed Performance Control of Servo Systems With Continuously Differentiable Friction Model
57(18)
4.1 Introduction
57(1)
4.2 Problem Formulation and Preliminaries
58(2)
4.2.1 Dynamic Model of Servo System
58(1)
4.2.2 Continuously Differentiable Friction Model
59(1)
4.2.3 Neural Network Approximation
60(1)
4.3 Adaptive Prescribed Performance Control Design
60(6)
4.3.1 Prescribed Performance Function and Error Transform
60(3)
4.3.2 Control Design and Stability Analysis
63(2)
4.3.3 Practical Implementation
65(1)
4.4 Experimental Validation
66(5)
4.4.1 Experimental Setup
66(2)
4.4.2 Experimental Results
68(3)
4.5 Conclusion
71(4)
References
72(3)
5 RISE Based Asymptotic Prescribed Performance Control of Servo Systems With Continuously Differentiable Friction Model
75(18)
5.1 Introduction
75(1)
5.2 Problem Formulation and Preliminaries
76(2)
5.2.1 Dynamic Model of Servo System
76(1)
5.2.2 Function Approximation Using ESN
77(1)
5.2.3 Prescribed Performance Function and Error Transform
77(1)
5.3 RISE Based Adaptive Control Design and Analysis
78(8)
5.3.1 Derivation of Filtered Tracking Error
79(2)
5.3.2 Adaptive Control Design With RISE
81(2)
5.3.3 Stability Analysis
83(3)
5.4 Experimental Validation
86(3)
5.4.1 Experimental Setup
86(1)
5.4.2 Experimental Results
86(3)
5.5 Conclusion
89(4)
References
89(4)
6 Adaptive Control for Manipulation Systems With Discontinuous Piecewise Parametric Friction Model
93(16)
6.1 Introduction
93(1)
6.2 System Dynamics and Problem Statement
94(1)
6.2.1 Manipulation System Dynamics
94(1)
6.2.2 Problem Formulation
94(1)
6.3 Modeling and Identification of Friction
95(5)
6.3.1 Discontinuous Piecewise Parametric Representation (DPPR)
95(1)
6.3.2 DPPR Modeling of Friction
96(1)
6.3.3 Data Acquisition and Model Validation
97(3)
6.4 Adaptive Control With Friction Compensation and Stability Analysis
100(2)
6.5 Simulations
102(2)
6.6 Conclusion
104(5)
References
105(4)
Part 3 Modeling and Control of Uncertain Systems With Input Dead-Zone
7 Dead-Zone Dynamics and Modeling
109(10)
7.1 Introduction
109(1)
7.2 Dead-Zone Models
110(3)
7.2.1 Linear Dead-Zone Model
110(1)
7.2.2 Non-linear Dead-Zone Model
111(2)
7.3 Examples With Dead-Zone
113(2)
7.3.1 Upper-Limb Model
113(1)
7.3.2 Ultrasonic Motor
114(1)
7.3.3 Servo-Valve
115(1)
7.4 Conclusion
115(4)
References
116(3)
8 Adaptive Finite-Time Neural Control of Servo Systems With Non-linear Dead-Zone
119(16)
8.1 Introduction
119(1)
8.2 Problem Formulation and Preliminaries
120(2)
8.3 Adaptive Finite-Time Control Design and Stability Analysis
122(7)
8.3.1 Fast Terminal Sliding Mode Manifold
122(3)
8.3.2 Adaptive Controller Design
125(1)
8.3.3 Stability Analysis
126(3)
8.4 Experimental Validation
129(3)
8.4.1 Experimental Setup
129(3)
8.4.2 Experimental Results
132(1)
8.5 Conclusion
132(3)
References
132(3)
9 Adaptive Neural Dynamic Surface Control of Strict-Feedback Systems With Non-linear Dead-Zone
135(20)
9.1 Introduction
135(1)
9.2 Problem Formulation and Preliminaries
136(3)
9.2.1 Problem Statement
136(2)
9.2.2 High-Order Neural Networks (HONNs)
138(1)
9.3 Control Design and Stability Analysis
139(11)
9.3.1 Adaptive Neural Dynamic Surface Control
139(8)
9.3.2 Stability Analysis
147(2)
9.3.3 Practical Implementation
149(1)
9.4 Simulations
150(1)
9.5 Conclusion
151(4)
References
153(2)
10 Adaptive Prescribed Performance Control of Strict-Feedback Systems With Non-linear Dead-Zone
155(22)
10.1 Introduction
155(1)
10.2 Problem Formulation and Preliminaries
156(4)
10.2.1 Prescribed Performance Function and Error Transform
158(1)
10.2.2 High-Order Neural Network and Nussbaum-Type Function
159(1)
10.3 Control Design and Stability Analysis
160(9)
10.3.1 Adaptive Prescribed Performance Control
160(7)
10.3.2 Stability Analysis
167(2)
10.4 Simulations
169(3)
10.5 Conclusion
172(5)
References
173(4)
11 Adaptive Dynamic Surface Output Feedback Control of Pure-Feedback Systems With Non-linear Dead-Zone
177(18)
11.1 Introduction
177(1)
11.2 Problem Formulation and Preliminaries
178(1)
11.3 Coordinate Transformation and Observer Design
179(4)
11.3.1 Coordinate Transformation
179(3)
11.3.2 Non-linear Extended State Observer Design
182(1)
11.4 Control Design and Stability Analysis
183(6)
11.4.1 Tracking Differentiator
183(1)
11.4.2 Dynamic Surface Control Design
184(3)
11.4.3 Stability Analysis
187(2)
11.5 Simulations
189(2)
11.6 Conclusion
191(4)
References
191(4)
Part 4 Modeling and Control of Uncertain Systems With Saturation
12 Saturation Dynamics and Modeling
195(8)
12.1 Introduction
195(1)
12.2 Saturation Dynamics
196(1)
12.3 Saturation Approximation
197(1)
12.4 Examples With Saturations
198(2)
12.4.1 Active Micro-Gravity Isolation System
198(1)
12.4.2 Flight Control System
199(1)
12.4.3 ITER Cryogenic System
199(1)
12.5 Conclusion
200(3)
References
200(3)
13 ESO Based Adaptive Sliding Mode Control of Servo Systems With Input Saturation
203(12)
13.1 Introduction
203(1)
13.2 System Description and Saturation Model
204(2)
13.2.1 System Description
204(1)
13.2.2 Saturation Model
205(1)
13.3 Adaptive Sliding Mode Control Design and Stability Analysis
206(5)
13.3.1 Non-linear ESO Design
206(2)
13.3.2 Adaptive Sliding Mode Controller Design
208(1)
13.3.3 Stability Analysis
209(2)
13.4 Simulations
211(2)
13.5 Conclusion
213(2)
References
214(1)
14 Non-singular Terminal Sliding Mode Funnel Control of Servo Systems With Input Saturation
215(14)
14.1 Introduction
215(1)
14.2 Problem Formulation and Preliminaries
216(2)
14.2.1 System Description and Problem Formulation
216(1)
14.2.2 Saturation Model
217(1)
14.2.3 Neural Network Approximation
217(1)
14.3 Non-singular Terminal Sliding Mode Funnel Control
218(5)
14.3.1 Funnel Error Variable
218(1)
14.3.2 Controller Design
219(2)
14.3.3 Stability Analysis
221(2)
14.4 Simulations
223(3)
14.5 Conclusion
226(3)
References
226(3)
15 Adaptive Neural Dynamic Surface Control for Pure-Feedback Systems With Input Saturation
229(20)
15.1 Introduction
229(1)
15.2 Problem Formulation and Preliminaries
230(5)
15.2.1 System Description
230(1)
15.2.2 Coordinate Transformation
231(3)
15.2.3 High-Order Sliding Mode (HOSM) Differentiator
234(1)
15.3 Sliding Mode Dynamic Surface Control Design and Stability Analysis
235(6)
15.3.1 Sliding Mode Dynamic Surface Control Design
235(3)
15.3.2 Stability Analysis
238(3)
15.4 Simulations
241(2)
15.5 Conclusion
243(6)
References
245(4)
Part 5 Modeling and Control of Uncertain Systems With Hysteresis
16 Hysteresis Dynamics and Modeling
249(8)
16.1 Introduction
249(1)
16.2 Hysteresis Models
250(4)
16.2.1 Preisach Model
250(1)
16.2.2 Prandtl-Ishlinskii (PI) Model
250(2)
16.2.3 Krasnoselskii-Pokrovskii (KP) Model
252(1)
16.2.4 Backlash-Like Model
253(1)
16.3 Examples With Hysteresis
254(1)
16.3.1 Magneto-Rheological (MR) Dampers for Suspension
254(1)
16.3.2 Piezoelectric Motor
254(1)
16.3.3 Hysteresis Motor
255(1)
16.4 Conclusion
255(2)
References
255(2)
17 Identification and Inverse Model Based Control of Uncertain Systems With Backlash
257(18)
17.1 Introduction
257(1)
17.2 System Description and Problem Formulation
258(1)
17.2.1 Uncertain System With Input Backlash
258(1)
17.2.2 Problem Formulation
259(1)
17.3 System Identification With Unknown Backlash
259(5)
17.3.1 System Reformulation
259(2)
17.3.2 Discontinuous Piecewise Parametric Representation of Backlash
261(2)
17.3.3 Parameter Estimation
263(1)
17.4 Inverse Compensation Based Control Design and Stability Analysis
264(5)
17.4.1 Inverse Model of Backlash
265(2)
17.4.2 Controller Design With Inverse Compensation
267(1)
17.4.3 Stability Analysis
268(1)
17.5 Simulations
269(3)
17.6 Conclusion
272(3)
References
272(3)
18 Identification and Control of Hammerstein Systems With Hysteresis Non-linearity
275(20)
18.1 Introduction
275(1)
18.2 Problem Formulation
276(1)
18.3 Identification of Hammerstein System With Hysteresis
277(8)
18.3.1 Estimation of System Order
277(1)
18.3.2 Estimation of Transfer Function
278(2)
18.3.3 Estimation of Preisach Non-linearity
280(4)
18.3.4 Implementation of Identification Algorithm
284(1)
18.4 Composite Control Design and Analysis
285(2)
18.5 Simulations
287(4)
18.5.1 Identification of Linear Dynamics
287(2)
18.5.2 Identification of Hysteresis
289(1)
18.5.3 Tracking Control Results
290(1)
18.6 Conclusion
291(4)
References
291(4)
19 Adaptive Estimation and Control of Magneto-Rheological Damper for Semi-Active Suspensions
295(18)
19.1 Introduction
295(1)
19.2 Modeling of Magneto-Rheological (MR) Damper
296(5)
19.2.1 MR Damper Dynamics
296(3)
19.2.2 Hyperbolic MR Model and Parameter Estimation
299(2)
19.3 Adaptive Estimation and Control for Vehicle Suspension With MR Damper
301(6)
19.3.1 Quarter Car Model and Control Objectives
301(2)
19.3.2 Adaptive Control Design With Parameter Estimation
303(3)
19.3.3 Suspension Performance Analysis
306(1)
19.4 Simulations
307(3)
19.5 Conclusion
310(3)
References
310(3)
Index 313
Jing Na received his B.Eng. and Ph.D. degrees from the School of Automation, Beijing Institute of Technology, Beijing, China, in 2004 and 2010, respectively. He was a Monaco/ITER Postdoctoral Fellow at the ITER Organization, Saint-Paul-lès-Durance, France, and also a Marie Curie Intra-European Fellow with the Department of Mechanical Engineering, University of Bristol, U.K. Since 2010, he has been with the Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China, where he became a professor in 2013. He has co-authored one monograph and more than 100 international journal and conference papers. His current research interests include intelligent control, adaptive parameter estimation, nonlinear control. Qiang Chen received his MSc degree in Measurement and Control Technology and Instrumentation from Hebei Agricultural University, Baoding, China, in 2006, and his PhD in Control Science and Engineering from the Beijing Institute of Technology, Beijing, China, in 2012. Since then, he has been with the School of Information Engineering at Zhejiang University of Technology, Hangzhou, China, where he is currently a Professor. His research interests include adaptive control and iterative learning control, with a focus on applications in motion control systems.

Xuemei Ren received her B.S. degree from Shandong University, Shandong, China, in 1989, and M.S. and Ph.D. degrees in control engineering from the Beijing University of Aeronautics and Astronautics, Beijing, China, in 1992 and 1995, respectively. She worked at the School of Automation, Beijing Institute of Technology as a professor from 2002. She has published more than 100 academic papers. Her research interests include nonlinear systems, intelligent control, neural network control, adaptive control, multi- drive servo systems and time delay systems.
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