Robust adaptive iterative learning control for nonrepetitive systems with iteration-varying parameters and initial state

This paper explores how to construct an adaptive iteration learning control (AILC) mechanism for a class of discrete-time nonrepetitive systems subject to iteration-varying unknown parameters and unidentical initial condition. Firstly, for the linear discrete-time nonrepetitive systems, by minimizin...

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Vydáno v:International journal of machine learning and cybernetics Ročník 12; číslo 8; s. 2327 - 2337
Hlavní autoři: Geng, Yan, Ruan, Xiaoe, Zhou, Qinghua, Yang, Xuan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2021
Springer Nature B.V
Témata:
Adaptation
Adaptive control
Algorithms
Artificial Intelligence
Batch processes
Complex Systems
Computational Intelligence
Control
Discrete time systems
Engineering
Interactive control
Kalman filters
Learning
Mathematical analysis
Matrices (mathematics)
Mechatronics
Nonlinear systems
Original Article
Parameter estimation
Pattern Recognition
Robotics
Robust control
Systems Biology
Adaptive iteration learning control (AILC)
Iteration-varying parameters
Parameter estimation
Robustness
Nonrepetitive systems
ISSN:1868-8071, 1868-808X
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Popis
Shrnutí:This paper explores how to construct an adaptive iteration learning control (AILC) mechanism for a class of discrete-time nonrepetitive systems subject to iteration-varying unknown parameters and unidentical initial condition. Firstly, for the linear discrete-time nonrepetitive systems, by minimizing the discrepancy of the real system outputs from the estimated system outputs, a gradient-type adaptation law is designed to estimate the system lower triangular parameter matrix and the system initial state. Especially, the current parametric estimation is updated by virtue of the input-output data and the previous estimation. Secondly, an AILC mechanism is constructed based on the estimated system lower triangular parameter matrix, where the control input algorithm and the adaptation law are scheduled in an interactive mode. Thirdly, the boundedness of the estimation error between the real system matrix and the estimation one is derived by means of vector norm theory. Based on the boundedness of the estimation error, the robust condition of the proposed AILC is given. Finally, the proposed AILC is investigated for a class of nonlinear affine systems and the corresponding results are captured. Simulation results illustrate the validity and effectiveness of the proposed AILC schemes.
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ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-021-01313-9