Composite learning control for strict feedback systems with neural network based on selective memory

This paper addresses the high‐precision control problem for nonlinear strict feedback systems with external time‐varying disturbances and proposes a novel composite learning control algorithm. Unlike previous research that only uses tracking errors for neural network updates, this paper prioritizes...

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Bibliographic Details
Published in:International journal of robust and nonlinear control Vol. 34; no. 17; pp. 11335 - 11350
Main Authors: Hu, Zhiyu, Fei, Yiming, Li, Jiangang, Li, Yanan
Format: Journal Article
Language:English
Published: Bognor Regis Wiley Subscription Services, Inc 25.11.2024
Subjects:
Algorithms
composite learning control
Control systems
Control theory
disturbance observer
Disturbance observers
Feedback
Machine learning
Neural networks
Nonlinear control
Nonlinear systems
Nonlinearity
radial basis function neural network
selective memory recursive least squares
strict‐feedback system
Tracking errors
ISSN:1049-8923, 1099-1239
Online Access:Get full text
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Summary:This paper addresses the high‐precision control problem for nonlinear strict feedback systems with external time‐varying disturbances and proposes a novel composite learning control algorithm. Unlike previous research that only uses tracking errors for neural network updates, this paper prioritizes the accuracy of neural network learning. The article uses a selective memory recursive least squares algorithm to construct system information prediction errors, which are combined with tracking errors to update the neural network weights. A new composite learning control algorithm is developed to design dynamic surface control and neural network disturbance observers, which achieves high‐precision control of nonlinear strict feedback systems under external time‐varying disturbance conditions. Lyapunov's method demonstrates the stability of the closed‐loop system and the boundedness of errors. The simulation results show that the proposed control algorithm can effectively estimate system nonlinearity and suppress the impact of disturbances.
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ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.7572