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Composite learning control for strict feedback systems with neural network based on selective memory
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DOI：10.1002/rnc.7572.png)
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Abstract
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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.
Keywords:
composite learning control
disturbance observer
radial basis function neural network
selective memory recursive least squares
strict-feedback system
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