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首页> 外文期刊>Asian Journal of Control: Affiliated with ACPA, the Asian Control Professors Association >Adaptive Recurrent Neural Network Enhanced Variable Structure Control for Nonlinear Discrete Mimo Systems
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Adaptive Recurrent Neural Network Enhanced Variable Structure Control for Nonlinear Discrete Mimo Systems

机译：Adaptive Recurrent Neural Network Enhanced Variable Structure Control for Nonlinear Discrete Mimo Systems

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摘要

The stability and performance of robust control for a nonlinear complex dynamic system deteriorates due to large uncertainties. To address this, a discrete variable structure control (DVSC) outside of a convex set is designed to force the operating point into a convergent set, which is verified by Lyapunov stability theory. Due to the dynamic features of the lumped uncertainties, they are learned on-line by a recurrent neural network (RNN) in a specific convex set containing the convergent set of the DVSC. Between this convergent set and the convex set, a switching mechanism determines whether a learning RNN for the lumped uncertainties is executed. An adaptive recurrent-neural-network enhanced discrete variable structure control is then established to improve both transient and steady performances. Simulations, including compared examples and application to the trajectory tracking of a mobile robot, validate the effectiveness and robustness of the proposed control.

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