具有未知类逆激滞后的多输入多输出纯反馈非线性系统的自适应FNN动态表面控制

Yong-ming Li, Tie-shan Li
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引用次数: 1

摘要

针对一类多输入多输出(MEMO)纯反馈非线性系统,提出了一种模糊神经网络(FNN)输出反馈控制方法。设计了状态观测器来估计未测状态,引入滤波信号来解决控制器实现中遇到的代数环路问题,采用自适应补偿技术解决未知类逆冲滞后问题。在设计状态观测器的基础上,结合反步和动态面控制(DSC)技术,提出了一种自适应FNN输出反馈反步控制方法。该方法不仅克服了退步控制设计中固有的“复杂性爆炸”问题,而且保证了闭环系统的所有信号都是半全局一致最终有界的,跟踪误差收敛到原点的一个小邻域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive FNN dynamic surface control for MIMO pure-feedback nonlinear systems with unknown backlash-like hysteresis
An adaptive fuzzy neural networks (FNN) output feedback control approach is proposed for a class of multi-input and multi-output (MEMO) pure-feedback nonlinear systems with unknown backlash-like hysteresis and immeasurable states. The state observers are designed to estimate the unmeasured states, the filtered signals are introduced to circumvent algebraic loop problem encountered in the implementation of the controller, and an adaptive compensation technique are used to solve the problem of unknown backlash-like hysteresis, respectively. Based on the designed state observers, and combining the backstepping and dynamic surface control (DSC) techniques, an adaptive FNN output feedback backstepping control approach is developed. The proposed method not only overcomes the problem of "explosion of complexity" inherent in the backstepping control design but also guarantees that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded (SUUB) and the tracking errors converge to a small neighborhood of the origin.
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