Adaptive Tracking Control for a Class of Uncertain Non-affine Delayed Systems Subjected to Input Constraints Using Self Recurrent Wavelet Neural Network

2010 International Conference on Advances in Recent Technologies in Communication and Computing Pub Date : 2010-10-16 DOI:10.1109/ARTCOM.2010.47

M. Sharma, Medicaps Instt

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引用次数: 8

Abstract

In this work an adaptive tracking control strategy for a class of non affine delayed systems subjected to actuator saturation is proposed. Self recurrent wavelet neural network (SRWNN) is used to approximate the uncertainties present in the system as well as to identify and compensate the nonlinearities introduced in the system due to actuator saturation. By using suitable transformation the system under consideration is first converted into an affine like form and subsequently an adaptive backstepping control strategy is developed to assure the stable tracking of nonlinear non affine system. In addition robust control terms are also designed to attenuate the approximation error due to SRWNN. Adaptation laws are developed for the online tuning of the wavelet parameters and the stability of the overall system is assured by using the Lyapunov-Krasovskii functional. The effectiveness of theoretical development is verified by a numerical example.

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一类受输入约束的不确定非仿射时滞系统的自循环小波神经网络自适应跟踪控制

针对一类受致动器饱和影响的非仿射时滞系统，提出了一种自适应跟踪控制策略。自递归小波神经网络(SRWNN)用于逼近系统中存在的不确定性，以及识别和补偿系统中由于执行器饱和而引入的非线性。通过适当的变换，首先将所考虑的系统转化为类仿射形式，然后提出一种自适应反演控制策略，以保证非线性非仿射系统的稳定跟踪。此外，还设计了鲁棒控制项，以减小SRWNN引起的逼近误差。利用Lyapunov-Krasovskii泛函，建立了小波参数在线整定的自适应律，保证了整个系统的稳定性。通过数值算例验证了理论推导的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2010 International Conference on Advances in Recent Technologies in Communication and Computing

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