非严格反馈非线性系统的自适应神经网络控制

发布时间：2018-07-15 07:38

【摘要】：近几十年来,自适应非线性控制作为非线性控制理论的重要组成部分一直备受众多研究者的广泛关注。尤其在处理不确定非线性系统的控制问题时,基于backstepping方法的自适应神经网络控制方法非常有效,并取得很多研究成果。然而,自适应backstepping设计方法有一个不利点就是要求非线性系统具有所谓的严格反馈形式。目前研究成果大多限于结构为严格反馈形式的非线性系统,对于非严格反馈形式的非线性系统的控制问题仍待深入研究。故而,本篇论文研究了一类具有非严格反馈形式的不确定非线性系统的自适应输出反馈跟踪控制问题。论文首先研究了一类非严格反馈非线性系统的跟踪控制问题。由于系统状态未知不可用,一个新颖的输入驱动型滤波器被引入。基于李雅普诺夫函数和反推法,控制器连同自适应律被最终设计出并得到半全局一致最终有界的研究结果。此研究成果突破了系统的非严格反馈结构的限制并适当放宽了未知非线性函数的条件限制,大大降低了保守性。然后,论文接着研究了一类带有时滞的多输入多输出非严格反馈非线性系统的输出反馈跟踪控制问题。所研究系统不但考虑了时滞因素的影响,也考虑了各子系统存在状态关联的问题。借助于神经网络逼近特性,自适应控制及反推技术等,控制器被最终设计出来。基于李雅普诺夫泛函稳定性分析,系统闭环信号是最终有界的。此研究成果进一步解决了时滞问题以及状态互联的复杂性控制问题。
[Abstract]:In recent decades, adaptive nonlinear control as an important part of nonlinear control theory has been widely concerned by many researchers. Especially in dealing with the control problem of uncertain nonlinear systems, the adaptive neural network control method based on backstepping method is very effective, and many research results have been obtained. However, one disadvantage of adaptive backstepping design is that nonlinear systems are required to have a so-called strict feedback form. At present, most of the research results are limited to nonlinear systems with strict feedback structure, but the control problem of nonlinear systems with non-strict feedback forms still needs to be deeply studied. Therefore, the problem of adaptive output feedback tracking control for a class of uncertain nonlinear systems with non-strict feedback is studied in this paper. Firstly, the tracking control problem for a class of non-strict feedback nonlinear systems is studied. Because the system state is unknown and unavailable, a novel input drive filter is introduced. Based on Lyapunov function and backstepping method, the controller and adaptive law are finally designed and the semi-globally uniformly bounded results are obtained. The results of this study break through the limitations of the nonstrict feedback structure of the system and relax the conditions of unknown nonlinear functions properly, thus greatly reducing the conservatism. Then, the problem of output feedback tracking control for a class of nonlinear systems with time delay is studied. The system not only considers the effect of time delay, but also considers the problem of state correlation among subsystems. With the help of neural network approximation, adaptive control and backstepping techniques, the controller is finally designed. Based on Lyapunov functional stability analysis, the closed-loop signal of the system is ultimately bounded. The research results further solve the delay problem and the complexity control problem of state interconnection.
【学位授予单位】：渤海大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP13;TP183

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