文章基本信息

标题：Output Feedback Tracking Control Based on Neural Network for a Class of SISO Strict Feedback Nonlinear Systems
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作者：Hu, Hui ; Hao, Zhongxiao ; Guo, Peng 等
期刊名称：Journal of Networks
印刷版ISSN：1796-2056
出版年度：2014
卷号：9
期号：9
页码：2521-2528
DOI：10.4304/jnw.9.9.2521-2528
语种：English
出版社：Academy Publisher
摘要：The paper proposes a new output feedback tracking controller using neural network (NN) for a class of SISO strict-feedback nonlinear systems that only the output variables can be measured. The distinguished aspect of the controller is that no backstepping design is employed, and the strict-feedback systems could be transformed into the standard affine form. The gains of observer and controller are simultaneously tuned according to output tracking error based on non-separation principle design. With the universal approximation property of NN and the simultaneous parametrisation, no Lipschitz assumption and SPR condition are employed which makes the system construct simple. The proposed neural network controller can guarantee that output tracking error and all the states in the closed-loop system are the semi-globally ultimately bounded by Lyapunov approach. Finally the simulation results are used to demonstrate the effectiveness of the control scheme.
关键词：Output Feedback;Neural Network;Tracking Control;Non-Separation Principle;Without Backstepping