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首页> 外文期刊>IEEE Transactions on Neural Networks >Discrete-time CMAC NN control of feedback linearizable nonlinear systems under a persistence of excitation
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Discrete-time CMAC NN control of feedback linearizable nonlinear systems under a persistence of excitation

机译:励磁持续性下线性化反馈非线性系统的离散CMAC NN控制

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摘要

The local structure of CMAC neural networks (NN) result in better and faster controllers for nonlinear dynamical systems. A CMAC neural network-based discrete-time controller which linearizes the unknown multiinput and multioutput nonlinear system through feedback is presented. Control action is defined in order to achieve tracking performance for this unknown nonlinear system. An efficient and localized weight addressing scheme for the CMAC NNs is described using an appropriate choice of the B-spline receptive field functions that form a basis. A uniform ultimate boundedness of the closed-loop system is given in the sense of Lyapunov using the persistency of excitation condition. Simulation results are shown to demonstrate the theoretical conclusions.
机译:CMAC神经网络(NN)的局部结构可为非线性动力学系统提供更好,更快的控制器。提出了一种基于CMAC神经网络的离散时间控制器,该控制器通过反馈使未知的多输入多输出非线性系统线性化。定义控制动作是为了对该未知的非线性系统实现跟踪性能。通过适当选择构成基础的B样条接收场函数,描述了一种针对CMAC NN的有效且局部的权重寻址方案。在李雅普诺夫意义上,利用激励条件的持久性给出了闭环系统的一致极限极限。仿真结果表明了理论结论。

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