A Direct Self-Constructing Neural Controller Design for a Class of Nonlinear Systems
A Direct Self-Constructing Neural Controller Design for a Class of Nonlinear Systems
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DOI：
10.1109/tnnls.2015.2401395
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发表时间：
2015-02
影响因子：
10.4
通讯作者：
Hong-gui Han;Wendong Zhou;J. Qiao;G. Feng
中科院分区：
文献类型：
--
作者：
Hong-gui Han;Wendong Zhou;J. Qiao;G. Feng
研究了一类不确定或不确定非仿射非线性系统的自适应神经控制问题。利用自组织径向基函数神经网络（RBFNN）设计了一种直接自构造神经控制器（DSNC），可以逼近未知的非线性，使闭环系统保持稳定。提出的DSNC设计方案的主要特点可以总结如下。首先，与已有的文献结果不同，针对DSNC在线构建了自适应阈值的自组织RBFNN，提高了控制性能。其次，根据李雅普诺夫稳定性理论，建立了RBFNN的控制律和权值自适应律，使闭环系统稳定；第三，借助附加的鲁棒控制项，保证跟踪误差一致渐近收敛于零。最后通过实例验证了该方法的设计过程和性能。仿真结果表明了该方法的有效性。
This paper is concerned with the problem of adaptive neural control for a class of uncertain or ill-defined nonaffine nonlinear systems. Using a self-organizing radial basis function neural network (RBFNN), a direct self-constructing neural controller (DSNC) is designed so that unknown nonlinearities can be approximated and the closed-loop system is stable. The key features of the proposed DSNC design scheme can be summarized as follows. First, different from the existing results in literature, a self-organizing RBFNN with adaptive threshold is constructed online for DSNC to improve the control performance. Second, the control law and adaptive law for the weights of RBFNN are established so that the closed-loop system is stable in the term of Lyapunov stability theory. Third, the tracking error is guaranteed to uniformly asymptotically converge to zero with the aid of an additional robustifying control term. An example is finally given to demonstrate the design procedure and the performance of the proposed method. Simulation results reveal the effectiveness of the proposed method.