Zhihong M, Wu H R, Palaniswami M
Department of Electrical and Electronic Engineering, The University of Tasmania, Hobart 7001, Australia.
IEEE Trans Neural Netw. 1998;9(5):947-55. doi: 10.1109/72.712168.
A neural-network-based adaptive tracking control scheme is proposed for a class of nonlinear systems in this paper. It is shown that RBF neural networks are used to adaptively learn system uncertainty bounds in the Lyapunov sense, and the outputs of the neural networks are then used as the parameters of the controller to compensate for the effects of system uncertainties. Using this scheme, not only strong robustness with respect to uncertain dynamics and nonlinearities can be obtained, but also the output tracking error between the plant output and the desired reference output can asymptotically converge to zero. A simulation example is performed in support of the proposed neural control scheme.
本文针对一类非线性系统提出了一种基于神经网络的自适应跟踪控制方案。结果表明，径向基函数（RBF）神经网络用于在李雅普诺夫意义下自适应学习系统不确定性界限，然后将神经网络的输出用作控制器的参数，以补偿系统不确定性的影响。使用该方案，不仅可以获得对不确定动态和非线性的强鲁棒性，而且被控对象输出与期望参考输出之间的输出跟踪误差可以渐近收敛到零。进行了一个仿真示例以支持所提出的神经控制方案。
