Liu Yan-Jun, Chen C L Philip, Wen Guo-Xing, Tong Shaocheng
School of Sciences, Liaoning University of Technology, Jinzhou, China.
IEEE Trans Neural Netw. 2011 Jul;22(7):1162-7. doi: 10.1109/TNN.2011.2146788. Epub 2011 Jun 9.
This brief studies an adaptive neural output feedback tracking control of uncertain nonlinear multi-input-multi-output (MIMO) systems in the discrete-time form. The considered MIMO systems are composed of n subsystems with the couplings of inputs and states among subsystems. In order to solve the noncausal problem and decouple the couplings, it needs to transform the systems into a predictor form. The higher order neural networks are utilized to approximate the desired controllers. By using Lyapunov analysis, it is proven that all the signals in the closed-loop system is the semi-globally uniformly ultimately bounded and the output errors converge to a compact set. In contrast to the existing results, the advantage of the scheme is that the number of the adjustable parameters is highly reduced. The effectiveness of the scheme is verified by a simulation example.
本文简要研究了离散时间形式的不确定非线性多输入多输出（MIMO）系统的自适应神经输出反馈跟踪控制。所考虑的MIMO系统由n个子系统组成，子系统之间存在输入和状态耦合。为了解决非因果问题并解耦耦合，需要将系统转换为预测器形式。利用高阶神经网络逼近期望控制器。通过李亚普诺夫分析，证明了闭环系统中的所有信号是半全局一致最终有界的，并且输出误差收敛到一个紧致集。与现有结果相比，该方案的优点是可调参数的数量大大减少。通过一个仿真例子验证了该方案的有效性。
