一类通用神经网络非线性系统模型参考自适应控制(英文)

General Nonlinear System MRAC Strategy Based on Neural Network

针对任意复杂的具有最小相位,滞后环节和非最小相位特性的离散非线性系统,提出一种通用的直接神经网络模型参考自适应控制。并采用具有在线学习功能的最近邻聚类算法训练RBF神经网络控制器,同时引入优化策略对聚类半径进行自动调整,并利用构造伪系统的方法构成一种对非最小相位同样有效的神经网络模型参考自适应控制器。仿真研究证明,该控制策略不仅能使多种非线性对象跟踪多种参考信号,而且抗干扰能力和鲁棒性也很好。

A novel scheme of neural network model reference control was proposed for arbitrary complex nonlinear system, which ranged from discrete time minimum phase, time delay to non-minimum phase system. The method used the nearest neighbors clustering algorithm to train the RBF neural network. The optimization strategy which regulated the clustering radius automatically was introduced to guarantee the rationality of radius. Through constructing pseudo-plant, the neural network MRAC which was effective to the nonlinear non-minimum tihase system was put forward. In simulation, MRAC based on RBFNN can not only make the multi-dimension nonlinear plant track multi-dimension reference signals quickly, but also endow the control system with satisfying robustness.