Adaptive optimal tracking control for a class of nonlinear systems with fully unknown parameters

H. Mohammadi, Hamid Shiri
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Abstract

In this paper, a new adaptive optimal tracking approximate solution for the infinite-horizon function is presented to design a new controller for a class of fully unknown continuous-times nonlinear systems. A dynamic neural network identifier (DNN) derived from a Lyapunov function, is achieved to approximate the unknown system dynamics. We utilize an adaptive steady-state controller based on the identified plant to keep tracking performance and an adaptive optimal controller is used to stabilize the systems. A critic neural network is utilized for estimating optimal value function of the Hamilton-Jacobi-Bellman (HJB). The simulation examples are presented to confirm the effectiveness of the proposed controller method.
一类参数完全未知非线性系统的自适应最优跟踪控制
针对一类完全未知连续时间非线性系统,提出了一种新的自适应最优跟踪逼近解。利用李雅普诺夫函数实现了一种动态神经网络辨识器(DNN)来逼近未知的系统动态。我们利用基于被识别对象的自适应稳态控制器来保持跟踪性能,并使用自适应最优控制器来稳定系统。利用评价神经网络估计Hamilton-Jacobi-Bellman (HJB)的最优值函数。仿真实例验证了所提控制方法的有效性。
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