College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China.
Comput Intell Neurosci. 2021 Nov 16;2021:2952115. doi: 10.1155/2021/2952115. eCollection 2021.
Model uncertainties are usually unavoidable in the control systems, which are caused by imperfect system modeling, disturbances, and nonsmooth dynamics. This paper presents a novel method to address the robust control problem for uncertain systems. The original robust control problem of the uncertain system is first transformed into an optimal control of nominal system via selecting the appropriate cost function. Then, we develop an adaptive critic leaning algorithm to learn online the optimal control solution, where only the critic neural network (NN) is used, and the actor NN widely used in the existing methods is removed. Finally, the feasibility analysis of the control algorithm is given in the paper. Simulation results are given to show the availability of the presented control method.
模型不确定性通常是不可避免的控制系统，这是由于不完善的系统建模、干扰和非光滑动力学。本文提出了一种解决不确定系统鲁棒控制问题的新方法。首先，通过选择合适的代价函数，将不确定系统的原始鲁棒控制问题转化为标称系统的最优控制。然后，我们开发了一种自适应评论家学习算法来在线学习最优控制解决方案，其中仅使用评论家神经网络（NN），并去除了现有方法中广泛使用的演员神经网络（NN）。最后，本文给出了控制算法的可行性分析。仿真结果表明了所提出的控制方法的有效性。
