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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Data-Driven Zero-Sum Neuro-Optimal Control for a Class of Continuous-Time Unknown Nonlinear Systems With Disturbance Using ADP
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Data-Driven Zero-Sum Neuro-Optimal Control for a Class of Continuous-Time Unknown Nonlinear Systems With Disturbance Using ADP

机译：基于ADP的一类连续时间未知非线性系统的数据驱动零和神经最优控制

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摘要

This paper is concerned with a new data-driven zero-sum neuro-optimal control problem for continuous-time unknown nonlinear systems with disturbance. According to the input–output data of the nonlinear system, an effective recurrent neural network is introduced to reconstruct the dynamics of the nonlinear system. Considering the system disturbance as a control input, a two-player zero-sum optimal control problem is established. Adaptive dynamic programming (ADP) is developed to obtain the optimal control under the worst case of the disturbance. Three single-layer neural networks, including one critic and two action networks, are employed to approximate the performance index function, the optimal control law, and the disturbance, respectively, for facilitating the implementation of the ADP method. Convergence properties of the ADP method are developed to show that the system state will converge to a finite neighborhood of the equilibrium. The weight matrices of the critic and the two action networks are also convergent to finite neighborhoods of their optimal ones. Finally, the simulation results will show the effectiveness of the developed data-driven ADP methods.
机译：本文关注的是具有扰动的连续时间未知非线性系统的新的数据驱动零和神经最优控制问题。根据非线性系统的输入输出数据，引入了有效的递归神经网络来重构非线性系统的动力学。将系统扰动作为控制输入，建立了两人零和最优控制问题。开发了自适应动态规划（ADP），以在最坏的干扰情况下获得最佳控制。采用三个单层神经网络，包括一个批评家和两个动作网络，分别对性能指标函数，最优控制律和扰动进行近似，以简化ADP方法的实现。开发了ADP方法的收敛特性，以表明系统状态将收敛到平衡的有限邻域。评论家和两个动作网络的权重矩阵也收敛到其最佳邻域的有限邻域。最后，仿真结果将表明开发的数据驱动ADP方法的有效性。

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