In this article, an actor-critic neural network (NN)-based online optimal adaptive regulation of a class of nonlinear continuous-time systems with known state and input delays and uncertain system dynamics is introduced. The temporal difference error (TDE), which is dependent upon state and input delays, is derived using actual and estimated value function and via integral reinforcement learning. The NN weights of the critic are tuned at every sampling instant as a function of the instantaneous integral TDE. A novel identifier, which is introduced to estimate the control coefficient matrices, is utilized to obtain the estimated control policy. The boundedness of the state vector, critic NN weights, identification error, and NN identifier weights are shown through the Lyapunov analysis. Simulation results are provided to illustrate the effectiveness of the proposed approach.

中文翻译：

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具有输入和状态延迟的不确定非线性连续时间系统的最优自适应控制


在本文中，介绍了一种基于行动批评神经网络（NN）的在线最优自适应调节，用于具有已知状态和输入延迟以及不确定系统动力学的一类非线性连续时间系统。时间差误差 (TDE) 取决于状态和输入延迟，是使用实际值函数和估计值函数并通过积分强化学习导出的。批评者的神经网络权重在每个采样时刻根据瞬时积分 TDE 进行调整。引入一种新颖的标识符来估计控制系数矩阵，用于获得估计的控制策略。通过 Lyapunov 分析显示状态向量、批评神经网络权重、识别误差和神经网络标识符权重的有界性。提供仿真结果来说明所提出方法的有效性。