Neural-Network-Based Robust Control Schemes for Nonlinear Multiplayer Systems With Uncertainties via Adaptive Dynamic Programming
针对多人非线性系统中未解决的执行器不确定性（有界非线性扰动和未知常数故障），利用自适应动态规划提出两种鲁棒控制方案，并通过单神经网络架构降低计算负担。
This paper investigates the robust control issues of nonlinear multiplayer systems by utilizing adaptive dynamic programming (ADP) methods and fills a gap in the ADP field, where actuator uncertainties for multiplayer systems are still not addressed. Two types of actuator uncertainties including bounded nonlinear perturbation and unknown constant actuator fault are taken into consideration. First, a data-driven reinforcement learning (RL) approach is derived to learn the optimal solutions of multiplayer nonzero-sum games. Then, based on the obtained optimal control policies, two robust control schemes are developed to handle these two different types of uncertainties, respectively, and the associated stability analysis is also provided. To implement the proposed iterative RL approach, a single neural network (NN) architecture with least-square-based updating law is given, which reduces the computation burden compared with the traditional dual NN architecture. Finally, two numerical examples are shown to test the feasibility of our proposed schemes.