In this article, the event-triggered robust control of unknown multiplayer nonlinear systems with constrained inputs and uncertainties is investigated by using adaptive dynamic programming. To relax the requirement of system dynamics, a neural network-based identifier is constructed by using the system input-output data. Subsequently, by designing a nonquadratic value function, which contains the bounded functions, the system states, and the control inputs of all players, the event-triggered robust stabilization problem is converted into an event-triggered constrained optimal control problem. To obtain the approximate solution of the event-triggered Hamilton–Jacobi (HJ) equation, a critic network for each player is established with a novel weight updating law to relax the persistence of excitation condition based on the experience replay technique. Furthermore, according to the Lyapunov stability theorem, the present event-triggered robust optimal control ensures the multiplayer system to be uniformly ultimately bounded. Finally, two simulation examples are employed to show the effectiveness of the present method.

中文翻译：

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未知动态的多人非零和博弈的基于自适应动态规划的事件触发鲁棒控制


在本文中，使用自适应动态规划研究了具有约束输入和不确定性的未知多人非线性系统的事件触发鲁棒控制。为了放宽系统动力学的要求，利用系统输入输出数据构建基于神经网络的标识符。随后，通过设计一个包含有界函数、系统状态和所有参与者的控制输入的非二次值函数，将事件触发的鲁棒稳定问题转化为事件触发的约束最优控制问题。为了获得事件触发的汉密尔顿-雅可比（HJ）方程的近似解，为每个玩家建立了一个批评者网络，并采用新颖的权重更新法则来放松基于经验回放技术的激励条件的持续性。此外，根据李亚普诺夫稳定性定理，本发明的事件触发鲁棒最优控制保证了多人系统最终一致有界。最后通过两个仿真例子验证了该方法的有效性。