This paper studies the optimal event-triggered control for constrained-input discrete-time switched nonlinear systems. Firstly, the optimal time-triggered control problem is analyzed based on the necessary optimality condition. Secondly, the optimal event-triggered control problem is presented, and the optimal results are presented based on the periodic time-triggered ones, and an event-triggered integer-mixed adaptive dynamic programming algorithm is put forward to obtain the optimal results. The proposed algorithm is only executed at trigger instants, which decreases the execution times compared with the periodic time-triggered algorithm, and the neural networks are applied to approximate the control input and costate vector functions of each subsystem based on the triggered states. Thirdly, an event-triggered condition is designed to make the closed-loop switched system asymptotically stable. Fourthly, it is shown that during the iteration process, the value function sequence corresponding to the costate vector converges to the optimal value function. Finally, the simulation results demonstrate the effectiveness of the presented algorithm.

中文翻译：

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具有有界输入的切换非线性系统的事件触发整数混合自适应动态规划

本文研究了约束输入离散时间切换非线性系统的最优事件触发控制。首先，基于必要的最优性条件分析了最优时间触发控制问题。其次，提出了最优事件触发控制问题，并在周期性时间触发的基础上给出了最优结果，并提出了一种事件触发整数混合自适应动态规划算法以获得最优结果。该算法仅在触发时刻执行，与周期性时间触发算法相比，减少了执行时间，并且基于触发状态，应用神经网络来逼近每个子系统的控制输入和辅助向量函数。第三，事件触发条件旨在使闭环切换系统渐近稳定。第四，表明在迭代过程中，costate向量对应的值函数序列收敛到最优值函数。最后，仿真结果证明了所提出算法的有效性。