In order to solve the constrained-input problem and reduce the computing resources, a novel event-triggered optimal control method is proposed for a class of discrete-time nonlinear systems. In the proposed method, the event-triggered control policy is applied to the globalized dual heuristic dynamic programming (GDHP) algorithm. Compared with the traditional adaptive dynamic programming (ADP) control, the event-triggered GDHP control can reduce the computation while ensuring the system performance. In this paper, a non-quadratic function is given to code the control constraints and the trigger condition with the stability analysis is provided. Neural networks (NN) are constructed in the GDHP structure, where the model network is designed to identify the unknown nonlinear system, the critic network is used to learn the cost function and its partial derivative, and the action network is designed to obtain the approximate optimal control law. Three simulation examples are presented to demonstrate the performance of the proposed event-triggered design for constrained discrete-time nonlinear systems.

为了解决输入受限问题，减少计算资源，针对一类离散时间非线性系统，提出了一种新颖的事件触发最优控制方法。在该方法中，将事件触发的控制策略应用于全局双重启发式动态规划算法。与传统的自适应动态规划（ADP）控制相比，事件触发的GDHP控制可以在确保系统性能的同时减少计算量。本文给出了一个非二次函数来对控制约束进行编码，并提供了具有稳定性分析的触发条件。在GDHP结构中构建了神经网络（NN），其中模型网络旨在识别未知的非线性系统，批评者网络用于学习成本函数及其偏导数，而动作网络则用于获得近似最优控制律。给出了三个仿真示例，以证明所提出的事件触发设计在受限离散时间非线性系统中的性能。