In this paper, a novel integral reinforcement learning (IRL)-based event-triggered adaptive dynamic programming scheme is developed for input-saturated continuous-time nonlinear systems. By using the IRL technique, the learning system does not require the knowledge of the drift dynamics. Then, a single critic neural network is designed to approximate the unknown value function and its learning is not subjected to the requirement of an initial admissible control. In order to reduce computational and communication costs, the event-triggered control law is designed. The triggering threshold is given to guarantee the asymptotic stability of the control system. Two examples are employed in the simulation studies, and the results verify the effectiveness of the developed IRL-based event-triggered control method.

本文针对输入饱和的连续时间非线性系统，提出了一种基于积分增强学习（IRL）的事件触发自适应动态规划方案。通过使用IRL技术，学习系统不需要了解漂移动力学。然后，设计单个评论者神经网络来近似未知值函数，并且其学习不受初始允许控制的要求。为了减少计算和通信成本，设计了事件触发的控制律。给出触发阈值以确保控制系统的渐近稳定性。在仿真研究中采用了两个例子，结果验证了所开发的基于IRL的事件触发控制方法的有效性。