School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China.
School of Automation, Central South University, Changsha 410083, China; Peng Cheng Laboratory, Shenzhen 518000, China.
Neural Netw. 2020 Nov;131:144-153. doi: 10.1016/j.neunet.2020.07.016. Epub 2020 Jul 30.
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.
本文提出了一种新的基于积分强化学习（IRL）的事件触发自适应动态规划方案，用于输入饱和的连续时间非线性系统。通过使用 IRL 技术，学习系统不需要漂移动力学的知识。然后，设计了一个单批评估神经网络来逼近未知的价值函数，并且其学习不受初始可接受控制的要求的限制。为了降低计算和通信成本，设计了事件触发控制律。给出了触发阈值，以保证控制系统的渐近稳定性。在仿真研究中采用了两个实例，结果验证了所提出的基于 IRL 的事件触发控制方法的有效性。
