Static and Dynamic Event-Triggered Control for Disturbed Interconnected Systems Through Adaptive Critic Designs
针对存在外部干扰和互联匹配的连续时间非线性系统，提出了分散式静态和动态事件触发控制策略，通过自适应评判框架求解最优策略，并用带动量的梯度下降法更新评判神经网络权重，以降低计算和通信负担。
This article develops decentralized static and dynamic event-triggered control (ETC) strategies for continuous-time (CT) nonlinear systems featuring unmatched external disturbances and matched interconnections. Both static and dynamic ETC schemes can be used separately to alleviate the computational load and communication resources. The proposed static and dynamic event-triggering conditions differ from existing approaches in terms of condition design and stability proof. The static event-triggered mechanism (ETM) relies solely on the system state, whereas the dynamic ETM integrates the system state with an internal variable derived from a differential equation. The decentralized event-triggered (ET) controller is established for the large-scale system, which is comprised of many ETC policies for each individual subsystem. Based on the critic-only architecture, the optimal policies are obtained within the adaptive critic framework by solving the relevant ET Hamilton–Jacobi–Isaacs (HJI) equation. Specifically, the weight vectors of critic neural networks (NNs) are updated using a novel gradient descent strategy with momentum. Finally, the effectiveness of the developed method is validated through two simulation examples.