Hybrid Event-Triggered Tracking Control With Critic Learning for Nonlinear Networked Systems
针对离散时间非线性网络化控制系统，提出一种结合自适应评论技术的混合事件触发控制框架，通过构建增广系统将最优跟踪问题转化为最优调节问题，并设计混合事件触发机制以节省网络带宽，最后通过实验验证了方法的有效性。
In this article, a novel hybrid event-triggered (ET) control framework is constructed based on the adaptive critic technique, aiming to address the optimal tracking issue of discrete-time nonlinear networked control systems. First, an augmented plant is created by combining the system state with the reference trajectory, transforming the optimal tracking control design into the optimal regulation problem of the reconstructed nonlinear error system. Subsequently, to conserve communication network resources and ensure the stability of the error system, a hybrid ET mechanism is developed to determine a constant interval for event silence. This approach not only alleviates the limited network bandwidth but also eliminates the need for continuous evaluation of triggering conditions, as seen in traditional event-based methods. Regarding algorithm implementation, the model, critic, and action networks are established to execute the online adaptive critic algorithm, which allows the tracking control policy to be adjusted in real-time to reach the optimal level. Finally, an experimental plant with nonlinear characteristics is presented to illustrate the overall performance of the proposed online tracking control method with the hybrid ET mechanism.