Event-Triggered Globalized Dual Heuristic Programming and Its Application to Networked Control Systems

Networked control systems (NCSs) provide many benefits, such as higher control accuracy and better robustness with the successively increasing computational complexity and communication burden. This results in the traditional adaptive dynamic programming control method having difficulty meeting the real-time requirements of industrial systems. In this paper, a novel event-triggered globalized dual heuristic programming method is proposed to reduce the required samples while guaranteeing the stability of the system. In the proposed method, the NCSs can communicate and update the control law only when the designed event-triggered condition is violated. Furthermore, the Elman neural network, which is a dynamic feedback network with a memory function is implemented to reconstruct the state variables as an approximator, and it depends only on the input and output data. To obtain fewer event-triggered times, two optimization methods, i.e., the unscented Kalman filter and the multiobjective quantum particle swarm optimization, are used to optimize the initial weights of the networks and the positive constant in the event-triggered condition, respectively. The simulation results on industrial system of aluminum electrolysis production are included to verify the performance of the controller.