Event-based Optimal Regulator Design for Nonlinear Networked Control Systems

This paper presents a novel stochastic event-based near optimal control strategy to regulate a networked control system (NCS) represented as an uncertain nonlinear continuous time system. An online stochastic actor-critic neural network (NN) based approach is utilized to achieve the near optimal regulation in the presence of network constraints, such as, network induced time-varying delays and random packet losses under event-based transmission of the feedback signals. The transformed nonlinear NCS in discrete-time after the incorporation the delays and packet losses is utilized for the actor-critic NN based controller design. To relax the knowledge of the control coefficient matrix, a NN based identifier is used. Event sampled state vector is utilized as NN inputs and their respective weights are updated non-periodically at the occurrence of events. Further, an event-trigger condition is designed by using the Lyapunov technique to ensure ultimate boundedness of all the closed-loop signals and save network resources and computation. Moreover, policy and value iterations are not utilized for the stochastic optimal regulator design. Finally, the analytical design is verified by using a numerical example by carrying out Monte-Carlo simulations.