Event-Triggered Robust Stabilization Incorporating an Adaptive Critic Mechanism
In this chapter, we investigate the robust feedback stabilization for a class of continuous-time uncertain nonlinear systems via event-triggering mechanism and adaptive critic learning technique. The main idea is to combine the event-triggering mechanism with adaptive critic designs, so as to solve the nonlinear robust control problem under uncertain environment. This can not only make better use of computation and communication resources, but also conduct controller design from the view of intelligent optimization. Through theoretical analysis, the nonlinear robust stabilization can be achieved by deriving an event-triggered optimal controller of the nominal system with a newly defined cost function and a certain triggering condition. The adaptive critic technique is employed to facilitate the event-triggered control design, where a critic neural network is constructed to serve as the approximator of the learning phase. The performance of the event-triggered robust control strategy is verified via simulation studies and comparisons. The present method extends the application domain of both event-triggered control and adaptive critic learning control to nonlinear systems possessing dynamical uncertainties.
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