Event-triggered adaptive tracking control for a class of uncertain stochastic nonlinear systems with Markov jumping parameters

AssumptionDefinitionExampleLemmaProofRemarkTheorem This paper aims to investigate the problem of event-triggered adaptive tracking control for a class of stochastic nonlinear systems with Markov jumping parameters. Since the stochastic system contains unknown parameters, the assumption of the stochastic input-to-state stability is a difficult task to check. To overcome the design difficulty, mode-dependent adaptive controllers and the event-triggered strategy are designed simultaneously with the help of backstepping technique. The stochastic input-to-state stability assumption is avoided by adding correction terms in controller to compensate the measurement errors. The proposed control schemes guarantee that all signals in the closed-loop system remain bounded in probability and the tracking error signals eventually converge to the compact set in the sense of mean quartic value. Finally, simulation results show the effectiveness of the proposed approach.