Event-based adaptive finite-time control scheme with improved performance assurance for nonlinear Markov jumping systems

초록

This article investigates the adaptive finite-time control design with performance assurance for nonlinear Markov jumping systems. First, the radial basis function neural networks are applied to tackle unknown nonlinear terms. Subsequently, within the framework of the command-filtered backstepping control, the new error compensation signals are devised to remove the effect caused by filter error. Additionally, an adaptive finite-time prescribed performance control law with an event-triggered mechanism is developed to achieve a balance between tracking performance and bandwidth consumption, which ensures that the closed-loop system is practically finite-time stable in mean square and guarantees that the tracking error adjusts to the predetermined area. Finally, two simulation examples are provided to verify the validity and superiority of the devised scheme.

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