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Adaptive Event-Triggered SMC for Stochastic Switching Systems With Semi-Markov Process and Application to Boost Converter Circuit Model

Wenhai Qi, Guangdeng Zong, Wei Xing Zheng

2020IEEE Transactions on Circuits and Systems I Regular Papers307 citationsDOI

Abstract

In this article, the sliding mode control (SMC) design is studied for a class of stochastic switching systems subject to semi-Markov process via an adaptive event-triggered mechanism. Network-induced communication constraints, semi-Markov switching parameters, and uncertain parameters are considered in a unified framework for the SMC design. Due to the constraint of measuring transducers, the system states always appear with unmeasurable characteristic. Compared with the traditional event-triggered mechanism, the adaptive event-triggered mechanism can effectively reduce the number of triggering than the static event-triggered mechanism. During the data transmission of network communication systems, network-induced delays are characterized from the event trigger to the zero-order holder. The aim of this work is to design an appropriate SMC law based on an adaptive event-triggered communication scheme such that the resulting closed-loop system could realize stochastic stability and reduce communication burden. By introducing the stochastic semi-Markov Lyapunov functional, sojourn-time-dependent sufficient conditions are established for stochastic stability. Then, a suitable SMC law is designed such that the system state can be driven onto the specified sliding surface in a finite-time region. Finally, the simulation study on boost converter circuit model (BCCM) illustrates the effectiveness of the theoretical findings.

Topics & Concepts

Control theory (sociology)Computer scienceMarkov processMarkov chainTransmission (telecommunications)Stochastic processEvent (particle physics)Stability (learning theory)Telecommunications networkLyapunov functionMathematicsControl (management)TelecommunicationsQuantum mechanicsNonlinear systemStatisticsPhysicsMachine learningArtificial intelligenceStability and Control of Uncertain SystemsNeural Networks Stability and SynchronizationAdaptive Control of Nonlinear Systems