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A sampling sub-interval error-based stochastic feedback sampled-data control for nonlinear stochastic Markovian jump systems

Author

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  • Wang, Yingchun
  • Song, Siyong
  • Mu, Yunfei
  • Zhang, Huaguang

Abstract

This paper addresses the exponential stabilization of nonlinear stochastic Markovian jump systems (NSMJSs) based on stochastic feedback sampled-data controllers. Firstly, a stochastic sampled-data-based switching control framework is established for NSMJSs, in which the switching controller modes do not match the system mode. The advantages are that it does not require real-time detection of system modes and relaxes the strict condition that the system mode remains unchanged between the sampling intervals. Secondly, a novel sampling sub-interval error analysis method is developed for the design of the stochastic feedback sampling controller, such that the NSMJSs are p-th exponentially stable, and the conditions for the maximum allowable upper bound of sampling intervals (UBOSIs) are achieved, which can not only increase the sampling intervals significantly but also decrease the computation resource. Moreover, some enhanced p-th exponential stabilization results for general stochastic systems are given. Compared with some existing important results, the conservatism of the UBOSIs is reduced greatly by simulation examples.

Suggested Citation

  • Wang, Yingchun & Song, Siyong & Mu, Yunfei & Zhang, Huaguang, 2026. "A sampling sub-interval error-based stochastic feedback sampled-data control for nonlinear stochastic Markovian jump systems," Applied Mathematics and Computation, Elsevier, vol. 517(C).
    • Handle: RePEc:eee:apmaco:v:517:y:2026:i:c:s0096300325006204
    DOI: 10.1016/j.amc.2025.129895
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