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arXiv · 2306.15317

Output Regulation of Stochastic Sampled-Data Systems with Post-processing Internal Model

Himadri Basu·Francesco Ferrante·Mirko Fiacchini
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Abstract

This paper deals with the output regulation problem (ORP) of a linear time-invariant (LTI) system in the presence of sporadically sampled measurement streams with the inter-sampling intervals following a stochastic process. Under such sporadically available measurement streams, a regulator consisting of a hybrid observer, continuous-time post-processing internal model, and stabilizer are proposed, which resets with the arrival of new measurements. The resulting system exhibits a deterministic behavior except for the jumps that occur at random sampling times and therefore the overall closed-loop system can be categorized as a piecewise deterministic Markov process (PDMP). In existing works on ORPs with aperiodic sampling, the requirement of boundedness on inter-sampling intervals precludes extending the solution to the random sampling intervals with possibly unbounded support. Using the Lyapunov-like theorem for the stability analysis of stochastic systems, we offer sufficient conditions to ensure that the overall closed-loop system is mean exponentially stable (MES) and the objectives of the ORP are achieved under stochastic sampling of measurement streams. The resulting LMI conditions lead to a numerically tractable design of the hybrid regulator. Finally, with the help of an illustrative example, the effectiveness of the theoretical results are verified.

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BibTeXRIS

Himadri Basu, Francesco Ferrante, Mirko Fiacchini. 2023-06-27. Output Regulation of Stochastic Sampled-Data Systems with Post-processing Internal Model. https://doi.org/10.1016/j.ejcon.2023.100866

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