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Sliding mode control for Markov jump power systems: Asynchronous learning-based method
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DOI：10.1016/j.engappai.2026.115035.png)
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Abstract
En 中文
This paper studies the problem of asynchronous learning-based sliding mode control (LSMC) of a single-machine infinite bus (SMIB) power system. Due to the influence of instantaneous faults and stochastic switching, the power system may experience dynamic changes in the structural parameters. To describe this phenomenon, the power system is expressed as a Markov jump power system (MJPS) model. In addition, a common sliding surface is designed to overcome the problem that the sliding surface may be unreachable due to mode switching. By constructing the Lyapunov function, sufficient conditions to ensure the stochastic stability of the system are derived, and a suitable sliding matrix is solved. In order to facilitate the design of learning parameters, an LSMC law related to the hidden Markov observation mode is designed. Compared with the mode-independent LSMC, the proposed method further accelerates the convergence speed and has better control performance. Finally, the superiority of the proposed method is verified by a simulation example.
Keywords:
Sliding mode control
Markov jump power system
Asynchronous learning
Stochastic stability
Sliding surface design
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5.3K
Citations:
3.5W
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