A learning-based sliding mode control for switching systems with dead zone

Bo Wang, Fucheng Zou, Junhui Wu and Jun Cheng

Applied Mathematics and Computation, 2025, vol. 494, issue C

Abstract: This paper focuses on the problem of adaptive neural network sliding mode control for switching systems affected by dead zones. Distinct from existing rules defined by transition and sojourn probabilities, a broader switching rule is proposed based on duration-time-dependent sojourn probabilities. A neural network strategy for compensation is implemented to mitigate the effects of the dead zone. Moreover, a sliding mode control law incorporating a learning term is designed, effectively reducing chattering compared to conventional sliding mode control. Employing a stochastic Lyapunov function grounded in the joint distribution of duration time and system mode, sufficient criteria for designing the adaptive neural network-based controller are established. Finally, the effectiveness of the proposed method is demonstrated through two simulated examples.

Keywords: Switching system; Dead zone; Sliding mode control; Adaptive neural network (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:494:y:2025:i:c:s0096300325000104

DOI: 10.1016/j.amc.2025.129283

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