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Memory-Gain-Scheduling of Fuzzy Cyber–Physical Systems: A Dual-Mode Polynomial-Based Framework and Its Applications to Active Vehicle Suspension Systems
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DOI：10.1109/tfuzz.2026.3694906.png)
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Abstract
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This article investigates the critical challenges of mode information loss and complex transition probability uncertainties, collectively termed as dual-domain uncertainty, in stochastic nonlinear cyber–physical systems. Oriented toward the emerging wireless architectures in future vehicles, a memory-gain-scheduling framework is developed, which strategically incorporates memory-dependent membership functions to enhance controller design by leveraging past and present scheduling information. Unlike existing scaling-based methods, our approach utilizes a polytopic reconstruction of the transition probability matrix to fundamentally eliminate structural conservatism. This is integrated with a dual-mode polynomial controller that adaptively switches based on real-time mode information availability, exhibiting enhanced robustness against data loss disruptions. Furthermore, by employing homogeneous polynomial techniques, this memory-gain-scheduling strategy effectively enlarges the feasible solution region by guaranteeing structural consistency. The practical effectiveness and superior performance of the proposed method are conclusively validated through rigorous hardware-in-the-loop experiments on an active vehicle suspension system, demonstrating significant improvements in ride comfort and stabilization under various road disturbances.
Keywords:
Active vehicle suspension systems
complex transition information
dual-domain uncertainty
fuzzy Markov jump systems (F-MJSs)
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11.9
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