Secure-Oriented Gain-Scheduling for IT-2 Fuzzy Active Vehicle Suspension Systems under Malicious Attacks: Polynomial-Driven Relaxation and Experimental Validation

Malicious cyber intrusions have posed an escalating threat to the operational reliability of transportation cyber–physical systems (TCPSs). Motivated by this issue, this study investigates the resilient security protection problem for active vehicle suspension systems (AVSSs) embedded within the TCPS framework against cyberattacks. First, an interval type-2 (IT-2) fuzzy model is constructed to tackle the uncertainties arising from time-varying spring stiffness and damping coefficients. Second, a secure-oriented multiinstant gain-scheduling (SMG) defense strategy is developed. By systematically utilizing normalized fuzzy weighted membership degrees (NFWMDs) over consecutive sampling instants, the proposed high-order strategy can not only alleviate conservatism in the stability criteria but also guarantee smooth vehicle operation in the presence of concurrent cyberattacks and road disturbances. Third, a dedicated slack variable technique tailored to the proposed SMG strategy is introduced to exploit the inherent algebraic properties of the NFWMDs, which yields more relaxed exponential stability conditions for AVSSs under stochastically triggered cyberattacks compared to those reported in recent studies. Finally, the hardware-in-the-loop (HIL) testing is performed to validate the superiority of the developed method.