Active suspension system control in electric vehicles: A performance comparison of PID, T2FLC, and T2FLC-PID controllers

The active suspension system (ASS) is considered a highly promising solution for effectively addressing the inherent conflicts between ride comfort, vehicle stability, and driving safety. However, its performance is significantly influenced by the control strategies employed. To address this, the present study investigates the effectiveness of various control strategies applied to the ASS of an electric vehicle (EV). For this purpose, a half-car EV model with five degrees of freedom is developed for the deployment of the ASS. Several control strategies are proposed for the ASS, including the classical Proportional-Integral-Derivative (PID) controller, a Type-2 Fuzzy Logic Controller (T2FLC), a hybrid controller that combines PID and T2FLC referred to as HT2FLCPID, and an adaptive type of the controller called AT2FLCPID. The Particle Swarm Optimization (PSO) algorithm is selected to optimize the parameters of these control strategies. The ASS with the proposed control strategies is developed in the MATLAB/Simulink environment. The results show that the root mean square (RMS) values of seat acceleration ( a zs ), body acceleration ( a zb ), pitching body acceleration ( a φb ), suspension working space ( SWS ), and dynamic tire load ( DTL ) in the ASS with the proposed control strategies demonstrate significantly improved vibration absorption compared to the passive suspension system (PSS). Among these strategies, the AT2FLCPID controller demonstrates the best performance in comparison to the other control strategies. The findings of this study provide valuable insights into the advantages and limitations of each control strategy, offering a solid foundation for the development of smarter and more efficient suspension systems for future electric vehicle applications.