Intelligent sliding mode controller for active suspension system using particle swarm optimization

This paper considers the control of an active suspension system (ASS) for a quarter car model based on the fusion of robust control and computational intelligence techniques. The objective of designing a controller for the car suspension system is to improve the ride comfort while maintaining the constraints on to the suspension travel and tire deformation subjected to different road profile. However, due to the mismatched uncertainty in the mathematical model of the ASS, sliding mode control (SMC) cannot be applied directly to control the system. Thus, the purpose of this work is to adapt the SMC technique for the control of ASS, where particle swarm optimization (PSO) algorithm is utilized to design the sliding surface such that the effect of the mismatched uncertainty can be minimized. The performance of the proposed sliding mode controller based on the PSO algorithm is compared with the linear quadratic optimal control (LQR) and the existing passive suspension system. In comparison with the other control methods, the simulation results demonstrate the superiority of the proposed controller, where it significantly improved the ride comfort 67% and 25% more than the passive suspension system and the LQR controller, respectively