Design of a genetic-algorithm-based self-tuning sliding fuzzy controller for an active suspension system

Abstract

Due to the fixed damping coefficient of the passive suspension system, it is difficult to enhance ride comfort and driving quality. An active suspension system provides extra force through the actuator which allows it to effectively isolate energy from vibrations and improve passenger comfort. This paper investigates ride comfort of a quarter-vehicle active suspension system according to the ISO 2631-1 standard. Using feedback information about the sprung mass acceleration, unsprung mass acceleration, and suspension deflection, an extended Kalman filter is developed to estimate the six unknown state variables: the sprung mass displacement and velocity; unsprung mass displacement and velocity; actuating force; and spool valve displacement. In addition, the road surface irregularities are also estimated. As compared to conventional controllers, the hydraulic actuator has time-varying, uncertain, and highly non-linear characteristics; thus, this paper applies a genetic-algorithm-based self-tuning sliding fuzzy controller and feedback linearization technique to design the actuating force. Simulation results are presented that show that the proposed controller demonstrates significant improvements in the vibration suppression of the vehicle body and ride comfort compared to a linear quadratic Gaussian controller design.