Development of a Semi-Active Suspension Controller Using Adaptive-Fuzzy with Kalman Filter

Following the developments in controlled suspension system components, the studies on the vertical dynamics analysis of vehicles increased their popularity in recent years. The objective of this study is to develop a semi-active suspension system controller using Adaptive-Fuzzy Logic control theories together with Kalman Filter for state estimation. A quarter vehicle ride dynamics model is constructed and validated through laboratory tests performed on a hydraulic four-poster shaker. A Kalman Filter algorithm is constructed for bounce velocity estimation, and its accuracy is verified through measurements performed with external displacement sensors. The benefit of using adaptive control with Fuzzy-Logic to maintain the optimal performance over a wide range of road inputs is enhanced by the accuracy of Kalman Filter in estimating the controller inputs. A gradient-based optimization algorithm is applied for improving the Fuzzy-Logic controller parameters. The vehicle model is simulated with the developed semi-active suspension controller on different road profiles. A comparison is performed between the Adaptive-Fuzzy semi-active controller, the optimal LQR semi-active controller, and the optimal passive suspension system in terms of ride comfort and road holding. The results showed that, the proposed semi-active suspension system controller provides significant improvements in both ride comfort and road holding of the vehicle on different road profiles.