A New Optimization Control Policy for Fuzzy Vehicle Suspension Systems Under Membership Functions Online Learning

Abstract

Active vehicle suspension systems play a crucial role in isolating vibration between the car body and pavement. In this article, based upon Takagi–Sugeno (T–S) fuzzy model, a novel optimization control algorithm is first proposed to furthest improve driving comfort level for fuzzy suspension systems compared with traditional existing approach. First, sufficient criteria for designing non parallel distribution compensation (non-PDC) fuzzy controller to improve driving comfort level and guarantee corresponding suspension constrained requirements are presented. Then, in the context of ensuring stability of studied systems, considering the feasible areas of membership functions (MFs) of non-PDC controller, a new MFs online optimization strategy is built for fuzzy suspension systems for the first time. By this proposed novel optimization algorithm, the values of controller MFs (CMFs) can be updated in real-time so as to obtain a better driving comfort level while improving suspension-constrained requirements. Sufficient criteria is obtained to guarantee the convergence of the designed cost function with the help of the Lyapunov stability theory. In the end, the usefulness, as well as superiority of the proposed online optimization strategy, are illustrated by contrastive verification.