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

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 member-ship 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 improv-ing 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.

Automated summary

This article introduces an optimization control algorithm based on the Takagi-Sugeno (T-S) fuzzy model, aiming to improve the driving comfort level of fuzzy suspension systems. By designing a non-PDC fuzzy controller and an online optimization strategy, the values of the controller's membership functions can be updated in real-time to achieve better driving comfort.