Fuzzy-Scheduled Constrained Control of Active Vehicle Suspension System

The active suspension system (ASS) is an automated system that isolates the car body from road irregularities while satisfying the physical restrictions of the system. In this paper, a fuzzy-scheduled optimal control system is developed for the ASS to intelligently solve the constraints in various road conditions. The proposed control method is formulated using a nonlinear prediction approach that utilizes a continuous model of the vehicle suspension system. Subsequently, the optimal control law is determined by constructing a nonlinear optimization problem. The weighting factors existing in the optimal control law are adaptively adjusted by fuzzy rules, enabling effective and smooth satisfaction of state and input constraints. Simulation studies are conducted to show the efficiency of the proposed control system. The obtained results demonstrate that the suggested soft-computing controller satisfies the practical constraints intelligently, when reduces the body acceleration below the standard range defined by ISO. Meanwhile, it is fast and suitable for online implementation when compared to other optimal methods, such as conventional nonlinear model predictive control (NMPC) methods.