Optimization of Sliding Mode Control for a Vehicle Suspension System via Multi-objective Genetic Algorithm with Uncertainty

In this paper, Sliding Mode Controller (SMC) is designed for a quarter-car active suspension system with parametric uncertainty in the mass of vehicle body and also Genetic Algorithms are employed to find optimal parameters of controller. An ideal suspension must be able to provide passengers comfort and improve safety performance. Furthermore, the necessity of trading off among the conflicting requirements of the suspensions in terms of comfort and road holding capability led to the use of multiobjective optimization techniques. The important conflicting objective functions that have been considered in this work are, namely, sprung mass acceleration, suspension deflection and energy consumption. Moreover, this approach returns the optimum answers in Pareto form that designer can, by making trade-offs, select desired answer. Finally, the obtained results demonstrate that use of the proposed controller provides good performance in improving and enhancing the road holding ability and riding quality compared with passive suspension system.