Optimization of weighting function selection for H<inf>∞</inf>control of semi-Active suspensions

Multi-objective optimization is a popular problem in engineering design. In semi-Active suspension control, comfort and road holding are two essential but conflicting performance objectives. In a previous work, the authors proposed an LPV formulation for semi-Active suspension control of a realistic nonlinear suspension model where the nonlinearities (i.e the bi-viscous and the hysteresis) have been taken into account; an H∞/LPV controller to handle the comfort and road holding has been also designed. The present paper aims at improving the method of [6] by using Genetic Algorithms (GAs) to select the optimal weighting functions for the H∞/LPV synthesis. First, a general procedure for the optimization of weighting function for the H∞/LPV synthesis is proposed and then applied to the semi-Active suspension control. Thanks to GAs, the comfort and road holding conflicting objectives are handled using a single high level parameter and illustrated via the Pareto optimality. The simulation results performed on a nonlinear vehicle model emphasize the efficiency of the method.