Multi-objective optimization by genetic algorithms in H∞/LPV control of semi-active suspension

Abstract 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) were taken into account; an H∞/LPV controller to handle the comfort and road holding was also designed. The present paper aims at improving the method of Do et al. (2010) by using Genetic Algorithms (GAs) to select the optimal weighting functions for the H∞/LPV synthesis. First, a general procedure for the optimization of the weighting functions for the H∞/LPV synthesis is proposed and then applied to the semi-active suspension control. Thanks to GAs, the comfort and road holding 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.

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