Multi-objective Controller Design: Evolutionary Algorithms and Bilinear Matrix Inequalities for a Passive Suspension

In this paper we present two multi-objective optimization techniques for the design of a passive suspension for a quarter-car vehicle model. These techniques are based on meta-heuristic optimization and bilinear matrix inequalities (BMI) respectively. The use of multi-objective optimization is motivated by the need to optimize simultaneously various performance measures when designing a vehicle suspension. These performance measures capture desirable properties such as ride comfort and handling. The use of the BMI technique was motivated by the fact that both the performance measures and the passive nature of the suspension can be formulated using matrix inequalities. The characterization of a positive real passivity constraint using matrix inequalities and the use of a new mechanical element, the inerter, permit optimization over the entire class of positive real admittances and the realization of the resulting admittance using passive elements. The meta-heuristic optimization is based on evolutionary algorithms (EAs) and it has proved more favorable than the BMI technique from a computational point of view, although both techniques give similar results. Copyright c ©2006 IFAC