A hybrid evolutionary approach for robust active suspension design of light rail vehicles
This paper is concerned with the design of a robust active suspension controller for light rail vehicles aimed at providing superior ride comfort within the suspension's traveling range. A multibody dynamic model of a three-car train is set up and the control parameters are optimized. Force cancellation, skyhook damper, and track-following concepts are used to synthesize the active controller. Selection of the active suspension parameters is aided by an evolutionary computation algorithm to get the best compromise between ride quality, suspension deflections due to irregular gradient tracks, and robust stability of the control system. A mixed gradient and evolutionary multiobjective optimization approach accompanied with the Pareto set and variable weights are developed to deal with the complicated control design task. Extensive simulations and comparisons are performed to verify the proposed design.