Adaptive fuzzy controller for vehicle active suspension system based on traffic conditions

Fuzzy logic controller; Adaptive; Traffic conditions; Genetic algorithm; Multi-objective; Pareto; Full-vehicle model. Abstract Vehicle ride comfort is a function of the frequency content of transmitted vibrations to passengers from road irregularities. However, this frequency content varies with vehicle speed fluctuations, which occur under real traffic conditions. The design of an adaptive active suspension system, in order to simultaneously improve ride comfort and travel suspension under various traffic conditions, is addressed in this paper. For this purpose, using a full-vehicle model, with eight degrees of freedom, two separate fuzzy controllers are designed for front and rear suspensions. The parameters of the fuzzy controllers are then tuned for various traffic conditions of a driving pattern, using a multi-objective Pareto-optimal solution. The optimization objectives are: the ride comfort index, evaluated according to the ISO 2631-1 standard, and the maximum suspension travel. Simulation results prove that the multiobjective fuzzy controller conventionally tuned, based on the constant speed driving pattern, results in simultaneous improvement of ride comfort, travel suspension and energy consumption. However, this controller does not work optimally under all traffic conditions. On the other hand, the proposed adaptive multi-objective controller not only results in optimal ride comfort and travel suspension under various traffic conditions, but also leads to a considerable drop in active suspension energy consumption. © 2012 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.