A dynamic-reliable multiple model adaptive controller for active vehicle suspension underuncertainties

Abstract

The inherent uncertainties of vehicle suspension systems challenge not only the capabilityof ride comfort and handling performance, but also the reliability requirement. In thisresearch, a dynamic-reliable multiple model adaptive (MMA) controller is developed toovercome the difficulty of suspension uncertainties while considering performance andreliability at the same time. The MMA system consists of a finite number of optimalsub-controllers and employs a continuous-time based Markov chain to guide the jumpingamong the sub-controllers. The failure mode considered is the bottoming and topping ofsuspension components. A limitation on the failure probability is imposed to penalizethe performance of the sub-controllers and a gradient-based genetic algorithmyields their optimal feedback gains. Finally, the dynamic reliability of the MMAcontroller is approximated by using the integration of state covariances and a judgingcondition is induced to assert that the MMA system is dynamic-reliable. In numericalsimulation, a long scheme with piecewise time-invariant parameters is employed toexamine the performance and reliability under the uncertainties of sprung mass,road condition and driving velocity. It is shown that the dynamic-reliable MMAcontroller is able to trade a small amount of model performance for extra reliability.