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

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