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

Abstract : 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.
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Smart Materials and Structures, 2010, 4, 19, pp.045007. 〈10.1088/0964-1726/19/4/045007〉
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Soumis le : vendredi 14 octobre 2016 - 14:50:05
Dernière modification le : vendredi 10 novembre 2017 - 01:20:59

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Xiaopin Zhong, Mohamed Ichchou, Frederic Gillot, Alexandre Saidi. A dynamic-reliable multiple model adaptive controller for active vehicle suspension under uncertainties. Smart Materials and Structures, 2010, 4, 19, pp.045007. 〈10.1088/0964-1726/19/4/045007〉. 〈hal-01381604〉

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