In general, a vehicle suspension system can be characterized as a nonlinear dynamic system that is subjected to unknown vibration sources, dependent on road roughness and vehicle speed. In this paper, we will present a nonlinear-model-based adaptive semiactive control algorithm developed for nonlinear systems exposed to broadband non-stationary random vibration sources that are assumed to be unknown or not measurable. If there exist unknown and/or varying parameters of the dynamic system such as mass and stiffness, then the adaptive algorithm can include a recursive least square (RLS) method for on-line system identification. Since the adaptive algorithm is developed for semiactive systems, stability is guaranteed based on the fact that the system is energy conservative. The convergence of the adaptive system, however is not guaranteed, and is investigated through a numerical approach for a specific case. The simulation results for a magneto-rheological seat suspension system with the suggested adaptive control are presented. The results are compared with low-damping and high-damping cases, as well æ other configurations of skyhook control, in order to show the extent of the procurement that can be expected with the suggested adaptive skyhook control provides a better broadbandk performance for the suspension, as compared to the other damping configurations that are included here.

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