LAUSR

The riding conditions for a high-speed tracked vehicle are quite complex. To enhance the adaptability of suspension systems to different riding conditions, a semiactive and self-adaptive hybrid control strategy based on disturbance velocity and frequency identification was proposed. A mathematical model of the semiactive, self-adaptive hybrid suspension control system, along with a performance evaluation function, was established. Based on a two-degree-of-freedom (DOF) suspension system, the kinematic relations and frequency zero-crossing detection method were defined, and expressions for the disturbance velocity and disturbance frequency of the road were obtained. Optimal scheduling of the semiactive hybrid damping control gain ( , , ) and self-adaptive control gain ( ) under different disturbances were realized by exploiting the particle swarm multiobjective optimization algorithm. An experimental study using a carefully designed test rig was performed under a number of typical riding conditions of tracked vehicles, and the results showed that the proposed control strategy is capable of accurately recognizing different disturbances, shifting between control modes (semiactive/self-adaptive), and scheduling the damping control gain according to the disturbance identification outcomes; hence, the proposed strategy could achieve a good trade-off between ride comfort and ride safety and efficiently increase the overall performance of the suspension under various riding conditions.