在建立二自由度1/4车辆主、被动悬架模型和LQG控制器的基础上,设计了卡尔曼滤波器实现对悬架状态的估计.针对汽车主动悬架作动器增益故障,设计卡尔曼滤波器获得状态残差,实现对故障的在线诊断,得到悬架故障增益.对LQG控制率进行实时控制力补偿,实现主动悬架作动器的容错控制,并利用MATLAB/Simulink进行了仿真.结果表明,设计的状态估计器能及时发现故障,并进行控制力补偿,使故障悬架快速恢复原有性能,提高主动悬架的可靠性.%Based on the 1/4 active and passive vehicle suspension model of 2-DOF and LQG controller, a Kalman filter was designed to estimate the suspension state. To eliminate suspension actuator gain fault, Kalman filter was designed to obtain state residual, and realize online diagnosis of fault, and obtain fault gain of suspension. By real-time control force compensation of LQG control rate, the fault-tolerant control of the active suspension actuator was realized. Finally the control was simulated and verified by MATLAB/Simulink software. The simulation results show that designed Kaiman filter state estimator can detect fault timely, and make control force compensation quickly to resume the original performance of the malfunctioning suspension rapidly,thereby improving the reliability of the suspension.
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