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基于二自由度线性化模型,分析了质心侧偏角和横摆角速度之间的耦合现象;在七自由度非线性汽车模型基础上,利用BP神经网络构造了自适应解耦控制器。设计了2个车辆姿态参数控制器,以状态参数理想模型的输出作为控制目标,分别控制汽车的质心侧偏角和横摆角速度,实现车辆主动安全控制。仿真结果表明,基于BP神经网络的解耦控制能够跟踪状态参数的理想值,提高汽车在极限工况下的行驶安全性和操纵稳定性。
Based on the two-degree-of-freedom linearization model, the coupling between the center-of-mass slip angle and the yaw rate is analyzed. Based on the seven-degree-of-freedom nonlinear vehicle model, an adaptive decoupling controller is constructed by BP neural network. Two vehicle attitude parameters controllers are designed. The output of the ideal model of the state parameters is used as the control target to control the vehicle center of mass side slip angle and yaw rate respectively to realize the active safety control of the vehicle. The simulation results show that the decoupling control based on BP neural network can track the ideal value of the state parameters and improve the driving safety and handling stability of the vehicle under the limit conditions.