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针对自动泊车系统中路径跟踪控制困难这一问题,文中采用云模型不确定性推理方法,以汽车低速运动学模型中方向盘的转角偏差作为输入,将1维和2维云模型有机组合,构建路径跟踪多规则推理模型,以后悬架几何中点位置的准确度为控制目标,建立路径跟踪控制规则库,推导泊车入库过程中的最优转向角控制参数。设计自动泊车路径跟踪控制原型,通过仿真试验,验证云数字特征值对泊车跟踪效果的影响,结果表明,所设计的控制原型路径跟踪效果良好。
Aiming at the difficulty of path tracking control in automatic parking system, this paper uses cloud model uncertainty inference method to take the steering wheel steering angle deviation in the low-speed kinematics model of the automobile as input, and combines the 1D and 2D cloud models to build the path After tracking the multi-rule reasoning model, the accuracy of the mid-point position of the suspension geometry is the control target, the path tracking control rule base is established, and the optimum steering angle control parameters are deduced during parking. The prototype of automatic parking path tracking control is designed. The simulation test verifies the effect of cloud digital eigenvalues on parking tracking performance. The results show that the proposed control prototype path tracking is effective.