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Using Of Neural Network Controller And Fuzzy PID Control To Improve Electric Vehicle Stability Based On A14-DOF Model

机译:基于A14-DOF模型的神经网络控制器和模糊PID控制在提高电动汽车稳定性中的应用

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Vehicle safety and control are attracting increased attention among researchers to improve the stability and maneuverability of the electric vehicles EVs. This paper describes the design and implementation for a control system that aims to enhance lateral stability and vehicle handling based on a 14-DOF full vehicle model and using two control methods, which are fuzzy PID control theory and neural network controller. When the control system has been created using the two controllers, the vehicle’s performance is examined at two completely different front steering angles, which are a lane change maneuver and a step steering. Also, this paper aims to compare the performance of the vehicle when using the fuzzy PID controller and the neural network controller. The performance and output analysis of the control systems implemented have shown the efficiency of the controllers proposed. The yaw rate for the vehicle, which indicates the lateral stability and handling has been improved satisfactorily in comparison with the uncontrolled case of the vehicle.
机译:车辆安全和控制正引起研究人员越来越多的关注,以提高电动汽车EV的稳定性和可操纵性。本文介绍了一种旨在提高横向稳定性和车辆操纵性能的控制系统的设计和实现,该系统基于14自由度整车模型并使用模糊PID PID控制理论和神经网络控制器这两种控制方法。当使用两个控制器创建了控制系统时,将在两个完全不同的前转向角(车道变更操纵和步进转向)下检查车辆的性能。此外,本文旨在比较使用模糊PID控制器和神经网络控制器时车辆的性能。所执行的控制系统的性能和输出分析显示了所提出的控制器的效率。与车辆的不受控制的情况相比,表示横向稳定性和操纵性的车辆的横摆率得到了令人满意的改善。

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