Neural-network-based car drive train control

T. Hrycej
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引用次数: 4

Abstract

The optimization of the drive train string for high comfort requirements involves several control problems. One is the determination of optimal torque trajectory. Classical solutions of this problem suffer from strongly nonmonotonic torque trajectory, resulting from the difficulty of formulating the monotonicity requirement in the classical quadratic objective function form. A model-based neural-network trainable controller has been applied to this problem. In contrast to previous neural-network approaches, it fully exploits the available information about the plant. By its capability of using an arbitrary nonlinear differentiable control objective function (as well as an arbitrary nonlinear differentiable plant), it allows a direct formulation of the torque monotonicity requirement. The development time for the controller has been only two days. No control engineering competence has been required-the design procedure is very general and automatic.<>
基于神经网络的汽车传动系统控制
为了满足高舒适性要求,传动系管柱的优化涉及到几个控制问题。一是最佳转矩轨迹的确定。由于难以用经典的二次目标函数形式来表述单调性要求,该问题的经典解具有强烈的非单调力矩轨迹。一种基于模型的神经网络可训练控制器被应用于该问题。与以前的神经网络方法相比,它充分利用了关于植物的可用信息。由于其使用任意非线性可微控制目标函数(以及任意非线性可微对象)的能力,它允许直接表述转矩单调性要求。控制器的开发时间仅为两天。不需要控制工程能力-设计过程非常通用和自动化。
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