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Fuel economy and torque tracking in camless engines through optimization of neural networks

机译：通过优化神经网络实现无凸轮发动机的燃油经济性和扭矩跟踪

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摘要

The feed forward controller of a camless internal combustion engine is modeled by inverting a multi-input multi-output feed forward artificial neural network (ANN) model of the engine. The engine outputs, pumping loss and cylinder air charge, are related to the inputs, intake valve lift and closing timing, by the artificial neural network model, which is trained with historical input-output data. The con-troller selects the intake valve lift and closing timing that will mimimize the pumping loss and achieve engine torque tracking. Lower pumping loss means better fuel economy, whereas engine torque tracking gurantees the driver's torque demand. The inversion of the ANN is performed with the complex method constrained optimization. How the camless engine inverse controller can be augmented with adaptive techniques to maintain accuracy even when the engine parts degrade is discussed. The simulation results demonstrate the effectiveness of the developed camless engine controller.
机译：通过倒置发动机的多输入多输出前馈人工神经网络（ANN）模型，对无凸轮内燃机的前馈控制器进行建模。发动机输出，泵送损失和气缸空气充气通过人工神经网络模型与输入，进气门升程和关闭正时相关，并使用历史输入-输出数据进行训练。控制器选择进气门升程和关闭正时，以最小化泵送损失并实现发动机扭矩跟踪。较低的泵送损失意味着更好的燃油经济性，而发动机扭矩跟踪可确保驾驶员的扭矩需求。人工神经网络的反演是通过复杂的方法约束优化来进行的。讨论了如何通过自适应技术扩展无凸轮发动机逆控制器，以在发动机零件降级时保持精度。仿真结果证明了开发的无凸轮发动机控制器的有效性。

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