The paper presents an engine control algorithm, based on an artificial neural network (ANN), which ensures a quick and smooth inertia phase of an upshift in a dual-clutch transmission (DCT). The main emphasis is placed on 3D characteristics, which can be directly implemented in the ECU, to control the inertia phase, when the engine speed decreases to reach the target clutch speed. They are developed based on an ANN model which approximates data obtained during engine test bed measurements in dynamic states. Moreover, to provide an overall gear change algorithm, the dual-clutch assembly activation mechanism was also analysed.

中文翻译：

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基于神经发动机模型的双离合变速器换挡过程非线性控制

本文提出了一种基于人工神经网络 (ANN) 的发动机控制算法，该算法可确保双离合变速器 (DCT) 升档的快速平稳惯性阶段。主要重点放在 3D 特性上，可以直接在 ECU 中实现，以控制惯性阶段，当发动机速度降低以达到目标离合器速度时。它们是基于 ANN 模型开发的，该模型近似于动态状态下发动机试验台测量期间获得的数据。此外，为了提供整体换档算法，还分析了双离合器总成的启动机制。