Research on data-driven control algorithm of automotive electronic control opening and closing system

This article presents a data-driven control algorithm for automotive electronic control opening and closing system. By collecting the data of various mixed complex working conditions, the automatic decision-making model is formed on the basis of data learning, which can reduce the design and research of the algorithm itself and improve the intelligence degree of ECU. The untrained data can be collected at any time to learn to get the new model, and the original model can be quickly replaced. According to a large number of data collected in complex mixed conditions, the control method has better adaptability, simplifies the electronic control algorithm, and has certain generality in the field of automotive electronics. This article takes the anti-pinch control of power lift door as an example, proposes a data-driven BP neural network anti-pinch force classification method, selects good data for network training, and the recognition accuracy reaches 99.7%. Based on DSPACE, the rapid prototype bench test results show that the anti-pinch forces are in the range of 70N-100N, which verifies the feasibility of the control method.