MATEC Web of Conferences
Volume 166, 2018The 2nd International Conference on Mechanical, Aeronautical and Automotive Engineering (ICMAA 2018)
|Number of page(s)||5|
|Section||Material Science and Mechanical Engineering|
|Published online||23 April 2018|
Neural Network Optimized Model Predictive Multi-Objective Adaptive Cruise Control
State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, People’s Republic of China
2 Innovation Center of Electric Vehicles, Beijing, People’s Republic of China
A model predictive multi-objective adaptive cruise control (MPC MO-ACC) system, designed to consider both the tracking performance and the fuel consumption, is optimized by a neural network in this paper, reducing the computational complexity without sacrificing the control performance. The optimized MO-ACC control system is built by training a neural network with the control results of the MPC MO-ACC system. Simulation tests are conducted in Matlab/Simulink in conjunction with the high-fidelity CarMaker software. Influences of four driving conditions (the learning track, NEDC, JP05, FTP75) and two kinds of sensor models (ideal radar sensor and 77GHz physical radar sensor) are analysed. Simulation results have shown that the neural network optimized model predictive MO-ACC has the same control capability and strong robustness as the original MPC MO-ACC. Meanwhile, the optimized control system has much lower computational complexity, which shows potentials for the application in real-time vehicle control and industry.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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