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ORIGINAL RESEARCH article

Front. Mech. Eng.
Sec. Engine and Automotive Engineering
Volume 10 - 2024 | doi: 10.3389/fmech.2024.1378175

Particle Swarm Optimization based Neural Network Automatic Controller for Stability Steering Control of Four-Wheel Drive Electric Vehicle

Provisionally accepted
Ying Li Ying Li *
  • Yunnan Vocational Institute of Energy Technology, Qujing, China

The final, formatted version of the article will be published soon.

    In addressing the steering stability issues of four-wheel-drive electric vehicles on surfaces such as wet, slippery, frozen, and soft terrains, a novel control method based on particle swarm optimization for neural networks is proposed in this study. The approach integrates the advantages of Proportional-Integral-Derivative control, particle swarm optimization, and neural networks. By constructing a neural network model with input, hidden, and output layers, the study introduces particle swarm optimization algorithm for weight and structure optimization. Fuzzy logic and slip control theory are integrated into the steering stability control. The results demonstrated that, under wet and slippery road conditions, the model exhibited a system response time of 15ms with a steering prediction accuracy of up to 92%. On frozen road surfaces, the model showed a system response time of 18ms, with a steering prediction accuracy reaching 90%. Compared to other models, it significantly demonstrated superior steering stability control. This suggests that the designed model performs well in handling complex driving environments, indicating high application potential in the field of electric vehicle steering stability control.

    Keywords: electric vehicle, steering, Particle swarm, stability control, PID 1. Introduction

    Received: 29 Jan 2024; Accepted: 16 Jul 2024.

    Copyright: © 2024 Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ying Li, Yunnan Vocational Institute of Energy Technology, Qujing, China

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