Particle swarm optimization based neural network automatic controller for stability steering control of four-wheel drive electric vehicle

Abstract

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 15 ms with a steering prediction accuracy of up to 92%. On frozen road surfaces, the model showed a system response time of 18 ms, 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.