ABSTRACT The distributed electric drive vehicle is a highly nonlinear and time‐varying system. To address the issue of drive slip control under varying driving forces and road surface coefficients, a novel drive slip control strategy is proposed, which considers axle load transfer during vehicle acceleration. The strategy employs an improved PSO algorithm to obtain optimal parameters for the BP neural network, uses the BP neural network for forward propagation to calculate PID parameters in real‐time, and adjusts the weight matrix through backward propagation to achieve real‐time adaptive PID control for vehicle slip. Experimental results indicate that this strategy improves the ITAE index by 13.6% and response time by 74.8% compared to the anti‐saturation PID.

Abstract Due to the significant differences in the imaging principles of infrared images and visible images, the feature points and descriptors between the two cannot be effectively matched directly by traditional feature extraction methods such as SIFT. To solve this problem, this study proposes a registration algorithm for infrared and visible images based on phase information and edge information. The algorithm extracts the feature points of the infrared image and the visible image through the principle of phase agreement and the edge binary map of the image and then calculates the descriptors of the gradient images of the infrared image and the visible image, and the descriptor calculation draws on some SIFT principles. Finally, the cosine similarity was used to match the feature points, and the improved random sample consensus algorithm was used to screen out the correct registration points. Experiments show that this method can effectively register between infrared and visible images and is also suitable for the registration of infrared and visible images with different rotation angles and similar structures.

Abstract Around the new four‐wheel independent steering system, the adjustment principle of Akman angle is studied in this article. In order to improve steering stability and reduce tire wear, a new algorithm for optimal Ackerman angle allocation at full speed is proposed. Furthermore, combined with the minimum load rule, the four‐wheel rotation angle and driving force are optimized. In this process, Carsim vehicle model and MATLAB/Simulink force and angle allocation algorithm model are established. The final system simulation and performance evaluation demonstrate the effectiveness of the proposed force and angle distribution algorithm for vehicle stability and tire wear optimization.