Adaptive Slip Control of Distributed Electric Drive Vehicles Based on Improved PSO-BPNN-PID

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.