Research on track vehicle path tracking algorithm based on Improved PSO

Aiming at the problems of low tracking accuracy and more steering control times of the existing unilateral braking tracked vehicle tracking control algorithm, an adaptive path tracking algorithm for unilateral braking tracked vehicle based on Particle swarm optimization (PSO) is proposed. Based on the preview tracking model, the track vehicle path tracking method is studied; In order to improve the adaptive ability of the preview tracking model, a fitness function is constructed based on the tracking accuracy and steering control times. The lateral error is used as the main decision parameter, and the forward-looking distance in the preview tracking model is determined in real time by particle swarm optimization algorithm; In order to reduce the calculation time of particle swarm optimization and carry out local search as soon as possible, the inertia weight coefficient and particle state update strategy in PSO algorithm are improved, and chaos factor is introduced. In this paper, the tracking accuracy and steering control times of the algorithm are comprehensively evaluated through simulation and actual tests on the test platform of the modified 3b55 tracked transport vehicle. Compared with SSA algorithm, the improved PSO algorithm has faster convergence speed, higher tracking accuracy and fewer steering control times. The research results can provide innovative ideas and technical support for the automatic navigation technology of unilateral braking tracked vehicles.