Research on steering control performance of electric forklift with steer by wire

Abstract

Forklift plays an important role in cargo handling in the warehouse; therefore, it is necessary to ensure the stability of the forklift when turning to guarantee the safety of transportation. In this study, the particle swarm optimization (PSO) algorithm was improved by a genetic algorithm (GA), and the parameters of the proportion, integration, and differentiation (PID) controller were calculated using the improved algorithm for forklift steering control. Then simulation experiments were carried out using MATLAB. The results showed that the convergence speed of the improved PSO algorithm was faster than that of GA, and its adaptive value after convergence stability was significantly lower than that of the PSO algorithm; whether it was low-speed or high-speed steering, the three algorithms responded to the steering signal quickly; the yaw velocity and sideslip angle of the forklift steering under the improved PSO algorithm were more suitable for stable steering, and the increase of the steering speed would increase the yaw velocity. The novelty of this paper is that the traditional PSO algorithm is improved by GA and the particle swarm jumps out of the locally optimal solution through the crossover and mutation operations.