Model free predictive path tracking control of variable-configuration unmanned ground vehicle

Abstract

Unmanned ground vehicle (UGV) is developing towards high mobility and intelligence, where path tracking plays a particularly important role. This paper investigated the path tracking control strategy of variable-configuration unmanned ground vehicle. In order to overcome the structural and unstructured uncertainties, a model free predictive control (MFAPC) strategy using particle swarm optimization (PSO) is presented. The control scheme of MFAPC is improved by integrating vehicle state parameters. Then, the main parameters of the improved control scheme are optimized by PSO algorithm. The effectiveness of the proposed method under different operation conditions is verified by simulation. The experimental results show that the proposed scheme does not require the accurate mathematical model and can quickly track the reference path.