Hierarchical path-following control for distributed tracked unmanned vehicles: MPC-PID-PSO design for lateral error and energy optimization

As a key technical foundation for tracked unmanned ground vehicles (UGVs) to autonomously execute missions in high-risk environments, path-following control is a core prerequisite for achieving autonomous driving capability. To address the lack of research on path-following for a new configuration of the Distributed Tracked Unmanned Vehicle (DTUV), this paper first establishes the dynamics and kinematics models of the DTUV, including the track model, motor model, and whole-vehicle model. Subsequently, a hierarchical path-following controller is designed, with a model predictive control (MPC) modified by proportional integral derivative (PID) control as the upper layer and a torque-distribution algorithm based on adaptive weight particle swarm optimization (AWPSO) as the lower layer, namely the MPC-PID-PSO controller. Finally, the effectiveness of the proposed controller under three typical paths is verified through co-simulation. The results show that the proposed control strategy algorithm has better lateral error performance than other control algorithms, with a 2.7%–53.8% lateral error reduction. Meanwhile, it also has certain energy-saving effects, with a 5.5%–16.5% reduction in energy consumption. The research findings of this paper can provide a theoretical basis and control strategy guidance for the engineering practice of the DTUV and other UGVs.