Collaborative high-precision trajectory control for heavy excavators based on an improved particle swarm optimization algorithm

Heavy excavators suffer from low trajectory accuracy due to nonlinear dynamics, inter-joint coupling, and synchronization errors among electro-hydraulic systems. Conventional methods inadequately address these issues during high-speed operation. To overcome these limitations, this research proposes a cooperative control framework that integrates a collaborative evaluation method and an improved particle swarm optimization. A dual error metric is designed based on the tracking error of the single servo system and mean-coupled collaborative error. The inertia weight adaptive method, asynchronous learning coefficient adjustment method, and elite mutation method are introduced to improve the algorithm's performance. Experimental results demonstrate that under 400 mm/s high-speed condition, the proposed controller achieves a root mean square error of 10.90 mm, representing reductions of 61.65 % and 72.26 % compared to master-slave collaborative trajectory controller and traditional independent parallel controller respectively. The proposed collaborative high-precision trajectory controller enables precise and robust control of excavators across different speed scenarios.