Event-Triggered Data-Driven Trajectory Tracking Control for Networked Mobile-Robot Systems: Application to Workpiece Transport

This paper proposes an event-triggered data-driven trajectory tracking control method for a single networked mobile-robot system (NMS) of networked control systems. In practical applications, tracking performance is often affected by unknown external disturbances such as workpiece vibrations, indoor ground unevenness, and mechanical inconsistencies, while high-bandwidth pose estimation systems (e.g., LiDAR, vision) impose significant communication burdens. To address these challenges, a input-reference dynamic linearization (DL) data model is proposed by introducing the reference control input increment and unknown disturbance term, which improves the adaptability and equivalence of DL data model. Correspondingly, an input error term, as an additional constraint, is incorporated into the control input cost function to further enhance tracking accuracy and improve control smoothness. Given that the external disturbances are unknown, a compensation mechanism based on a radial basis function neural network is developed. As for the communication constraint, an event-triggered mechanism combining static and decaying threshold schemes is proposed to alleviate network resource consumption in NMSs. The effectiveness of the proposed method is validated through numerical simulations and further demonstrated in a long workpiece transportation scenario under ground-disturbance-only and lump-disturbance conditions. Note to Practitioners - This work targets trajectory tracking for networked mobile-robot systems under limited bandwidth and external disturbances. A data-driven event-triggered control method is developed, which reduces network usage by updating robot pose information only when necessary. The approach is data-driven control and includes a neural-network-based disturbance estimator for enhanced robustness. Practitioners implementing this method should focus on careful parameter tuning to balance tracking accuracy and communication cost. This strategy is especially suited for warehouse transport, aircraft stringer transport, and collaborative multi-robot systems. Future improvements could automate tuning and adapt to unstructured environments.