Resilient Memory-Event-Triggered Predictive Tracking Control for Unmanned Ground Vehicle

Albeit model predictive control (MPC) has been broadly applied for the trajectory tracking of unmanned ground vehicles (UGVs), certain issues still remain to be further investigated, particularly with regard to the tracking stability analysis and computational efficiency. This paper formulates the trajectory tracking for Ackerman-steering UGV as an optimization-constrained control problem within the MPC framework, incorporating stability analysis through the specification of terminal ingredients. To relax the computational burdens of MPC in network transmission, this paper integrates historical triggered signals into the event-triggered scheme synthesis, ensuring that critical moments, such as peaks and troughs in the system dynamics, are triggered. Additionally, an upper limit on untriggered signals is imposed to safeguard against potential abnormal behaviors on event generators. The effectiveness of the proposed resilient memory-event-triggered MPC algorithm is validated through both computer simulations and hardware experiments of tracking circular and '8'-shaped trajectories.