Trajectory Tracking Problem for Wheeled Robots Based on Self-triggered MPC

In this paper, we introduce a self-triggered Model Predictive Control (MPC) framework tailored for precise control of two-wheeled non-holonomic robots. This framework considers the intricate dependencies among input constraints and bounded external disturbances. By leveraging Lyapunov theory and carefully selecting parameters, we design a terminal region to ensure the feasibility of the system. Our approach reduces the computational burden of solving optimization problems during trajectory tracking while ensuring accurate tracking of the reference trajectory. Additionally, it guarantees the stability and suboptimal performance of the closed-loop system. Comparative analysis with traditional methods highlight the effectiveness of the self-triggered MPC scheme. Numerical simulations validate its potential in real-world robotic control applications.