Robust Prescribed-Time Predictive Control for Mobile Robot Navigation

This paper presents a robust control strategy for point-to-point navigation of mobile robotic systems under pre-scribed time specifications and with state and input constraints. Based on the concept of Nonlinear Model Predictive Control (NMPC), we propose a stable dynamically decreasing horizon robust optimal control strategy that achieves the aforementioned task while considering state and input constraints as well as system uncertainties and disturbances. We guarantee that, under the proposed control scheme, the mobile robotic system reaches the desired set in the specific given time, provided an upper bound of the disturbances. This can have great applications in collaborative tasks including a team of mobile robots, where in addition to mobility and flexibility, the mobile robots are required to be enhanced individually with abilities to satisfy time-constrained navigation tasks, in order to fulfill high-level planning specifications. Finally, simulation results verify the performance of the proposed framework.