Prescribed-Time Extended State Observer-Based Model Predictive Control for Wheeled Mobile Robots

Abstract

Wheeled mobile robots often face external disturbances during trajectory tracking tasks, which can significantly degrade control system performance, especially in high-precision applications. To address this issue, this article proposes a framework called PTESO-MPC that integrates model predictive control (MPC) with a prescribed-time extended state observer (PTESO). The PTESO is designed to provide a rapid and accurate estimation of external disturbances within a predefined settling time. To further enhance robustness against residual disturbances, the MPC optimization problem is reformulated with tightened constraints and rigorously designed terminal conditions, ensuring improved disturbance rejection and system stability. Experimental results on the Turtlebot4 platform demonstrate the effectiveness and superiority of the PTESO-MPC approach, highlighting its potential for practical applications in high-precision robotic systems.