Trajectory Tracking for Wheeled Mobile Robot Using Lyapunov-Based Model Predictive Control

This paper investigates the trajectory tracking control problem for wheeled mobile robots (WMRs) and proposes a novel Lyapunov-based model predictive control (LMPC) strategy. By incorporating the Lyapunov direct method into the model predictive control (MPC) framework, optimal control signals are obtained to achieve accurate trajectory tracking. The proposed method utilizes the Lyapunov direct method to design the control laws, and then incorporate the control laws into the optimization problem to construct the contractive constraint. Based on the contractive constraint, the stability of the controlled system is ensured. Besides, the recursive feasibility of the LMPC problem is analyzed and guaranteed by ensuring that the auxiliary control laws satisfy the input limitations. Finally, simulations and real-time experiments are conducted to verify the applicability and effectiveness of the proposed method.

I. Introduction

With the increasing attention to intelligent unmanned systems, wheeled mobile robots (WMRs) have attracted significant attention in transportation and intelligent driving due to their versatility and adaptability [1]. However, some research challenges and difficulties still exist in this field. A key issue is the nonholonomic constraint that restricts the system from freely manoeuvring within the entire state space [2]. Besides, how to effectively handle nonlinearities and input constraints simultaneously in a performance-optimizing way is still challenging for trajectory tracking control problems.