Laguerre based model predictive control for trajectory tracking of nonholonomic mobile robots

This paper presents a Laguerre parametrization approach to employ Model Predictive Control (MPC) for the trajectory tracking problem of a non-holonomic mobile robot with input and state constraints. A time-varying error model is obtained for the trajectory tracking of the mobile robot. Then, a Laguerre based MPC (LMPC) for time-varying systems is designed and tuned to ensure asymptotic stability of the system. The proposed algorithm considers input and states, including velocity and acceleration, constraints to provide stability. It is shown that the proposed method is able to reduce the computation times. In order to confirm the effectiveness of the proposed method, extensive simulations results are provided.