Trajectory Tracking of Differential Driven AGV Based on Kalman Filter and Model Predictive Control

Trajectory tracking is one of the key technologies of automated guided vehicles. However, the existing relevant research seldom considers the influence of noise on tracking accuracy in the real environment. Considering the automated guided vehicle model with additive noise, a new trajectory tracking algorithm is proposed in this paper. In the algorithm, the initial state and control are first given. Then state estimation and optimal control are implemented alternatively. The former is realized by the Kalman filter and the latter is resorted to model predictive control. The algorithm can effectively reduce the influence of noises on trajectory tracking. Finally, the proposed algorithm is evaluated by simulation experiments.