Trajectory Tracking Control of Unmanned Vehicles Based on Adaptive MPC

doi:10.12068/j.issn.1005-3026.2020.06.013

Abstract

Abstract: According to the principle of adaptive MPC(model predictive control)， a trajectory tracking control strategy for unmanned vehicles is designed. Based on the vehicle dynamics model， the trajectory tracking controller is established， and the objective function and related constraints are designed. The adaptive MPC control algorithm is used for solution. At each control point of operation， the Kalman state estimator correlation gain coefficient matrix and the state of the controller are continuously updated to adapt to the current working environment of the unmanned vehicle， thereby compensating for the nonlinearity of the vehicle and the effect of state measurement noise. The simulation model is built in MATLAB and verified by simulation. It is concluded that the adaptive MPC has better control precision and robustness for the trajectory tracking of unmanned vehicles. The effectiveness of the algorithm in the trajectory tracking control layer is verified. In addition， it provides a reference for the research of future trajectory tracking control.

Key words: unmanned vehicle, Kalman state estimator, adaptive MPC(model predictive control), trajectory tracking control, MATLAB/Simulink

CLC Number:

U461

LIANG Zhong-chao， ZHANG Huan， ZHAO Jing， WANG Yong-fu. Trajectory Tracking Control of Unmanned Vehicles Based on Adaptive MPC[J]. Journal of Northeastern University Natural Science, 2020, 41(6): 835-840.

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