基于自适应MPC的无人驾驶车辆轨迹跟踪控制

根据自适应模型预测控制相关原理，设计一种无人驾驶车辆的轨迹跟踪控制策略.基于车辆动力学模型，建立轨迹跟踪控制器，并设计目标函数与相关约束，利用自适应MPC(model predictive control)控制算法对其进行求解.在每一个控制时刻工作点，不断更新卡尔曼状态估计器相关增益系数矩阵以及控制器的状态来适应无人驾驶车辆当前的工作环境，以此补偿车辆的非线性以及状态测量噪声带来的影响.在MATLAB中搭建仿真模型并进行仿真验证，得出自适应MPC对于无人驾驶车辆的轨迹跟踪拥有较好的控制精度与鲁棒性，验证了该算法应用在轨迹跟踪控制层的有效性，为轨迹跟踪控制的研究提供了参考.

Trajectory Tracking Control of Unmanned Vehicles Based on Adaptive MPC

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.