Trajectory tracking control of quadrotor using MPC and Gaussian process regression compensation

Nowadays, the market for UAV is huge, and they are widely used in civilian, commercial and even military. In order to achieve so many functions, the drone's track tracking strategy is fundamental. Nowadays the PID control technology is quite mature, but this strategy is less robust and easily affected by external factors such as wind disturbance. On the other hand, since the UAV dynamic model is not a black box and most parts can be mathematically modeled, simply using PID cannot make good use of the UAV dynamic model. In order to achieve precise and robust flight control and management and improve the performance and safety of UAV systems, we design a MPC controller by utilizing the UAV's dynamic model and target constraints enables it to perform adaptive control in changing environments and provide more reliable and accurate flight performance. At the same time, Gaussian Process Regression is used to learn the historic data and predict the error, which can be used to compensate the MPC controller, thus enhancing the robustness and adaptability of the system. In the simulation by using SIMULINK and MATLAB, UAV trajectory tracking curves have higher accuracy and higher robustness compared with the results of PID control and ordinary MPC control. This paper uses MPC for the position loop of the UAV with the auxiliary compensation strategy of Gaussian process prediction, optimizes the UAV's more advanced MPC strategy which is also suitable for multi-UAV control, which has certain promoting significance.