基于MPC的无人机航迹跟踪控制器设计

Aiming at the problem of trajectory tracking of fixed-wing unmanned aerial vehicle, the controller is designed by using the dual-feedback model predictive control theory based on state expansion. Firstly, the unmanned aerial vehicle side trajectory tracking model based on the lateral deviation is derived, and the dynamic inverse method is used to linearize the model. Based on this, a dual feedback model predictive controller based on state expansion is designed, and the parameters of the controller are optimized using quantum particle swarm optimization (QPSO). Then, considering the unknown interference encountered during the flight, the extended states observer (ESO) is introduced to observe the interference, which further improves the robustness of the system. Finally, the system is mathematically simulated in combination with actual engineering applications. Simulation results show that the side trajectory tracking controller of unmanned aerial vehicle based on the state expansion dual feedback model predictive control can accurately and stably track the expected trajectory when the system has model uncertainty and is subject to dynamic interference.