Trajectory Tracking Control for Flapping-wing UAV Based on Model-Free Predictive Control

This paper proposes a trajectory tracking algorithm that uses Model Predictive Control (MPC) to address the issue of poor tracking performance of the Flapping-wing UAV. However, due to the difficulty in constructing a prediction model, the high computational complexity, and the poor real-time performance of MPC, we propose a Model-Free Predictive Control (MFPC) algorithm that uses a dynamic linearized model as the prediction model. It greatly reduces complexity and running time while satisfying control performance. To address the effects of the inaccuracy of the prediction model and disturbances such as crosswind on the tracking performance of MFPC, we optimize the feedback correction law of MFPC using the L1 algorithm idea and significantly improve the robustness of the resulting MFPC-L1. Simulation results demonstrate that MFPC-L1 can reduce the computation time for a given trajectory by approximately 46% compared to the classical MPC. Moreover, it reduces the tracking errors of circular and S-shaped trajectories by over 96% compared to PID and Model-Free Adaptive Control (MFAC) algorithm. In the presence of crosswind, MFPC-L1 reduces the tracking errors by approximately 85% compared to the MFPC-PD algorithm based on the lateral offset PD feedback correction law without L1 algorithm optimization. Therefore, the simulation results indicate that MFPC-L1 has strong robustness in the trajectory-tracking control of the Flapping-wing UAV.