Trajectory planning of unmanned aerial vehicles in uncertain environments based on model predictive control and dual-model control

Abstract

Aiming at the difficulties of autonomous trajectory planning for unmanned aerial vehicles (UAVs) in unknown and dynamically uncertain environments, such as insufficient prediction ability, easy falling into local optimum, poor environmental adaptability, and information scarcity caused by passive obstacle avoidance, traditional single-model predictive control is difficult to balance computational efficiency and uncertainty compensation. Therefore, this paper proposes a UAV trajectory planning method based on Model Predictive Control dual-model cooperative control, which organically integrates model predictive control with the exploration-exploitation strategy. The method adopts a dual architecture of nominal model + online learning model: the simplified nominal model ensures real-time solution efficiency, and the online learning model dynamically compensates for unmodeled dynamics and environmental disturbances. By introducing information gain and uncertainty attenuation indicators into the MPC optimization objective, the UAV is guided to actively explore unknown areas, avoid local optimum traps, and achieve a dynamic balance between exploration and exploitation. Simulation verification based on MATLAB and CasAdi toolbox shows that the proposed method can significantly reduce environmental uncertainty, improve trajectory tracking accuracy and active obstacle avoidance ability, and exhibit stronger robustness, decision-making intelligence and flight efficiency in complex unstructured environments.