Based on Non-Inertial Frame Modeling for High-Dynamic Companion Flight Control Research

Aijun Xu; Jiang Lu

doi:10.54097/6650ef71

Authors

Aijun Xu
Jiang Lu

DOI:

https://doi.org/10.54097/6650ef71

Keywords:

Air-ground collaboration, Non-inertial frame, Relative motion modeling, Model predictive control, Unscented kalman filter, Continuous gain scheduling

Abstract

This paper investigates the intelligent companion flight control problem for mobile platforms (UGVs) and unmanned aerial vehicles (UAVs) under highly dynamic conditions. To address the Coriolis, Euler, and centrifugal fictitious forces introduced by a non-inertial reference frame and rapidly varying disturbances, an integrated approach of "non-inertial relative dynamics modeling + parameter preview estimation based on the Unscented Kalman Filter (UKF) + Continuous Gain Scheduling (CGS) MPC" is proposed. First, a complete and reproducible set of relative kinematics and dynamics under platform-fixed connections is presented, unifying gravity and thrust modeling. Subsequently, a unified UKF for platform angular velocity/angular acceleration and apparent acceleration is constructed, with its output fed into the MPC as time-varying parameters and tightening radii. Finally, a continuously varying weight scheduling law dependent on non-inertia intensity and uncertainty is proposed, balancing tracking accuracy and robustness. Simulation results indicate that, compared to fixed-weight MPC and simplified models that ignore fictitious forces, the proposed method significantly reduces peak errors and constraint violations under high-maneuvering and strong-noise conditions while maintaining control smoothness and real-time performance.

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