Airborne gimbal control improves aiming accuracy under vibrations
Physics-Informed and Data-Driven Composite Disturbance Separation and Predictive Control for Airborne Electro-Optical Gimbals with Multiple Constraints
Robotics
Summary
Keeping the eyes of a flying camera steady is tough because planes or drones shake in regular and irregular ways. The authors design a smart control system that separately predicts and corrects for these different shakes using both physics knowledge and learning from data. Their method better anticipates vibrations and delays while keeping commands within safe limits, helping the camera aim more accurately. Testing on a drone setup showed this approach reduced aiming errors significantly compared to older methods.
What this means in practice
- •For drone system engineers: Control airborne gimbals to stabilize and track targets more accurately despite vibrations and delays using the proposed disturbance-separated MPC.
- •For camera stabilization teams: Improve pointing precision of electro-optical platforms on moving vehicles by compensating both regular and irregular disturbances separately.
Authors
Yaokun Lu, Yifan Li, Zhixuan Zhang, Enmei Wang, Hao Teng, Jianzhong Qiao
Abstract
This paper studies the constrained pointing control problem of airborne electro-optical gimbals subjected to composite periodic and aperiodic disturbances. Under carrier vibration, input delay, and actuator constraints, achieving line-of-sight stabilization and tracking is challenging. A disturbance-separated model predictive control (MPC) scheme is proposed to compensate these heterogeneous disturbances according to their physical characteristics. An adaptive periodic observer predicts multi-harmonic vibrations over the MPC horizon using linear-regression frequency estimation. A linear observer estimates the aperiodic disturbance, and a bounded long short-term memory (LSTM) network predicts its change over the input delay. Embedding periodic predictions into the MPC model while applying aperiodic compensation outside the optimizer limits amplification of aperiodic estimation errors and improves pointing accuracy under frequency changes and parameter mismatch. On the stated headroom envelope, input-set tightening provides feasible commands within the prescribed limits, and a regional practical tracking bound is established under explicit input-sequence and predicted-state margin conditions. A UAV-mounted two-axis gimbal platform is assembled for comparative indoor hovering experiments, complemented by simulations under frequency changes and parameter mismatch. In the reported trials, the proposed method achieves yaw and pitch relative-angle root-mean-square errors (RMSEs) of 0.770 degrees and 0.350 degrees, reducing tracking errors by 25.1% and 45.9% compared with linear active disturbance rejection control.