研究目的
To improve the stability and tracking performance of airborne opto-electronic platforms on multi-rotor unmanned aerial vehicles by compensating for disturbances using an improved velocity signal-based disturbance observer and an adaptive fuzzy control system.
研究成果
The proposed IVDOB and adaptive fuzzy control system effectively compensate for disturbances, achieving high stability precision (0.13 mrad) and accurate tracking with bounded errors. The method is robust and meets the requirements for airborne opto-electronic platform tasks on multi-rotor UAVs.
研究不足
The paper does not explicitly discuss limitations, but potential constraints may include the specific UAV and platform setup used, which might not generalize to all conditions, and the reliance on identified parameters that could vary in real-world applications.
1：Experimental Design and Method Selection:
The study involves designing an improved velocity signal-based disturbance observer (IVDOB) and an adaptive fuzzy control system to compensate for disturbances. Theoretical models include state space models of the airborne opto-electronic platform and actuator dynamics, with control laws derived using Lyapunov stability analysis.
2：Sample Selection and Data Sources:
Experiments are conducted on a Hex-Rotor UAV with an airborne opto-electronic platform. Physical parameters of the UAV and platform are provided, and data is collected from sensors including a photoelectric encoder and a sensor with integrated gyro and accelerometer.
3：List of Experimental Equipment and Materials:
Equipment includes a Hex-Rotor UAV, TMS320F28335 processor, photoelectric encoder, and a sensor with three-axis gyro and accelerometer. Materials involve carbon fiber structures for the platform.
4：Experimental Procedures and Operational Workflow:
The control system is implemented with a sampling period of 30 ms. Experiments include LOS stabilization control and tracking of given position signals under wind conditions. Data is sampled at 100 Hz for angle information.
5：Data Analysis Methods:
Results are analyzed by comparing angle errors and tracking performance between different control methods, using stability precision metrics and visual comparisons of error curves.
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