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PPO-Based Reinforcement Learning Control of a Flapping-Wing Robot with a Bio-Inspired Sensing and Actuation Feather
Saddam Hussain1, Mohammed Messaoudi2, Muhammad Imran3
1School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.
Sensors (Basel, Switzerland)
|February 13, 2026
Summary
Bio-inspired feathers for flying robots act as sensors and actuators, improving stability in gusts. Reinforcement learning enables autonomous aerodynamic control for enhanced resilience and performance in flapping-wing drone designs.
Area of Science:
- Robotics
- Bio-inspired Engineering
- Aerodynamics
Background:
- Flapping-wing flying robots (FWFRs) require enhanced stability in dynamic environments.
- Bio-inspired mechanisms offer potential for improved flow-sensing and actuation.
- Current FWFRs lack adaptive aerodynamic control for gusty conditions.
Purpose of the Study:
- To introduce a bio-inspired sensing and actuation feather unit (SAFU) for FWFRs.
- To develop a physics-based model for training a reinforcement learning controller.
- To enable autonomous aerodynamic adaptation and improve FWFR stability.
Main Methods:
- Developed a bio-inspired SAFU mimicking falcon covert feathers.
- Created a reduced-order bond-graph model of coupled aero-electromechanical dynamics.
- Utilized proximal policy optimization (PPO) reinforcement learning for control.
Main Results:
- The PPO-driven SAFU achieved fast, well-damped responses (rise time < 0.5s, settling time < 1.4s).
- Demonstrated up to 50% alleviation of airflow-induced disturbance effects in gusty conditions.
- Showcased autonomous regulation of feather displacement and load reduction.
Conclusions:
- Bio-inspired SAFUs combined with reinforcement learning enhance FWFR resilience.
- This approach enables intelligent aerodynamic control and autonomous flow adaptation.
- Highlights potential for advanced flapping-wing drone designs.
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