Gust Estimation and Rejection with a Disturbance Observer for Proprioceptive Underwater Soft Morphing Wings
Tobias Cook, Leo Micklem, Huazhi Dong, Yunjie Yang, Michael Mistry, Francesco Giorgio-Serchi
AI summary
Problem
Underwater soft robots lack reliable external sensors to detect and counteract sudden hydrodynamic disturbances like gusts, compromising stability and maneuverability in turbulent environments.
Approach
The authors combine a Piecewise Constant Curvature dynamic model with Thin Airfoil Theory to represent the wing, then use an Extended State Observer to infer flow disturbances directly from the wing's measured camber, feeding this into a disturbance-rejection controller.
Key results
- Validated PCC-Thin Airfoil dynamic model against experimental flume data
- Demonstrated real-time gust estimation from wing camber using an Extended State Observer
- Designed a baseline control law that successfully mitigates lift deviations from flow disturbances
- Achieved low prediction error for curvature and lift across dynamic test conditions
Why it matters
Provides a sensor-efficient pathway for soft underwater robots to maintain stability and perform reliable operations in hazardous, turbulent marine environments.
Abstract
Unmanned underwater vehicles are increasingly employed for maintenance and surveying tasks at sea, but their operation in shallow waters is often hindered by hydrodynamic disturbances such as waves, currents, and turbulence. These unsteady flows can induce rapid changes in direction and speed, compromising vehicle stability and manoeuvrability. Marine organisms contend with such conditions by combining proprioceptive feedback with flexible fins and tails to reject disturbances. Inspired by this strategy, we propose soft mor- phing wings endowed with proprioceptive sensing to mitigate environmental perturbations. The wing’s continuous deforma- tion provides a natural means to infer dynamic disturbances: sudden changes in camber directly reflect variations in the oncoming flow. By interpreting this proprioceptive signal, a disturbance observer can reconstruct flow parameters in real time. To enable this, we develop and experimentally validate a dynamic model of a hydraulically actuated soft wing with controllable camber. We then show that curvature-based sensing allows accurate estimation of disturbances in the angle of attack. Finally, we demonstrate that a controller leveraging these proprioceptive estimates can reject disturbances in the lift response of the soft wing. By combining proprioceptive sensing with a disturbance observer, this technique mirrors biological strategies and provides a pathway for soft underwater vehicles to maintain stability in hazardous environments.