As a core member of our dynamic stabilization project team, I am deeply engaged in developing an intelligent closed-loop control system that actively reduces platform roll for stand-up paddleboards. Our system integrates dual-IMU sensor fusion with a hybrid feedforward-feedback control architecture, delivering real-time stabilization through differential thrust from vertical thrusters. While the current prototype demonstrates strong performance under controlled conditions, the next phase of our project aims to address two critical frontiers: (1) enabling the system to adapt seamlessly across diverse user profiles and paddleboard models through transfer learning, and (2) refining the control algorithm for enhanced robustness and responsiveness. This statement outlines my individual scope, challenges encountered, intended contributions, team role, areas for deep technical inquiry, and expected deliverables in the coming semester.
a) Limited Generalizability Across Platforms
Our stabilization system was initially tuned and validated on a single paddleboard model with a fixed user weight. During preliminary cross-platform tests, we observed noticeable degradation in stabilization performance when the system was mounted on boards of different sizes, flex characteristics, or when subjected to varying rider weights and paddling styles. The current control parameters are statically configured, which limits the system’s adaptability.
b) Sensitivity to Environmental Disturbances
Although the Dual-IMU fusion algorithm provides high-fidelity state estimation in calm conditions, we encountered intermittent noise and drift in more dynamic environments—such as choppy water or abrupt rider movements. This introduced phase delays and occasionally caused over-correction from the PID controller.
c) Manual Tuning Overhead
Each new platform or user currently requires extensive manual recalibration of sensor fusion weights and PID gains. This process is time-consuming, requires expert oversight, and is impractical for a consumer-ready product.
a) Implementing Transfer Learning for Rapid Adaptation
To overcome the generalization barrier, I will lead the development of a transfer learning framework. The core idea is to pre-train a lightweight model on a diverse dataset collected from multiple board types and rider profiles. When deployed on a new, unseen platform, the system will require only a few seconds of interaction data to fine-tune a small set of latent parameters. This will enable the stabilization logic to adjust automatically to the new mechanical and inertial properties, significantly reducing calibration effort while maintaining high performance.
b) Enhancing Control Algorithm Robustness
I will extend the current PID+feedforward architecture by incorporating adaptive control elements. Rather than relying solely on fixed gains, the controller will learn to adjust its behavior based on real-time performance feedback. This will mitigate the effects of sensor noise and unmodeled dynamics, resulting in smoother, more accurate counter-torque generation.
c) Automating Sensor Fusion Calibration
Leveraging the same transfer learning pipeline, I will integrate an auto-calibration routine for the adaptive fusion algorithm. This routine will dynamically adjust the weighting between the two IMUs based on learned confidence metrics, reducing susceptibility to transient errors and improving overall system reliability.