Operating in wave-dominated environments poses significant challenges for underwater robotic systems, particularly in renewable energy sectors such as offshore wind and tidal energy. A new study from the University of Edinburgh [55.9°N, 3.26°W] offers a cutting-edge control framework combining wave disturbance prediction with advanced nonlinear model predictive control (NMPC), demonstrating a transformative approach to dynamic positioning for remotely operated vehicles (ROVs). Here’s how this innovation could revolutionise renewable energy operations:
Highlights of the Framework
- Deterministic Wave Prediction:
- The system uses a deterministic sea wave predictor (DSWP) to forecast wave elevations based on upstream measurements.
- These predictions inform hydrodynamic load estimates, enabling precise disturbance mitigation.
- Nonlinear Model Predictive Control (NMPC):
- Incorporates wave forecasts to optimise control actions over short-term horizons.
- Outperforms traditional methods, reducing position errors by up to 52%, even under noisy conditions.
Key Performance Insights
- Enhanced Stability:
- The proposed system maintains positional accuracy for ROVs exposed to significant wave disturbances.
- Pitch, heave, and surge errors were significantly lower compared to baseline controllers.
- Resilience to Noise and Delays:
- The framework retains high accuracy despite sensor noise and communication delays, demonstrating robustness in real-world applications.
- Effective even with signal-to-noise ratios as low as 5 dB.
- Energy Efficiency:
- While power consumption increases due to advanced predictive control, the trade-off yields superior accuracy, essential for sensitive tasks such as turbine inspections or cable maintenance.
Implications for Renewable Energy Progress
- Safer Offshore Operations:
- Predictive control ensures ROVs operate reliably in harsh environments, minimising risks during inspections of offshore wind turbines or wave energy converters.
- Cost Optimisation:
- Enhanced stability reduces wear-and-tear on robotic systems, lowering maintenance costs and extending operational lifetimes.
- Grid Integration:
- Improved reliability in underwater robotics supports the seamless integration of offshore renewable systems, a critical step in expanding clean energy capacity.
Looking Forward
This hybrid wave prediction and control model exemplifies how interdisciplinary approaches can solve complex engineering challenges. By integrating advanced robotics with renewable energy systems, the study underscores the potential for safer, more efficient operations in offshore environments. As wave energy and offshore wind play growing roles in decarbonisation efforts, technologies like these will be instrumental in supporting the transition to a sustainable energy future.
Source
Kyle L. Walker, Laura-Beth Jordan, Francesco Giorgio-Serchi. Nonlinear model predictive dynamic positioning of a remotely operated vehicle with wave disturbance preview. The International Journal of Robotics Research, 2024
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