Robust Path Following Control of Autonomous Underwater Vehicles via Gain Scheduling and Integral Quadratic Constraints

This paper addresses the design and analysis of linear parameter-varying (LPV) path-following controllers for an autonomous underwater vehicle (AUV) operating in environments affected by ocean currents and subject to measurement noise. Leveraging a recently developed robustness analysis framework ba...

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Bibliographic Details
Published inProceedings of the IEEE Conference on Decision & Control pp. 1473 - 1479
Main Authors Sinha, Sourav, Farhood, Mazen, Stilwell, Daniel J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.12.2024
Subjects
Analytical models
Autonomous underwater vehicles
Process control
Robustness
Sea measurements
Testing
Tuning
Uncertainty
Vehicle dynamics
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ISSN2576-2370
DOI10.1109/CDC56724.2024.10886214

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Summary:This paper addresses the design and analysis of linear parameter-varying (LPV) path-following controllers for an autonomous underwater vehicle (AUV) operating in environments affected by ocean currents and subject to measurement noise. Leveraging a recently developed robustness analysis framework based on integral quadratic constraints (IQCs), the work focuses on constructing LPV controllers capable of effectively steering the AUV along planar paths with bounded curvature. The approach involves approximating the path-following dynamics of the AUV as a linear fractional transformation (LFT) on uncertainties, followed by a comprehensive IQC-based robustness analysis of the uncertain LFT system. This approach not only facilitates comparing the performances of various LPV controllers but also guides the control design process. The robust performance level estimates derived through IQC analysis are validated through nonlinear simulations. Subsequently, the most effective LPV controller is deployed on the AUV for underwater testing.
ISSN:2576-2370
DOI:10.1109/CDC56724.2024.10886214