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Energy-optimal Planning and Control of Autonomous Underwater Vehicles Under Ocean Currents.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Energy-optimal Planning and Control of Autonomous Underwater Vehicles Under Ocean Currents./
Author:	Yang, Niankai.
Description:	1 online resource (138 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-01, Section: B.
Contained By:	Dissertations Abstracts International84-01B.
Subject:	Naval engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29274996click for full text (PQDT)
ISBN:	9798438780861

Energy-optimal Planning and Control of Autonomous Underwater Vehicles Under Ocean Currents.
Yang, Niankai.

Energy-optimal Planning and Control of Autonomous Underwater Vehicles Under Ocean Currents. - 1 online resource (138 pages)

Source: Dissertations Abstracts International, Volume: 84-01, Section: B.

Thesis (Ph.D.)--University of Michigan, 2022.

Includes bibliographical references

Autonomous underwater vehicles (AUVs) can broaden the scope of underwater deployments thanks to their high level of autonomy. Yet, one of the major challenges in using AUVs for long-range or deepwater missions is the limited vehicle endurance, i.e., the ability to consistently conduct missions. This dissertation develops advanced energy management strategies involving energy-optimal planning and control to improve the AUV operational energy efficiency, extending AUV endurance. The proposed planner takes into account the effect of future vehicle motion on the flow prediction accuracy to ensure good robustness in energy saving. Given the planned references (e.g., waypoints or paths), controllers are proposed to further enhance energy-saving performance by considering the vehicle dynamics and real-time ocean current information.For performance evaluation/quantification, a virtual test-bed is developed based on the test-bed vehicle, DROP-Sphere. The virtual test-bed includes a six-degrees-of-freedom vehicle motion model and an empirical thruster energy consumption model fitted based on the experimental data of DROP-Sphere. Control-oriented ocean current and vehicle models are further derived from the virtual test-bed to facilitate planner and controller designs.Optimal path planning requires accurate flow predictions to ensure robust energy-saving performance. Active flow perception, which refers to strategies that optimize vehicle actions so that the flow information collected along the vehicle path reduces uncertainties in flow prediction, is leveraged to develop the energy-optimal planner. Assuming negligible unmodeled dynamics in the control-oriented ocean current model, the flow prediction uncertainty is evaluated by the Cramer-Rao (CR) bound of estimated model parameters. The planner is established by minimizing the cost associated with the estimated propulsion energy and CR bound. Simulations demonstrate improved robustness in energy saving by considering the trade-offs between exploration and exploitation of flow information during path planning.To track planned waypoints with enhanced energy efficiency, an economic model predictive control (EMPC) is proposed. The EMPC optimizes stage costs capturing the control energy consumed within the prediction horizon and a terminal cost approximating the energy-to-go, i.e., the energy required to reach the desired waypoint from the end of the prediction horizon. To retain the optimality achieved by globally-optimized solutions with reduced complexity, the energy-to-go is formulated based on the maneuvering characteristics observed in trajectory optimization solutions. Further analysis reveals that the proposed EMPC can balance the trade-offs among energy components spent for vehicle surge, heave, and yaw controls.For following planned paths with reduced energy use, a robust energy-optimal controller is proposed, taking setpoint computation and tracking steps to achieve the three-dimensional (3D) path following. The surge velocity, heave velocity, and pitch angle setpoints are optimized by minimizing vehicle propulsion energy considering the uncertainty set defined by the state estimate and its uncertainty. A line-of-sight-based guidance law is established to compute the yaw angle setpoints, which integrates direct and indirect drift angle compensation for reduced path-following error and path-convergence time. Two setpoint-tracking MPCs are designed to control horizontal and vertical vehicle motion with a small computational overhead. It is shown that the proposed controller can optimize the vertical motion in 3D path-following under ocean currents for increased energy savings.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798438780861Subjects--Topical Terms:

3173824
Naval engineering.
Subjects--Index Terms:

AUVIndex Terms--Genre/Form:

542853
Electronic books.

Energy-optimal Planning and Control of Autonomous Underwater Vehicles Under Ocean Currents.
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520 $a Autonomous underwater vehicles (AUVs) can broaden the scope of underwater deployments thanks to their high level of autonomy. Yet, one of the major challenges in using AUVs for long-range or deepwater missions is the limited vehicle endurance, i.e., the ability to consistently conduct missions. This dissertation develops advanced energy management strategies involving energy-optimal planning and control to improve the AUV operational energy efficiency, extending AUV endurance. The proposed planner takes into account the effect of future vehicle motion on the flow prediction accuracy to ensure good robustness in energy saving. Given the planned references (e.g., waypoints or paths), controllers are proposed to further enhance energy-saving performance by considering the vehicle dynamics and real-time ocean current information.For performance evaluation/quantification, a virtual test-bed is developed based on the test-bed vehicle, DROP-Sphere. The virtual test-bed includes a six-degrees-of-freedom vehicle motion model and an empirical thruster energy consumption model fitted based on the experimental data of DROP-Sphere. Control-oriented ocean current and vehicle models are further derived from the virtual test-bed to facilitate planner and controller designs.Optimal path planning requires accurate flow predictions to ensure robust energy-saving performance. Active flow perception, which refers to strategies that optimize vehicle actions so that the flow information collected along the vehicle path reduces uncertainties in flow prediction, is leveraged to develop the energy-optimal planner. Assuming negligible unmodeled dynamics in the control-oriented ocean current model, the flow prediction uncertainty is evaluated by the Cramer-Rao (CR) bound of estimated model parameters. The planner is established by minimizing the cost associated with the estimated propulsion energy and CR bound. Simulations demonstrate improved robustness in energy saving by considering the trade-offs between exploration and exploitation of flow information during path planning.To track planned waypoints with enhanced energy efficiency, an economic model predictive control (EMPC) is proposed. The EMPC optimizes stage costs capturing the control energy consumed within the prediction horizon and a terminal cost approximating the energy-to-go, i.e., the energy required to reach the desired waypoint from the end of the prediction horizon. To retain the optimality achieved by globally-optimized solutions with reduced complexity, the energy-to-go is formulated based on the maneuvering characteristics observed in trajectory optimization solutions. Further analysis reveals that the proposed EMPC can balance the trade-offs among energy components spent for vehicle surge, heave, and yaw controls.For following planned paths with reduced energy use, a robust energy-optimal controller is proposed, taking setpoint computation and tracking steps to achieve the three-dimensional (3D) path following. The surge velocity, heave velocity, and pitch angle setpoints are optimized by minimizing vehicle propulsion energy considering the uncertainty set defined by the state estimate and its uncertainty. A line-of-sight-based guidance law is established to compute the yaw angle setpoints, which integrates direct and indirect drift angle compensation for reduced path-following error and path-convergence time. Two setpoint-tracking MPCs are designed to control horizontal and vertical vehicle motion with a small computational overhead. It is shown that the proposed controller can optimize the vertical motion in 3D path-following under ocean currents for increased energy savings.
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