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An optimization approach to extend control period for dynamics control of Autonomous Underwater Vehicles with X-form rudders
https://orcid.org/0000-0003-3942-5308
Abstract This paper addresses the challenge of dynamics control with a long control period for an Autonomous Underwater Vehicle (AUV) equipped with four independent stern control planes. The goal is to minimize frequent actuator operations. To tackle the difficulties posed by the extended control period, the paper proposes a hierarchy controller architecture comprising high-level dynamics control and low-level control allocation. The dynamics controller is developed using the Nonlinear Model Predictive Control (NMPC) technique. It aims to generate optimal virtual steering moments while considering the differences in the periods of model updating, state prediction, and control output. Additionally, a constrained optimization problem is formulated for control allocation, taking into account the characteristics of the control planes. To validate the proposed approach, a test AUV is designed for experimental validation. The hydrodynamic parameters required for state prediction are identified through Computational Fluid Dynamics (CFD) analysis and verified by comparing characteristic indexes from maneuvering trials. Comparison with the Linear Quadratic Regulator (LQR) in dynamics control highlights that the proposed control methods offer a stable and effective control framework, particularly when dealing with a long control period.
An optimization approach to extend control period for dynamics control of Autonomous Underwater Vehicles with X-form rudders
https://orcid.org/0000-0003-3942-5308
Abstract This paper addresses the challenge of dynamics control with a long control period for an Autonomous Underwater Vehicle (AUV) equipped with four independent stern control planes. The goal is to minimize frequent actuator operations. To tackle the difficulties posed by the extended control period, the paper proposes a hierarchy controller architecture comprising high-level dynamics control and low-level control allocation. The dynamics controller is developed using the Nonlinear Model Predictive Control (NMPC) technique. It aims to generate optimal virtual steering moments while considering the differences in the periods of model updating, state prediction, and control output. Additionally, a constrained optimization problem is formulated for control allocation, taking into account the characteristics of the control planes. To validate the proposed approach, a test AUV is designed for experimental validation. The hydrodynamic parameters required for state prediction are identified through Computational Fluid Dynamics (CFD) analysis and verified by comparing characteristic indexes from maneuvering trials. Comparison with the Linear Quadratic Regulator (LQR) in dynamics control highlights that the proposed control methods offer a stable and effective control framework, particularly when dealing with a long control period.
An optimization approach to extend control period for dynamics control of Autonomous Underwater Vehicles with X-form rudders
https://orcid.org/0000-0003-3942-5308
Abstract This paper addresses the challenge of dynamics control with a long control period for an Autonomous Underwater Vehicle (AUV) equipped with four independent stern control planes. The goal is to minimize frequent actuator operations. To tackle the difficulties posed by the extended control period, the paper proposes a hierarchy controller architecture comprising high-level dynamics control and low-level control allocation. The dynamics controller is developed using the Nonlinear Model Predictive Control (NMPC) technique. It aims to generate optimal virtual steering moments while considering the differences in the periods of model updating, state prediction, and control output. Additionally, a constrained optimization problem is formulated for control allocation, taking into account the characteristics of the control planes. To validate the proposed approach, a test AUV is designed for experimental validation. The hydrodynamic parameters required for state prediction are identified through Computational Fluid Dynamics (CFD) analysis and verified by comparing characteristic indexes from maneuvering trials. Comparison with the Linear Quadratic Regulator (LQR) in dynamics control highlights that the proposed control methods offer a stable and effective control framework, particularly when dealing with a long control period.
An optimization approach to extend control period for dynamics control of Autonomous Underwater Vehicles with X-form rudders
Chen, Ying (author) / Liu, Gang (author) / Wang, Wenjin (author)
Applied Ocean Research ; 141
2023-10-26
Article (Journal)
Electronic Resource
English
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