Project and Control Allocation of a 3 DoF Autonomous Surface Vessel With Aerial Azimuth Propulsion System

Silva, Mathaus Ferreira da; Honório, Leonardo de Mello; Santos, M. F.; Neto, Accacio Ferreira dos Santos; Cruz, Nuno; Matos, Aníbal; Westin, Luis Gustavo Fortes

doi:10.1109/access.2020.3048330

Cited by 11 publications

(15 citation statements)

References 35 publications

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“…1. This topology was first presented in [33], [34]. It provides an over-actuated ASV with eight actuators (four directional servos and four propulsion motors) and 3Degrees of Freedom (DoFs) (surge, sway and yaw).…”

Section: A Topologymentioning

confidence: 99%

“…where M ∈ R 3×3 is the Rigid Body Inertia Matrix, C(ν) ∈ R 3×3 the Matrix of Coriolis, J(•) the Jacobian matrix of velocities in the rigid body and inertial frame, and τ ∈ R 3 represents the forces and moments generated by the propulsion system [33]. The contact between the water and the hull [20]generates added mass τ A ∈ R 3 and hydrodynamic damping τ D ∈ R 3 .…”

Section: Mν + C(ν)νmentioning

confidence: 99%

“…Moreover, it is also presents the proposed method for modeling and identifying dynamic ASVs, which consists of two stages: (a) design of identification signals using the new approach called Robust SOESGOPE and, later, (b) parametric estimation in two phases. Note that, although the modeling and results consider the over-actuaded ASV presented in [33], [34], the proposed framework can be used for any other vessel.…”

Section: Identification Of the Asvs Dynamicsmentioning

confidence: 99%

“…In many over-actuated dynamical systems, it is common to use the DoFs as controlled states instead of the real signals that are sent to the actuators [33]. This approach reduces the system's complexity and makes easier the control laws definition.…”

Section: Excitation Signals Designingmentioning

confidence: 99%

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Development of Optimal Parameter Estimation Methodologies Applied to a 3DOF Autonomous Surface Vessel

et al. 2021

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Autonomous Surface Vessels (ASVs) are reliable and robust vehicles. They perform autonomous missions in lakes, rivers and even open waters. Those are dangerous environments that requires precise and secure navigation. Under these conditions, the knowledge of a robust and accurate mathematical model is a fundamental aspect for adjusting the control system for reaching safety and performance. Moreover, traditional mathematical models disregard assimetries and coupling between the degrees of freedom. While those models work fine for bigger vessels, to ignore these characteristics in small ASVs compromises the model's quality. In this context, this work presents a new methodology for modeling and identifying the dynamics of ASVs along with the uncertainties arising from disturbances and non-modeled dynamics. As for the uncertainties and disturbances, this work considers the coupling parameters into the mathematical modeling, which synthesizes the divergences between the model and the real application, allowing to incorporate asymmetries and model deficiencies. Regarding the parameter identification, the proposal is based on (i) the design of optimal input excitation signals from a double layer optimization methodology and (ii) a parametric estimation concept in two steps, dividing the original set of parameters into two partially coupled sub-problems. Finally, this work also presents a full discussion and analysis about the importance to manage the trade-off between precision and complexity of mathematical models, its respective solution spaces and the impact over the optimization algorithms. To validate the approach, a real 3 Degree of Freedom ASV with aerial holonomic propulsion system is used. The results show that it is possible to successfully capture the complex set of parameters and identify physical characteristics not considered by the model.

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Section: A Topologymentioning

confidence: 99%

Section: Mν + C(ν)νmentioning

confidence: 99%

Section: Identification Of the Asvs Dynamicsmentioning

confidence: 99%

Section: Excitation Signals Designingmentioning

confidence: 99%

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Development of Optimal Parameter Estimation Methodologies Applied to a 3DOF Autonomous Surface Vessel

et al. 2021

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“…Finally, the control allocation is responsible for transforming the virtual control forces into real actions. A deeper description of the control levels and control allocation methodology can be found in [34].…”

Section: Overall Control Structurementioning

confidence: 99%

Hull and Aerial Holonomic Propulsion System Design for Optimal Underwater Sensor Positioning in Autonomous Surface Vessels

Regina

Honório

Pancoti

et al. 2021

Sensors

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Acoustic Doppler Current Profiler (ADCP) sensors measure water inflows and are essential to evaluate the Flow Curve (FC) of rivers. The FC is used to calibrate hydrological models responsible for planning the electrical dispatch of all power plants in several countries. Therefore, errors in those measures propagate to the final energy cost evaluation. One problem regarding this sensor is its positioning on the vessel. If placed on the bow, it becomes exposed to flowing obstacles, and if it is installed on the stern, the redirected water from the boat and its propulsion system change the sensor readings. To improve the sensor readings, this paper proposes the design of a catamaran-like Autonomous Surface Vessel (ASV) with an optimized hull design, aerial propulsion, and optimal sensor placement to keep them protected and precise, allowing inspections in critical areas such as ultra-shallow waters and mangroves.

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A Convolutional System Identification Approach Mixing Optimal Parameter Estimation and Deep Learning

Souza

Neto

Honório

et al. 2023

Int. J. Control Autom. Syst.

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Project and Control Allocation of a 3 DoF Autonomous Surface Vessel With Aerial Azimuth Propulsion System

Cited by 11 publications

References 35 publications

Development of Optimal Parameter Estimation Methodologies Applied to a 3DOF Autonomous Surface Vessel

Development of Optimal Parameter Estimation Methodologies Applied to a 3DOF Autonomous Surface Vessel

Hull and Aerial Holonomic Propulsion System Design for Optimal Underwater Sensor Positioning in Autonomous Surface Vessels

A Convolutional System Identification Approach Mixing Optimal Parameter Estimation and Deep Learning

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