Unmanned surface vehicle energy consumption modelling under various realistic disturbances integrated into simulation environment

Touzout, Walid; Benmoussa, Yahia; Benazzouz, Djamel; Moréac, Erwan; Diguet, Jean-Philippe

doi:10.1016/j.oceaneng.2020.108560

Cited by 23 publications

(10 citation statements)

References 26 publications

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“…In order to reveal the energy consumption of the USV, we construct the energy consumption model of the USV. USV's energy consumption model based on the dynamic model is described as the following equation in Touzout et al [36], that the total consumption is obtained by integrating of the instantaneous power…”

Section: Energy Consumption Modelmentioning

confidence: 99%

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Adaptive NN state error PCH trajectory tracking control for unmanned surface vessel with uncertainties and input saturation

Chen

et al. 2023

Asian Journal of Control

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A novel robust state error port controlled Hamiltonian (PCH) trajectory tracking controller of an unmanned surface vessel (USV) subject to time‐varying disturbances, dynamic uncertainties and control input saturation is presented. The proposed control scheme combines the advantages of the high robustness and energy minimization of the state error PCH approach and the approximation capability of adaptive radial basis function neural networks (RBFNNs). Adaptive RBFNNs are used to the time‐varying disturbances of the environment and unknown dynamics uncertainties of the USV model. The state error PCH control approach is designed such that the system can optimize energy consumption, and the state error PCH technique makes the designed trajectory tracking controller be easy to implement in practice. To handle the effect of the control input saturation, a Gaussian error function model is employed. It has been demonstrated that the proposed approach can maintain the USV's trajectory at the desired trajectory, while the closed‐loop control system can guarantee the uniformly ultimate boundedness. The energy consumption model of the USV is constructed to reveal to the energy consumption. Simulation results demonstrate the effectiveness of the proposed controller.

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Section: Energy Consumption Modelmentioning

confidence: 99%

“…Substituting Equation ( 8), Equation (20), Equation (34), Equation (35), Equation (36) and Equation (37), we obtain…”

Section: Stabilitymentioning

confidence: 99%

Adaptive NN state error PCH trajectory tracking control for unmanned surface vessel with uncertainties and input saturation

Chen

et al. 2023

Asian Journal of Control

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“…Most of existing studies about USVs mission planning focus on path planning [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29]. However, the objective in these works is to find an optimal route from a starting point to an arrival point which is not suitable for monitoring mission where the main objective is to cover the maximum navigation area.…”

Section: Usv Covering Path Planningmentioning

confidence: 99%

Near-Optimal Covering Solution for USV Coastal Monitoring using PAES

Ouelmokhtar

Benmoussa

Diguet³

et al. 2022

J Intell Robot Syst

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This paper addresses a multi-objective optimization problem for marine monitoring using USV. The objectives are to cover the maximum area with the lowest energy cost while avoiding collisions. The problem is solved using an exact and heuristic methods. First, a multi-objective Mixed Integer Programming formulation is proposed to model the USV monitoring problem. It consists of a combination of the Covering Salesman Problem (CSP) and Travelling Salesman Problem with Profit (TSPP). Then, we use CPLEX software to provide exact solutions. On the other hand, a customized chromosome-size algorithm is used to find heuristic solution. The latter is a multi-objective evolutionary algorithm known as Pareto Archived Evolution Strategy (PAES). The obtained results showed that the exact solving of the USV monitoring mission problem with mixed-integer programming (MIP) methods needs extensive computational costs. However, the customized PAES was able to provide Near-optimal solutions for large-size graphs in much faster time as compared to the exact one.

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“…D is hydrodynamic drag matrix. X u , Y v , and N r are collectively referred to as the hydrodynamic derivative, and the specific values of the above three variables are calculated by using the formula of literature [17]. F thrust ,F env are the thruster and environment forces (F wind , F current ), respectively, applied on the USV.…”

Section: Dynamic Model Of Usv Under the Influence Of Wind And Currentmentioning

confidence: 99%

“…Therefore, the autonomy of the USV is essential. This depends on two complex and changeable environmental parameters: wind and current [17,18]. Ignoring the environmental impact in motion planning would not only lead to a great waste of energy when the USV navigates strong ocean currents, but would also increase the potential risk of hitting obstacles.…”

Section: Introductionmentioning

confidence: 99%

Ship Motion Planning for MASS Based on a Multi-Objective Optimization HA* Algorithm in Complex Navigation Conditions

Zhang

Gao

et al. 2021

JMSE

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Ship motion planning constitutes the most critical part in the autonomous navigation systems of marine autonomous surface ships (MASS). Weather and ocean conditions can significantly affect their navigation, but there are relatively few studies on the influence of wind and current on motion planning. This study investigates the motion planning problem for USV, wherein the goal is to obtain an optimal path under the interference of the navigation environment (wind and current), and control the USV in order to avoid obstacles and arrive at its destination without collision. In this process, the influences of search efficiency, navigation safety and energy consumption on motion planning are taken into consideration. Firstly, the navigation environment is constructed by integrating information, including the electronic navigational chart, wind and current field. Based on the environmental interference factors, the three-degree-of-freedom kinematic model of USVs is created, and the multi-objective optimization and complex constraints are reasonably expressed to establish the corresponding optimization model. A multi-objective optimization algorithm based on HA* is proposed after considering the constraints of motion and dynamic and optimization objectives. Simulation verifies the effectiveness of the algorithm, where an efficient, safe and economical path is obtained and is more in line with the needs of practical application.

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Unmanned surface vehicle energy consumption modelling under various realistic disturbances integrated into simulation environment

Cited by 23 publications

References 26 publications

Adaptive NN state error PCH trajectory tracking control for unmanned surface vessel with uncertainties and input saturation

Adaptive NN state error PCH trajectory tracking control for unmanned surface vessel with uncertainties and input saturation

Near-Optimal Covering Solution for USV Coastal Monitoring using PAES

Ship Motion Planning for MASS Based on a Multi-Objective Optimization HA* Algorithm in Complex Navigation Conditions

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