Analysis of an Unmanned Underwater Vehicle Propulsion Model for Motion Control

Bibuli, Marco; Zereik, Enrica; Palma, D. De; Ingrosso, Roberta; Indiveri, Giovanni

doi:10.2514/1.g005692

Cited by 6 publications

(5 citation statements)

References 33 publications

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“…He described the motion of the vehicle as an equation of six degrees of freedom. When considering the influence of the external environment, the coupling relationship is ignored and only the simplified uncoupling model is considered [12]. In order to characterize the motion of the USV in a simple manner, the three degrees of freedom (surge, sway, and yaw) in horizontal planar motion are taken into account in this work.…”

Section: Usv Dynamic Modelmentioning

confidence: 99%

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Application of Modified BP Neural Network in Identification of Unmanned Surface Vehicle Dynamics

Zhang,

Liu,

Cheng

2024

JMSE

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Over the past few decades, unmanned surface vehicles (USV) have drawn a lot of attention. But because of the wind, waves, currents, and other sporadic disturbances, it is challenging to understand and collect correct data about USV dynamics. In this paper, the Modified backpropagation neural network (BPNN) is suggested to address this issue. The experiment was conducted in the Qinghuai River, and the receiver collected the data. The modified BPNN outperforms the conventional BPNN in terms of ship trajectory forecasting and the rate of convergence. The updated BPNN can accurately predict the rotational velocity during the propeller’s acceleration and stability stages at various rpms.

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Section: Usv Dynamic Modelmentioning

confidence: 99%

“…The neural network's -th i output value is compared to the real value, and the difference is known as the unit error of the output layer. Its error function is depicted below: Substituting Equation (12) into Equation (11) and the output O j can be expressed as:…”

Section: The Back Propagation Neural Network Principlementioning

confidence: 99%

Application of Modified BP Neural Network in Identification of Unmanned Surface Vehicle Dynamics

Zhang,

Liu,

Cheng

2024

JMSE

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“…Their results showed the high efficiency of the system, both in processing sensor information and in providing the dynamic control of the movements of UUVs. Bibuli et al [54] analyzed the UUV propulsion model for motion control. They designed a procedure for small-size, low-speed vehicles.…”

Section: Uuv Control System Designmentioning

confidence: 99%

A Review of the Various Control Algorithms for Trajectory Control of Unmanned Underwater Vehicles

Bashir,

Khan,

Iqbal

et al. 2023

Sustainability

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Unmanned underwater vehicles (UUVs) have become increasingly popular in recent years due to their use in various applications. The motivations for using UUVs include the exploration of difficult and dangerous underwater environments, military tasks in mine detection, intelligence gathering and surveillance, the inspection of offshore oil and gas infrastructure in the oil and gas industry, scientific research for studying marine life, and the search and rescue of missing persons or submerged airplanes or boats in underwater environments. UUVs offer many advantages in achieving the desired applications with increased safety, efficiency, and cost-effectiveness. However, there are also several challenges associated with their communication, navigation, power requirements, maintenance, and payload limitations. These types of vehicles are also prone to various disturbances caused by currents of the ocean, propulsion systems, and unmolded uncertainties. Practically, it is a challenging task to design a controller that will ensure optimal performance under these conditions. Therefore, the control system design is of prime importance in the overall development of UUVs. Also, the UUV controller receives input from different sensors, and the data from these sensors are used by the controller to perform different tasks. The control systems of UUVs should take into account all uncertainties and make them stable so that all sensors can perform optimally. This paper presents a complete review of different control system design algorithms for UUVs. The basic logic designs of several control system algorithms are also presented. A comparison is made based on reliability, robustness, precession, and the ability of the controller to handle the nonlinearity that is faced by UUVs during their missions. Simulation and experimental results are thoroughly studied to gain insight into each algorithm. The advantages and disadvantages of each algorithm are also presented, which will facilitate the selection of a suitable algorithm for the control system design of UUVs.

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“…Autonomous underwater vehicles (AUVs) have been widely used in marine scientific research, underwater resource exploration, underwater oil and gas pipeline and structure overhaul, seabed hydrothermal research, and military fields [1,2]. When AUVs perform underwater tasks, they usually need to complete path-tracking tasks [3].…”

Section: Introductionmentioning

confidence: 99%

Tube-Based Event-Triggered Path Tracking for AUV against Disturbances and Parametric Uncertainties

Chen,

Bian

2023

Electronics

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In order to enhance the performance of disturbance rejection in AUV’s path tracking, this paper proposes a novel tube-based event-triggered path-tracking strategy. The proposed tracking strategy consists of a speed control law and an event-triggered tube model predictive control (tube MPC) scheme. Firstly, the speed control law using linear model predictive control (LMPC) technology is obtained to converge the nominal path-tracking deviation. Secondly, the event-triggered tube MPC scheme is used to calculate the optimal control input, which can enhance the performance of disturbance rejection. Considering the nonlinear hydrodynamic characteristics of AUV, a linear matrix inequality (LMI) is formulated to obtain tight constraints on the AUV and the feedback matrix. Moreover, to enhance real-time performance, tight constraints and the feedback matrix are all calculated offline. An event-triggering mechanism is used. When the surge speed change command does not exceed the upper bound, adaptive tight constraints are obtained. Finally, numerical simulation results show that the proposed tube-based event-triggered path-tracking strategy can enhance the performance of disturbance rejection and ensure good real-time performance.

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Analysis of an Unmanned Underwater Vehicle Propulsion Model for Motion Control

Cited by 6 publications

References 33 publications

Application of Modified BP Neural Network in Identification of Unmanned Surface Vehicle Dynamics

Application of Modified BP Neural Network in Identification of Unmanned Surface Vehicle Dynamics

A Review of the Various Control Algorithms for Trajectory Control of Unmanned Underwater Vehicles

Tube-Based Event-Triggered Path Tracking for AUV against Disturbances and Parametric Uncertainties

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