An extreme learning-based adaptive control design for an autonomous underwater vehicle

Rath, Biranchi Narayan; Subudhi, Bidyadhar

doi:10.1016/b978-0-12-819443-0.00012-x

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…In [10], the authors conducted a study where they forecasted the heading angle with a horizon of 1s, resulting in an RMSE of ∼ 0. In [11], the authors formulated a state prediction algorithm for an Autonomous Underwater Vehicle (AUV) by leveraging Extreme Learning Machines (ELMs). They conducted tests on models of the pitch (θ), pitch rate ( θ), heave (Z), and heave velocity (w) of an underwater vehicle.…”

Section: Related Workmentioning

confidence: 99%

Shallow and Deep Learning Models for Vessel Motions Forecasting during Adverse Weather Conditions

M. Walker,

Coraddu,

Savio

et al. 2023

Moses

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Accurately forecasting vessel motions is a critical step towards achieving fast and accurate intelligent vessel control systems. Intelligent vessel control relies on accurate predictions of vessel motion to make informed decisions regarding control, maneuvering, and positioning, particularly during times of exogenous loading caused by adverse weather conditions. Hence, by accurately forecasting vessel motion accurately, the control system can anticipate potential issues (i.e., excessive trim or roll) and prescribe corrective actions before they become problematic. In this study, the authors propose two approaches to address the problem of vessel motion forecasting. The first approach relies on classical shallow learning models, whereas the second approach involves the use of state-of-the-art deep learning models for improved accuracy at further forecast horizons. Unlike shallow models, deep models can learn the required features directly from the data and therefore do not require a priori knowledge or additional features engineering. By leveraging deep learning models, the authors show that vessel motions can be forecasted further into the future without a significant loss in accuracy, thereby improving the overall effectiveness of the intelligent vessel control system. To support their statements, the authors use real operational data and compare the performance of the shallow and deep learning models. The results show that deep learning outperforms shallow learning models in terms of accuracy without a significant increase in the computational demand. Additionally, the authors demonstrate that their models remain accurate even under adverse weather conditions, indicating that they have practical applicability for vessel motions forecasting and can potentially improve the overall effectiveness of intelligent vessel control systems.

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Section: Related Workmentioning

confidence: 99%

Shallow and Deep Learning Models for Vessel Motions Forecasting during Adverse Weather Conditions

M. Walker,

Coraddu,

Savio

et al. 2023

Moses

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“…Adaptive control can achieve optimal or suboptimal control after obtaining the mathematical model of the control object. Rath B N ( Rath et al, 2020 ) proposed an adaptive controller with a time delay estimator that successfully predicted the state of the AUV. An accurate model of the control object is obtained and the control effect is very extremely good.…”

Section: Related Workmentioning

confidence: 99%

Attitude Stabilization Control of Autonomous Underwater Vehicle Based on Decoupling Algorithm and PSO-ADRC

Xiong

Yun

et al. 2022

Front. Bioeng. Biotechnol.

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Autonomous Underwater Vehicle are widely used in industries, such as marine resource exploitation and fish farming, but they are often subject to a large amount of interference which cause poor control stability, while performing their tasks. A decoupling control algorithm is proposed and A single control volume–single attitude angle model is constructed for the problem of severe coupling in the control system of attitude of six degrees of freedom Autonomous Underwater Vehicle. Aiming at the problem of complex Active Disturbance Rejection Control (ADRC) adjustment relying on manual experience, the PSO-ADRC algorithm is proposed to realize the automatic adjustment of its parameters, which improves the anti-interference ability and control accuracy of Autonomous Underwater Vehicle in dynamic environment. The anti-interference ability and control accuracy of the method were verified through experiments.

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An extreme learning-based adaptive control design for an autonomous underwater vehicle

Cited by 2 publications

References 25 publications

Shallow and Deep Learning Models for Vessel Motions Forecasting during Adverse Weather Conditions

Shallow and Deep Learning Models for Vessel Motions Forecasting during Adverse Weather Conditions

Attitude Stabilization Control of Autonomous Underwater Vehicle Based on Decoupling Algorithm and PSO-ADRC

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