2022
DOI: 10.2478/pomr-2022-0025
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An Improved Dynamic Surface Sliding Mode Method for Autonomous Cooperative Formation Control of Underactuated USVS with Complex Marine Environment Disturbances

Abstract: In this paper, a novel dynamic surface sliding mode control (DSSMC) method, combined with a lateral velocity tracking differentiator (LVTD), is proposed for the cooperative formation control of underactuated unmanned surface vehicles (USVs) exposed to complex marine environment disturbances. Firstly, in view of the kinematic and dynamic models of USVs and the design idea of a virtual control law in a backstepping approach, the trajectory tracking control problem of USVs’ cooperative formation is transformed in… Show more

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Cited by 12 publications
(4 citation statements)
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“…As a branch of artificial intelligence, ML is an effective approach that allows computer systems to acquire knowledge and improve without being explicitly programmed. Machine learning is centered around the creation of algorithms that can discover patterns, make predictions, and produce insights from data autonomously 33,115,116 . The process typically entails training a model on a labeled dataset, which allows the algorithm to learn to detect correlations between input characteristics and matching output labels.…”
Section: Fundamentals Of MLmentioning
confidence: 99%
“…As a branch of artificial intelligence, ML is an effective approach that allows computer systems to acquire knowledge and improve without being explicitly programmed. Machine learning is centered around the creation of algorithms that can discover patterns, make predictions, and produce insights from data autonomously 33,115,116 . The process typically entails training a model on a labeled dataset, which allows the algorithm to learn to detect correlations between input characteristics and matching output labels.…”
Section: Fundamentals Of MLmentioning
confidence: 99%
“…Thanks to its strongly autonomous navigation capability, environmental adaptability and modular design advantage, it has been widely used in hydrologic reconnaissance, maritime search and rescue, and navigation of formation and other fields [1][2][3][4][5][6]. To solve the nonlinear interference problem caused by the overlay and the interaction of multi interference, and the poor robustness of control methods due to the uncertainty of the model parameters [7][8], active disturbance rejection control [9][10] (ADRC), fuzzy control [11][12], backstepping control [13][14][15], sliding mode control [16][17][18][19][20][21] (SMC), and proportion integration differentiation (PID) control [22][23][24], combined with intelligent algorithms and artificial neural networks [25][26][27] (ANN), have been introduced by scholars.…”
Section: Introductionmentioning
confidence: 99%
“…This is further complicated by the uncertainties of model parameters and the complex ocean environment. To deal with model parameter uncertainties and ocean disturbances, a variety of algorithms have been developed, mainly based on neural network approximation algorithms [6][7][8] and disturbance-observer-based algorithms [9][10][11]. The neural-network-based technique uses the neural network's excellent approximation capabilities to assess model parameter uncertainties and ocean disturbances.…”
Section: Introductionmentioning
confidence: 99%