Finite-time unknown observer based coordinated path-following control of unmanned underwater vehicles

Liang, Xiao; Qu, Xingru; Hou, Yuanhang; Li, Ye; Zhang, Rubo

doi:10.1016/j.jfranklin.2021.01.028

Cited by 28 publications

(9 citation statements)

References 43 publications

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“…Literature 84 designed a dynamic controller to realize marine vehicle trajectory tracking by incorporating the first-order filter into the dynamic surface control. Considering the external disturbances in the complex ocean environment, literature 85 developed a coordinated guidance law to guide the unmanned underwater vehicles’ surge speed and yaw speed and adopted the path update law based on neighbor information to achieve synchronous tracking. It successfully realized the process of multiple unmanned underwater vehicles tracking multiple parameterized paths in a complex environment.…”

Section: Musv Collaborative Round-up Methodsmentioning

confidence: 99%

Review of multiple unmanned surface vessels collaborative search and hunting based on swarm intelligence

Sun

et al. 2022

International Journal of Advanced Robotic Systems

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In recent years, the research of multiple unmanned surface vessels collaboration has received great attention. More and more researchers have proposed different methods of multiple unmanned surface vessels collaboration, such as cooperative collision avoidance, formation, and rendezvous. Based on the significant advantages of biological swarm intelligence applications in these collaborative methods, this article summarizes the research methods of multiple unmanned surface vessels collaborative search and hunting from the perspective of swarm intelligence. First of all, this article summarizes the key technologies of multiple unmanned surface vessels collaborative search and hunting from the aspects of the multi-robot system, group communication, environment modeling, collaboration mechanism, and path planning. Then, it reviews some classic swarm intelligence algorithms, analyzes the advantages and disadvantages of these algorithms, and proposes optimization directions for existing disadvantages based on relevant literature. Finally, the article points out some existing problems in every stage and suggestions for future research.

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Section: Musv Collaborative Round-up Methodsmentioning

confidence: 99%

Review of multiple unmanned surface vessels collaborative search and hunting based on swarm intelligence

Sun

et al. 2022

International Journal of Advanced Robotic Systems

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“…Consider the underactuated AUV model (Equations ( 1) and ( 2)) with multiple uncertainties, and suppose that Assumptions 1-4 are satisfied. If the kinematic uncertainty and the lumped dynamic uncertainties are estimated by Equation (6a-d), the desired LOS angle is calculated by Equation ( 8), the parameter of desired path is updated by Equation ( 9), the derivative of the desired pitch speed is obtained by Equation ( 15), and the control laws are obtained from Equation (20), then the following proposition holds:…”

Section: Stability Analysis Of the Closed-loop Systemmentioning

confidence: 99%

“…In order to maintain stability and improve robustness in the presence of multiple disturbances, many control strategies such as back-stepping control [13,14], sliding mode control (SMC) [15,16], fuzzy control [17], neural network control [18,19], observer-based method [20,21], model predictive control (MPC) [22], and their combinations [23,24] have been widely utilized for path-following control of underactuated AUVs. In [25], an adaptive switching supervisory control method was utilized to solve the trajectory-tracking problems of underactuated vehicles suffering from large parametric uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Robust Path-Following Control of Underactuated AUVs with Multiple Uncertainties in the Vertical Plane

Jin

Deng

Zhang

et al. 2022

JMSE

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The current study employs a novel nonlinear robust control approach for path-following control of underactuated autonomous underwater vehicles (AUVs) with multiple uncertainties in the vertical plane. Firstly, a nonlinear underactuated AUV model is established to characterize the dynamics of AUV and path-following error. To resolve dependence on a detailed model that appeared in previous studies, the unknown time-varying attack angular velocity in the dynamic model of the path-following error is considered as the kinematic uncertainty, while the linear superposition of the external environmental disturbances, the perturbations in the internal model parameters, and other unmodeled dynamics in the dynamic model is chosen as lumped dynamic uncertainties. Several reduced-order extended state observers (ESOs) are designed for estimating both of these uncertainties. Secondly, to reduce the impact of input saturation and avoid the “explosion of complexity” associated with traditional back-stepping method, a nonlinear track differentiator (NTD) is utilized to follow the virtual control signal and its derivative. Thirdly, the constructed reduced-order ESOs and NTD are adopted to establish an augmented back-stepping controller, where its ability to stabilize the overall system is demonstrated using the Lyapunov theorem. Finally, extensive simulations and analyses in various working conditions, including the nominal working condition without disturbances, the working condition with multiple uncertainties, and the conditions which better replicate the actual environment, are performed to demonstrate the effectiveness, superiority, and robustness of the designed controller.

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“…With regard to this, Liu et al [23] designed an integral sliding surface for the tracking control of AUV, where the model uncertainties and external disturbances were estimated by time-delay technique. In [24] and [25], two finite-time unknown observers were developed to compensate the uncertainties and disturbances, through which adaptive dynamics controllers can be successfully conducted to ensure the asymptotic stability of AUV tracking system. In order to improve convergence speed, Kim et al [26] developed a finite-time sliding mode tracking controller for AUV.…”

Section: Introductionmentioning

confidence: 99%

Finite-Time Tracking Control of Autonomous Underwater Vehicle Without Velocity Measurements

Yan

Guo

Yang

et al. 2022

IEEE Trans. Syst. Man Cybern, Syst.

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Human-on-the-loop (HOTL) system is regarded as a promising technology to allow autonomous underwater vehicle (AUV) to track the most adequate target point as soon as possible. However, the unique characteristics of the underwater environment make it challenging to perform the tracking task. This article is concerned with a finite-time tracking control issue for AUV, subjected to unavailable velocity signals in the measurement side and uncertain model parameters in physical side. A HOTL system, including operator, buoys, AUV and sensors, is first provided to construct a cooperative tracking network. For such system, operator in surface control center decides the tracking mission based on all available data. Then, a buoy-assisted localization estimator is utilized by AUV to acquire its position, through which a fast terminal sliding mode observer is developed to estimate the velocity of AUV in finite time. With the estimated velocity information, an adaptive-nonsingular fast terminal sliding mode tracking controller is designed to drive AUV to the target point in finite time. For the proposed velocity observer and tracking controller, the signum and differential functions are employed together to improve the convergence speed and reduce the chattering. Besides that, the proposed solution can not only guarantee finite-time velocity observation, but also achieve finite-time tracking control. Finally, simulation and experimental results are both presented to verify the effectiveness.

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Finite-time unknown observer based coordinated path-following control of unmanned underwater vehicles

Cited by 28 publications

References 43 publications

Review of multiple unmanned surface vessels collaborative search and hunting based on swarm intelligence

Review of multiple unmanned surface vessels collaborative search and hunting based on swarm intelligence

Robust Path-Following Control of Underactuated AUVs with Multiple Uncertainties in the Vertical Plane

Finite-Time Tracking Control of Autonomous Underwater Vehicle Without Velocity Measurements

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