Tracking control of an underactuated ship by modified dynamic inversion

Ye, Linqi; Zong, Qun

doi:10.1016/j.isatra.2018.09.007

Cited by 37 publications

(27 citation statements)

References 20 publications

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“…A smart ship is considered in the ship industry as having the advantages of less carbon emissions, lower risk for the ship crew at sea, higher traffic efficiency, larger cargo carriage capability, etc., and thus it has attracted much research attention in the maritime traffic community [1][2][3]. For the purpose of helping in smart ship maritime navigational environments, varied ship tracking techniques and data sources are employed to obtain informative static and kinematic ship data from varied maritime sources.…”

Section: Introductionmentioning

confidence: 99%

Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter

Chen

et al. 2020

Sensors

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Maritime surveillance videos provide crucial on-spot kinematic traffic information (traffic volume, ship speeds, headings, etc.) for varied traffic participants (maritime regulation departments, ship crew, ship owners, etc.) which greatly benefits automated maritime situational awareness and maritime safety improvement. Conventional models heavily rely on visual ship features for the purpose of tracking ships from maritime image sequences which may contain arbitrary tracking oscillations. To address this issue, we propose an ensemble ship tracking framework with a multi-view learning algorithm and wavelet filter model. First, the proposed model samples ship candidates with a particle filter following the sequential importance sampling rule. Second, we propose a multi-view learning algorithm to obtain raw ship tracking results in two steps: extracting a group of distinct ship contour relevant features (i.e., Laplacian of Gaussian, local binary pattern, Gabor filter, histogram of oriented gradient, and canny descriptors) and learning high-level intrinsic ship features by jointly exploiting underlying relationships shared by each type of ship contour features. Third, with the help of the wavelet filter, we performed a data quality control procedure to identify abnormal oscillations in the ship positions which were further corrected to generate the final ship tracking results. We demonstrate the proposed ship tracker’s performance on typical maritime traffic scenarios through four maritime surveillance videos.

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Section: Introductionmentioning

confidence: 99%

Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter

Chen

et al. 2020

Sensors

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“…In addition, for the trajectory tracking problem of the underactuated USV, the biggest challenge is that the number of actuators is less than the degree of freedom, which increases the design difficulty of the controller. Some previous solutions have been published in [12,13]. A trajectory tracking controller is applied to underactuated surface ships in [12], where the model is represented by numerical methods.…”

Section: Introductionmentioning

confidence: 99%

“…Its main advantage is that the calculation of control actions are obtained, solving a system of linear equations. In [13], three modified dynamic inversion methods are proposed to solve the tracking control problem of the underactuated ship , which can remove some of its inherent limitations, making it applicable to a wide variety of underactuated systems. Based on the above analysis, there are still many problems to be solved for tracking control of the underactuated USV.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Trajectory Tracking Control for Unmanned Surface Vehicle with Modeling Uncertainties and Input Saturation

et al. 2019

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In the presence of modeling uncertainties and input saturation, this paper proposes a practical adaptive sliding mode control scheme for an underactuated unmanned surface vehicle (USV) using neural network, auxiliary dynamic system, sliding mode control and backstepping technique. First, the radial basis function neural network with minimum learning parameter method (MLP) is constructed to online approximate the uncertain system dynamics, which uses single parameter instead of all weights online learning, leading to a reduction in the computational burdens. Then a hyperbolic tangent function is adopted to reduce the chattering phenomenon due to the sliding mode surface. Meanwhile, the auxiliary dynamic system and the adaptive technology are employed to handle input saturation and unknown disturbances, respectively. In addition, a neural shunting model is introduced to eliminate the “explosion of complexity” problem caused by the backstepping method for virtual control derivation. The stability of the closed-loop system is guaranteed by the Lyapunov stability theory. Finally, simulations are provided to validate the effectiveness of the proposed control scheme.

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“…The difference between them is that trajectory tracking is time-dependent, while path following is time-independent. More specifically, trajectory tracking needs to reach a specific location at a specific time, which has a more important realization significance [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Control for Unmanned Surface Vehicle Subject to Unmeasurable Disturbance and Input Saturation

Zhao

Wang

et al. 2020

IEEE Access

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This paper presents a trajectory tracking control controller for an unmanned surface vehicle in the presence of unmeasurable disturbance and input saturation. An adaptive trajectory tracking control strategy is developed with the aid of disturbance observer, neural shunting model, adaptive technology, and auxiliary design system. Disturbance observer is used to estimate unmeasurable disturbances in order to compensate for them. The functions of neural shunting model and adaptive technology are respectively used to deal with the "computational explosion" caused by derivation of virtual control law and to enhance the robustness of the controlled system. Besides, to avoid the potential input saturation issue, auxiliary design system is employed in the design of control strategy. By Lyapunov stability theory, it is proved that all the error signals in the trajectory tracking control system are uniform ultimate bounded. The simulation results are given to prove the correctness of the proposed control scheme. INDEX TERMS unmanned surface vehicle, trajectory tracking, input saturation, neural shunting model

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Tracking control of an underactuated ship by modified dynamic inversion

Cited by 37 publications

References 20 publications

Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter

Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter

Adaptive Sliding Mode Trajectory Tracking Control for Unmanned Surface Vehicle with Modeling Uncertainties and Input Saturation

Trajectory Tracking Control for Unmanned Surface Vehicle Subject to Unmeasurable Disturbance and Input Saturation

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