Trajectory tracking control for a marine surface vessel with asymmetric saturation actuators

Zheng, Zewei; Cheng, Jin; Zhu, Ming; Sun, Kangwen

doi:10.1016/j.robot.2017.08.005

Cited by 77 publications

(45 citation statements)

References 47 publications

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“…In the works by Campos et al 6 a proportional–derivative (PD) nonlinear control based on saturation functions with varying parameter for depth and yaw set point regulation and trajectory tracking on an underwater vehicle is proposed. Zheng et al 7 deal with asymmetric saturation over the actuator of a marine vessel. In their work, a Gaussian error function–based continuous differentiable asymmetric model is employed, for the design of the base control with backstepping technique.…”

Section: Introductionmentioning

confidence: 99%

Experimental validation of constraint mitigation algorithm in underwater robot depth control

Rosendo

Clément

Garelli

2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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This work explores both modeling and control of the experimental Ciscrea autonomous underwater vehicle. A 6-degree-of-freedom model is presented and validated for turn and emerge/sink maneuvers. Then, a constraint compensating algorithm is proposed based on quasi-sliding mode conditioning ideas and added to a pre-existing inaccessible proportional-derivative controller in order to improve the overall closed-loop response. By considering actuator constraints, the employed technique allows path following at greater speed than the original controller for a given error tolerance. Experimental results on the so-called Ciscrea underwater robot are presented.

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Section: Introductionmentioning

confidence: 99%

Experimental validation of constraint mitigation algorithm in underwater robot depth control

Rosendo

Clément

Garelli

2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…In order to use the backstepping technique, the discontinuous saturation nonlinearity σ(•) is replaced by a smooth function [38]:…”

Section: Remarkmentioning

confidence: 99%

Distributed Adaptive Output Consensus for High-Order Multiagent Systems with Input Saturation and Uncertain Nonlinear Dynamics

Fan

Qin

2020

Mathematical Problems in Engineering

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This paper deals with the leader-following output consensus problem for a class of high-order affine nonlinear strict-feedback multiagent systems with unknown control gains and input saturation under a general directed graph. Nussbaum gain function technique is used to handle the unknown control gains, and the uncertain nonlinear dynamics of each agent is approximated by radial basis function neural networks. Distributed adaptive controllers are designed via the backstepping technique as well as the dynamic surface control approach. It is proved that the closed-loop multiagent systems are semiglobally uniformly ultimately bounded, and the output consensus error can converge to a small region around the origin. Finally, the theoretical results are supported by a numerical simulation.

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“…Compared the equation (22) with equation (11), the added term "Àd" is used to compensate the unknown external disturbance. Take equations (21) and (22) into equation (10), the derivative of the sliding surface variable can be written as…”

Section: Gftsm-nn Controller Design For Course Keepingmentioning

confidence: 99%

Global fast terminal sliding mode control based on radial basis function neural network for course keeping of unmanned surface vehicle

Wan

Zhang

et al. 2019

International Journal of Advanced Robotic Systems

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A scheme to solve the course keeping problem of the unmanned surface vehicle with nonlinear and uncertain characteristics and unknown external disturbances is investigated in this article. The chattering existing in global fast terminal sliding mode controller in solving the course keeping problem of the unmanned surface vehicle with external disturbance is analyzed. To reduce the chattering and eliminate the influence of the unknown disturbance, an adaptive global fast terminal sliding mode controller based on radial basis function neural network is developed. The equivalent control that usually requires a precise model information of the system is computed using the radial basis function neural network. The weights of the neural network are online adjusted according to the adaptive law that is derived using Lyapunov method to ensure the stability of the closed-loop system. Using the online learning of the neural network, the nonlinear uncertainty of the system and the unknown disturbance of external environment are compensated, and the system chattering is reduced effectively as well. The simulation results demonstrate that the proposed controller can achieve a good performance regarding the fast response and smooth control.

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Trajectory tracking control for a marine surface vessel with asymmetric saturation actuators

Cited by 77 publications

References 47 publications

Experimental validation of constraint mitigation algorithm in underwater robot depth control

Experimental validation of constraint mitigation algorithm in underwater robot depth control

Distributed Adaptive Output Consensus for High-Order Multiagent Systems with Input Saturation and Uncertain Nonlinear Dynamics

Global fast terminal sliding mode control based on radial basis function neural network for course keeping of unmanned surface vehicle

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