Dynamical sliding mode control for the trajectory tracking of underactuated unmanned underwater vehicles

Xu, Jian; Wang, Man; Qiao, Lei

doi:10.1016/j.oceaneng.2015.06.022

Cited by 205 publications

(109 citation statements)

References 20 publications

(26 reference statements)

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“…Specifically, in this simulation, we assume that all the parameters m ii and d ii will simultaneously increase 10% to the actual model. In addition, to verify the robustness and adaptation of the proposed controllers against systematic uncertainty and external disturbances, the following disturbances as in [7] are designed to act on the vehicle dynamics…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In recent years, given their increased number of engineering applications, trajectory tracking control of underactuated autonomous underwater vehicles (AUVs) has received considerable attention. The interested reader can be referred to recent books [1,2] and papers [3][4][5][6][7]. This paper is concerned with the design of control laws by using adaptive neural networks that make the vehicles track pre-specified feasible "state-space" trajectories [8], that is, trajectories that specify time evolution of the position and attitude, as well as the linear velocities and angular rates, all consistent with the vehicles' dynamics, even though in practical applications one often only needs to track a desired position.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, as the sliding mode method has outstanding characteristics, including insensitivity to parameter variations and good rejection of disturbances, it is suitable for the design of robust tracking controllers for AUVs. The interested reader is referred to the papers [5,7,23,24]. But a common weakness frequently discussed in the literature is that the resulting controller may suffer from high-frequency chatter, which is may cause controller performance degradation and premature wear in actuators.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Global Adaptive Neural Network Control of Underactuated Autonomous Underwater Vehicles with Parametric Modeling Uncertainty

Yan

Wang

2018

Asian Journal of Control

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This paper focuses on global adaptive neural network control for a class of underactuated autonomous underwater vehicles in the presence of possibly large modeling parametric uncertainty. As the control inputs cannot directly act in the sway and heave directions, two virtual velocities defined here, plus three actual control actions provided by the thrusters and rudders, are used to achieve the convergence of the system errors to around zero. Motivated by real-time characteristics in the trajectory tracking, the proposed controller presents a significant advantage because it contains only one adaptive parameter to be updated online rather than the neural network weights. In addition, we also consider the practical situation that the velocities of the vehicle may experience sharp speed jumps when the position tracking errors initially change suddenly, which always results in thruster saturation. The biologically inspired model is introduced to smooth the virtual velocity commands such that the vehicle satisfies the control input and velocity constraints. Finally, comparison simulations are given to show the effectiveness of the proposed scheme.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Global Adaptive Neural Network Control of Underactuated Autonomous Underwater Vehicles with Parametric Modeling Uncertainty

Yan

Wang

2018

Asian Journal of Control

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“…Selecting a suitable controller is of utmost importance, due to the remote control nature of unmanned vehicles Zare et al, 2014) or robots (Zakeri et al, 2012;Zakeri et al, 2013;Zakeri et al 2014;. So far, researchers have presented different control methods for UUVs, including, control of a high-speed underwater robot using H ∞ controller (Zhang et al, 2015) or control of an underwater robot using dynamic sliding mode controller (Xu et al, 2015). Among various controllers, the fuzzy controller, due to its nonlinear structure and also no need of the dynamic model of the system in its design, has been used a lot Moezi et al 2016) therefore, it has been used in much researches for controlling underwater vehicles, including, control of six DOF of an ROV or depth control of submarine robot using fuzzy controller (Ishaque, et al, 2010;Nag et al, 2013).…”

Section: Latin American Journal Of Solids and Structures 13 (2016) 10mentioning

confidence: 99%

Path Planning for Unmanned Underwater Vehicle in 3D Space with Obstacles Using Spline-Imperialist Competitive Algorithm and Optimal Interval Type-2 Fuzzy Logic Controller

Zakeri

Farahat

Moezi

et al. 2016

Lat. Am. j. solids struct.

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In this research, generation of a short and smooth path in three-dimensional space with obstacles for guiding an Unmanned Underwater Vehicle (UUV) without collision is investigated. This is done by utilizing spline technique, in which the spline control points positions are determined by Imperialist Competitive Algorithm (ICA) in three-dimensional space such that the shortest possible path from the starting point to the target point without colliding with obstacles is achieved. Furthermore, for guiding the UUV in the generated path, an Interval Type-2 Fuzzy Logic Controller (IT2FLC), the coefficients of which are optimized by considering an objective function that includes quadratic terms of the input forces and state error of the system, is used. Selecting such objective function reduces the control error and also the force applied to the UUV, which consequently leads to reduction of energy consumption. Therefore, by using a special method, desired signals of UUV state are obtained from generated three-dimensional optimal path such that tracking these signals by the controller leads to the tracking of this path by UUV. In this paper, the dynamical model of the UUV, entitled as "mUUV-WJ-1", is derived and its hydrodynamic coefficients are calculated by CFD in order to be used in the simulations. For simulation by the method presented in this study, three environments with different obstacles are intended in order to check the performance of the IT2FLC controller in generating optimal paths for guiding the UUV. In this article, in addition to ICA, Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) are also used for generation of the paths and the results are compared with each other. The results show the appropriate performance of ICA rather than ABC and PSO. Moreover, to evaluate the performance of the IT2FLC, optimal Type-1 Fuzzy Logic Controller (T1FLC) and Proportional Integrator Differentiator (PID) controller are designed and applied to the UUV and compared with each other. The simulation results show the superiority of the IT2FLC than the other two controllers.

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“…16 Xu et al have proposed a novel adaptive dynamical sliding mode control-based methodology to enhance the robustness in the presence of environmental disturbances; it utilized a virtual velocity variable to represent the attitude error so as to avoid the representation singularities and simplify the analytical expression of the controller. 17 In order to reduce the chattering effect in the trajectory control of underwater vehicle, Soylu et al proposed an adaptive concept to replace the switching term in conventional sliding mode control. 18 Fuzzy and neural network scheme can be used to estimate the unknown dynamic uncertainties and parameter uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Autonomous underwater vehicle precise motion control for target following with model uncertainty

Huang

Zhang

Hongde

et al. 2017

International Journal of Advanced Robotic Systems

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Target following plays an important role in oceanic detection and target capturing for autonomous underwater vehicles. Due to the model nonlinearity and external disturbance, the dynamic model of a portable autonomous underwater vehicle was usually established with parameter uncertainties. In this article, a petri-based recurrent type 2 fuzzy neural network has been built to approximate the unknown autonomous underwater vehicle dynamics. The type 2 fuzzy logic system has been applied to the network to improve the approximation accuracy for systematic nonlinearity, and the petri layer in the network can improve estimation speed and reduce energy consumption. A petri-based recurrent type 2 fuzzy neural network-based adaptive robust controller has been proposed for target tracking. In the offshore experiments, the proposed controller has not only realized stable position and pose control but also successfully followed mobile target on the surface. In the tank underwater experiments, the pipeline target has been successfully followed to further verify the controller performance.

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Dynamical sliding mode control for the trajectory tracking of underactuated unmanned underwater vehicles

Cited by 205 publications

References 20 publications

Global Adaptive Neural Network Control of Underactuated Autonomous Underwater Vehicles with Parametric Modeling Uncertainty

Global Adaptive Neural Network Control of Underactuated Autonomous Underwater Vehicles with Parametric Modeling Uncertainty

Path Planning for Unmanned Underwater Vehicle in 3D Space with Obstacles Using Spline-Imperialist Competitive Algorithm and Optimal Interval Type-2 Fuzzy Logic Controller

Autonomous underwater vehicle precise motion control for target following with model uncertainty

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