Extended State Observer-Based Integral Sliding Mode Control for an Underwater Robot With Unknown Disturbances and Uncertain Nonlinearities

Cui, Rongxin; Chen, Lepeng; Yang, Chenguang; Chen, Mou

doi:10.1109/tie.2017.2694410

Cited by 470 publications

(222 citation statements)

References 45 publications

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“…Because of the insensitivity to the dimension of problems, it is able to be used to search time invariant parameters [19]. It is a fact that AUVs have been widely applied in the marine resources exploration and utilization [20][21][22][23]. In order to map a scalar field, mutual information based multidimensional RRT algorithm was presented for multiple AUVs in [15].…”

Section: Journal Of Advanced Transportationmentioning

confidence: 99%

Liveness-Based RRT Algorithm for Autonomous Underwater Vehicles Motion Planning

Zhang

et al. 2017

Journal of Advanced Transportation

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Motion planning is a crucial, basic issue in robotics, which aims at driving vehicles or robots towards to a given destination with various constraints, such as obstacles and limited resource. This paper presents a new version of rapidly exploring random trees (RRT), that is, liveness-based RRT (Li-RRT), to address autonomous underwater vehicles (AUVs) motion problem. Different from typical RRT, we define an index of each node in the random searching tree, called "liveness" in this paper, to describe the potential effectiveness during the expanding process. We show that Li-RRT is provably probabilistic completeness as original RRT. In addition, the expected time of returning a valid path with Li-RRT is obviously reduced. To verify the efficiency of our algorithm, numerical experiments are carried out in this paper.

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Section: Journal Of Advanced Transportationmentioning

confidence: 99%

Liveness-Based RRT Algorithm for Autonomous Underwater Vehicles Motion Planning

Zhang

et al. 2017

Journal of Advanced Transportation

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“…Alongside the linear control method like PID, the nonlinear methods are also developed widely from the underwater robot like ROV [6,7] and AUV [8,9], to the aerial robot like quadrotor. Several applied methods for the quadrotor and unmanned robots are the backstepping control [10][11][12][13], sliding mode control [9,[14][15][16][17], and the learning-based control method like fuzzy logic and artificial neural-network-based controllers [18].…”

Section: Introductionmentioning

confidence: 99%

“…Several applied methods for the quadrotor and unmanned robots are the backstepping control [10][11][12][13], sliding mode control [9,[14][15][16][17], and the learning-based control method like fuzzy logic and artificial neural-network-based controllers [18]. This article compared one of the artificial neural-network-based controllers, that is, the artificial neural network's direct inverse control (DIC-ANN), with the PID controller in the quadrotor attitude dynamics.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Control System of UAV Altitude Dynamics and Its Comparison with the PID Control System

Muliadi

Kusumoputro

2018

Journal of Advanced Transportation

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This article proposes a comparative method to assess the performance of artificial neural network's direct inverse control (DIC-ANN) with the PID control system. The comparison served as an analysis tool to assess the advantages of DIC-ANN over conventional control method for a UAV attitude controller. The development of ANN method for UAV control purposes arises due to the limitations of the conventional control method, which is the mathematical based model, involving complex expression, and most of them are difficult to be solved directly into analytic solution. Although the linearization simplified the solving process for such mathematical based model, omitting the nonlinear and the coupling terms is unsuitable for the dynamics of the multirotor vehicle. Thus, the DIC-ANN perform learning mechanism to overcome the limitation of PID tuning. Therefore, the proposed comparative method is developed to obtain conclusive results of DIC-ANN advantages over the linear method in UAV attitude control. Better achievement in the altitude dynamics was attained by the DIC-ANN compared to PID control method.

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“…Dealing with nonlinear uncertainties is an important issue in motion control for mobile robots [22][23][24] . This is even more prominent for the underwater vehicles [25] .…”

Section: Introductionmentioning

confidence: 99%