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

Wan, Lili; Su, Yixin; Zhang, Huajun; Tang, Yongchuan; Shi, Binghua

doi:10.1177/1729881419829961

Cited by 15 publications

(9 citation statements)

References 48 publications

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“…The problem of course keeping control is highly non-linear in nature and has been studied from a perspective of observed disturbance control using sliding mode control (SMC) approach. The SMC problem for USVs, subjected to, higher order non linear operational disturbances, have been studied with varying control approaches like sliding mode [3][4][5][6]; fuzzy sliding mode [7]; proportional derivative fuzzy [8]; backstepping [9][10][11][12]; backstepping with adaptive radial basis function neural network [13]; sine function-based non-linear feedback [14]; hyperbolic tangent based nonlinear control [15]; sigmoid based nonlinear control [16]; function adaptive neural path following control [17]; model predictive control [18,19]; eventtriggered control approach [20] and non-linear feedback power functions [21].…”

Section: State Of the Artmentioning

confidence: 99%

Adaptive Integral Sliding Mode Based Course Keeping Control of Unmanned Surface Vehicle

González-Prieto¹,

Pérez-Collazo²,

Singh³

2022

JMSE

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This paper investigates the course keeping control problem for an unmanned surface vehicle (USV) in the presence of unknown disturbances and system uncertainties. The simulation study combines two different types of sliding mode surface based control approaches due to its precise tracking and robustness against disturbances and uncertainty. Firstly, an adaptive linear sliding mode surface algorithm is applied, to keep the yaw error within the desired boundaries and then an adaptive integral non-linear sliding mode surface is explored to keep an account of the sliding mode condition. Additionally, a method to reconfigure the input parameters in order to keep settling time, yaw rate restriction and desired precision within boundary conditions is presented. The main strengths of proposed approach is simplicity, robustness with respect to external disturbances and high adaptability to static and dynamics reference courses without the need of parameter reconfiguration.

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Section: State Of the Artmentioning

confidence: 99%

Adaptive Integral Sliding Mode Based Course Keeping Control of Unmanned Surface Vehicle

González-Prieto¹,

Pérez-Collazo²,

Singh³

2022

JMSE

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“…Radial basis function (RBF) is a numerical method for solving the interpolation problems. 16 It uses symmetric functions to transform the multivariate data approximation problems into the essentially unified approximation problems. In 1988, Broomhead and Lowe proposed an RBF-based neural network, which can approximate any continuous function with arbitrary accuracy and have a very fast learning convergence rate.…”

Section: Residual Correction Modeling and Data Acquisition Based On Rmentioning

confidence: 99%

Residual correction for robotic articulated arm coordinate measuring machine with radial basis function neural network

Gao

Wang

et al. 2020

International Journal of Advanced Robotic Systems

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The robotic articulated arm coordinate measuring machine (RAACMM) is a special robotic structural measuring instrument used to perform industrial field inspection. The accuracy of a RAACMM in different poses presents certain characteristics due to the influence of various dynamic factors. However, the existing error compensation model of the RAACMM cannot include dynamic factors, which imposes certain limits on improving the accuracy of the RAACMM. In this article, a residual correction method for the RAACMM based on a radial basis function neural network (RBFNN) is proposed to compensate the dynamic factors and improve the accuracy of the RAACMM. Firstly, the influence of the pose configuration of the RAACMM on the residual error of the probe is analyzed. The periodic characteristics of the residual error are obtained based on the analysis results. Secondly, a relationship model between the residual errors and the structural parameters is established in the cylindrical coordinate system. Then, a residual correction model based on the single point repeatability and RBFNN is proposed to further enhance the accuracy of the RAACMM. The probe of the RAACMM is constrained with a cone-hole gauge to acquire the single point repeatability data. The residual correction model is trained with the data of single point repeatability, and residual errors are calculated via the residual correction model. Experimental results show that the repeatability and measurement accuracy of the RAACMM are all improved after the residual correction, which validates the effectiveness of the residual correction method.

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“…Moreover, the design of the DSC method depends on local errors of the system, which makes the control system robust to system's uncertainties, but the control performance of the controller is easily affected by the perturbation of the system's parameters. Sliding-mode control [25][26][27][28][29][30][31] has strong robustness to unmodeled dynamics of nonlinear systems. Liu et al [32] constructed a nonlinear gain function and proposed an improved DSC strategy with sliding-mode control for a class of nonlinear systems to enhance the non-fragility of the control law.…”

Section: Introductionmentioning

confidence: 99%

Variable Gain Prescribed Performance Control for Dynamic Positioning of Ships with Positioning Error Constraints

Gong

Zhang

2022

JMSE

Self Cite

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In this paper, a variable gain prescribed performance control law is proposed for dynamic positioning (DP) of ships with positioning error constraints, input saturation and unknown external disturbances. The error performance index functions are designed to preset the prescribed performance bounds and the error mapping functions are constructed to incorporate the prescribed performance bounds into the DP control design. The variable gain technique is used to limit the output amplitude of the control law to avoid input saturation of the system by dynamically adjusting the control gain of the DP control law according to the positioning errors, and the error mapping function replaces the positioning error as a recursive sliding-mode surface to realize the prescribed performance control of the system and guarantee the stability of the closed-loop system with variable control gains. It has been proved that the proposed DP control law can make the uniformly ultimately boundedness of all signals in the DP closed-loop control system. The numerical simulation results illustrate that the proposed control law can make the ship’s position and heading maintain at the desired value with positioning error constraints, enhance the non-fragility of the DP control law to the perturbation of system’s parameters and improve the system’s rejection ability to external disturbances.

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

Cited by 15 publications

References 48 publications

Adaptive Integral Sliding Mode Based Course Keeping Control of Unmanned Surface Vehicle

Adaptive Integral Sliding Mode Based Course Keeping Control of Unmanned Surface Vehicle

Residual correction for robotic articulated arm coordinate measuring machine with radial basis function neural network

Variable Gain Prescribed Performance Control for Dynamic Positioning of Ships with Positioning Error Constraints

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