Adaptive terminal sliding mode control of uncertain robotic manipulators based on local approximation of a dynamic system

Tran, Minh-Duc; Kang, Hee-Jun

doi:10.1016/j.neucom.2016.09.089

Cited by 84 publications

(66 citation statements)

References 21 publications

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“…Thus, the h(x) defined on the [−11] can be approximated by Legendre polynomial in the form of (6), which the coefficients α i (i = 0, 1,..., m) are calculated by Equations (3) and (4), and the ϕ i (x) (i = 0, 1,..., m) are given by Equation (7). Finally, the transformation of the matrix expression of the Equation (6) can be obtained by the Equations (8) and (9).…”

Section: Legendre Polynomial Function Approximationmentioning

confidence: 99%

“…At present, underwater manipulators are mostly control objects with joint redundancy, and the accuracy mainly depends on the control performance of joints [6]. However, due to the nonlinear, time-variation of dynamic properties and other factors (i.e., external disturbances such as ocean current disturbance) [7,8], the system dynamic model has uncertainties, which will affect the joint control of the manipulator based on the dynamic model. Besides, for most hydraulic manipulators, the joint control performance is also affected by joint lag [6,9,10], which means that the joint lag will affect the control accuracy of manipulator joint.…”

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confidence: 99%

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Adaptive Sliding Mode PID Control for Underwater Manipulator Based on Legendre Polynomial Function Approximation and Its Experimental Evaluation

et al. 2020

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The joint control problem of the underwater manipulator is addressed in this paper, under the influence of uncertainty factors such as model uncertainty, external disturbance, and manipulator joint lag. In general, for the uncertainty factors, it is usually approximated online, but it is difficult to select a reasonable value for the approximation error boundary, too conservative estimated values would cause chattering problem easily. And the influence of joint lag on the manipulator control should be considered in actual work. Unlike most previous control method, in this paper, the function approximation technique (FAT), which uses the Legendre polynomial, is adopted to approximate the uncertainty factors online. Then, based on the proportion integral differential (PID) sliding manifold with the integral and differential of tracking error, a sliding model PID controller is designed to speed up the response and reduce the effects of joint lag. For the error boundary, the adaptive law is proposed, and it will reduce chattering of the control quantity under the steady state of the system. It was proved that the joint error of the control system is uniformly asymptotic convergence through the stability analysis. Finally, the effectiveness of the proposed approach is demonstrated with pool comparison experiments of the underwater manipulator installed in the autonomous underwater vehicles (AUVs).Appl. Sci. 2020, 10, 1728 2 of 15 developed and equipped on multipurpose intervention-AUV [4]. An underwater electric manipulator (MARIS 7080, 7 DOF manipulator) was equipped on semi-autonomous underwater vehicle for the intervention mission (SAUVIM) [5].In UVMS, the underwater manipulator is a key component, and its joint control accuracy directly determines the operating accuracy of the UVMS. At present, underwater manipulators are mostly control objects with joint redundancy, and the accuracy mainly depends on the control performance of joints [6]. However, due to the nonlinear, time-variation of dynamic properties and other factors (i.e., external disturbances such as ocean current disturbance) [7,8], the system dynamic model has uncertainties, which will affect the joint control of the manipulator based on the dynamic model. Besides, for most hydraulic manipulators, the joint control performance is also affected by joint lag [6,9,10], which means that the joint lag will affect the control accuracy of manipulator joint. In conclusion, under the influence of the above factors, it has great research significance and practical value to develop the joint control technology of underwater manipulators for improving the accuracy and efficiency of underwater operation.Currently, many methods have achieved high control accuracy, which uses the online approximation to deal with the uncertainty factors, and determine the controller and adaptive law of parameter through stability analysis [11,12]. Among them, in view of the nonlinear and uncertain identification ability [11][12][13][14][15][16], neural network and fuzzy control has b...

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Section: Legendre Polynomial Function Approximationmentioning

confidence: 99%

mentioning

confidence: 99%

Adaptive Sliding Mode PID Control for Underwater Manipulator Based on Legendre Polynomial Function Approximation and Its Experimental Evaluation

et al. 2020

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“…SMC has recognized itself as an effectual control method that was confirmed to be robust in opposition to external disturbances and system uncertainties. The SMC method [12] is modeled for driving the system state constraints to equilibrium by exploiting a discontinuous feedback control theory. This approach encloses constructive invariance features in terms of irregularities in the plant representation and therefore, it is an excellent aspirant for controlling uncertain nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Sliding Mode Controller for Robot Manipulator: Effect on External Interferences

2019

IJITEE

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In the previous decades, the SMC approach has attained unique consideration as this technique offers a systematic model to maintain robust performance and asymptotic stability. As robotic manipulators turn out to be gradually more significant in industrial automation, robotic manipulators by means of SMC have raised as a significant region of research. Hence, this paper intends to model and establish an adaptive sliding mode controller (SMC) for robotic manipulator. As it is not feasible to match up the SMC functions with the system model each time, this paper implements a Fuzzy Inference System (FIS) to replace the system model. It effectively achieves the experimentation in two phases. Accordingly, in the first phase, it attains the accurate features of the system model based on varied samples to characterize the robotic manipulator. Consequently, it derives the obtained features as fuzzy rules. In the subsequent phase, it signifies the derived fuzzy rules depending on adaptive fuzzy membership functions. Moreover, it establishes the self-adaptiveness using Grey Wolf Optimization (GWO) to attain the adaptive fuzzy membership functions. The analysis distinguishes the efficiency of the adopted technique with the optimal investigational scheme and the traditional schemes such as SMC, Fuzzy SMC (FSMC) and GWO-SMC. Moreover, the comparative analysis is also performed by including the external disturbances and noise and validates the effectiveness of the proposed and conventional models. IV. RESULTS AND DISCUSSIONS A. Experimental ProcedureThe proposed SAGWO-FSMC scheme that adopts the fuzzy model to support the SMC to control the robotic manipulator was simulated in MATLAB, and the outcomes were attained. The count of iteration was fixed as 100. For analyzing the performance of the proposed model, it was distinguished with the traditional experimental schemes such as SMC [1], FSMC [19], and GWO-SMC [17] in case of external disturbances and noises. The basic Simulink model of the SAGWO-FSMC was shown in Fig. 3 and the SAGWO block was broadly modeled in Fig. 4.

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“…Another well-known way to reduce chattering problem in TSM is to use tanh function instead of the sign function [23]. Neural networks can learn and approximate any arbitrary nonlinear functions [24]. Therefore, neural networks are implemented to approximate uncertain disturbances in systems as well as to reduce the chattering [25].…”

Section: Introductionmentioning

confidence: 99%

“…It has been adopted in TSM control systems [24,26]. Wavelet neural networks, which are applied to terminal sliding mode control [27,28], are a new class of neural networks that have been developed using a combined method of multi-layer artificial neural networks and wavelet analysis.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive B-Spline Neural Network and Its Application in Terminal Sliding Mode Control for a Mobile Satcom Antenna Inertially Stabilized Platform

Zhang

Zhao

Guo

et al. 2017

Sensors

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The mobile satcom antenna (MSA) enables a moving vehicle to communicate with a geostationary Earth orbit satellite. To realize continuous communication, the MSA should be aligned with the satellite in both sight and polarization all the time. Because of coupling effects, unknown disturbances, sensor noises and unmodeled dynamics existing in the system, the control system should have a strong adaptability. The significant features of terminal sliding mode control method are robustness and finite time convergence, but the robustness is related to the large switching control gain which is determined by uncertain issues and can lead to chattering phenomena. Neural networks can reduce the chattering and approximate nonlinear issues. In this work, a novel B-spline curve-based B-spline neural network (BSNN) is developed. The improved BSNN has the capability of shape changing and self-adaption. In addition, the output of the proposed BSNN is applied to approximate the nonlinear function in the system. The results of simulations and experiments are also compared with those of PID method, non-singularity fast terminal sliding mode (NFTSM) control and radial basis function (RBF) neural network-based NFTSM. It is shown that the proposed method has the best performance, with reliable control precision.

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Adaptive terminal sliding mode control of uncertain robotic manipulators based on local approximation of a dynamic system

Cited by 84 publications

References 21 publications

Adaptive Sliding Mode PID Control for Underwater Manipulator Based on Legendre Polynomial Function Approximation and Its Experimental Evaluation

Adaptive Sliding Mode PID Control for Underwater Manipulator Based on Legendre Polynomial Function Approximation and Its Experimental Evaluation

Adaptive Fuzzy Sliding Mode Controller for Robot Manipulator: Effect on External Interferences

An Adaptive B-Spline Neural Network and Its Application in Terminal Sliding Mode Control for a Mobile Satcom Antenna Inertially Stabilized Platform

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