Adaptive Tracking Control for Robot Manipulators Using Fuzzy Wavelet Neural Networks

Long, Mai Thang; Wang, Yaonan; Ngo, Thanh-Quyen

doi:10.2316/journal.206.2015.1.206-3986

Cited by 5 publications

(10 citation statements)

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“…However, at the practical operation scene, the robotic control systems is always influenced by the external stochastic disturbances, including the Coulomb force and the Friction force, and the inside parameter perturbations. Therefore, the mathematic model established under the face of ideal instance cannot work well and finding the robust controller which can compensate the uncertainties of practical mathematic model is very important [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Control of Uncertain Robotic Manipulator

Zhou

Qunli

2018

Mathematical Problems in Engineering

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This paper designs a kind of adaptive fuzzy controller for robotic manipulator considering external disturbances and modeling errors. First, n-link uncertain robotic manipulator dynamics based on the Lagrange equation is changed into a two-order multiple-input multiple-output (MIMO) system via feedback technique. Then, an adaptive fuzzy logic control scheme is studied by using sliding theory, which adopts the adaptive fuzzy logic systems to estimate the uncertainties and employs a filtered error to make up for the approximation errors, hence enhancing the robust performance of robotic manipulator system uncertainties. It is proved that the tracking errors converge into zero asymptotically by using Lyapunov stability theory. Last, we take a two-link rigid robotic manipulator as an example and give its simulations. Compared with the existing results in the literature, the proposed controller shows higher precision and stronger robustness.

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Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Control of Uncertain Robotic Manipulator

Zhou

Qunli

2018

Mathematical Problems in Engineering

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“…The dynamics of n -links robot manipulator system can be considered by the following Lagrange function form [1]:…”

Section: Preliminaries 21 Problem Formulationmentioning

confidence: 99%

“…are the gravity vector, friction vector, the unknown disturbances vector and the torque inputs vector, respectively. The following properties of the RM system are considered as [1]: Property 1. The ( ) M q is a positive definite symmetric matrix and it is uniformly bounded as:…”

Section: Preliminaries 21 Problem Formulationmentioning

confidence: 99%

“…In this study, our purpose is to improve the traditional PD tracking control effectiveness for RM. So, based on the model of RM (1), we firstly define the tracking errors ( ) e t and filtered-tracking errors ( ) r t as the following forms [1,17] rn K is positive constant matrix. The conventional control input can be applied as [1,17]:…”

Section: Preliminaries 21 Problem Formulationmentioning

confidence: 99%

“…4, will be used in the simulation process to prove the effectiveness of the proposed control method. The model of RM is constructed in the Lagrange equations form, with its dynamics is given as in [1]. In order to exhibit the superior control performance of the proposed control scheme (APDNN), the traditional PD controller, the conventional PDNN strategy in [1] will be examined.…”

Section: Proofmentioning

confidence: 99%

See 2 more Smart Citations

An Adaptive Proportional-Derivative Control Method for Robot Manipulator

Long¹,

TOAN²,

Hung³

et al. 2022

jst-iuh

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This research presents an improved control method for the robot manipulator system based on the proportional-derivative technique and neural networks. In the proposed strategy, the proportional-derivative controller based on the filtered tracking error technique has been modified such that the proportional-derivative gain parameters are adaptively updated. Similar to the conventional intelligent control methods, the neural networks approximator is applied to relax the unknown dynamics of the robot control system. In addition, the compensator-typed robust controller is also considered to eliminate inevitable approximating errors and unknown disturbances of the control system. By using the Lyapunov stability theorem for the proposed control design procedure, the tracking control and stability are guaranteed. The comparative simulation results will provide clearly the evident to prove effectiveness of the proposed approach.

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Global adaptive neural backstepping control of a flexible hypersonic vehicle with disturbance estimation

Zhao

Yang

2021

AEAT

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Purpose This paper aims to discuss the precise altitude and velocity tracking control of a hypersonic vehicle, a global adaptive neural backstepping controller was studied based on a disturbance observer (DOB). Design/methodology/approach The DOB combined with a radial basis function (RBF) neural network (NN) was used to estimate the disturbance terms that are generated by the flexible modes of the hypersonic vehicle system. A global adaptive neural method was introduced to approximate the unknown system dynamics, with robust control terms pulling the system transient states back into the neural approximation domain externally. Findings The globally uniformly ultimately bounded for all signals of a closed-loop system can be guaranteed by the proposed control algorithm. Additionally, the command filtered backstepping methods can avoid the explosion of the complexity problem caused by the backstepping design process. In addition, the effectiveness of the proposed controller can be verified by the simulation used in this study. Research limitations/implications Normally lateral dynamics issue should be discussed in the process of control system designed, the lateral dynamics are not included in the nonlinear dynamic model of hypersonic vehicle used in this paper, merely the longitudinal flight dynamics are discussed in this paper. Originality/value The flexible states in rigid modes are considered as the disturbance of the system, which is estimated by structuring DOB with NN approximations. The compensating tracking error and prediction error are used in the update law of RBF NN weight. The differential explosions complexity derived from the backstepping procedure is dealt with by using command filters.

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Adaptive Tracking Control for Robot Manipulators Using Fuzzy Wavelet Neural Networks

Cited by 5 publications

References 0 publications

Adaptive Fuzzy Control of Uncertain Robotic Manipulator

Adaptive Fuzzy Control of Uncertain Robotic Manipulator

An Adaptive Proportional-Derivative Control Method for Robot Manipulator

Global adaptive neural backstepping control of a flexible hypersonic vehicle with disturbance estimation

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