D-type iterative learning control without resetting condition for robot manipulators

Bouakrif, Farah

doi:10.1017/s0263574711000191

Cited by 26 publications

(14 citation statements)

References 29 publications

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“…The NI-USB 6009 is setup to use the QET's analog command port to drive the motor's control/input signal and the QET's analog tachometer port as the motor's response/output speed signal, and finally the NI-USB is C. Numerical simulations 1) Control of a DC motor: In this part, we assume that the motor dynamics is subject to a nonlinear friction verifying all the conditions imposed to system (6). The friction is assumed to be a Lipschitz function of the speed with unknown parameters.…”

Section: B Real-time Control Using Ni Usb-6009mentioning

confidence: 99%

“…Subsequently, iterative learning control has been further explored in many engineering areas as robotics [23], [6], [14], multi-agent control [15], mechatronics [13], motor control applications [5], [22] and more. Referring to the extensive literature in this area of research, it has been shown that iterative-learning procedures can be implemented for continuous-time systems [20], [25] and discrete-time systems as well [16], [18], [24], [21].…”

Section: Introductionmentioning

confidence: 99%

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Iterative learning control schemes for a class of nonlinear systems: Theory and real-time implementation

Ibrir

Ramlal

2014

2014 12th IEEE International Conference on Industrial Informatics (INDIN)

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Iterative Learning Control (ILC) is a powerful intelligent tool for stabilization of repetitive-task systems. However, choosing and fixing the gains of ILC schemes is not an easy task due to the fact that information propagation is present in two independent directions. In this paper, we propose new sufficient linear matrix inequality conditions for the design of iterative learning trackers for a special class of discretetime nonlinear systems. The control designs have been testified by the application of different real-time experiments using dsPIC 33 microcontroller and software in the loop simulation. The application of the developed results to speed control of a DC motor subject to a nonlinear friction shows satisfactory tracking performance in real time.

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Section: B Real-time Control Using Ni Usb-6009mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Iterative learning control schemes for a class of nonlinear systems: Theory and real-time implementation

Ibrir

Ramlal

2014

2014 12th IEEE International Conference on Industrial Informatics (INDIN)

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“…Over the last two decades, this technique has been the center of interest of many researchers. [2][3][4][5][6][7][8][9][10][11][12][13][14][15] dead-zone may severely degrade system performance. How to deal with these uncertainties becomes another direction in the development of ILC.…”

Section: Introductionmentioning

confidence: 99%

Adaptive P‐type iterative learning radial basis function control for robot manipulators with unknown varying disturbances and unknown input dead zone

Bensidhoum

Bouakrif

2020

Intl J Robust & Nonlinear

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This article proposes an adaptive iterative learning radial basis function (RBF) scheme to solve the trajectory-tracking problem for perturbed robot manipulators with unknown iteration varying disturbances and unknown dead-zone input. It is well known that the presence of the dead zone in actuators and mechatronics devices gives rise to extra difficulty due to the presence of singularity in the input channels. Hence, it is interesting to take this problem into account when synthesizing a controller. This synthesis is made here. In addition, the control design is very simple in the sense that we use, only, the proportional gain. Therefore, the considerable amount noise caused by the sensors for velocity measurements of robot manipulators is avoided. Another advantage of this work is that the unknown disturbances are assumed to be time varying and also varying from iteration to iteration. Thus, the RBF neural network is used to approximate these unknown nonlinear functions. Using the Lyapunov theory, the analysis of the stability of the closed-loop system is guaranteed when the iteration number tends to infinity. Finally, simulation results on the PUMA 560 arm are provided to illustrate the effectiveness of the proposed method. In order to evaluate the performance of our controller, a comparison of our results with other method is also given. K E Y W O R D SIterative learning control, Radial basis function, Dead-zone, Adaptive, Robot manipulators, Lyapunov theory 1 Indeed, input uncertainties such as dead-zone, saturation, backlash, and hysteresis are kind of nonsmooth and nonaffine in input factor widely existing in actuators and sensors. In fact, in many practical applications, the presence of Int J Robust Nonlinear Control. 2020;30:4075-4094.wileyonlinelibrary.com/journal/rnc

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“…Since ILC method was proposed by Uchiyama and presented as a formal theory by Arimoto et al, this technique has been the center of interest of many researchers over the last decades [3]. After nearly thirty years' development, ILC has made great progress.…”

Section: Introductionmentioning

confidence: 99%

A New Fuzzy Iterative Learning Control Algorithm for Single Joint Manipulator

Wang

Bian

2016

Archives of Control Sciences

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This paper present a new fuzzy iterative learning control design to solve the trajectory tracking problem and performing repetitive tasks for rigid robot manipulators. Several times' iterations are needed to make the system tracking error converge, especially in the first iteration without experience. In order to solve that problem, fuzzy control and iterative learning control are combined, where fuzzy control is used to tracking trajectory at the first learning period, and the output of fuzzy control is recorded as the initial control inputs of ILC. The new algorithm also adopts gain self-tuning by fuzzy control, in order to improve the convergence rate. Simulations illustrate the effectiveness and convergence of the new algorithm and advantages compared to traditional method.

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D-type iterative learning control without resetting condition for robot manipulators

Cited by 26 publications

References 29 publications

Iterative learning control schemes for a class of nonlinear systems: Theory and real-time implementation

Iterative learning control schemes for a class of nonlinear systems: Theory and real-time implementation

Adaptive P‐type iterative learning radial basis function control for robot manipulators with unknown varying disturbances and unknown input dead zone

A New Fuzzy Iterative Learning Control Algorithm for Single Joint Manipulator

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