Velocity observer-based iterative learning control for robot manipulators

Bouakrif, Farah; Boukhetala, Djamel; Boudjema, Farès

doi:10.1080/00207721.2011.600467

Cited by 59 publications

(27 citation statements)

References 30 publications

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“…In fact, using Lyapunov-like function, Tayebi derived in [10] an adaptive ILC schemes to solve the trajectory tracking problem of rigid robot manipulators. To solve the same problem for robot manipulators without using the velocity measurement, Bouakrif et al proposed in [11] a velocity observer having an iteratively form to reconstruct the velocity signal in the control laws.…”

Section: Introductionmentioning

confidence: 99%

Iterative learning control for strictly unknown nonlinear systems subject to external disturbances

Bouakrif

2011

Int. J. Control Autom. Syst.

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This paper deals with Iterative Learning Control ILC schemes to solve the trajectory tracking problem of strictly unknown nonlinear systems subject to external disturbances, and performing repetitive tasks. Two ILC laws are presented, the first law is the high order, i.e., the information (error) of several iterations are used in the control law. The second law is the ILC with forgetting factor, i.e., the control of the preceding iteration is multiplied by a matrix of the gains. Indeed, the advantage of these algorithms, it is not only applicable for nonlinear systems with model uncertainty, but also for nonlinear systems with no data exists, neither in the structure model nor in the system parameters. In addition, the control design is very simple in the sense that there is no requirement on the choice of the learning gains. Furthermore, the convergence of our algorithms is independent of initial conditions. The asymptotic stability of the closed loop system is guaranteed. This proof is based upon the use of a Lyapunov-like positive definite sequence, which is shown to be monotonically decreasing under the proposed control schemes. Finally, simulation results on nonlinear system are provided to illustrate the effectiveness of the proposed controllers.

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Section: Introductionmentioning

confidence: 99%

Iterative learning control for strictly unknown nonlinear systems subject to external disturbances

Bouakrif

2011

Int. J. Control Autom. Syst.

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“…Based on the Lyapunov approach, the developed control system is proved as stable. In [14], the issue in the process of developing an iterative learning control system for the trajectory tracking of robotic mechanisms that have external disturbances with conducting repetitive works and with no knowledge of the velocity measurement is demonstrated. To handle this issue, the authors put forward a velocity observer that contains an iterative form which is used to recreate the velocity signal.…”

Section: Iterative Learning Control and Its Variantsmentioning

confidence: 99%

On the Development of Learning Control for Robotic Manipulators

Zhang

Wei

2017

Robotics

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Learning control for robotic manipulators has been developed over the past decade and to the best of the authors' knowledge, it is still in its infant development stage; the authors believe that it will become one of the most promising directions in the control area in robotic manipulators. Learning control in robotic manipulators is mainly used to address the issue that the friction at the joints of robotic mechanisms and other uncertainties may exist in the dynamic models, which are very complex and may even be impossible to model mathematically. In this paper, the authors review and discuss the learning control in robotic manipulators and some issues in learning control for robotic manipulators are also illustrated. This review is able to give a general guideline for future research in learning control for robotic manipulators.

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“…Finally, many of the available ILC approaches require identical resetting initial conditions (at the beginning of each iteration: the well known resetting condition); however, in real applications, the perfect resetting condition may be not realizable [6,8]. Therefore, under different tests on resetting initial conditions, the boundedness along the time evolution and asymptotic stability on each iteration of an ILC system were well proven in [8].…”

Section: Introductionmentioning

confidence: 99%

“…To note, ILC theory has also been used in system modeling, two-dimensional systems analysis, linear matrix inequality system design, and in adaptive and robust control innovation (see, for instance, [5,6] and references therein). In the meantime, some other ILC strategies are mainly based on Lyapunov's theory [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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A Chaotic Secure Communication System Design Based on Iterative Learning Control Theory

Acho

2016

Applied Sciences

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This paper presents an application of Iterative Learning Control (ILC) theory to secure communication system design by using chaotic signals, where the logistic-map is employed as a source of chaos. Meanwhile, the ILC scheme is employed as a tool to encrypt and decrypt a message. A set of numerical experiments is realized to evidence the performance of our system, including the noisy case on the channels of communication of the proposed scheme.

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Velocity observer-based iterative learning control for robot manipulators

Cited by 59 publications

References 30 publications

Iterative learning control for strictly unknown nonlinear systems subject to external disturbances

Iterative learning control for strictly unknown nonlinear systems subject to external disturbances

On the Development of Learning Control for Robotic Manipulators

A Chaotic Secure Communication System Design Based on Iterative Learning Control Theory

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