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Model reference learning control for rigid robots

Guruprasad, KR and Ghosal, A (1999) Model reference learning control for rigid robots. In: ASME 1999 Design Engineering Technical Conferences, DETC 1999, 12-16 Sep 1999, pp. 445-454.

Full text not available from this repository.
Official URL: https://doi.org/10.1115/DETC99/DAC-8659


The equations of motion of a rigid robot are often known only approximately, as some of the parameters are not known exactly and there are also unmodelled nonlinearities. Most adaptive control schemes can estimate the parameters if the structure of the equations is known, but are not very useful if structure itself is not known. In this paper we propose a model reference learning control scheme using Adaptive Network based Fuzzy Inference System (ANFIS) for control of rigid robots whose model may have parametric and structural uncertainties. The approximate model of a robot, which may differ very significantly from the actual robot in parametric values and structure, is used as a reference plant and a nonlinear model based controller is designed based on this model. The ANFIS corrector provides an additional correction to control input as a function of the present and desired states of the plant. The error between states of plant and that of reference plant is used to tune the ANFIS corrector. The proposed control scheme has been implemented for a two-degree-of-freedom serial rigid robot. The results of the simulation experiments carried out show that the proposed control scheme can learn to control the unmodelled dynamics. The ANFIS controller is shown to give improved performance for parameter as well as structural uncertainties. Copyright © 1999 by ASME.

Item Type: Conference Paper
Publication: Proceedings of the ASME Design Engineering Technical Conference
Publisher: American Society of Mechanical Engineers (ASME)
Additional Information: The copyright of this article belongs to American Society of Mechanical Engineers (ASME)
Keywords: Computer aided design; Control nonlinearities; Controllers; Degrees of freedom (mechanics); Equations of motion; Fuzzy inference; Fuzzy neural networks; Learning algorithms; Learning systems; Robots, Adaptive control schemes; Adaptive network based fuzzy inference system; Approximate model; Control schemes; Non-linear model; Structural uncertainty; Two-degree of freedom; Unmodelled dynamics, Model reference adaptive control
Department/Centre: Division of Mechanical Sciences > Mechanical Engineering
Date Deposited: 16 Mar 2021 07:49
Last Modified: 16 Mar 2021 07:49
URI: http://eprints.iisc.ac.in/id/eprint/68264

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