Two-way fuzzy adaptive identification and control of a flexible-joint robot arm

Date

2002-08-01

Author

Gurkan, E
Erkmen, İsmet
Erkmen, Aydan Müşerref

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

194
views

0
downloads

The objective in this paper is to apply our proposed two-way fuzzy adaptive system that makes use of intuitionistic fuzzy sets to the identification and model-based control of a flexible-joint robot arm. Uncertainty and inconsistency are modelled in the proposed system such as uncertainty is the width of the interval introduced by the independent assignment of membership and nonmembership functions of the intuitionistic fuzzy sets; and inconsistency is the violation of the consistency inequality in this assignment. We reduce uncertainty and inconsistency through a two phase training. The first phase is to reduce inconsistency introduced by the inconsistent assignment of membership and nonmembership functions. The resultant system is an almost consistent two-way fuzzy adaptive system. Thus, an evaluation of the degree of reduction of inconsistency is needed and is carried out at the end of this phase by forming the shadowed set patterns of the membership and nonmembership functions after first phase of training. The system is further trained for a second phase in order to reduce uncertainty. The system performance has shown that this second phase of training renders the system totally one-way fuzzy adaptive. (C) 2002 Elsevier Science Inc. All rights reserved.

Subject Keywords

Inconsistency modelling and evaluation, Uncertainty reduction, Intuitionistic fuzzy sets, Shadowed sets, Fuzzy adaptive identification for control

URI

https://hdl.handle.net/11511/48877

Journal

INFORMATION SCIENCES

DOI

https://doi.org/10.1016/s0020-0255(02)00222-0

Collections

Department of Electrical and Electronics Engineering, Article

Suggestions

OpenMETU
Core

Evaluation of inconsistency in a 2-way fuzzy adaptive system using shadowed sets Gurkan, E; Erkmen, Aydan Müşerref; Erkmen, İsmet (2001-05-24) Our objective in this paper is to evaluate inconsistency for our proposed 2-way fuzzy adaptive system that makes use of intuitionistic fuzzy sets. Uncertainty is modeled as the width of the interval introduced by the independent assignment of membership and nonmembership functions of the intuitionistic fuzzy sets. There is only a consistency constraint in this assignment, violation of which gives rise to inconsistency in the system. The inconsistency model using this fact is reduced through training. There ...
Reducing inconsistencies in intuitionistic 2-way adaptive fuzzy control systems Gurkan, E; Erkmen, Aydan Müşerref; Erkmen, İsmet (2000-08-30) Our objective in this paper is to model and reduce inconsistency in expert knowledge for our proposed 2-way adaptive fuzzy system that makes use of intuitionistic fuzzy sets. Intuitionistic fuzzy sets model an interval valued distribution of information in the adaptive control architecture with the necessity at the lower bound as the degree of membership functions and the possibility at the upper bound as the complement of the degree of nonmembership functions. Uncertainty is modelled as the width of this i...
Intuitionistic, 2-way adaptive fuzzy control Gurkan, E; Erkmen, Aydan Müşerref; Erkmen, İsmet (1999-05-15) Our objective in this paper is to develop a 2-way adaptive fuzzy control system that makes use of the intuitionistic fuzzy sets for modeling expert knowledge bearing uncertainty. Adaptive fuzzy control systems are fuzzy logic systems whose rule parameters are automatically adjusted through training. The training of such system was applied until now, to supports of rule propositions with single distribution such that they can be termed 1-way adaptive. In our system, all supports to propositions have interval...
The enhancement of the cell-based GIS analyses with fuzzy processing capabilities Yanar, Tahsin Alp; Akyürek, Sevda Zuhal; Department of Geodetic and Geographical Information Technologies (2003) In order to store and process natural phenomena in Geographic Information Systems (GIS) it is necessary to model the real world to form computational representation. Since classical set theory is used in conventional GIS software systems to model uncertain real world, the natural variability in the environmental phenomena can not be modeled appropriately. Because, pervasive imprecision of the real world is unavoidably reduced to artificially precise spatial entities when the conventional crisp logic is used...
Genetically tuned fuzzy scheduling for flexible manufacturing systems. Erkmen, Aydan Müşerref; Anlagan, O; Unver, O (1997-04-25) This paper focuses on the development and implementation of a Genetically Tuned Fuzzy Scheduler (GTFS) for heterogeneous FMS under uncertainty. The scheduling system takes input from a table and creates an optimum master schedule. The GTFS uses fuzzy rulebase and inferencing where fuzzy sets are generated by a genetic algorithm to tune the optimization. The fuzzy optimization is based on time criticality in deadline and machine need, taking into account machine availability, uniformity, process time and sel...

Citation Formats

E. Gurkan, İ. Erkmen, and A. M. Erkmen, “Two-way fuzzy adaptive identification and control of a flexible-joint robot arm,” INFORMATION SCIENCES, pp. 13–43, 2002, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48877.