By Topic

Evaluating uncertainty resiliency of Type-2 Fuzzy Logic Controllers for parallel delta robot

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Ondrej Linda ; University of Idaho, Idaho Falls, USA ; Milos Manic

As a consequence of recent theoretical advancements in Type-2 (T2) fuzzy logic, applications of T2 Fuzzy Logic Controllers (FLCs) are becoming increasingly popular in various engineering areas. Nevertheless, the qualitative comparison of Type-1 (T1) and T2 FLCs and the assessment of the potential of T2 fuzzy logic can still be considered open questions. Despite this fact, researchers commonly claim superiority of T2 FLC in uncertain conditions based on a very limited exploration of the design parameter space. This manuscript provides a systematic analysis of the uncertainty resiliency of T2 FLC used for position control of parallel delta robot. In order to allow for objective comparison among different T1 and T2 FLCs, the controllers were constructed using a partially-dependent approach. Here, the T2 FLC is created based on an initially optimized T1 FLC. In this, manner the constrained design space allows for its full systematic exploration and analysis. The performance of each controller was evaluated on the real parallel delta robot under various levels of dynamic uncertainty. The experimental results support the theoretical claims about the superiority of T2 FLC. However, it was also demonstrated that there is a clear upper bound on the amount of “type-2 fuzziness” in the controller design, which can result in performance improvement. Exceeding such upper bound leads to performance deterioration.

Published in:

Human System Interactions (HSI), 2011 4th International Conference on

Date of Conference:

19-21 May 2011