International Journal of Computational Intelligence Systems

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Volume 11, Issue 1, 2018, Pages 183 - 194

An Improved Nonstationary Fuzzy System Approach versus Type-2 Fuzzy System for the Lifting Motion Control with Human-in-the-Loop Simulation

Authors
Mehmet Karakose*, mkarakose@firat.edu.tr
* Computer Engineering Department, Firat University, Elazig, 23119, Turkey
*Corresponding author: Mehmet Karakose, Computer Engineering Department, Firat University, 23119, Central Elazig, Turkey, Tel: +90-424-2370000, Email address: mkarakose@firat.edu.tr.
Corresponding Author
Mehmet Karakosemkarakose@firat.edu.tr
Received 6 August 2016, Accepted 10 October 2017, Available Online 1 January 2018.
DOI
10.2991/ijcis.11.1.14How to use a DOI?
Keywords
Human lifting motion; Nonstationary fuzzy system; Human body modeling; Type-2 fuzzy sets; Human in the loop simulation
Abstract

People working in the fields of robotics, animation, computer graphics and computer vision, and biomechanics, find it difficult to conduct human motion simulations, such as lifting, walking and running. This is because it is difficult to predict all of the motion strategies in a variety of situations. The human lifting motion is hard work; at the same time, lifting is the most demanded robotic motion. In this paper, a fuzzy integral based nonstationary fuzzy inference system is proposed to control a five-segment human model for the human lifting motion with human-in-the-loop simulation. This system was designed to reduce the computational complexity of nonstationary fuzzy systems. Hence, this paper contributes to the literature in two ways: as an improved nonstationary fuzzy system and as a human lifting motion simulation framework. A fuzzy integral algorithm between the fuzzification and defuzzification stages has been used to aggregate the inference outputs of the nonstationary fuzzy controller. The fuzzy integral algorithm uses fuzzy values obtained during the fuzzification stage as the attribute values and the fuzzy values obtained by a one-loop quantum particle swarm optimization algorithm as the importance values. The computational complexity in the nonstationary fuzzy systems and type-2 fuzzy systems can be reduced between 25 to 60 percent with the improved nonstationary fuzzy system. An experimental application of the human lifting motion was carried out to demonstrate the effectiveness of the proposed approach. The results illustrated that the proposed algorithm can achieve increased simplicity, improved effectiveness, good robustness, and a higher precision of computation.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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<Previous Article In Issue
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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
11 - 1
Pages
183 - 194
Publication Date
2018/01/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.11.1.14How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Mehmet Karakose
PY  - 2018
DA  - 2018/01/01
TI  - An Improved Nonstationary Fuzzy System Approach versus Type-2 Fuzzy System for the Lifting Motion Control with Human-in-the-Loop Simulation
JO  - International Journal of Computational Intelligence Systems
SP  - 183
EP  - 194
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.14
DO  - 10.2991/ijcis.11.1.14
ID  - Karakose2018
ER  -