Designing interval type-2 fuzzy controllers by Sarsa learning

Author

Mohajeri, Nooshin Nasri ; Sistani, Mohammad Bagher Naghibi

Author_Institution

Ferdowsi Univ. of Mashhad, Mashhad, Iran

fYear

2013

fDate

14-16 May 2013

Firstpage

1

Lastpage

6

Abstract

In this paper, new Interval type-2 fuzzy controllers, which are designed by Sarsa learning, are proposed. The porposed controllers are A2-C0 Takagi-Sugeno-Kang type. Therefore, the antecedent part of rules is formed by fuzzy type-2 sets and the consequent part is comprised of possible actions. In the output processing section of fuzzy type-2 controllers, in addition to, Karnik-Mendel type reducer accompanied by centroid deffuzification, another output processor called BMM method is applied. Consequently, IT2FSL-KM and IT2FSL-BMM are generated. These new controllers are compared with other fuzzy controllers designed by RL methods in truck backing control problem. Simulation results represent the efficiency and effectiveness of proposed controllers in noiseless and noisy environment.

Keywords

control system synthesis; fuzzy control; fuzzy set theory; learning (artificial intelligence); A2-C0 Takagi-Sugeno-Kang type; BMM method; IT2FSL-BMM; IT2FSL-KM; Karnik-Mendel type reducer; Sarsa learning; centroid deffuzification; fuzzy Q-learning; fuzzy type-2 sets; interval type-2 fuzzy controller design; truck backing control problem; Algorithm design and analysis; Equations; Fuzzy sets; Learning (artificial intelligence); Mathematical model; Noise measurement; Robustness; Fuzzy Q-learning (FQL); fuzzy Sarsa learning (FSL); interval type-2 fuzzy set (IT2F Set); reinforcement learning (RL); type-2 fuzzy logic systems (T2FLS);

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical Engineering (ICEE), 2013 21st Iranian Conference on

Conference_Location

Mashhad

Type

conf

DOI

10.1109/IranianCEE.2013.6599660

Filename

6599660