기관회원 [로그인]
소속기관에서 받은 아이디, 비밀번호를 입력해 주세요.
개인회원 [로그인]

비회원 구매시 입력하신 핸드폰번호를 입력해 주세요.
본인 인증 후 구매내역을 확인하실 수 있습니다.

회원가입
서지반출
Analysis of FLC with Changing Fuzzy Variables in Frequency Domain
[STEP1]서지반출 형식 선택
파일형식
@
서지도구
SNS
기타
[STEP2]서지반출 정보 선택
  • 제목
  • URL
돌아가기
확인
취소
  • Analysis of FLC with Changing Fuzzy Variables in Frequency Domain
  • Analysis of FLC with Changing Fuzzy Variables in Frequency Domain
저자명
Lee. Kyoung-Woong,Choi. Han-Soo
간행물명
International Journal of Control, Automation and Systems
권/호정보
2010년|8권 3호|pp.695-701 (7 pages)
발행정보
제어로봇시스템학회
파일정보
정기간행물|ENG|
PDF텍스트
주제분야
기타
이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
서지반출

기타언어초록

This paper discusses a simple method for analyzing FLC in frequency domain based on describing function. Since nonlinear characteristics of FLC make it difficult FLC analysis, it usually requires a big deal of trial-and-error procedures based on computer simulation. The proposed method is simple and easy to understand, because it is based on the Nyquist stability criterion used to analyze absolute and relative stability, phase and gain margin of a linear system. To linearize in frequency domain, a describing function for FLC is derived by using a piecewise linearization of the FLC response plot. This describing function is represented as a function of magnitude of input sinusoid and nonlinear parameters $x_1$ and $x_2$ which change consequence fuzzy variables and nonlinearity of FLC. The describing function is redefined without the magnitude of sinusoid input because maximum values of the describing function can explain the stability of the system. This redefined describing function is used to get minimum stability characteristic, an absolute stability, phase margin and gain margin, of FLC. Using this function, we can explicitly figure out various characteristic of FLC according to $x_1$ and $x_2$ in frequency domain. In this work, we suggest a minimum phase margin (MPM) and a minimum gain margin (MGM) for FLC which can be used to determine whether the system is stable or not and how stable it is. For simplicity, we use one-input FLC with three rules. For various nonlinear response of FLC, changing fuzzy variables of a consequence membership function is used. Simulation results show that these parameters are effective in analyzing FLC.