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H∞놈과 Pre-filter을 이용한 비선형시스템의 안정화 제어기 설계
서강면(Kang-Myun Seo) 한국산업기술융합학회(구. 산업기술교육훈련학회) 2021 산업기술연구논문지 (JITR) Vol.26 No.4
This study analyzes the design of stabilization controllers for nonlinear systems. The proposed method is based on the synthesis control method of the H∞ norm and prefilter, which is a low-pass filter. The low-pass filter is coupled to structure an open-loop transfer function such that sensitivity and complementary sensitivity functions possess desirable frequency characteristics. The H∞ controller, which was designed to validate the proposed controller method, was applied to a representative nonlinear system of an inverted pendulum system, and results were compared with the with that of a PID controller application. Simulation results show that the designed controller well adhered to control objectives while reducing the influence of modeling error and noise.
박정훈(Park Jung-Hoon),김진근(Kim Jin-Keun),홍성훈(Hong Sung-Hoon),서강면(Seo Kang-Myun),강문성(Kang Moon-Sung) 대한전기학회 2006 대한전기학회 학술대회 논문집 Vol.2006 No.7
This paper describes the development of temperature controller using program control method for small heating system. This system consists of three parts; sensing part, control part that includes the PID control algorithm, and actuating part. We introduce a zero-crossing control method of TRIAC and firmware technique using single chip micro-controller(ATmega8535) in the control part.
Kohonen Network을 이용한 로보트 매니퓰레이터의 퍼지 제어기 구성
서강면,강문성 청주대학교 산업과학연구소 1994 産業科學硏究 Vol.12 No.-
This paper presents a fuzzy controller and neuro-fuzzy controller which fuzzy rules are automatically generated, to control robot manipulator. Performances of these controllers were evaluated by comparing with simulation results for a two-link robot manipulator. The membership functions of the fuzzy controller first approximated by studying the response of a traditional PD controller and then tuned to achieve the best response by trial and error method. In a neuro-fuzzy controller, the fuzzy rules can be obtained with the fuzzy clustering scheme using Kohonen network and differential competitive learning algorithm based on input/output data. The computer simulation results show that neuro-fuzzy controller, generates automatically fuzzy rules, is more robust to the disturbance than the traditional PD controller in the transient state response.