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중력 가속도로 인한 의식상실 감지 및 자동 회복 시스템 개발
김종섭(Chong-sup Kim),황병문(Byung-moon Hwang),강임주(Im-ju Kang),장순용(Soon-ryong Jang),김광윤(Kwang-yun Kim),박명환(Myung-hwan Park) 제어로봇시스템학회 2011 제어·로봇·시스템학회 논문지 Vol.17 No.7
For many years, many pilots lost their lives and aircrafts due to GLOC(Gravity-induced Loss Of Consciousness). Due to the emergence of high-gravity maneuvering aircraft such as the F-16, F-15 and T-50, the automatic GLOC detection and recovery systems are necessary to increase the aircraft safeties even when the pilot loses his consciousness due to high-G maneuvering. This paper addresses the design of GLOC detection, warning and recovery algorithm based on a model of supersonic jet trainer. The system is solely controlled by the pilot"s control input(i.e., control stick force) and aircraft status such as attitude, airspeed, altitude, altitude and so forth. And, moreover, it does not depend upon any pilot physiological condition. The test evaluation results show that the developed system supports the recovery of an aircraft from the unusual aircraft attitude and imporves the aircraft safeties even when the pilot loses his consciousness due to high-G maneuvering.
김관수,최재석,이순영,장순용 慶尙大學校 工科大學 自動化및컴퓨터應用技術硏究所 1999 自動化 및 컴퓨터應用技術 Vol.6 No.1
In this paper, the direct and indirect neural adaptive controller are combined based on the Lyapunov synthesis approach. The proposed adaptive controller is constructed from RBF neural network and a set of fuzzy IF-THEN rules. And the weighting parameters are adjusted on-line according to some adaptation law for the purpose of controlling the plant to track a given trajectory. In this scheme, fuzzy IF-THEN rules are used to decide the combined weighting factor. It is shown that all the signals in the closed-loop system are uniformly bounded under mild assumptions. The effectiveness of the proposed control scheme is demonstrated through the control of one-link rigid robotics manipulator.
퍼지 結合 加重値에 의한 混合 신경망 適應制御器의 强靭性 硏究
장순용,권대업,최재석,이순영 慶尙大學校生産技術硏究所 1999 生産技術硏究所論文集 Vol.15 No.-
In this paper, direct and indirect adaptive controllers are combined based on the Lyapunov synthesis approach. The proposed adaptive controller is constructed from RBF Neural Network and a set of fuzzy IF-THEN rules whose parameters are adjusted on-line according to some adaptation law for the purpose of controlling a plant to track a given trajectory. A control input is constructed using combined adaptive neural control and the supervisory control input with a variable structure method(SCVS). The SCVS improves the robustness and ensures the stability of system. In this scheme, fuzzy IF-THEN rules are used to decide the combined weighting factor. A parameter projection method is employed so that neural network weights are bounded. The effectiveness of the proposed control scheme is demonstrated through simulation results of control for one-link rigid robotics manipulator.