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하인중,김영균,최계근,Ha, In-Jung,Kim, Yeong-Gyun,Choe, Gye-Geun The Institute of Electronics and Information Engin 1982 전자공학회지 Vol.19 No.2
본 논문에서는 관성 항해 시스뎀(INS) 수직 찬넬의 bias error 감소를 위해 Kalman filter 방법과 재래식 방법이 적용, 비교되어졌다. 이 두가지 방법들은 예측 error와 반응면에서 다른 보통 쓰이는 방법들 보다 더 잘 수행됨을 보였다. 비교 연구 결과에 의하면, Kalman filter 방법 방호이 별무리없이 재래식 방법보다 효과적으로 더 잘 수행됨을 알 수 있다. In this paper, two methods (Kalman filter and Conventional) are investigated to reduce the bias error in the INS (Intertial Navigation System) vertical channel. The schemes by these methods show better performance (estimation error and response) than the others commonly used. Comparison results show that the scheme by Kalman filter method gives much better performance than the Conventional method.
주기적인 외란 제거에 있어서 빠른 오프라인 학습 제어 접근 방식
하인중(In-Joong Ha),장정국(Jung-Kook Jang),박진원(Jin-Won Park),권정훈(Jung-Hoon Kwon) 대한전기학회 2007 대한전기학회 학술대회 논문집 Vol.2007 No.4
The recently-developed off-line learning control approaches for the rejection of periodic disturbances utilize the specific property that the learning system tends to oscillate in steady state. Unfortunately, the prior works have not clarified how closely the learning system should approach the steady state to achieve the rejection of periodic disturbances to satisfactory level. In this paper, we address this issue extensively for the class of linear systems. We also attempt to remove the effect of other aperiodic disturbances on the rejection of the periodic disturbances effectively. In fact, the proposed learning control algorithm can provide very fast convergence performance in the presence of aperiodic disturbance. The effectiveness and practicality of our work is demonstrated through mathematical performance analysis as well as various simulation results.