• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.421-421
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• 2000

In this paper, robust two-degree-of-freedom controller for satellite antenna system which tracks reference signal is designed. Two-degree-of-freedom controller consists of a prefilter and a feedback controller to solve trade-off between robust stability and command response. The feedback controller is designed from specifications like stability, disturbance rejection and robustness via H$_{\infty}$ design technique. In the sequel, H$_2$ optimal prefilter is introduced to improve the command response. This suggests a two-step design, with different types of performance specifications at each stage. In practical problems, this may easily lead to a prefilter of unacceptably high order. In order to avoid high order prefilter we use a particular structure in which both the prefilter and the feedback controller share the same dynamics. H$_2$-prefilter technique proposed in this paper is verified by simulation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.81-84
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• 1998

For ANC systems applied to aircrafts or passenger ships, engines from which reference signals are usually measured are located so far from seats where main part of controllers are placed. It can make feedforward ANC scheme difficult to implement or very costly. Feedback ANC algorithms which do not require reference signals and use error signals alone to update the filter, are usually sensitive to measurement noise ' and impulse noise. In this paper, reference signal needed for the feedforward control is not measured directly but generated with the estimated frequencies. Cascade adaptive notch filter (ANF), which has the low computational burden, is used to implement ANC system in real time. Several ANFs of order 2 are connected in series to estimate multiple sinusoids. Computer simulations and experiments in the laboratory for verifying efficacy of the proposed algorithm are carried out.

• 한국통신학회논문지
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• 제21권7호
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• pp.1734-1745
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• 1996

본 논문에서는 디지탈 자기 기록 채널의 비선형 왜곡 보상을 위한 개선된 fixed-delay tree search with decision feedback(FDTS/DF)에 대하여 논한다. 고밀도로 갈수록 두드러지게 나타나는 비선형 왜곡은 일반적으로 이산 Volterra 급수로 잘 모델링되므로 비선형 feedforward 필터, 선형 궤환 필터, 그리고 비선형 왜곡 테이블에 의하여 등화기가 구성되며, 이 때 왜곡 테이블의 값은 미래의 기록 데이터에 의한 비선형 왜곡 영향을 고려하여 결성된다. FDTS를 사용한 결정단에서는 이전의 결정값, 현재의 추정치와 미래의 추정치를 고려하여 최소의 branch metric을 갖는 path가 선택된다. 모의 실험을 통하여 선형 FDTS/DF, 기존의 비선형 FDTS/DF, 그리고 제안된 비선형 FDTS/DF/DF의 성능이 비교되며, 제안된 비선형 FDTS/DF가 고밀도 기록시 가장 우수한 성능을 보임을 확인한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1557-1560
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• 1997

In this paper, we have built an experimental magnetic levitation system for a possible use of control education. We have give a mathermatical model of the nonlinear system and have shown the stability region of the linearized system when it is controlled by a PD controller. We also proposed a neural network control system which uses a neural network as a feedforward controller thgether with a conventional feedback PF controller. We have generated a desired output trajectory, which was designed for the benefit of the generalization of the neural network controller, and trained the desired output trajectory, which was desigend for the benefit of the generalization of the neural netowrk controller, and trained a neural network controller with the data of the actual input and the output of the system obtained by applying the desired output trajectroy. A good tracking performance was observed for both the desired trajectiories used and not used for the neural network training.

• 한국정밀공학회지
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• 제29권1호
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• pp.79-86
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• 2012

This paper proposes PID and RIC (Robust Internal-loop Compensator) based motion controller using dual learning algorithm for position control of linear synchronous motor respectively. Its gains are auto-tuned by using two learning algorithms, reinforcement learning and neural network. The feedback controller gains are tuned by reinforcement learning, and then the feedforward controller gains are tuned by neural network. Experiments prove the validity of dual learning algorithm. The RIC controller has better performance than does the PID-feedforward controller in reducing tracking error and disturbance rejection. Neural network shows its ability to decrease tracking error and to reject disturbance in the stop range of the target position and home.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.1055-1059
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• 1990

This paper proposed a predictive tracking controller for the continuous-time systems by using the receding horizon concept in the optimal tracking control. This controller is the continuous-time version of the previous RHTC (Receding Horizon Tracking Control) for the discrete-time state space models. The problems in implementing the feedforward part of this controller is discussed and a approximate method of implementing this controller is presented. This approximate method utilizes the information of the command signals on the receding horizon and has simple constant feedback and feedforward gain. To perform the offset free control, the integral action is included in the continuous time RHTC. By simulation it is shown that the proposed method gives better performance than the conventional steady state tracking control.

• 한국정밀공학회지
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• 제13권12호
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• pp.22-29
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• 1996

In this paper the nodes of the multilayer hidden layers have been modified for modeling the nonlinear systems. The structure of nodes in the hidden layers is built with the feedforward, the cross talk and the recurrent connections. The feedforward links are mapping the nonlinear function and the cross talks and the recurent links memorize the dynamics of the system. The cross talks are connected between the modes in the same hidden layers and the recurrent connection has self feedback, and these two connections receive one time delayed input signals. The simplified steam boiler and the analytic multi input multi output nonlinear system which contains process noise have been modeled using this neural networks.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.150-155
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• 1993

The neural network MRAC system is presented. The purpose of this paper is applied to a plant that is to be controlled in a strongly nonlinear environment. The proposed system has a learning and adaptive ability in the varying environment by using the back-propagation learning algorithm based on Lyapunov stability theory. N.N. regulator is a part of overall system and is guaranteed to be stable in initial stage. Nonlinear terms of the varying mass, colilori, centifugal, and gravity are compensated for by feedforward N.N. regulator. And the feedback controller (adaptive mechanism) works to eliminate errors of position, velocity which the feedforward controller cannot compensate for. Finally, the proposed system will be demonstrated by simulation of a two d.o.f robot manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.66.3-66
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• 2001

A piezoelectric actuator yields hysteresis effect due to its composed ferroelectric. Hysteresis nonlinearty is neglected when a piezoelectric actuator moves with short stroke. However when it moves with long stroke and high frequency, the hysteresis nonlinearty can not be neglected. The hysteresis nonlinearty of piezoelectric actuator degrades the control performance in precision position control. In this paper, in order to improve the control performance of piezoelectric actuator, an inverse modeling scheme is proposed to compensate the hysteresis nonlinearty problem. And feedforward - feedback controller is proposed to give a good tracking performance. The Feedforward controller is inverse hysteresis model ,and PID control is used ...

• 대한전기학회논문지
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• 제36권3호
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• pp.188-196
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• 1987

This paper describes a design method for the buck type switching regulator to improve transient and steady state performances. Necessary design considerations on the power stage are given before designing the controller to obtain better trandient responses with less control effort and a feedforward compensation is also given to effectively improve the steady state performance. In the design of the controller, a PIM (proportional-integral-measurable) control method with optimized constant feedback gains is presented to get better tansient and steady state performances without complicating the implementation of controller. Computer simulations and experimental results are given to show the usefulness of the presented technique.