• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.1492-1495
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• 1996

In this paper, an approach to autopilot design based on the robust nonlinear dynamic inversion method is proposed. Both unknown parameters and uncertainty bounds are estimated and parameter estimates are used in the fast inversion. Furthermore, to get more robustness slow inversion is incorporated with MRAC(Model Reference Adaptive Control) and sliding mode control where the estimates of uncertainty bounds are used. The proposed method is applied to the pitch autopilot design of a missile system and excellent performance is shown via computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1987년도 한국자동제어학술회의논문집(한일합동학술편); 한국과학기술대학, 충남; 16-17 Oct. 1987
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• pp.717-720
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• 1987

This paper presents a discrete-time model reference adaptive control technique for periodically time-varying plants. It is shown that the identification problem for periodic parameters can be reduced to that of constant unknown parameters case. The global stability of the resulting closed-loop system is established using the key technical lemma of Goodwin, Ramadge and Caines.

• 전자공학회논문지B
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• 제29B권10호
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• pp.53-58
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• 1992

This paper presents a decentralized model reference adaptive control scheme for an interconnected linear system composed of a number of single-input single-output subsystems in which outgoing interactions pass through the measurement channel and are subjected to bounded external disturbances. The scheme can treat the unknown strength of interactions as well as uncertainties in subsystem dynamics, and allows for the case when the relative degree of each decoulped subsystem does not exceed two.

• 제어로봇시스템학회논문지
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• 제7권11호
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• pp.891-895
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• 2001

An alternative inverse feedback structure for adaptive active control of periodic noise is introduced for systems with nonminimum phase cancellation path. To obtain the inverse model of the nonminimum phase cancellation path, the cancellation path model can be factorized into a minimum phase term and a maximum phase term. The maximum phase term containing unstable zeros makes the inverse model unstable. To avoid the instability, we alter the inverse model of the maximum phase system into an anti-causal FIR one. An LMS predictor estimates the future samples of the noise, which are necessary for causality of both anti-causal FIR approximation for the stable inverse of the maximum phase system and time-delay existing in the cancellation path. The proposed method has a faster convergence behavior and a better transient response than the conventional filtered-x LMS algorithms with the same internal model control structure since a filtered reference signal is not required. We compare the proposed methods with the conventional methods through simulation studies.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권4호
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• pp.154-157
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• 2006

This paper is proposed hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design..of this algorithm based on fuzzy-neural network(FNN) controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• Journal of Advanced Marine Engineering and Technology
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• 제22권3호
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• pp.320-327
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• 1998

The purpose of this study is to design the intelligent speed control system for marine diesel engine by combining the Model Matching Method and the Nominal Model Tracking Method. Recently for the speed control of a diesel engine some methods using the advanced control techniques such as LQ control Fuzzy control or H$\infty$ control etc. have been reported. However most of speed controllers of a marine diesel engine developed are still using the PID control algorithm But the performance of a marine diesel engine depends highly on the parameter setting of the PID controllers. The authors proposed already a new method to tune efficiently the PID parameters by the Model Mathcing Method typically taking a marine diesel engine as a non-oscillatory second-order system. It was confirmed that the previously proposed method is superior to Ziegler & Nichols's method through simulations under the assumption that the parameters of a diesel engine are exactly known. But actually it is very difficult to find out the exact model of the diesel engine. Therefore when the model and the actual diesel engine are unmatched as an alternative to enhance the speed control characteristics this paper proposes a Model Refernce Adaptive Speed Control system of a diesel engine in which PID control system for the model of a diesel engine is adopted as the nominal model and a Fuzzy controller is adopted as the adaptive controller, And in the nominal model parameters of a diesel engine are adjusted using the Model Matching Method. it is confirmed that the proposed method gives better performance than the case of using only Model Matching Method through the analysis of the characteristics of indicial responses.

• 전력전자학회논문지
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• 제10권1호
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• pp.52-60
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• 2005

펄스 폭 변조 전압원 인버터에서는 전압 명령과 실제 전압 사이에 전압 차 또는 전압 왜곡이 존재한다. 이 전압 왜곡은 동작 온도, DC 링크 전압, 및 상전류 수준에 따라 달라진다. 또한 전압 왜곡은 전류 왜곡, 전동기 토크 맥동, 그리고 제어 성능에 영향을 미친다. 본 논문에서는 펄스 폭 변조 전압원 인버터의 전압 왜곡을 분석하고, 모델기준 적응 시스템(Model Reference Adaptive System)을 기반으로 하여 영구자석 동기 전동기의 파라미터 변화에 강인한 새로운 실시간 전압 왜곡 관측 기법을 제안한다. 그리고 제안된 전압 왜곡 관측 및 보상 기법에 대한 모의실험 및 실험을 통해서 그 효용성을 증명한다.

• 한국산업융합학회 논문집
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• 제2권2호
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• pp.91-102
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• 1999

The paper describes a robust adaptive control algorithm for induction motor drive without speed sensor at low speed range. The control algorithm use only current sensors in a space vector pulse width modulation within loop control with rotor speed estimation and voltage source inverter. On-line rotor speed estimation is based on utilizing parallel model reference adaptive control system. MRAC of the modified flux model for flux and rotor speed estimator uses dual-adaptation mechanism, ${\omega}_r$ and ${\omega}_e$ scheme. The estimated flux components in the model can be compensated from the effects of offset errors on pure integrals. It can be compensated to the parameter variations and torque fluctuation with speed estimation in less then 10 rad/sec. In a simulation, the proposed induction motor control algorithm without speed sensor at very low speed range are shown to operate very well in spite of variable rotor time constant and fluctuating load without change the controller parameters. The suggested control strategy and estimation method have been validated by simulation study, and it proposed the designed system for the implementation using TI320C31 DSP/ASIC controller.

• 한국멀티미디어학회논문지
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• 제2권1호
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• pp.30-37
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• 1999

광대역 능동소음제거는 수 백개의 적응필터 텝 수를 갖는다. 탭 수가 긴 적응필터는 많은 계산량이 요구된다. 본 논문에서는 적응 계수벡터가 서브밴드로 계산되는 M-채널 QMF 펼터뱅크를 이용한 능동소음제거 시스템올 제안한다. 분해 필터뱅크와 합성 펼터뱅크는 cosine-modulated pseudo QMF 펼터를 사용한다. 오차경로의 전달특성을 온라인 인식하기 위한 기준신호는 적응필터의 출력신호와 저주파 대역의 서브 밴드 출력신호와의 차신호를 사용한다. 따라서 본 논문의 능동소음제거기는 계산량이 적고, 수렴속도가 빠른 견실한 시스템이 되도록 제안한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.162.5-162
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• 2001

This paper presents a indirect vector control system for induction motors using an adaptive fuzzy logic(AFL) speed estimator. The proposed speed estimator is based on the MRAS(Mode Referece Adaptive System) scheme. In general, the MRAS speed estimation approaches are more simple than any other strategies. However, there are some difficulties in the scheme, which are strong sensitivity to the motor parameters variations and necessity to detune the estimator gains caused by different speed area. In this paper, the AFL speed estimator is proposed to solve the problems. The structure of the proposed AFL is very simple. The input of the AFL is the rotor flux error difference between reference and adjustable model, and the output is the estimated incremental rotor speed. Moreover, the back propagation algorithm is combined to adjust the parameters of the fuzzy logic to the most appropriate values during the operating the system. Finally, the validity of the ...