• 제어로봇시스템학회논문지
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• 제17권11호
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• pp.1138-1144
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• 2011

This article deals with the problem of fault detection of a spacecraft's actuators. The authors introduce an extended unknown input observer for nonlinear systems. This is an extended form of unknown input observers which are used for linear systems. Since faults are not available, those are considered as unknown inputs. Unknown input observers can estimate states without full information of inputs if some conditions are satisfied. The authors suggest a continuous-time extended UIO (eUIO) and prove the convergence of state estimation errors. Since the dynamic equation of a spacecraft is nonlinear, an extended UIO can be applied. Three eUIOs are designed to monitor three reaction wheels. The moving averages of each eUIO's residuals are selected for decision logic. The proposed method is verified by numerical simulations.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 A
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• pp.3-5
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• 1996

A sensorless drive of induction motor has several advantage: low cost and availability in a harsh environment. Most of sensorless control schemes are based on the direct estimation of rotor speed from state observer. This study proposes a new sensorless control scheme. The proposed scheme is based on a reference model control which the error between the model and plant outputs decays to zero as time proceeds. The actuating signal is calculated from the fuzzy controller which increases the system stability and robustness. The simulation results indicate a good dynamic performance.

• International Journal of Aeronautical and Space Sciences
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• 제18권4호
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• pp.757-766
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• 2017

For implementing the proportional navigation guidance law on passive homing missiles equipped with strapdown imaging infrared seekers, the line-of-sight angles and rates with respect to the inertial frame should be estimated by carefully handling the parasitic instability effect due to the seeker's latency. By introducing a new state vector representation along with the Pade approximation for compensating the time-delay of the seeker, this paper proposes a new guidance filter structure, stochastic dynamic models and measurement equations, in three-dimensional homing problem. Then, it derives the line-of-sight angle and rate estimator in general two-dimensional engagement by applying the extended Kalman filter to the proposed structure. The estimation performance and the characteristics of the proposed filter were evaluated via a series of numerical experiments.

• KSBB Journal
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• 제6권1호
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• pp.15-25
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• 1991

The optimal glucose concentration for the high-density culture of recombinant yeasts was obtained using dynamic simulation. An adaptive and predictive algoritilm complimented by the rule base was proposed for the control of the fed-batch fermentation process. The measurement of process variables has relatively long sampling period and relatively long time delay characteristics. As one of the solution on these problems, prediction techniques and rule bases were added to a classical recursive identification and control algorithm. Rule bases were used in the determination of control input considering the difference between the predicted value and the measured value. A mathelnatical model was used in the estimation and interpretation of the changes of state variables and parameters. Better performances were obtained by employing the control algorithm proposed in the present study compared to the conventional adaptive control method.

• 전자공학회논문지B
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• 제30B권11호
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• pp.84-92
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• 1993

Calibration of Engine models is a painstaking process but very important for successful application to automotive industry problems. A combined heuristic and machine learning approach has therefore been adopted to improve the efficiency of model calibration. We developed an intelligent calibration program called ICALIB. It has been used on a daily basis for engine model applications, and has reduced the time required for model calibrations from many hours to a few minutes on average. In this paper, we describe the heuristic control strategies employed in ICALIB such as a hill-climbing search based on a state distance estimation function, incremental problem solution refinement by using a dynamic tolerance window, and calibration target parameter ordering for guiding the search. In addition, we present the application of amachine learning program called GID3*for automatic acquisition of heuristic rules for ordering target parameters.

• Journal of Power Electronics
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• 제9권4호
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• pp.582-592
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• 2009

This paper proposes a new speed sensorless direct torque and flux controlled interior permanent magnet synchronous motor (IPMSM) drive. Closed-loop control of both the torque and stator flux linkage are achieved by using two proportional-integral (PI) controllers. The reference voltage vectors are generated by a SVM unit. The drive uses an adaptive sliding mode observer for joint stator flux and rotor speed estimation. Global asymptotic stability of the observer is achieved via Lyapunov analysis. At low speeds, the observer is combined with the high frequency signal injection technique for stable operation down to standstill. Hence, the sensorless drive is capable of exhibiting high dynamic and steady-state performances over a wide speed range. The operating range of the direct torque and flux controlled (DTFC) drive is extended into the high speed region by incorporating field weakening. Experimental results confirm the effectiveness of the proposed method.

• 전산구조공학
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• 제2권2호
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• pp.113-119
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• 1989

기존구조물의 안전성검토와 관련하여 구조물의 강성에 대한 현재상태를 추정하는 방법에 대하여 연구하였다. 구조계의 동특성계수들을 입력지진기록과 이에 의한 거동응답의 측정기록을 바탕으로 추정하였는데, 이때에 Extened Kalman Filtering기법을 사용하였다. 이를 위하여 상태방정식을 통상의 시간영역에서 구성하는 방법과 주파수영역에서 구성하는 방법을 제시하였다. 이 기법들의 검증은 시뮬레이션기법에 의한 방법과 함께 Shaking Table에 설치된 소형구조모형을 이용한 실험적 방법으로 수행되었다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집B
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• pp.105-109
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• 2001

This paper discuss the real-time self-tuning learning control based on evolutionary computation, which proves its the superiority in the finding of the optimal solution at the off-line learning method. The individuals are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because the learning process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes.

• 대한기계학회논문집A
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• 제27권9호
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• pp.1463-1468
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• 2003

This paper presented the real-time self-tuning learning control based on evolutionary computation, which proves its superiority in finding of the optimal solution at the off-line learning method. The individuals of the populations are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations is proposed. It is possible to control the control object slightly varied as time changes. As the state value of the control object is generated, evolutionary strategy is applied each sampling time because the learning process of an estimation, selection, mutation is done in real-time. These algorithms can be applied; the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2615-2617
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• 2003

The Kalman filter is a recursive Linear Estimator for the linear dynamic systems(LDS) affected by two different noises called process noise and measurement noise both of which are uncorrelated white. The Expectation Maximization(EM) algorithm is employed in this paper as a preprocessor to reinforce the effectiveness of Kalman estimator. Particularly, we focus on the relation between Kalman filter and EM algorithm in the LDS. In this paper, we propose a new algorithm to improve the performance on the parameter estimation via EM algorithm, which improves the overall process of Kalman filtering. Since Kalman filter algorithm not only needs the system parameters but also is very sensitive the initial state conditions, the initial conditions decided through EM turns out to be very effective. In experiments, the computer simulation results ate provided to demonstrate the superiority of the proposed algorithm.