• Title/Summary/Keyword: unknown-input

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Slope Displacement Data Estimation using Principal Component Analysis (주성분 분석기법을 적용한 사면 계측데이터 평가)

  • Jung, Soo-Jung;Kim, Yong-Soo;Ahn, Sang-Ro
    • Proceedings of the Korean Geotechical Society Conference
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    • 2010.03a
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    • pp.1358-1365
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    • 2010
  • Estimating condition of slope is difficult because of nonlinear time dependency and seasonal effects, which affect the displacements. Displacements and displacement patterns of landslides are highly variable in time and space, and a unique approach cannot be defined to model landslide movements. Characteristics of movements are obtained by using a statistical method called Principal Component Analysis(PCA). The PCA is a non-parametric method to separate unknown, statistically uncorrelated source processes from observed mixed processes. In the non-parametric approaches, no physical assumptions of target systems are required. Instead, since the "best" mathematical relationship is estimated for given data sets of the input and output measured from target systems. As a consequence, non-parametric approaches are advantageous in modeling systems whose geomechanical properties are unknown or difficult to be measured. Non-parametric approaches are consequently more flexible in modeling than parametric approaches. This method is expected to be a useful tool for the slope management of and alarm systems.

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Indirect Adaptive Fuzzy Observer Design

  • Yang, Jong-Kun;Hyun, Chang-Ho;Kim, Jae-Hun;Kim, Eun-Tai;Park, Mi-Gnon
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2004.10a
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    • pp.192-196
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    • 2004
  • This paper proposes an alternative observation scheme, T-S fuzzy model based indirect adaptive fuzzy observer. Nonlinear systems are represented by fuzzy models since fuzzy logic systems are universal approximators. In order to estimate the unmeasurable states of a given nonlinear system, T-S fuzzy modeling method is applied to get the dynamics of an observation system. T-S fuzzy system uses the linear combination of the input state variables and the modeling applications of them to various kinds of nonlinear systems can be found. The adaptive fuzzy scheme estimates the parameters comprising the fuzzy model representing the observation system. The proposed indirect adaptive fuzzy observer based on T-S fuzzy model can cope with not only unknown states but also unknown parameters. In the process of deriving adaptive law, the Lyapunov theory and Lipchitz condition are used. To show the performance of the proposed observation method, it is applied to an inverted pendulum on a cart.

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Indirect Adaptive Fuzzy Observer Design

  • Yang, Jong-Kun;Hyun, Chang-Ho;Kim, Jae-Hun;Kim, Eun-Tai;Park, Mignon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.14 no.7
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    • pp.927-933
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    • 2004
  • This paper proposes an alternative observation scheme, T-S fuzzy model based indirect adaptive fuzzy observer. Nonlinear systems are represented by fuzzy models since fuzzy logic systems are universal approximators. In order to estimate the unmeasurable states of a given nonlinear system, T-S fuzzy modeling method is applied to get the dynamics of an observation system. T-S fuzzy system uses the linear combination of the input state variables and the modeling applications of them to various kinds of nonlinear systems can be found. The adaptive fuzzy scheme estimates the parameters comprising the fuzzy model representing the observation system. The proposed indirect adaptive fuzzy observer based on T-S fuzzy model can cope with not only unknown states but also unknown parameters. In the process of deriving adaptive law, the Lyapunov theory and Lipchitz condition are used. To show the performance of the proposed observation method, it is applied to an inverted pendulum on a cart.

A Learning Fuzzy Logic Controller Using Neural Networks (신경회로망을 이용한 학습퍼지논리제어기)

  • Kim, B.S.;Ryu, K.B.;Min, S.S.;Lee, K.C.;Kim, C.E.;Cho, K.B.
    • Proceedings of the KIEE Conference
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    • 1992.07a
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    • pp.225-230
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    • 1992
  • In this paper, a new learning fuzzy logic controller(LFLC) is presented. The proposed controller is composed of the main control part and the learning part. The main control part is a fuzzy logic controller(FLC) based on linguistic rules and fuzzy inference. For the learning part, artificial neural network(ANN) is added to FLC so that the controller may adapt to unknown plant and environment. According to the output values of the ANN part, which is learned using error back-propagation algorithm, scale factors of the FLC part are determined. These scale factors transfer the range of values of input variables into corresponding universe of discourse in the FLC part in order to achieve good performance. The effectiveness of the proposed control strategy has been demonstrated through simulations involving the control of an unknown robot manipulator with load disturbance.

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Robust adaptive control for unknown uncertain systems (미지의 불확실한 시스템에 대한 강인한 적응 제어)

  • 김진환;이정휴;정사철;함철주;함운철
    • 제어로봇시스템학회:학술대회논문집
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    • 1992.10a
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    • pp.760-765
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    • 1992
  • In this paper, robust adaptive control algorithms which can be applied to unknown uncertain systems are suggested. Transform matrix for dividing states into "uncontrolled" states and "controlled" states and general searching procedure for the transform matrix which assign arbitrary n-1 eigen values for the uncontrolled subsystem of n-th order single-input single-output systems is also studied and utilized for the design of new-type controllers. We derived new-type control laws by using adaptive control theory and variable structure system and its stability is proved by using Lyapunov stability theory. From computer simulation results, we can see that the proposed adaptive control algorithm is robust and stable.s robust and stable.

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Identification of Discrete-Time Low-Order Model from Pulse Response (펄스응답에 의한 저차 이산시간 모델의 식별)

  • Hwang, Jiho;Cha, Seungpyo;Kim, Young Chol
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.67 no.8
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    • pp.1062-1070
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    • 2018
  • This paper presents a simple identification method for discrete-time low-order model of unknown delay process from pulse response. The key idea is to find the parameters of the model such that the first N moments of the unknown process and the model are equal. We first show that the k-th moment of a process can be determined by the moments of the input and output. The parameters and delay are estimated separately. It is shown that for a given delay, the parameters of the low-order model can be determined by solving linear equations in a matrix form. Delay of the model is estimated such that the integral of the absolute errors (IAE) of the candidate models with possible delays minimizes. The illustrative example shows that the proposed method can directly identify low-order models without order reduction process from a single pulse response.

T-S Fuzzy Model Based Indirect Adaptive Fuzzy Observer Design

  • Hyun Chang-Ho;Kim You-Keun;Kim Euntai;Park Mignon
    • Proceedings of the IEEK Conference
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    • summer
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    • pp.348-353
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    • 2004
  • This paper proposes an alternative observation scheme, T-S fuzzy model based indirect adaptive fuzzy observer. Nonlinear systems arc represented by fuzzy models since fuzzy logic systems arc universal approximators. In order to estimate the unmeasurable states of a given nonlinear system, T-S fuzzy modeling method is applied to get the dynamics of an observation system. T-S fuzzy system uses the linear combination of the input state variables and the modeling applications of them to various kinds of nonlinear systems can be found. The adaptive fuzzy scheme estimates the parameters comprising the fuzzy model representing the observation system. The proposed indirect adaptive fuzzy observer based on T-S fuzzy model can cope with not only unknown states but also unknown parameters. In the process of deriving adaptive law, the Lyapunov theory and Lipchitz condition are used. To show the performance of the proposed observation method, it is applied to an inverted pendulum on a cart.

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A PID learning controller for DC motors (DC 전동기를 위한 PID 학습제어기)

  • Baek, Seung-Min;Kuc, Tae-Yong
    • Journal of Institute of Control, Robotics and Systems
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    • v.3 no.6
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    • pp.555-562
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    • 1997
  • With only the classical PID controller applied to control of a DC motor, good (target) performance characteristic of the controller can be obtained if all the model parameters of DC motor and operating conditions such as external load torque, disturbance, etc. are known exactly. However, in case when some of system parameters or operating conditions are uncertain or unknown, the fixed PID controller does not guarantee good performance, which is assumed with precisely known system parameters and operating conditions. In view of this and the robustness enhancement of DC motor control system, we propose a PID learning controller which consists of a set of learning rules for PID gain tuning and learning of an auxiliary input. The proposed PID learning controller is shown to drive the state of uncertain DC motor system with unknown system parameters and external load torque to the desired one world wide asymptotically. Computer simulation and experimental results are given to demonstrate the effectiveness of the proposed PID learning controller, thereby showing its superiority to the conventional fixed PID controller.

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MCMC Approach for Parameter Estimation in the Structural Analysis and Prognosis

  • An, Da-Wn;Gang, Jin-Hyuk;Choi, Joo-Ho
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.23 no.6
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    • pp.641-649
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    • 2010
  • Estimation of uncertain parameters is required in many engineering problems which involve probabilistic structural analysis as well as prognosis of existing structures. In this case, Bayesian framework is often employed, which is to represent the uncertainty of parameters in terms of probability distributions conditional on the provided data. The resulting form of distribution, however, is not amenable to the practical application due to its complex nature making the standard probability functions useless. In this study, Markov chain Monte Carlo (MCMC) method is proposed to overcome this difficulty, which is a modern computational technique for the efficient and straightforward estimation of parameters. Three case studies that implement the estimation are presented to illustrate the concept. The first one is an inverse estimation, in which the unknown input parameters are inversely estimated based on a finite number of measured response data. The next one is a metamodel uncertainty problem that arises when the original response function is approximated by a metamodel using a finite set of response values. The last one is a prognostics problem, in which the unknown parameters of the degradation model are estimated based on the monitored data.

Design of Adaptive Controller using Switching Mode with Fuzzy inference and its application for industry Automation Facility (퍼지추론의 스위칭 특성을 이용한 적응제어기 설계 및 산업용 자동화 설비에의 응용)

  • 이형찬
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.13 no.1
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    • pp.60-68
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    • 1999
  • This paper deals with the tracking control problem of industrial robotic manipulators with unknown or changing dynamics. The proposed method makes use of multiple moodels and switching mechanism by fuzzy inference of the manipulator in an indirect adaptive controller architecture. The models used for the indmtification of the manipliator are identical, except for the initial estimates of the unknown inertial pararmeters of the manipulator and its load. The torque input that is applied to the joint actuators is determined at every instant by the identification model that best approximates the robot dynamics. Simulation results are also included to dermnstrate the improvement in the tracking perfermance when the proposed method is used.s used.

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