• Title/Summary/Keyword: control parameter

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Robust Saturation Controller for the Stable LTI System with Structured Real Parameter Uncertainties (구조적 파라미터 불확실성을 갖는 안정한 선형계에 대한 강인 포화 제어기)

  • Lim Chae-Wook;Park Young-Jin;Moon Seok-Jun;Park Youn-Sik
    • Journal of Institute of Control, Robotics and Systems
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    • v.12 no.6
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    • pp.517-523
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    • 2006
  • This paper is focused on a robust saturation controller for the stable linear time-invariant (LTI) system involving both actuator's saturation and structured real parameter uncertainties. Based on affine quadratic stability and multi-convexity concept, a robust saturation controller is newly proposed and the linear matrix inequality (LMI)-based sufficient existence conditions for this controller are presented. The controller suggested in this paper can analytically prescribe the lower and upper bounds of parameter uncertainties, and guarantee the closed-loop robust stability of the system in the presence of actuator's saturation. Through numerical simulations, it is confirmed that the proposed robust saturation controller is robustly stable with respect to parameter uncertainties over the prescribed range defined by the lower and upper bounds.

Neural Network Parameter Estimation of IPMSM Drive using AFLC (AFLC를 이용한 IPMSM 드라이브의 NN 파라미터 추정)

  • Ko, Jae-Sub;Choi, Jung-Sik;Chung, Dong-Hwa
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.60 no.2
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    • pp.293-300
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    • 2011
  • A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance and adaptive fuzzy learning contrroller(AFLC) for speed control in IPMSM Drives. AFLC is chaged fuzzy rule base by rule base modifier for robust control of IPMSM. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator and AFLC is confirmed by comparing to conventional algorithm.

Precision Speed Control of PMSM Using Neural Network Disturbance observer and Parameter compensation (신경망 외란관측기와 파라미터 보상기를 이용한 PMSM의 속도제어)

  • Ko Jong-Sun;Lee Yong-Jae;Kim Kyu-Gyeom
    • Proceedings of the KIPE Conference
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    • 2001.07a
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    • pp.389-392
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    • 2001
  • This paper presents neural load disturbance observer that used to deadbeat load torque observer and regulation of the compensation gain by parameter estimator. As a result, the response of PMSM follows that of the nominal plant. The load torque compensation method is compose of a neural deadbeat observer. To reduce of the noise effect, the post-filter, which is implemented by MA process, is proposed. The parameter compensator with RLSM (recursive least square method) parameter estimator is suggested to increase the performance of the load torque observer and main controller. The proposed estimator is combined with a high performance neural torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation, are shown in this paper.

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A Study on the Real-Time Parameter Estimation of DURUMI-II for Control Surface Fault Using Flight Test Data (Longitudinal Motion)

  • Park, Wook-Je;Kim, Eung-Tai;Song, Yong-Kyu;Ko, Bong-Jin
    • International Journal of Control, Automation, and Systems
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    • v.5 no.4
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    • pp.410-418
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    • 2007
  • For the purpose of fault detection of the primary control surface, real-time estimation of the longitudinal stability and control derivatives of the DURUMI-II using the flight data is considered in this paper. The DURUM-II, a research UAV developed by KARI, is designed to have split control surfaces for the redundancy and to guarantee safety during the fault mode flight test. For fault mode analysis, the right elevator was deliberately fixed to the specified deflection condition. This study also mentions how to implement the multi-step control input efficiently, and how to switch between the normal mode and the fault mode during the flight test. As a realtime parameter estimation technique, Fourier transform regression method was used and the estimated data was compared with the results of the analytical method and the other available method. The aerodynamic derivatives estimated from the normal mode flight data and the fault mode data are compared and the possibility to detect the elevator fault by monitoring the control derivative estimated in real time by the computer onboard was discussed.

Development of a self-Tuning fuzzy controller for the speed control of an induction motor (유도전동기 속도 제어를 위한 뉴로 자기 동조 퍼지 제어기 개발)

  • Kim, Do-Han;Han, Jin-Wook;Lee, Chang-Goo
    • Proceedings of the KIEE Conference
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    • 2003.04a
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    • pp.248-252
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    • 2003
  • This paper has a control method proposed for the effective self-tuning fuzzy speed control based on neural network of the induction motor indirect vector control. The vector control of an induction motor provides the decoupled control of the rotor flux magnitude and the torque producing current to performance is desirable. But, the drive performance often degrades for the machine parameter variations and its condition give rise to coupling of flux and torque current. The fuzzy speed control of an induction motor has the robustness about machine parameter variations compared with conventional PID speed control in a way. That proved to be some waf from the true. The purpose of this paper is to improve the adaptation by offering self-turning function to fuzzy speed controller. In this paper, the adaptive mechanism of fuzzy speed control in used ANN(Artificial Neural Network) technique is applied in an IFO induction machine drive, such that the machine can follow a reference model (an ideal field oriented machine) to achieve desired speed. In this paper proved the self-turning method of fuzzy controller has the robustness about parameter variation and the wide range of adaptation by simulation.

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The Performance Improvement of an Efficient Usage Parameter Control Algorithm in ATM Networks (ATM망에서의 효율적인 UPC 알고리즘의 성능 개선)

  • Park, Sung-Kon
    • The Transactions of the Korea Information Processing Society
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    • v.4 no.12
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    • pp.3150-3158
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    • 1997
  • In the ATM networks, there are two method in traffic control as schemes to improve the quality of service; one is the reactive control after congestion and the other is the preventive control before congestion. The preventive control include the CAC(Connection Admission Control), the UPC(Usage Parameter Control), the NPC(Network Parameter Control) and the PC(Priority co ntrol). In this paper, we propose an efficient UPC algorithm that has a complex structure using the Jumping window algorithm within the Leaky Bucket algorithm. The proposed algorithm controls peak hit rate by the Leaky Bucket algorithm, then it does the traffic control to evaluate by the Jumping Window whether violates mean bit rate or not. As we assume On/Off traffic source model, our simulation results showed cell loss rate less than the pre-existential Leaky Bucket algorithm method, and it could decrease the demanded Bucket size.

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On-Line Parameter Estimation Scheme for Uncertain Takagi-Sugeno Fuzzy Models

  • Cho, Young-Wan;Park, Chang-Woo
    • International Journal of Control, Automation, and Systems
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    • v.2 no.1
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    • pp.68-75
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    • 2004
  • In this paper, an estimator with an appropriate adaptive law for updating parameters is designed and analyzed based on the Lyapunov theory. The adaptive law is designed so that the estimation model follows the parameterized plant model. Using the proposed estimator, the parameters of the T-S fuzzy model can be estimated by observing the behavior of the system and it can be a basis for indirect adaptive fuzzy control.

Descriptor Type Linear Parameter Dependent System Modeling And Control of Lagrange Dynamics

  • Kang, Jin-Shik
    • 제어로봇시스템학회:학술대회논문집
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    • 2003.10a
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    • pp.444-448
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    • 2003
  • In this paper, the Lagrange dynamics is studied. A state space representation of Lagrange dynamics and control algorithm based on the state feedback pole placement are presented. The state space model presented is descriptor type linear parameter dependent system. It is shown that the control algorithms based on the linear system theory can be applicable to the state space representation of Lagrange dynamics. To show that the linear system theory can be applicable to the state space representation of Lagrange dynamics, the LMI based regional pole-placement design algorithm is developed and present two examples.

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Improving Current Control Performance by Parameter Estimation of PWM Converter (PWM 컨버터의 상수추정을 통한 전류제어 성능 개선)

  • 이진우
    • Proceedings of the KIPE Conference
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    • 2000.07a
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    • pp.286-289
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    • 2000
  • from the viewpoint of model-based current control it is indispensable to use the accurate system parameters for the high control performance. This paper adopts the Least-Squares algorithm as a parameter estimation scheme because it has the fast convergence rate and the low sensitivity to noises. in case of the PI current controller with high gains the simulation results show that the adopted estimation scheme can be successfully applied to PWM converters and also show that the control performance can be improved by using the estimated parameters.

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Robust Model Predictive Control Using Polytopic Description of Input Constraints

  • Lee, Sang-Moon
    • Journal of Electrical Engineering and Technology
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    • v.4 no.4
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    • pp.566-569
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    • 2009
  • In this paper, we propose a less conservative a linear matrix inequality (LMI) condition for the constrained robust model predictive control of systems with input constraints and polytopic uncertainty. Systems with input constraints are represented as perturbed systems with sector bounded conditions. For the infinite horizon control, closed-loop stability conditions are obtained by using a parameter dependent Lyapunov function. The effectiveness of the proposed method is shown by an example.