• Title/Summary/Keyword: Iterative Learning Control

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Thrust and Mixtrue Control of Liquid Propellant Rocket Engine using Q-ILC (Q-ILC를 이용한 액체추진제로켓엔진의 추력 및 혼합비 제어)

  • Jung, Young-Suk;Lim, Seok-Hee;Cho, Kie-Joo;Oh, Seung-Hyub
    • Proceedings of the Korean Society of Propulsion Engineers Conference
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    • 2006.11a
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    • pp.139-145
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    • 2006
  • LRE(Liquid propellant Rocket Engine) is one of the important parts to control the trajectory and dynamics of rocket. The purpose of control of LRE is to control the thrust according to requiredthrust profile and control the mixture ratio of propellants fed into gas generator and combustor for constant mixture ratio. It is not easy to control thrust and mixture ratio of propellants since there are co-interferences among the components of LRE. In this study, the dynamic model of LRE was constructed and the dynamic characteristics were analyzed with control system as PID control and PID+Q-ILC(Iterative Learning Control with Quadratic Criterion) control. From the analysis, it could be observed that PID+Q-ILC control logic is more useful than standard PID control system for control of LRE.

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Adaptive Feedrate Neuro-Control for High Precision and High Speed Machining (고정밀 고속가공을 위한 신경망 이송속도 적응제어)

  • Lee, Seung-Soo;Ha, Soo-Young;Jeon, Gi-Joon
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.35S no.9
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    • pp.35-42
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    • 1998
  • Finding a technique to achieve high machining precision and high productivity is an important issue for CNC machining. One of the solutions to meet better performance of machining is feedrate control. In this paper we present an adaptive feedrate neuro-control method for high precision and high speed machining. The adaptive neuro-control architecture consists of a neural network identifier(NNI) and an iterative learning control algorithm with inversion of the NNI. The NNI is an identifier for the nonlinear characteristics of feedrate and contour error, which is utilized in iterative learning for adaptive feedrate control with specified contour error tolerance. The proposed neuro-control method has been successfully evaluated for machining circular, corner and involute contours by computer simulations.

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