• 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.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1143-1148
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• 2007

LRE(Liquid propellant Rocket Engine) is one of the important parts to control the motion of rocket. For operation of rocket in error boundary of the set-up trajectory, it is necessarily to control the thrust of LRE according to the required thrust profile and control the mixture ratio of propellants fed into combustor for the 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.

• Korean Chemical Engineering Research
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• v.44 no.5
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• pp.425-434
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• 2006

The operability and productivity of continuous processes, especially in petrochemical industries have made remarkable improvement during the past twenty years through advanced process control (APC) typified by model-based predictive control. On the other hand, APC have not been actively practiced in industrial batch processes typified by batch polymerization reactors. Perhaps the main cause for this has been the lack of reliable batch process APC techniques that can overcome the unique problems in industrial batch processes. Recently, some noteworthy progress is being made in this area. New high-performance batch process control techniques that can accommodate and also overcome the unique problems of industrial batch processes have been proposed on the basis of iterative learning control (ILC). In this review paper, recent advancement in the batch process APC techniques are presented, with a particular focus on the variations of the so called Q-ILC method, with the hope that they are widely practiced in different industrial batch processes and enhance their operations.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.303-306
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• 1996

We propose a robust tuning method of the quadratic criterion based iterative learning control(Q-ILC) algorithm for discrete-time linear batch system. First, we establish the frequency domain representation for batch systems. Next, a robust convergence condition is derived in the frequency domain. Based on this condition, we propose to optimize the weighting matrices such that the upper bound of the robustness measure is minimized. Through numerical simulation, it is shown that the designed learning filter restores robustness under significant model uncertainty.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.299-302
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• 1996

A control algorithm is proposed for nonlinear multi-input multi-output(MIMO) batch processes by combining quadratic iterative learning control(Q-ILC) with model predictive control(MPC). Both controls are designed based on output feedback and Kalman filter is incorporated for state estimation. Novelty of the proposed algorithm lies in the facts that, unlike feedback-only control, unknown sustained disturbances which are repeated over batches can be completely rejected and asymptotically perfect tracking is possible for zero random disturbance case even with uncertain process model.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.57-60
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• 1999

An optimal control technique designed for simultaneous tracking and quality control for batch processes. The proposed technique is designed by transforming quadratic-criterion based iterative learning control(Q-ILC) into linear quadratic control problem. For real-time quality inferential control, the quality is modeled by linear combination of control input around target qualify and then the relationship between quality and control input can be transformed into time-varying linear state space model. With this state space model, the real-time quality inferential control can be incorporated to LQ control Problem. As a consequence, both the quality variable as well as other controlled variables can progressively reduce their control error as the batch number increases while rejecting real-time disturbances, and finally reach the best achievable states dictated by a quadratic criterion even in case that there is significant model error Also the computational burden is much reduced since the most computation is calculated in off-line. The Proposed control technique is applied to a semi-batch reactor model where series-parallelreactions take place.