• Journal of the Korean Society for Precision Engineering
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• v.26 no.5
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• pp.120-127
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• 2009

An electric furnace, inside which desired temperatures are kept constant by generating heat, is known to be a difficult system to control and model exactly because system parameters and response delay time vary as the temperature and position are changed. In this study the heating system of ceramic drying furnaces with time-varying parameters is mathematically modeled as a second order system and control parameters are estimated by using a RIV (Recursive Instrumental-Variable) method. A modified bang-bang control with magnitude tuning is proposed in the time optimal temperature control of ceramic drying electric furnaces and its performance is experimentally verified. It is proven that temperature tracking of adaptive time optimal control using a second order model is more stable than the GPCEW (Generalized Predictive Control with Exponential Weight) and rapidly settles down by pre-estimation of the system parameters.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.44-47
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• 1996

Bang-bang control law provides the optimal solution for a minimum-time control problem, but ignores the intermediate path except for the initial and final points. In this paper, a near minimum-time suboptimal fuzzy logic controller is introduced that can control the intermediate path. A dynamic model for a system is established using the average dynamics method of linearization. System model is continuously updated over the control time periods. This makes it suitable for high speed or variable payload applications. Bang-bang control theory is modified and used to derive the preliminary control law. A fuzzy logic algorithm is then applied to adjust and find the best solution. The solution will provide the suboptimal minimum-time control law which can avoid obstacles in the workspace.