• Proceedings of the KIEE Conference
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• 1988.11a
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• pp.56-59
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• 1988

The main purpose of Set-up control of hot strip mill plant is to obtain the most regular thickness. Then the learning or adaptive computer control in hot strip rolling mill has been developed. But it is very difficult to keep the inter-stands load distribution ratio uniform; so that the deviation of strip flatness is not avoidable. This leads to the degradation of quality of the products. In this report, an improved method base on the steepest descent method including the computation of optimum step size. This method is applied to the off-line simulation. In consequence, the better balances of inter-stands load distribution is achieved in addition to improvements of output thickness of hot strip mill in POSCO.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1539-1544
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• 2005

The gauge control of the fishing mill is very important because more and more accurately sized hot rolled coils are demanded by customers recently. Because the mill constant and the plasticity coefficient vary with the specifications of the mill, the classification of steel, the strip width, the strip thickness and the slab temperature, the variation of these parameters should be considered in the automatic gauge control system(AGC). Generally, the AGC gain is used to minimize the effect of the uncertain parameters. In a practical field, operators set the AGC gain as a constant value calculated by FSU (Finishing-mill Set-Up model) and it is not changed during the operating time. In this paper, the thickness data signals that occupy different frequency bands are respectively extracted by adaptive filters and then the main cause of the thickness variation is analyzed. Additionally, the AGC gain is adaptively tuned to reduce this variation using the online tuning model. Especially ANFIS(Adaptive-Neuro-based Fuzzy Interface System) which unifies both fuzzy logics and neural networks, is used for this gain adjustment system because fuzzy logics use the professionals' experiences about the uncertainty and the nonlinearity of the system. Simulation is performed by using POSCO's data and the results show that proposed on-line gain adjustment algorithm has a good performance.