Improving lookup table control of a hot coil strip process with online retrainable RBF network

This paper presents an online retrainable radial basis function (RBF) network to control the coiling temperature for a hot coil strip at the Pohang Iron and Steel Company, Pohang, Korea. The proposed RBF network is designed to replace the conventional rule-based lookup table, the output of which is a heat transmission coefficient in the temperature control system. In order to make the controller more adaptable to the changing environments in the steelmaking process, specific interconnection weights were additionally devised for the hidden-to-output weights of a conventional RBF network. These weights were locally adjustable to reduce the immediate temperature error of a coil strip, while the global information of the RBF network trained with offline past data was largely unaltered. As a result, the proposed RBF network substantially alleviated the effect of catastrophic interference-completely forgetting old information in the presence of new inputs. Moreover, a rejection network was incorporated within the proposed control scheme to ensure reliable operation in the actual process. Results applied to the real steelmaking process indicated an improvement of 2.2% in control performance compared to conventional methods