| Journal: | Journal of the Brazilian Society of Mechanical Sciences and Engineering |
| Database: | PERIÓDICA |
| System number: | 000312190 |
| ISSN: | 1678-5878 |
| Authors: | Gálvez, J.M1 Zarate, Luis E2 Helman, H3 |
| Institutions: | 1Universidade Federal de Minas Gerais, Departamento de Engenharia Mecanica, Belo Horizonte, Minas Gerais. Brasil 2Pontificia Universidade Catolica de Minas Gerais, Departamento de Ciencia da Computacao, Belo Horizonte, Minas Gerais. Brasil 3Universidade Federal de Minas Gerais, Departamento de Engenharia Metalurgica, Belo Horizonte, Minas Gerais. Brasil |
| Year: | 2003 |
| Season: | Ene-Mar |
| Volumen: | 25 |
| Number: | 1 |
| Pages: | 85-89 |
| Country: | Brasil |
| Language: | Inglés |
| Document type: | Artículo |
| Approach: | Aplicado, descriptivo |
| English abstract | A capital issue in roll-gap control for rolling mill plants is the difficulty to measure the output thickness without including time delays in the control loop. Time delays are a consequence of the possible locations for the output thickness sensor, which usually is located some distance away from the roll gap. In this work, a new model-based predictive control law is proposed. The new scheme is a neural network based predictive control structure which is applied to roll-gap control with outstanding results. It is shown that the neural network based predictive control permits to overcome the existing time delays in the system dynamics. The proposed scheme implements a virtual thickness sensor, which releases an accurate estimate of the actual output thickness. It is shown that the dynamic response of the rolling mill system can be substantially improved by using the proposed controller. Simulation results are presented to illustrate the controller performance |
| Disciplines: | Ingeniería |
| Keyword: | Equipo y maquinaria, Ingeniería mecánica, Molinos, Control predictivo, Redes neuronales, Control automático, Industria siderúrgica |
| Keyword: | Engineering, Equipment and machinery, Mechanical engineering, Mills, Predictive control, Neural networks, Automatic control, Steel industry |
| Full text: | Texto completo (Ver HTML) |
