Abstract
Multidimensional sensors can deliver vast and rich information about the industrial processes operation. At industrial level, they are becoming widely available for supervising tasks. However, their use at control level is not very widespread, since there are no standard methodologies for including the information provided by this type of sensors into existing control systems. This paper describes the traditional approach to include multidimensional information into conventional control systems, and proposes a new structure based on pattern recognition. The latter makes use of artificial neural networks and finite state machines as a framework for designing the control system. The main characteristics and limitations of both approaches are illustrated by the image based control of an experimental fluidization bed.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
