Abstract
Proportional-integral-derivative (PID) control schemes have been widely used in most industrial control systems. However, it is difficult to determine a suitable set of PID gains because most industrial systems have nonlinearity. On the other hands, the cerebellar model articulation controller (CMAC) classified as neural networks has been proposed, and design scheme of an intelligent PID controller has been proposed. However, the CMAC-PID controller has a problem that CMACs used as PID tuners must be trained in an online manner to get their optimal weights. In order to train CMACs in an offline manner, a combination of CMAC learning and a fictitious reference iterative tuning (FRIT) scheme, which is called CMAC-FRIT scheme, has been proposed in our previous research, and an effectiveness of the method has been evaluated only by simulations. FRIT is a scheme to determine control parameters of linear controllers by using a set of experimental data. According to the CMAC-FRIT scheme, a CMAC-PID tuner can be trained in an offline manner by using a set of operating data. In this research, the proposed CMAC-PID controller is implemented and applied to a magnetic levitation device.
Sign-in/Register to access full text options 
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.
