| dc.contributor.author | Das, S | |
| dc.contributor.author | Saha, S | |
| dc.contributor.author | Mukherjee, A | |
| dc.contributor.author | Pan, I | |
| dc.contributor.author | Gupta, A | |
| dc.date.accessioned | 2018-01-18T14:58:32Z | |
| dc.date.issued | 2011-08-12 | |
| dc.description.abstract | Gain and order scheduling of fractional order (FO) PI{\lambda}D{\mu} controllers are studied in this paper considering four different classes of higher order processes. The mapping between the optimum PID/FOPID controller parameters and the reduced order process models are done using Radial Basis Function (RBF) type Artificial Neural Network (ANN). Simulation studies have been done to show the effectiveness of the RBFNN for online scheduling of such controllers with random change in set-point and process parameters. | en_GB |
| dc.description.sponsorship | This work has been supported by
the Department of Science & Technology (DST), Govt. of India under the
PURSE programme. | en_GB |
| dc.identifier.citation | 2011 International Conference on Process Automation, Control and Computing (PACC), Coimbatore, India, 20-22 July 2011, article 5979047 | en_GB |
| dc.identifier.doi | 10.1109/PACC.2011.5979047 | |
| dc.identifier.uri | http://hdl.handle.net/10871/31073 | |
| dc.language.iso | en | en_GB |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_GB |
| dc.rights | © 2011 IEEE | en_GB |
| dc.subject | Process control | en_GB |
| dc.subject | Tuning | en_GB |
| dc.subject | Artificial neural networks | en_GB |
| dc.subject | Neurons | en_GB |
| dc.subject | Switches | en_GB |
| dc.subject | Job shop scheduling | en_GB |
| dc.title | Adaptive Gain and Order Scheduling of Optimal Fractional Order PIλDμ Controllers with Radial Basis Function Neural-Network | en_GB |
| dc.type | Conference paper | en_GB |
| dc.date.available | 2018-01-18T14:58:32Z | |
| dc.description | This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record. | en_GB |
