Abstract
This paper describes two methodologies for implementation of Hammerstein model by using different input-output representations into model predictive control schemes. The model nonlinearity is easily approximated using a simple Takagi-Sugeno inference, while the linear parts are flexibly introduced. As optimization procedures for predictive control are used a standard gradient optimization method and an implementation of Hildreth Quadratic Programming. A comparison between the proposed control strategies is made by simulation experiments for control of nonlinear lyophilization plant.
| Original language | English |
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| Title of host publication | 2012 6th IEEE International Conference Intelligent Systems |
| Publisher | IEEE Institute of Electrical and Electronic Engineers |
| Pages | 316-321 |
| ISBN (Electronic) | 978-1-4673-2278-2, 978-1-4673-2277-5 |
| ISBN (Print) | 978-1-4673-2276-8 |
| DOIs | |
| Publication status | Published - 28 Nov 2012 |
| MoE publication type | A4 Article in a conference publication |
| Event | 2012 6th IEEE International Conference Intelligent Systems, IS 2012 - Sofia, Bulgaria Duration: 6 Sept 2012 → 8 Sept 2012 |
Conference
| Conference | 2012 6th IEEE International Conference Intelligent Systems, IS 2012 |
|---|---|
| Country/Territory | Bulgaria |
| City | Sofia |
| Period | 6/09/12 → 8/09/12 |
Keywords
- fuzzy-neural models
- gradient descent
- Hildreth quadratic programming
- lyophilization
- optimization
- predictive control