Nonlinear control with linearized models and neural networks
A nonlinear control strategy involving a geometric feedback controller and adaptive approximation of the plant is presented. The plant is approximated by a linearized model and a neural network which approximates the higher order error terms. Online adaptation of the network is performed using steep...
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Published: |
IEE
1995
|
| Subjects: |
| _version_ | 1796945527636492288 |
|---|---|
| author | Hussain, Mohd Azlan Allwright, J.C. Kershenbaum, L.S. |
| author_facet | Hussain, Mohd Azlan Allwright, J.C. Kershenbaum, L.S. |
| author_sort | Hussain, Mohd Azlan |
| collection | UM |
| description | A nonlinear control strategy involving a geometric feedback controller and adaptive approximation of the plant is presented. The plant is approximated by a linearized model and a neural network which approximates the higher order error terms. Online adaptation of the network is performed using steepest descent with a dead zone function. The proposed strategy is applied to two case studies for output tracking of set points. The results show good tracking comparable with utilizing the actual model of the plant (usually unknown) and better than that obtained when using the linearized model alone. |
| first_indexed | 2024-03-06T05:18:02Z |
| format | Conference or Workshop Item |
| id | um.eprints-7099 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:18:02Z |
| publishDate | 1995 |
| publisher | IEE |
| record_format | dspace |
| spelling | um.eprints-70992021-02-10T03:15:53Z http://eprints.um.edu.my/7099/ Nonlinear control with linearized models and neural networks Hussain, Mohd Azlan Allwright, J.C. Kershenbaum, L.S. TA Engineering (General). Civil engineering (General) TP Chemical technology A nonlinear control strategy involving a geometric feedback controller and adaptive approximation of the plant is presented. The plant is approximated by a linearized model and a neural network which approximates the higher order error terms. Online adaptation of the network is performed using steepest descent with a dead zone function. The proposed strategy is applied to two case studies for output tracking of set points. The results show good tracking comparable with utilizing the actual model of the plant (usually unknown) and better than that obtained when using the linearized model alone. IEE 1995 Conference or Workshop Item PeerReviewed Hussain, Mohd Azlan and Allwright, J.C. and Kershenbaum, L.S. (1995) Nonlinear control with linearized models and neural networks. In: Proceedings of the 4th International Conference on Artificial Neural Networks, 1995, Cambridge, United Kingdom. http://www.scopus.com/inward/record.url?eid=2-s2.0-0029210291&partnerID=40&md5=8d9eba3baeeb805273478758fdb0a9b1 |
| spellingShingle | TA Engineering (General). Civil engineering (General) TP Chemical technology Hussain, Mohd Azlan Allwright, J.C. Kershenbaum, L.S. Nonlinear control with linearized models and neural networks |
| title | Nonlinear control with linearized models and neural networks |
| title_full | Nonlinear control with linearized models and neural networks |
| title_fullStr | Nonlinear control with linearized models and neural networks |
| title_full_unstemmed | Nonlinear control with linearized models and neural networks |
| title_short | Nonlinear control with linearized models and neural networks |
| title_sort | nonlinear control with linearized models and neural networks |
| topic | TA Engineering (General). Civil engineering (General) TP Chemical technology |
| work_keys_str_mv | AT hussainmohdazlan nonlinearcontrolwithlinearizedmodelsandneuralnetworks AT allwrightjc nonlinearcontrolwithlinearizedmodelsandneuralnetworks AT kershenbaumls nonlinearcontrolwithlinearizedmodelsandneuralnetworks |