Abstract
This paper presents the design and stability analysis of sampled-data neural-network-based control systems. A continuous-time nonlinear plant and a sampled-data three-layer fully-connected feed-forward neural-network-based controller are connected in a closed-loop to perform a control task. Stability conditions will be derived to guarantee the closed-loop system stability. Linear-matrix-inequality- and genetic-algorithm-based approaches will be employed to obtain the maximum sampling period and connection weights of the neural network subject to the considerations of the system stability and performance. An application example will be given to illustrate the design procedure and effectiveness of the proposed approach.
Original language | English |
---|---|
Title of host publication | Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005 |
Pages | 2249-2254 |
Number of pages | 6 |
Volume | 4 |
DOIs | |
Publication status | Published - 1 Dec 2005 |
Event | International Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada Duration: 31 Jul 2005 → 4 Aug 2005 |
Conference
Conference | International Joint Conference on Neural Networks, IJCNN 2005 |
---|---|
Country/Territory | Canada |
City | Montreal, QC |
Period | 31/07/05 → 4/08/05 |
ASJC Scopus subject areas
- Software