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Exponential Stabilization for Sampled-Data Neural-Network-Based Control Systems.
- Source :
- IEEE Transactions on Neural Networks & Learning Systems; Dec2014, Vol. 25 Issue 12, p2180-2190, 11p
- Publication Year :
- 2014
-
Abstract
- This paper investigates the problem of sampled-data stabilization for neural-network-based control systems with an optimal guaranteed cost. Using time-dependent Lyapunov functional approach, some novel conditions are proposed to guarantee the closed-loop systems exponentially stable, which fully use the available information about the actual sampling pattern. Based on the derived conditions, the design methods of the desired sampled-data three-layer fully connected feedforward neural-network-based controller are established to obtain the largest sampling interval and the smallest upper bound of the cost function. A practical example is provided to demonstrate the effectiveness and feasibility of the proposed techniques. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 25
- Issue :
- 12
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 100026712
- Full Text :
- https://doi.org/10.1109/TNNLS.2014.2306202