Abstract
This paper presents a Lyapunov function based neural network tracking control strategy for single-input-single-output nonlinear dynamic systems. The proposed architecture is composed of two feed-forward neural networks operating as controller and estimator in a unified framework. The network parameters are tuned online with a Lyapunov function based backpropagation learning algorithm. The closed-loop error convergence and stability are analyzed with Lyapunov stability theory. Two simulation case studies are included that successfully validate the proposed controller performance.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the IEEE International Conference on Industrial Technology |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 321-325 |
| Number of pages | 5 |
| Volume | 2015-June |
| Edition | June |
| DOIs | |
| Publication status | Published - Jun 16 2015 |
| Event | 2015 IEEE International Conference on Industrial Technology, ICIT 2015 - Seville, Spain Duration: Mar 17 2015 → Mar 19 2015 |
Other
| Other | 2015 IEEE International Conference on Industrial Technology, ICIT 2015 |
|---|---|
| Country | Spain |
| City | Seville |
| Period | 3/17/15 → 3/19/15 |
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Keywords
- direct adaptive inverse control
- indirect adaptive inverse control
- Lyapunov function
- neural inverse tracking
- stable adaptive tracking
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Science Applications
Cite this
Lyapunov stability criterion based neural inverse tracking for unknown dynamic plants. / Aftab, Muhammad Saleheen; Shafiq, Muhammad; Yousef, Hasan.
Proceedings of the IEEE International Conference on Industrial Technology. Vol. 2015-June June. ed. Institute of Electrical and Electronics Engineers Inc., 2015. p. 321-325 7125118.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Lyapunov stability criterion based neural inverse tracking for unknown dynamic plants
AU - Aftab, Muhammad Saleheen
AU - Shafiq, Muhammad
AU - Yousef, Hasan
PY - 2015/6/16
Y1 - 2015/6/16
N2 - This paper presents a Lyapunov function based neural network tracking control strategy for single-input-single-output nonlinear dynamic systems. The proposed architecture is composed of two feed-forward neural networks operating as controller and estimator in a unified framework. The network parameters are tuned online with a Lyapunov function based backpropagation learning algorithm. The closed-loop error convergence and stability are analyzed with Lyapunov stability theory. Two simulation case studies are included that successfully validate the proposed controller performance.
AB - This paper presents a Lyapunov function based neural network tracking control strategy for single-input-single-output nonlinear dynamic systems. The proposed architecture is composed of two feed-forward neural networks operating as controller and estimator in a unified framework. The network parameters are tuned online with a Lyapunov function based backpropagation learning algorithm. The closed-loop error convergence and stability are analyzed with Lyapunov stability theory. Two simulation case studies are included that successfully validate the proposed controller performance.
KW - direct adaptive inverse control
KW - indirect adaptive inverse control
KW - Lyapunov function
KW - neural inverse tracking
KW - stable adaptive tracking
UR - http://www.scopus.com/inward/record.url?scp=84937716362&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84937716362&partnerID=8YFLogxK
U2 - 10.1109/ICIT.2015.7125118
DO - 10.1109/ICIT.2015.7125118
M3 - Conference contribution
AN - SCOPUS:84937716362
VL - 2015-June
SP - 321
EP - 325
BT - Proceedings of the IEEE International Conference on Industrial Technology
PB - Institute of Electrical and Electronics Engineers Inc.
ER -