Abstract
Temperature is an important control variable in industrial processes. In this paper, an adaptive PID control algorithm has been discussed to track the process temperature. The presented control algorithm employs Lyapunov function based artificial neural networks for online tuning of proportional, integral and derivative actions. This algorithm has been successfully tested on the laboratory temperature control process trainer. For comparative analysis, the results have been contrasted with the conventional PID scheme. The experimental findings show that improved and stable tracking is achieved with the proposed adaptive PID controller.
| Original language | English |
|---|---|
| Title of host publication | 2015 IEEE 8th GCC Conference and Exhibition, GCCCE 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Print) | 9781479984220 |
| DOIs | |
| Publication status | Published - Mar 12 2015 |
| Event | 2015 IEEE 8th GCC Conference and Exhibition, GCCCE 2015 - Muscat, Oman Duration: Feb 1 2015 → Feb 4 2015 |
Other
| Other | 2015 IEEE 8th GCC Conference and Exhibition, GCCCE 2015 |
|---|---|
| Country | Oman |
| City | Muscat |
| Period | 2/1/15 → 2/4/15 |
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Keywords
- adaptive PID control
- error backpropagation
- first order time delay systems
- Lyapunov function neural network
- PID tuning
ASJC Scopus subject areas
- Energy(all)
- Engineering(all)
- Computer Science(all)
Cite this
Adaptive PID controller based on Lyapunov function neural network for time delay temperature control. / Aftab, Muhammad Saleheen; Shafiq, Muhammad.
2015 IEEE 8th GCC Conference and Exhibition, GCCCE 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7060094.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Adaptive PID controller based on Lyapunov function neural network for time delay temperature control
AU - Aftab, Muhammad Saleheen
AU - Shafiq, Muhammad
PY - 2015/3/12
Y1 - 2015/3/12
N2 - Temperature is an important control variable in industrial processes. In this paper, an adaptive PID control algorithm has been discussed to track the process temperature. The presented control algorithm employs Lyapunov function based artificial neural networks for online tuning of proportional, integral and derivative actions. This algorithm has been successfully tested on the laboratory temperature control process trainer. For comparative analysis, the results have been contrasted with the conventional PID scheme. The experimental findings show that improved and stable tracking is achieved with the proposed adaptive PID controller.
AB - Temperature is an important control variable in industrial processes. In this paper, an adaptive PID control algorithm has been discussed to track the process temperature. The presented control algorithm employs Lyapunov function based artificial neural networks for online tuning of proportional, integral and derivative actions. This algorithm has been successfully tested on the laboratory temperature control process trainer. For comparative analysis, the results have been contrasted with the conventional PID scheme. The experimental findings show that improved and stable tracking is achieved with the proposed adaptive PID controller.
KW - adaptive PID control
KW - error backpropagation
KW - first order time delay systems
KW - Lyapunov function neural network
KW - PID tuning
UR - http://www.scopus.com/inward/record.url?scp=84929104618&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84929104618&partnerID=8YFLogxK
U2 - 10.1109/IEEEGCC.2015.7060094
DO - 10.1109/IEEEGCC.2015.7060094
M3 - Conference contribution
SN - 9781479984220
BT - 2015 IEEE 8th GCC Conference and Exhibition, GCCCE 2015
PB - Institute of Electrical and Electronics Engineers Inc.
ER -