Back to Search
Start Over
Control of a chain pendulum: A fuzzy logic approach
- Source :
- International Journal of Computational Intelligence Systems, Vol 9, Iss 2 (2016)
- Publication Year :
- 2016
- Publisher :
- Atlantis Press, 2016.
-
Abstract
- In this paper we present a real application of computational intelligence. Fuzzy control of a non-linear rotary chain pendulum is proposed and implemented on real prototypes. The final aim is to obtain a larger region of attraction for the stabilization of this complex system, that is, a more robust controller. As it is well-known, fuzzy logic exploits the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost when dealing with complex systems. In this case, the control strategy is based on a Takagi-Sugeno fuzzy model of the strongly non-linear multivariable system. Simulation and experimental results on the real plant have been obtained and tested in a rotary inverted pendulum and in a double rotary inverted pendulum. They have been compared to other feedback control strategies such as Full State Feedback or Linear Quadratic Regulator with encouraging results. Fuzzy control allows to enlarge the stability region of control. Indeed, the region of attraction and therefore the stabilization has been enlarged up to over 17% for the real system.
- Subjects :
- Intelligent control
0209 industrial biotechnology
General Computer Science
Computational intelligence
02 engineering and technology
Linear-quadratic regulator
Fuzzy logic
lcsh:QA75.5-76.95
Inverted pendulum
rotary inverted pendulum
020901 industrial engineering & automation
Control theory
Robustness (computer science)
Full state feedback
0202 electrical engineering, electronic engineering, information engineering
Mathematics
Takagi-Sugeno model
Fuzzy control system
QA75.5-76.95
region of attraction
stabilization
Computational Mathematics
Electronic computers. Computer science
020201 artificial intelligence & image processing
lcsh:Electronic computers. Computer science
fuzzy logic
Subjects
Details
- Language :
- English
- ISSN :
- 18756883
- Volume :
- 9
- Issue :
- 2
- Database :
- OpenAIRE
- Journal :
- International Journal of Computational Intelligence Systems
- Accession number :
- edsair.doi.dedup.....2016b4612d719f2ca916791ac5bb636a