DocumentCode :
3391612
Title :
Hybrid LQG-neural controller for inverted pendulum system
Author :
Sazonov, E.S. ; Klinkhachorn, P. ; Klein, R.L.
Author_Institution :
Dept. of Electr. & Comput. Eng., Clarkson Univ., Potsdam, NY, USA
fYear :
2003
fDate :
16-18 March 2003
Firstpage :
206
Lastpage :
210
Abstract :
The paper presents a hybrid system controller, incorporating a neural and an LQG controller. The neural controller has been optimized by genetic algorithms directly on the inverted pendulum system. The failure-free optimization process stipulated a relatively small region of the asymptotic stability of the neural controller, which is concentrated around the regulation point. The presented hybrid controller combines benefits of a genetically optimized neural controller and an LQG controller in a single system controller. High quality of the regulation process is achieved through utilization of the neural controller, while stability of the system during transient processes and a wide range of operation are assured through application of the LQG controller. The hybrid controller has been validated by applying it to a simulation model of an inherently unstable system - the inverted pendulum.
Keywords :
asymptotic stability; genetic algorithms; linear quadratic Gaussian control; motion control; multilayer perceptrons; neurocontrollers; nonlinear control systems; pendulums; position control; failure-free optimization process; genetic algorithms; hybrid LQG-neural controller; inherently unstable system; inverted pendulum system; regulation process; stability; transient processes; Control system synthesis; Control systems; Equations; Genetic algorithms; Neural networks; Numerical models; Optimization methods; Sliding mode control; Stability; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
System Theory, 2003. Proceedings of the 35th Southeastern Symposium on
ISSN :
0094-2898
Print_ISBN :
0-7803-7697-8
Type :
conf
DOI :
10.1109/SSST.2003.1194559
Filename :
1194559
Link To Document :
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