Title: Improved neural network control of inverted pendulums
Authors: Teréz A. Várkonyi; József K. Tar; Imre J. Rudas
Addresses: Doctoral School of Applied Informatics, John von Neumann Faculty of Informatics, Óbuda University, 96/B Bécsi Street, Budapest, H-1034, Hungary; Doctoral School of Computer Science, Department of Computer Science, Universitá degli Studi di Milano – Crema Campus, 65 Via Bramante, Crema (CR), I-26013, Italy ' Institute of Applied Mathematics, John von Neumann Faculty of Informatics, Óbuda University, 96/B Bécsi Street, Budapest, H-1034, Hungary ' Institute of Applied Mathematics, John von Neumann Faculty of Informatics, Óbuda University, 96/B Bécsi Street, Budapest, H-1034, Hungary
Abstract: Nowadays, neural network controllers (NNCs) are getting more and more prevalent because they are able to handle unknown systems by learning them and adapt to their changing behaviour. The family of robust fixed point transformations (RFPT) has been partly developed to solve control tasks without knowing the exact parameters of a controlled system. When disturbances effect a plant or the neural network controller is not trained properly RFPT integrated to the controller is suitable to reduce the problems caused by the model approximation and make the controller robust to the unknown external forces. In this paper, a novel combination of neural networks and robust fixed point transformations is introduced to balance an inverted pendulum on the top of a cart of changing nominal position. The results show that the inaccuracies caused by the disturbances can be reduced significantly when RFPT is used in the control process.
Keywords: neural networks; robust fixed point transformations; RFPT; inverted pendulum; robust control; adaptive control; neural network control.
International Journal of Advanced Intelligence Paradigms, 2013 Vol.5 No.4, pp.270 - 283
Published online: 30 Jul 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article