A neural-network based technique for modelling and LPV control of an arm-driven inverted pendulum

Journal
Proceedings of the IEEE Conference on Decision & Control  
Volume
2008
Start Page
3860
End Page
3865
Article Number
4739222
Citation
Proceedings of the 47th IEEE Conference on Decision and Control (): 4739222 3860-3865 (2008)
Contribution to Conference
47th IEEE Conference on Decision and Control, CDC 2008  
Publisher DOI
10.1109/CDC.2008.4739222
Scopus ID
2-s2.0-62949230427
Publisher
IEEE
This paper presents a generalization of a recurrent neural-networks (RNNs) approach which was proposed previously in[1], together with stability and identifiability proofs based on the contraction mapping theorem and the concept of sign-permutation equivalence, respectively. A slight simplification of the generalized RNN approach is also proposed that facilitates practical application. To use the RNN for linear parameter-varying (LPV) controller synthesis, a method is presented of transforming it into a discrete-time quasi LPV model in polytopic and linear fractional transformation (LFT) representations. A novel indirect technique for closed-loop identification with RNNs is proposed here to identify a black box model for an arm-driven inverted pendulum (ADIP). The identified RNN model is then transformed into a quasi-LPV model. Based on such LPV models, two discrete-time LPV controllers are synthesized to control the ADIP. The first one is a full-order standard polytopic LPV controller and the second one is a fixed-structure LPV controller in LFT form based on the quadratic separator concept. Experimental results illustrate the practicality of the proposed methods. © 2008 IEEE.
DDC Class
600: Technik
620: Ingenieurwissenschaften