Lachhab, NabilNabilLachhabAbbas, Hossam El-Din Mahmoud SeddikHossam El-Din Mahmoud SeddikAbbasWerner, HerbertHerbertWerner2023-02-162023-02-162008Proceedings of the 47th IEEE Conference on Decision and Control (): 4739222 3860-3865 (2008)http://hdl.handle.net/11420/14844This 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.en0743-1546200838603865IEEETechnikIngenieurwissenschaftenConference Paper10.1109/CDC.2008.4739222Other