AVOIDING CONTROLLER SINGULARITIES IN ADAPTIVE RECURRENT NEURAL CONTROL Ramon A. Felix ∗ Edgar N. Sanchez ∗∗ Alexander G. Loukianov ∗∗ ∗ FIME, Universidad de Colima, Coquimatlan Colima Mexico. e-mail:rfelix@ucol.mx ∗∗ CINVESTAV, Unidad Guadalajara, A.P. 31-438, Plaza La Luna C.P. 45091, Guadalajara, Jalisco, México Abstract: In this paper, to overcome the controller singularity problems, a novel neural parameters adaptive law for on-line identification is proposed, such strategy avoid specific adaptive weights zero-crossing. Using a priori knowledge about the real plant, a recurrent neural network is proposed as identifier. Based on the neural identifier model, a discontinuous control law is derived, which combines Block Control and Sliding Modes. The proposed scheme is tested in a induction motor via simulations. Copyright R ° IFAC 2005 Keywords: Induction Motors, Sliding Modes, Neural Control, Block Control INTRODUCTION Although the large number of success applications of neural networks for control and identification systems, one important drawback of such neural approaches (Rovithakis and Christodolou , 1994), (Kosmatopoulus et. al. , 1995) is the requirement of full-connected recurrent neural networks. This usually implies a large number of synaptic con- nections, becoming such schemes unacceptable for real time applications. To alleviate this situation, certain level of insight about the system is utilized to improve the empirical modelling. For example, in Loukianov et al. (2002), the Nonlinear Block Controllable form (NBC-form) (Loukianov , 1998) and the relative degree are taken into account to design a dynamic neural network to identify the plant; based on such neural identifier, a control law is derived combining the Block Control and Sliding Modes techniques (Utkin , 1999), yielding the so called Neural Block Control (NBC). Comparing with others neural control techniques (see: Sanchez, Perez and Ricalde (2003) and Rovithakis and Christodolou (1994)) that re- quire full-state full-connected neural identifiers, the NBC strategy, has the advantage that only a partial-state partially-connected neural identifier is required, reducing significantly the mathemati- cal analysis and the computational burden. Nevertheless, as well as several feedback lin- earization like controllers (Ge and Wang , 2002), the NBC may present singularities, yielding fre- quently, closed-loop system instability. In this pa- per, to overcome such controller singularity prob- lem, a priori information about the parameters of the neural model is used to design the update law; such strategy avoids not only controller singular- ities, but also the drift parameter phenomenon. 1. HIGH ORDER RECURRENT NEURAL NETWORKS In this paper, for the identification task, expan- sions of the first order Hopfield model called High Order Recurrent Neural Networks (RHONN) are Copyright (c) 2005 IFAC. All rights reserved 16th Triennial World Congress, Prague, Czech Republic 109