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This paper present a neural predictive controller (NPC) based on improved quasi-ARX neural network (IQARXNN) for nonlinear dynamical systems. The IQARXNN is used as a model identifier with switching algorithm and switching stability analysis. The primary controller is designed based on a modified Elman neural network (MENN) controller using back-propagation(More)
This work exploits the idea on how to search parameter estimation and increase its convergence speed for the Liner Time Invariant (LTI) system. The convergence speed of parameter estimation is the one problem and plays an important role in the adaptive controller to increase performance. The well-known algorithm is the recursive least square algorithm.(More)
A quasi-ARX (quasi-linear ARX) neural network (QARXNN) model is able to demonstrate its ability for identification and prediction highly nonlinear system. The model is simplified by a linear correlation between the input vector and its nonlinear coefficients. The coefficients are used to parameterize the input vector performed by an embedded system called(More)
In this article, a fuzzy adaptive controller approach is presented for nonlinear systems. The proposed quasi-ARX neural network based on Lyapunov learning algorithm is used to update its weight for prediction model as well as to modify fuzzy adaptive controller. The improving performances of the Lyapunov learning algorithm are stable in the learning process(More)
In this paper, an improved switching mechanism based on quasi-linear auto regressive exogenous (quasi-ARX) neural network (QARXNN) is presented for the adaptive control of nonlinear systems. The proposed switching control is composed of a QARXNN-based prediction model and an improved switching mechanism using two new adaptive control laws, first is moving(More)
This paper presents a fuzzy adaptive controller applied to a non linear system modeled under a Quasi-linear ARX Neural Network, with stability proof by using the Lyapunov approach. This work exploits the new idea to use Lyapunov function to train multi-input multi-output neural network on the core-part sub-model. The proposed controller is designed between(More)
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