this paper addresses a nonlinear observer based control scheme to synchronize chaotic systems subject to uncertainties and external disturbances. it is assumed that the dynamic of slave system is not completely known. in order to compensate for the system perturbation resulting from parameter variations and mismodeling phenomena, an adaptive neural network observer is employed to handle this pr...

this paper presents a new observer-based control scheme for a class of nonlinear systems. in the proposed method, nonlinear observer and twisting algorithm controller are employed to realize a sensor-less control strategy for complex systems which makes use of non measurable process information instead of installing as many sensors as possible. due to lack of availability of the complex system ...

this paper presents a new control scheme for a class of nonlinear systems. in the proposed method, an adaptive neural network observer with rise feedback controller are applied to realize sensorless control scheme. this observer is tuned online and no exact information of nonlinear term of plant is required. so, this characteristic can compensate mismodeling phenomena. also, a new controller ca...

in this paper, a neural network model reference adaptive system speed observer is designed, which can be used in speed control of linear induction motors (lims). dynamical equations of lim have been considered accurate. in other words, the end effect and the electrical losses of the motor have been included in the motor equivalent circuit. then equations of the reference model and adaptive mode...

In this paper, a neural network model reference adaptive system speed observer is designed, which can be used in speed control of linear induction motors (LIMs). Dynamical equations of LIM have been considered accurate. In other words, the end effect and the electrical losses of the motor have been included in the motor equivalent circuit. Then equations of the reference model and adaptive mode...

This paper presents tools for the design of a neural network based adaptive output feedback controller for a class of nonlinear MIMO systems without zero dynamics. Each of the outputs is assumed to have relative degree less or equal to 2. Under the condition that the output functional dependence is unknown, a neural network based adaptive observer is designed to estimate the derivatives of the ...

This paper presents tools for the design of a neural network based adaptive output feedback controller for a class of nonlinear MIMO systems without zero dynamics. Each of the outputs is assumed to have relative degree less or equal to 2. Under the condition that the output functional dependence is unknown, a neural network based adaptive observer is designed to estimate the derivatives of the ...

The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. The supervised IAFC ...

This paper presents a method to integrate adaptive estimation and adaptive control designs for a class of uncertain nonlinear systems having both parametric uncertainties and unmodeled dynamics. The method is based on Lyapunov-like stability analysis of all the errors in the closed-loop system. The adaptive estimator considered is a linear, time-varying Kalman filter augmented by the output of ...