An adaptive neural network control scheme for thermal power system is described. No off-line training is required for the proposed neural network controller. The online tuning algorithm and neural network architecture are described. The performance of the controller is illustrated via simulation for different changes in process parameters. Performance of neural network controller is compared wi...

Although there have been some severe nuclear accidents such as Three Mile Island (USA), Chernobyl (Ukraine) and Fukushima (Japan), nuclear fission energy is still a source of clean energy that can substitute for fossil fuels in a centralized way and in a great amount with commercial availability and economic competitiveness. Since the pressurized water reactor (PWR) is the most widely used nucl...

The aim of this paper was to present an optimized method in order to use maximum capacity of the photovoltaic panels. In this regard, we presented a method for the maximum power point tracking in the photovoltaic systems by using the neural networks and adaptive controller. In the proposed system, we estimated an error by using neural network. If this error is lower than the allowable systems e...

This paper presents a new approach to solve synchronization problem of a large class of discrete chaotic systems. The chaotic systems can be reformulated as an appropriate class of linear parameter varying (LPV) systems. Then, based on the LPV representation, a neural network observer-based approach is proposed to solve the synchronization problem. The simulation results show the advantages of ...

Time series analysis develops models that can establish the relationship between different variables. For nonlinear systems using time series analysis we propose to combine the 4 techniques of: i) Radial Basis Function (RBF), ii) artificial neural networks, iii) adaptive control and iv) optimization, and explore the design of robust control algorithms for uncertain nonlinear systems. Then, base...

Neural network adaptive filters are mainly used for the interference cancellation techniques. The gradient based design methods are well developed for the design of neural network adaptive filter but they converge to local minima. This paper describes the global optimization interference cancelling techniques for adaptive filtering of interferences in the corrupted signal. The system is designe...

This article proposes an adaptive flight framework that integrates a discrete-time incremental nonlinear dynamic inversion controller and neural network (NN)-based observer for maneuvering flight. The is built on the feedback-inversion scheme in which augments disturbance loop. effects of modeling uncertainties exogenous perturbations are both taken into consideration alleviated by observer. By...

Fuzzy control is a model free approach, i.e., it does not require a mathematical model of the system under control. An observer-based indirect adaptive fuzzy neural tracking control equipped with VSS and H∞ control algorithms is developed for nonlinear SISO systems involving plant uncertainties and external disturbances. Three important control methods, i.e., adaptive fuzzy neural control schem...