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Go to Editorial ManagerIn this paper, a neuro-fuzzy network-based adaptive tracking controller is suggested for controlling a type of nonlinear system. Where two neuro-fuzzy networks have been used to learn the system dynamics uncertainty bounds by using Lyapunov method. Then the output of these two networks are used to build a sliding mode controller. The stability of the control system is proved and stable neuro-fuzzy controller parameters adjustment laws are selected using Lyapunov theory. Simulation case study shows that the controlled system tracking the reference model effectively with smooth control effort and robust performance has been achieved.
The aim of this work is to experimentally study the influence of fiber prestress and curing temperature on the tensile and flexural properties of carbon fiber-epoxy composite. Adaptive Neuro-Fuzzy Inference System model was used to predict the effect of fiber prestress and curing temperature on the tensile strength, tensile modulus, flexural strength and flexural modulus of carbon fiber-epoxy composite. It was found that, the best membership functions for predicting the tensile strength, tensile modulus and flexural modulus are Gaussian membership functions with 4 number of membership function, and for predicting the flexural strength are generalized bell membership functions with 4 number of membership functions. From the comparison between the experimental and predicted results of carbon fiber-epoxy composite properties, it is found that the prediction results of this model show a good agreement with experimental results.
The excitation and governing control of generator play an important role in improving the dynamic and transient stability of power system. Typically the excitation control and governing control are designed independently. This paper, presented Neuro-Fu;,.zy methods for the excitation and governing control . Neuro-Fuz.zy system is applied to generate two compensating signals to modify the controls dwing system disturbances. A single machine to infinite bus (SMIB) system is applied in simulation. The MATLAB SIMULIK and S-function technique is used to simulate the system and controllers
An intelligent and anticipatory speed controller for internal combustion engines was designed theoretically and examined experimentally. This design was based on the addition of a torque loop to the main speed loop. The model can sense the external load with the help of a load cell and send this signal to a soft computing unit for analysis and processing. This scheme will improve the ability of anticipation of controller since it treats the factors that affect the speed, not the speed itself. The experimental design was implemented using two types of actuating techniques; an intelligent throttling actuator and an intelligent injection actuator. The signal was analyzed by using intelligent techniques such as fuzzy logic, neural network and genetic algorithm. The experimental data were used to train the neural and the Adaptive Neuro–Fuzzy Inference System. The comparison of the results obtained in this work with other available models proved the efficiency and the robustness of the present model.