In this study, a black box modeling of the coupledtank system is obtained by using fuzzy sets. The derived model is tested via adaptive neuro fuzzy inference system (ANFIS). In order to achieve a better control performance, the parameters of three different controller types, classical proportional integral controller (PID), fuzzy PID and function tuner method, are tuned by one of the evolutiona...

The main objective of Automatic Generation Control (AGC) is to balance the total system generation against system load losses so that the desired frequency and power interchange with neighboring systems is maintained. Any mismatch between generation and demand causes the system frequency to deviate from its nominal value. Thus high frequency deviation may lead to system collapse. This necessita...

This paper presents an adaptive neuro-fuzzy controller (ANFC) for the switched reluctance motors (SRM). The proposed controller is used for speed control of this type of motors. The dynamic response of the SRM with the proposed controller is studied during the starting process and under different disturbances. The effectiveness of the proposed ANFC is then compared with that of the conventional...

A controller based on neuro-dynamic programming coupled with a fuzzy ARTMAP neural network for a fed-batch bioreactor was developed to roduce cloned invertase in Saccharomyces cerevisiae yeast in a fed-batch bioreactor. The objective was to find the optimal glucose feed rate profile eeded to achieve the highest fermentation profit in this reactive system where the enzyme expression is repressed...

Fuzzy systems are currently being used in a wide field of industrial and scientific applications. Since the design and especially the optimization process of fuzzy systems can be very time consuming, it is convenient to have algorithms which construct and optimize them automatically. One popular approach is to combine fuzzy systems with learning techniques derived from neural networks. Such app...

The aim of this work is to optimise the vibration of the plate with the help of Adaptive neuro-fuzzy inference system (ANFIS) controller. The contribution of piezoelectric sensor and actuator layers on the mass and stiffness of the plate is considered. As the plate is square so we get total 64 squares. To validate the present code, frequency response has been compared with exact solution as wel...

The paper presents a comparative study regarding the control (using an adaptive neuro-fuzzy controller and a PD controller) based on the simulation of wind energy conversion systems functioning. There are considered several simulations based on asynchronous generator usage, by using the dedicated MATLAB-PSB (Power System Blockset) toolbox implementations.

The control of walking robots has improved dramatically over the last few years. Several methodologies have been developed for this. As one of them, in this work, we used fuzzy logic controller that generates angular of the knees in order to active a desired position of the center of mass. We present a fuzzy gait generator, FGG, that generates the angular trajectories of a biped robot. The FGG,...

This paper presents a neuro-fuzzy internal model Cartesian controller for robot manipulators. An inductive learning technique is applied to generate the required inverse dynamics and inverse kinematics modelling rules from input/output measurements. A fully differentiable fuzzy neural network is used to construct the adaptive sections of the controller for on-line parameters adaptation. A fuzzy...

In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) method based on the Artificial Neural Network (ANN) is applied to design an Inverse Kinematic based controller forthe inverse kinematical control of SCORBOT-ER V Plus. The proposed ANFIS controller combines the advantages of a fuzzy controller as well as the quick response and adaptability nature of an Artificial Neural Network (AN...