In this paper, dynamic modeling of a Vestas 660 kW wind turbine and its validation are performed based on operational data extracted from Eoun-Ebn-Ali wind farm in Tabriz, Iran. The operational data show that the turbine under study, with a classical PI controller, encounters high fluctuations when controlling the output power at its rated value. The turbine modeling is performed by deriving th...

A neural network model reference adaptive controller for trajectory tracking of nonlinear systems is developed. The proposed control algorithm uses a single layer neural network that bypasses the need for information about the system’s dynamic structure and characteristics and provides portability. Numerical simulations are performed using nonlinear dynamic models of marine vehicles. Results ar...

A neural architecture that makes possible the integration of a kinematic adaptive neuro-controller for trajectory tracking and an obstacle avoidance adaptive neuro-controller is proposed for nonholonomic mobile robots. The kinematic adaptive neuro-controller is a real-time, unsupervised neural network that learns to control a nonholonomic mobile robot in a nonstationary environment, which is te...

This paper describes a hybrid approach to the problem of controlling flexible link manipulators for both structured and unstructured uncertainties conditions. First, a neural network controller based on the robot’s dynamic equation of motion is elaborated. It aims to produce a fast and stable control of the joint position and velocity, and to damp the vibration of each arm. Then, an adaptive ne...

This paper studies the iterative learning control of robotic systems with repetitive tasks. A fuzzy neural network is applied to design a direct adaptive iterative learning controller. The fuzzy neural network is introduced for compensation of the unknown certainty equivalent controller. A new adaptive law using mixed time-domain and iteration-domain adaptation is developed. It is shown that th...

This paper deals with the design of neural network controller for the slung-load system. There are many methods for modeling the slung-load system, we developed the entire system model based on the UKE (Udwadia-Kalaba Equations) in order to account for multiconstraints. Neural network was adapted to the attitude controller. Finally, considering all matters in the design process, numerical simul...

Induction Motors are widely used in Industries, because of the low maintenance and robustness. Speed Control of Induction motor can be obtained by maximum torque and efficiency. Apart from other techniques Artificial Intelligence (AI) techniques, particularly the neural networks, improves the performance & operation of induction motor drives. This paper presents dynamic simulation of induction ...

An on-line training functional-link neural network predictor/controller for dynamic positioning of water surface structures is described in this paper. To develop a neural network for time-evolving systems, the deterministic on-line training model in a traditional parameter identification theory and the functional-link network are combined. The system’s previous input and output are used to be ...

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 proposes a novel intelligent control scheme using type-2 fuzzy neural network type-2 FNN system. The control scheme is developed using a type-2 FNN controller and an adaptive compensator. The type-2 FNN combines the type-2 fuzzy logic system FLS , neural network, and its learning algorithm using the optimal learning algorithm. The properties of type-1 FNN system parallel computation ...