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 focuses on the development of a controller for grid connected photovoltaic energy conversion system. Control design of a single phase inverter interfacing a photovoltaic generator and an electrical grid is performed, based on Artificial Neural Networks. The developed controller is compared with a Proportional Integral (PI) controller through computer simulation. The obtained results ...

In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) based control is proposed for the tracking of a Micro-Electro Mechanical Systems (MEMS) gyroscope sensor. The ANFIS is used to train parameters of the controller for tracking a desired trajectory. Numerical simulations for a MEMS gyroscope are looked into to check the effectiveness of the ANFIS control scheme. It proves that the sy...

This paper addresses control design in networked control system by considering stochastic packet dropouts in the forward path of the control loop. The packet dropouts are modelled by mutually independent stochastic variables satisfying Bernoulli binary distribution. A sliding mode controller is utilized to overcome the adverse influences of stochastic packet dropouts in networked control system...

The paper is focused especially on presenting possibilities of applying off-line trained artificial neural networks at creating the system inverse models that are used at designing control algorithm for non-linear dynamic system. The ability of cascade feedforward neural networks to model arbitrary non-linear functions and their inverses is exploited. This paper presents a quasi-inverse neural ...

This paper presents a neural network predictive controller for three phase inverter fed induction machine speed control. Thus the three phase inverter fed induction motor drive has been simulated with and without step change using NNP controller. The performance comparisons of neural network predictive controller are achieved with the help of IAE (Integral absolute error) and ITAE (Integral tim...

We present a tool to estimate the performance of the neural network in a neural network based adaptive controller. Using a Bayesian approach, this tool supports verification and validation of the adaptive controller as well as on-line monitoring. In this paper, we discuss our approach and present simulation results using the adaptive controller developed for NASA’s IFCS (Intelligent Flight Cont...

In this paper, a fuzzy adaptive neural-network model-following speed controller for permanent-magnet synchronous motor (PMSM) drives is proposed. The fuzzy neuralnetwork model-following controller (FNNMFC) consist of a proportional plus integral (PI) like-fuzzy controller in addition to an on-line trained neural-network model-following controller (NNMFC). This controller, FNNMFC, combines the m...

Purpose – This paper seeks to present a feedback error learning neuro-controller for an unstable research helicopter. Design/methodology/approach – Three neural-aided flight controllers are designed to satisfy the ADS-33 handling qualities specifications in pitch, roll and yaw axes. The proposed controller scheme is based on feedback error learning strategy in which the outer loop neural contro...

This paper presents a novel approach to control the speed of BLDC motor by using PID, Fuzzy, Neural Network and Anti-windup Controllers. The PID controllers is set to optimize the motor parameters such as rise time, peak time, Maximum peak overshoot and Settling time. The fuzzy controller adopts fuzzy logic to retune the PID parameters. Based on the mathematical model of BLDC Motor, novel adapt...