R. Barzamini

Bio: R. Barzamini is an academic researcher from Amirkabir University of Technology. The author has contributed to research in topics: Network congestion & Control theory. The author has an hindex of 7, co-authored 30 publications receiving 187 citations. Previous affiliations of R. Barzamini include Power and Water University of Technology & Islamic University.

Tooth Profile Modification and its Effect on Spur Gear Pair Vibration in Presence of Localized Tooth Defect

Abstract: Vibration induced by gears includes important data about gearbox condition. We can use dynamic modeling of gear vibration for increasing our information about vibration generating mechanisms in gearboxes and dynamic behavior of gearbox in the presence of some kind of gear defects. In this paper a six degree-of-freedom nonlinear dynamic model including different gear errors and defects is developed for investigation of effects of tooth localized defect and profile modifications on overall gear dynamics. Interactions between tooth modifications and profile error are studied and the role of profile modification in dynamic response when a localized defect is incurred by a tooth is shown. It is indicated that although profile modifications and profile errors are micro-geometrical, they have considerable effects on vibrations of gear pair. Especially for the case of root relieved teeth that is shown to be more effective in reduction of vibration in the presence of tooth localized defect. Finally, the simulation results are compared with results from literature and the model is verified.

Short term load forecasting for Iran National Power System and its regions using multi layer perceptron and fuzzy inference systems

Abstract: Many researchers have investigated short term load forecasting (STLF) in recent decades because of its importance in power system operation. In this paper a multi layers perceptron (MLP) neural network (NN) is designed for load forecasting in normal weather condition and ordinary days. The architecture of the proposed network is a three-layer feedforward neural network whose parameters are tuned by Levenberg-Marquardt backpropagation (LMBP) augmented by an early stopping (ES) method tried out for increasing the speed of convergence. For abrupt weather changes and special holidays, we have added a fuzzy inference systems (FIS) to modify the forecasted load appropriately. We show that this method satisfy the Iran electricity market rule. Simulation examples for Iran National Power System (INPS) and any of its regions, Bakhtar Region Electric Co. (BREC) demonstrate capabilities of proposed method for load forecasting.

Model Reference Adaptive Path Following for Wheeled Mobile Robots

Abstract: Wheeled mobile robots are considered as the most widely used class of mobile robots This is due to their fast maneuvering, simple controllers, and energy saving characteristics Two new algorithms for tracking control of these robots are presented in this paper: an adaptive controller and a nonlinear controller Here, the two algorithms are compared with regarding noise resistance and disturbance A combination of model reference adaptive control and gain scheduling is used to control the robot motion by the adaptive controller It is shown via Lyapunov method that the adaptive algorithm guarantees the stability of the system We have also employed a feedback linearization controller, proved its stability and compared its performance with both of the aforementioned controllers The simulation results indicate that the designed controller will preserve the robot on its desired track even though the disturbance level is of high

Adaptive Control

Abstract: This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Construction of Optimal Prediction Intervals for Load Forecasting Problems

Abstract: Short-term load forecasting is fundamental for the reliable and efficient operation of power systems. Despite its importance, accurate prediction of loads is problematic and far remote. Often uncertainties significantly degrade performance of load forecasting models. Besides, there is no index available indicating reliability of predicted values. The objective of this study is to construct prediction intervals for future loads instead of forecasting their exact values. The delta technique is applied for constructing prediction intervals for outcomes of neural network models. Some statistical measures are developed for quantitative and comprehensive evaluation of prediction intervals. According to these measures, a new cost function is designed for shortening length of prediction intervals without compromising their coverage probability. Simulated annealing is used for minimization of this cost function and adjustment of neural network parameters. Demonstrated results clearly show that the proposed methods for constructing prediction interval outperforms the traditional delta technique. Besides, it yields prediction intervals that are practically more reliable and useful than exact point predictions.

Mobile Robot Control and Navigation: A Global Overview

Abstract: The aim of this paper is to provide a global overview of mobile robot control and navigation methodologies developed over the last decades. Mobile robots have been a substantial contributor to the welfare of modern society over the years, including the industrial, service, medical, and socialization sectors. The paper starts with a list of books on autonomous mobile robots and an overview of survey papers that cover a wide range of decision, control and navigation areas. The organization of the material follows the structure of the author’s recent book on mobile robot control. Thus, the following aspects of wheeled mobile robots are considered: kinematic modeling, dynamic modeling, conventional control, affine model-based control, invariant manifold-based control, model reference adaptive control, sliding-mode control, fuzzy and neural control, vision-based control, path and motion planning, localization and mapping, and control and software architectures.

A Hybrid Framework for Fault Detection, Classification, and Location—Part I: Concept, Structure, and Methodology

Abstract: Bridging the gap between the theoretical modeling and the practical implementation is always essential for fault detection, classification, and location methods in a power transmission-line network. In this paper, a novel hybrid framework that is able to rapidly detect and locate a fault on power transmission lines is presented. The proposed algorithm presents a fault discrimination method based on the three-phase current and voltage waveforms measured when fault events occur in the power transmission-line network. Negative-sequence components of the three-phase current and voltage quantities are applied to achieve fast online fault detection. Subsequently, the fault detection method triggers the fault classification and fault-location methods to become active. A variety of methods-including multilevel wavelet transform, principal component analysis, support vector machines, and adaptive structure neural networks-are incorporated into the framework to identify fault type and location at the same time. This paper lays out the fundamental concept of the proposed framework and introduces the methodology of the analytical techniques, a pattern-recognition approach via neural networks and a joint decision-making mechanism. Using a well-trained framework, the tasks of fault detection, classification, and location are accomplished in 1.28 cycles, significantly shorter than the critical fault clearing time.

Adaptive backstepping H∞ tracking control with prescribed performance for internet congestion.

Abstract: This paper extends the well-known control method, prescribed performance control (PPC), to network congestion control problems. An adaptive H∞ tracking problem for Transmission Control Protocol/Active Queue Management (TCP/AQM) system with external disturbance is studied. Firstly, a modified network model is given. And then, the model is changed to an equivalent error model by using error transformation. Next, to solve the network congestion problem, prescribed performance, backstepping technique, adaptive control and H∞ control are combined to design a congestion controller. Due to the use of prescribed performance, the controller can guarantee both the transient and steady state performance of the system. Meanwhile, the output of the system can track the desired queue, and unknown link capacity can be estimated. Finally, a simulation result is shown to clarify the feasibility and effectiveness of proposed approach.