Mahdi Aliyari Shoorehdeli

Bio: Mahdi Aliyari Shoorehdeli is an academic researcher from K.N.Toosi University of Technology. The author has contributed to research in topics: Fuzzy control system & Control theory. The author has an hindex of 20, co-authored 157 publications receiving 1812 citations. Previous affiliations of Mahdi Aliyari Shoorehdeli include Islamic Azad University, Science and Research Branch, Tehran & Islamic Azad University.

A Functional Unknown Input Observer for Linear Singular Fornasini–Marchesini First Model Systems: With Application to Fault Diagnosis

Abstract: Estimation of the state variables of dynamical systems in the presence of noise, disturbance, and fault is an important problem in the control and monitoring field. In this article, the mentioned issue is addressed for singular 2-D systems in Fornasini–Marchesini first model (FM-I). In this regard, a functional unknown input observer is proposed to eliminate the effects of unknown inputs and estimate a given linear combination of the system states. Moreover, the proposed observer can be used to estimate the faults and necessary and sufficient conditions for the simultaneous estimation of states and faults are derived. Finally, the performance of the proposed estimation methods is validated through a numerical example and a second-order parabolic heat equation.

Chaos synchronization for a class of chaotic systems with model uncertainty and external disturbances

Abstract: This paper proposes the generalized projective synchronization (GPS) for a class of nonlinear chaotic systems with model uncertainty and external disturbances via variable structure control. As chaotic signals are usually broadband and noise like, synchronized chaotic systems can be used as cipher generators for secure communication. This paper presents chaos synchronization of two identical chaotic systems named as the master and the slave systems. The slave system is considered with model uncertainty and external disturbances. A sliding surface is adopted to ensure the stability of the error dynamics in variable structure control. The control law applied to chaos synchronization has been established in the sense of Lyapunov function, thus the system can be guaranteed to be asymptotically stable. The proposed method allows us to arbitrarily adjust the desired scaling by controlling the slave system. It is not necessary to calculate the Lyapunov exponents and the eigen values of the Jacobian matrix, which makes it simple and convenient. Also, it is a systematic procedure for GPS of chaotic systems and it can be applied to a variety of chaotic systems no matter whether it contains external excitation or not. Notice that it needs only one controller to realize GPS no matter how much dimensions the chaotic system contains and the controller is easy to be implemented. The designed controller is robust versus model uncertainty and external disturbances. The proposed method is applied to two chaotic systems; chaotic Gyroscope system and Genesio system. Numerical simulations are presented to verify the synchronization approach.

An On-line Fuzzy Backstepping Controller for Rotary Inverted Pendulum System

Abstract: In this study a new combination of nonlinear backstepping scheme with on-line fuzzy system is presented for the rotary inverted pendulum system to achieve better performance in nonlinear controller. The inverted pendulum, a popular mechatronic application, exists in many different forms. The common thread among these systems is their goal: to balance a link on end using feedback control. The purpose of this study is to design a stabilizing controller that balances the inverted pendulum in the upright position.

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.

Learning from class-imbalanced data

Abstract: 527 articles related to imbalanced data and rare events are reviewed.Viewing reviewed papers from both technical and practical perspectives.Summarizing existing methods and corresponding statistics by a new taxonomy idea.Categorizing 162 application papers into 13 domains and giving introduction.Some opening questions are discussed at the end of this manuscript. Rare events, especially those that could potentially negatively impact society, often require humans decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.

A Survey on Evolutionary Computation Approaches to Feature Selection

Abstract: Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.