Ardashir Mohammadzadeh

Bio: Ardashir Mohammadzadeh is an academic researcher from University of Bonab. The author has contributed to research in topics: Fuzzy logic & Fuzzy control system. The author has an hindex of 17, co-authored 70 publications receiving 671 citations. Previous affiliations of Ardashir Mohammadzadeh include University of Tabriz & K.N.Toosi University of Technology.

An Interval Type-3 Fuzzy System and a New Online Fractional-Order Learning Algorithm: Theory and Practice

Abstract: The main reason of the extensive usage of the fuzzy systems in many branches of science is their approximation ability. In this paper, an interval type-3 fuzzy system (IT3FS) is proposed. The uncertainty modeling capability of the proposed IT3FS is improved in contrast to type-1 and type-2 fuzzy systems (T1FS and T2FS). Because in the proposed IT3FS, the membership is defined as an interval type-2 fuzzy set, whereas in T1FS and T2FS, the membership is crisp value and type-1 fuzzy set, respectively. An online fractional-order learning algorithm is given to optimize the consequent parameters of the IT3FS. The stability of the learning algorithm is proved by utilizing the Lyapunov method. The validity of the proposed fuzzy system is illustrated by both simulation and the experimental studies. It is shown that the proposed fuzzy system and associated learning algorithm result in better approximation performance in comparison with the other well-known approaches.

A New Online Learned Interval Type-3 Fuzzy Control System for Solar Energy Management Systems

Abstract: In this article, a novel method based on interval type-3 fuzzy logic systems (IT3-FLSs) and an online learning approach is designed for power control and battery charge planing for photovoltaic (PV)/battery hybrid systems. Unlike the other methods, the dynamics of battery, PV and boost converters are considered to be fully unknown. Also, the effects of variation of temperature, radiation, and output load are taken into account. The robustness and the asymptotic stability of the proposed method is analyzed by the Lyapunov/LaSalle’s invariant set theorems, and the tuning rules are extracted for IT3-FLS. Also, the upper bound of approximation error (AE) is approximated, and then a new compensator is designed to deal with the effects of dynamic AEs. The superiority of the proposed method is examined in several conditions and is compared with some other well-known methods. It is shown that the schemed method results in high performance under difficult conditions such as variation of temperature and radiation and abruptly changing in the output load.

Synchronization of uncertain fractional-order hyperchaotic systems by using a new self-evolving non-singleton type-2 fuzzy neural network and its application to secure communication

Abstract: This paper presents a robust control method for synchronization of the uncertain fractional-order hyperchaotic systems by using a new self-evolving non-singleton type-2 fuzzy neural network (SE-NT2FNN). The proposed SE-NT2FNNs are used for estimating the unknown functions in the dynamic of system. The effects of approximation error and external disturbance are eliminated by linear matrix inequality control scheme. The proposed SE-NT2FNN has one rule initially, the new rules and membership functions (MFs) are added based on the proposed simple algorithm and unnecessary rules and MFs are deleted. The proposed synchronization scheme is applied in a secure communication scheme. To show the effectiveness of the proposed method, three simulation examples are given. The results are compared with other methods, and it showed that the proposed control scheme results in the better performance than other methods.

Recent advances in neuro-fuzzy system: A survey

Abstract: Neuro-fuzzy systems have attracted the growing interest of researchers in various scientific and engineering areas due to its effective learning and reasoning capabilities. The neuro-fuzzy systems combine the learning power of artificial neural networks and explicit knowledge representation of fuzzy inference systems. This paper proposes a review of different neuro-fuzzy systems based on the classification of research articles from 2000 to 2017. The main purpose of this survey is to help readers have a general overview of the state-of-the-arts of neuro-fuzzy systems and easily refer suitable methods according to their research interests. Different neuro-fuzzy models are compared and a table is presented summarizing the different learning structures and learning criteria with their applications.

An adaptive RBF neural network control method for a class of nonlinear systems

Abstract: This paper focuses on designing an adaptive radial basis function neural network U+0028 RBFNN U+0029 control method for a class of nonlinear systems with unknown parameters and bounded disturbances The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method The novel adaptive control method is designed to reduce the amount of computations effectively The uniform ultimate boundedness of the closed U+002D loop system is guaranteed by the proposed controller A coupled motor drives U+0028 CMD U+0029 system, which satisfies the structure of nonlinear system, is taken for simulation to confirm the effectiveness of the method Simulations show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system

A new multi-stable fractional-order four-dimensional system with self-excited and hidden chaotic attractors: Dynamic analysis and adaptive synchronization using a novel fuzzy adaptive sliding mode control method

Abstract: Four-dimensional chaotic systems are a very interesting topic for researchers, given their special features. This paper presents a novel fractional-order four-dimensional chaotic system with self-excited and hidden attractors, which includes only one constant term. The proposed system presents the phenomenon of multi-stability, which means that two or more different dynamics are generated from different initial conditions. It is one of few published works in the last five years belonging to the aforementioned category. Using Lyapunov exponents, the chaotic behavior of the dynamical system is characterized, and the sensitivity of the system to initial conditions is determined. Also, systematic studies of the hidden chaotic behavior in the proposed system are performed using phase portraits and bifurcation transition diagrams. Moreover, a design technique of a new fuzzy adaptive sliding mode control (FASMC) for synchronization of the fractional-order systems has been offered. This control technique combines an adaptive regulation scheme and a fuzzy logic controller with conventional sliding mode control for the synchronization of fractional-order systems. Applying Lyapunov stability theorem, the proposed control technique ensures that the master and slave chaotic systems are synchronized in the presence of dynamic uncertainties and external disturbances. The proposed control technique not only provides high performance in the presence of the dynamic uncertainties and external disturbances, but also avoids the phenomenon of chattering. Simulation results have been presented to illustrate the effectiveness of the presented control scheme.

Fuzzy-logic based frequency controller for wind farms augmented with energy storage systems

Abstract: Displacement of conventional synchronous generators by non-inertial units such as wind or solar generators will result in reduced-system inertia affecting under-frequency response. Frequency control is important to avoid equipment damage, load shedding, and possible blackouts. Wind generators along with energy storage systems can be used to improve the frequency response of low-inertia power system. This paper proposes a fuzzy-logic based frequency controller (FFC) for wind farms augmented with energy storage systems (wind-storage system) to improve the primary frequency response in future low-inertia hybrid power system. The proposed controller provides bidirectional real power injection using system frequency deviations and rate of change of frequency (RoCoF). Moreover, FFC ensures optimal use of energy from wind farms and storage units by eliminating the inflexible de-loading of wind energy and minimizing the required storage capacity. The efficacy of the proposed FFC is verified on the low-inertia hybrid power system.