Mojtaba Ahmadieh Khanesar

University of Nottingham

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Erdal Kayacan

Paderborn University

Okyay Kaynak

Boğaziçi University

Mohammad Teshnehlab

K.N.Toosi University of Technology

Saima Hassan

Centre Hospitalier de l’Université de Montréal

David T. Branson

University of Nottingham

Jafreezal Jaafar

Universiti Teknologi Petronas

Abbas Khosravi

Deakin University

Mahdi Aliyari Shoorehdeli

K.N.Toosi University of Technology

Huijun Gao

Harbin Institute of Technology

Abbas Dideban

Semnan University

Cooperative Institutions

Semnan University

Islamic Azad University, Tehran

Nanyang Technological University

University of Nottingham

Islamic Azad University, Science and Research Branch

K.N.Toosi University of Technology

Kohat University of Science and Technology

Universiti Teknologi Petronas

KU Leuven

Ferdowsi University of Mashhad

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Guaranteed cost adaptive sliding mode fuzzy control systems

2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (2017)

Mojtaba Ahmadieh Khanesar M. R. Semsar S. M. Hassan Hosseini

In this paper, a novel sliding mode control technique is proposed which benefits from recursive least square adaptation laws. Since this method has high resistance against uncertainty and may result in a desirable transient response, this method is one of the most commonly used nonlinear control methods. In this method, the uncertain dynamics of system is stabilized by applying a discontinuous control signal. The principle of this type of controller is to force the states of the system towards sliding manifold and maintain the states of this stable manifold which defines the desired behavior of the system. It is possible to model dynamic behavior of physical systems in terms of fuzzy systems. In order to tune the parameters of fuzzy system, a cost function based on sliding mode is proposed. The solution to this cost function results in optimal adaptation laws whose stability analysis is done using an appropriate Lyapunov function. The proposed adaptive sliding mode fuzzy controller is simulated on an inverted pendulum to test its efficacy and performance in control of a benchmark system.

Benchmark (surveying)

10.1109/fuzz-ieee.2017.8015769

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Citations (1)

Sliding mode control of Rotary Inverted Pendulm

Mojtaba Ahmadieh Khanesar Mohammad Teshnehlab Mahdi Aliyari Shoorehdeli

This paper presents sliding mode control of Rotary Inverted Pendulum. Rotary Inverted Pendulum is a nonlinear, unstable and non-minimum-phase system. Designing sliding mode controller for such system is difficult in general. Here, first the desired performance is introduced and based on this performance two sliding surfaces are designed, then system is controlled by proper definition of a lyapunov function. The lyapunov function designed puts more emphasis on the control of the inverted pendulum rather than the control of the motor.

Mode (computer interface)

Control-Lyapunov function

10.1109/med.2007.4433653

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Citations (30)

Mathematical Preliminaries

Elsevier eBooks (2015)

Erdal Kayacan Mojtaba Ahmadieh Khanesar

Mathematical notation

10.1016/b978-0-12-802687-8.00001-3

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Citations (5)

Adaptive Sliding-Mode Fuzzy Control Systems: Gradient Descent Method

Studies in systems, decision and control (2021)

Mojtaba Ahmadieh Khanesar Okyay Kaynak Erdal Kayacan

Descent (aeronautics)

Descent direction

Mode (computer interface)

10.1007/978-3-030-69182-0_5

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Citations (0)

Improved Karnik-Mendel algorithm: Eliminating the need for sorting

Mojtaba Ahmadieh Khanesar Okyay Kaynak Huijun Gao

In this paper a novel type reducer for interval type-2 fuzzy systems is proposed. Type reduction of interval type-2 fuzzy systems requires the solution of two nonlinear constrained optimization problems. Existing exact solutions to these problems (Karnik-Mendel algorithms and their variants) require sorting which is known to be computationally very expensive. In this research, these optimization problems are reformulated and novel improved solutions to these problem are proposed which do not require sorting any more. Simulation results show that the results obtained using the proposed methods are exactly the same as that of enhanced Karnik-Mendel algorithms with at least 9% less computational time when the number of rules of fuzzy system is bigger than ten. For more number of rules, it is even possible that the proposed methods converge 37% faster than enhanced Karnik-Mendel algorithms. In addition, it is shown that the computational time required by the proposed methods grow linearly as the number of the rules increases.

10.1109/icmc.2014.7231548

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Citations (4)

Adaptive direct fuzzy control of SISO nonlinear systems using a fuzzy reference model

Sanaz Tahmasebi Mojtaba Ahmadieh Khanesar Mohammad Teshnehlab

This study presents a novel fuzzy adaptive controller comprising a fuzzy direct model reference mechanism to control uncertain nonlinear SISO systems. The proposed method benefits from a reference model which is itself fuzzy. Since the reference model is fuzzy it has more degrees of freedom to define a more appropriate dynamic behavior for the system to be controlled. The flexibility caused by the fuzzy reference model makes it possible for the system to outperform the case when the reference signal is linear in terms of rise time and settling time. In addition, using the proposed approach, tracking error of the reference system reduces significantly. The direct method of Lyapunov is used to prove the stability of the system. The proposed method is tested on a Duffing oscillator subject to disturbances.

Reference model

Settling time

10.1109/actea.2016.7560119

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Fuzzy Neural Networks for Real Time Control Applications: Concepts, Modeling and Algorithms for Fast Learning

Erdal Kayacan Mojtaba Ahmadieh Khanesar

AN INDISPENSABLE RESOURCE FOR ALL THOSE WHO DESIGN AND IMPLEMENT TYPE-1 AND TYPE-2 FUZZY NEURAL NETWORKS IN REAL TIME SYSTEMS Delve into the type-2 fuzzy logic systems and become engrossed in the parameter update algorithms for type-1 and type-2 fuzzy neural networks and their stability analysis with this book! Not only does this book stand apart from others in its focus but also in its application-based presentation style. Prepared in a way that can be easily understood by those who are experienced and inexperienced in this field. Readers can benefit from the computer source codes for both identification and control purposes which are given at the end of the book. A clear and an in-depth examination has been made of all the necessary mathematical foundations, type-1 and type-2 fuzzy neural network structures and their learning algorithms as well as their stability analysis. You will find that each chapter is devoted to a different learning algorithm for the tuning of type-1 and type-2 fuzzy neural networks; some of which are: ? Gradient descent ? Levenberg-Marquardt ? Extended Kalman filter In addition to the aforementioned conventional learning methods above, number of novel sliding mode control theory-based learning algorithms, which are simpler and have closed forms, and their stability analysis have been proposed. Furthermore, hybrid methods consisting of particle swarm optimization and sliding mode control theory-based algorithms have also been introduced. The potential readers of this book are expected to be the undergraduate and graduate students, engineers, mathematicians and computer scientists. Not only can this book be used as a reference source for a scientist who is interested in fuzzy neural networks and their real-time implementations but also as a course book of fuzzy neural networks or artificial intelligence in master or doctorate university studies. We hope that this book will serve its main purpose successfully.Parameter update algorithms for type-1 and type-2 fuzzy neural networks and their stability analysisContains algorithms that are applicable to real time systemsIntroduces fast and simple adaptation rules for type-1 and type-2 fuzzy neural networksNumber of case studies both in identification and controlProvides MATLAB® codes for some algorithms in the book

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Citations (50)

Sliding mode type-2 fuzzy control of robotic arm using ellipsoidal membership functions

2022 13th Asian Control Conference (ASCC) (2013)

Mojtaba Ahmadieh Khanesar Erdal Kayacan Okyay Kaynak Wouter Saeys

Several papers claim that the performance of the type-2 fuzzy logic systems is superior over their type-1 counterparts, especially under noisy conditions. In order to show the effectiveness of the noise reduction capabilities of the type-2 fuzzy logic systems, a novel type-2 fuzzy membership function, ellipsoidal membership function, has recently been proposed. The novel membership function has certain values on both ends of the support and the kernel, and some uncertain values on the other values of the support. The parameters responsible for the width of uncertainty are decoupled from the parameters responsible for the center and the support of the membership function. In this study, a sliding mode control theory based learning algorithm has been proposed to tune the consequent part parameters tuning of the ellipsoidal type-2 fuzzy membership functions. The applicability of the novel membership function with the proposed novel parameter update rules has been shown on the control of a 2DOF robotic arm. The simulation results show that the type-2 fuzzy neural networks working in parallel with conventional PD controllers have the ability of controlling the robotic arm with a high accuracy especially under noisy conditions.

Ellipsoid

Kernel (algebra)

Defuzzification

10.1109/ascc.2013.6606393

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Citations (2)

A novel complexity reduced Levenberge-Marquardt algorithm: Application to the training of interval type-2 fuzzy systems

2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (2017)

Mojtaba Ahmadieh Khanesar Erdal Kayacan

Levenberge-Marquardt (LM) algorithm is a well-known optimization technique which has the advantages of the steepest descent and the Gauss-Newton methods. Unfortunately, LM algorithm-based parameter update rules, regardless of being used to tune the parameters of artificial neural networks or neuro-fuzzy systems, require the calculation of inversion of high dimensional matrices. Matrix inversions are generally computationally expensive, and it is not desired in a real-time application where the computation speed is critical. In this paper, using matrix inversion lemma, LM algorithm is modified to avoid matrix inversion calculations, and therefore lessen its computational burden. The proposed algorithm is compared with the conventional LM algorithm for the training of interval type-2 fuzzy logic systems in terms of its speed. Extensive simulation results demonstrate that that the proposed novel method can increase the speed of LM algorithm by 50% while remaining the same performance.

Levenberg–Marquardt algorithm

10.1109/fuzz-ieee.2017.8015737

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Model of car wing active control in order to increase stability of the car on corners of roads

Mahdi Salehi Mojtaba Ahmadieh Khanesar Faezeh Farivar

Active wing has the potential to increase the stability of racing cars by down-force as a result of aerodynamics pressure energy. In this paper a novel way of wing modelling and control is introduced. The proposed model is driven by using the second and the third Newton law and is also theoretically calculated by some simple formulation. Moreover, a proof to show how the performance increases is given. There are ways to work on other aspects of the subject because it will be the first step to intelligently control wings in order to increase road-holding without any special features on the road corners. Numerical and analytical arguments are considered to show step by step modeling spoiler. First the history of wing and spoiler is described then some research and improvement in the subject are given. Next the model of the wing plus its legs are discussed in details. Eventually suspension modelling according to wing model are given. The numerical results show the efficacy of the proposed method.

Car model

Suspension

Wing loading

10.1109/icacdot.2016.7877760

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Citations (2)