Robust Fractional Order Controller for Chaotic Systems

Boudjehem, Djalil; Boudjehem, Badreddine

doi:10.1016/j.ifacol.2016.07.525

Cited by 9 publications

(11 citation statements)

References 20 publications

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“…In this technique, the controlled states stabilise towards a fixed points due to the absence of the approximate calculation in the feedback loop. More recent papers focus their studies on the predictive control of chaotic systems proposed by Ushio and Yamamoto (see Yoo et al, 2005;Boukabou et al, 2008;Sadaoui et al, 2011;Longge and Xiangjie, 2013;Senouci and Boukabou, 2014;Boudjehem and Boudjehem, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…The development of more robust controllers that can satisfies these new requirements may implies the use of fractional calculus (Boudjehem and Boudjehem, 2016). Where its use show high efficiency in control system and was applied as a new choice to improve performances requirements.…”

Section: Introductionmentioning

confidence: 99%

“…The ability of fractional controllers to reject disturbances, improve and maintain system's performances even with the presence of a noise in the systems under control, has made them an interesting subject to many researchers Machado, 2010;Jesus et al, 2010;Machado, 2013;Boudjehem and Boudjehem, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Fractional order PID controller for the stabilisation of chaotic systems using Takagi-Sugeno fuzzy model

Mecheri

Boudjehem

2021

IJSCC

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In this paper, we propose a new fractional PI α D β controller design to control chaotic systems. The controller design is based on the predictive control proposed by Yamamoto et al. (2001) and the fractional calculus. The parameters of the controller are determined by minimising the energy of the chaotic states using particle swarm optimisation. In order to obtain a simple model structure, we have used Takagi-Sugeno technique. A fractional PD β and conventional predictive controllers have been also used as a comparative technique, in order to show the effectiveness of the proposed design one. The simulation results on Lorenz and Chen chaotic systems show the efficiency of the proposed fractional controller to reject disturbances and noises. These results show also that the fractional controller gives better results and overcome those of the fractional PD β and conventional predictive controllers.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fractional order PID controller for the stabilisation of chaotic systems using Takagi-Sugeno fuzzy model

Mecheri

Boudjehem

2021

IJSCC

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“…In some studies, predictive control has been used to control and synchronize chaotic systems, such as synchronization of integer-order chaotic systems with output feedback robust predictive control (Jiang et al, 2017), T-S fuzzy predictive control for the fractional-order system (Muthukumar et al, 2016), and robust predictive control for fractional-order systems (Boudjehem and Boudjehem, 2016). In most of these studies, linearized predictive control with a step forward has been used.…”

Section: Introductionmentioning

confidence: 99%

A new approach for security of wireless sensor networks based on anti-synchronization of the fractional-order hyper-chaotic system

Kaheni

Yaghoobi

2020

Transactions of the Institute of Measurement and Control

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Chaotic systems have wide applications in secure communication engineering and cryptography. In this paper, improved nonlinear predictive control with whale algorithm for anti-synchronization of a fractional-order economic hyper-chaotic system is used for increasing the security of wireless sensor networks and preventing intrusion. By chaotic masking method and the T-S fuzzy model, the message signal is encoded at the wireless sensor side and it is placed along the transmitter route. In the central station, the message signal is decoded using the T-S fuzzy model and the predictive control by anti-synchronizing the fractional-order hyper-chaotic slave system. To reduce the effect of disturbances, a sign function of error is added to the predictive control. Finally, simulation results indicate the proper performance of the proposed nonlinear predictive control for anti-synchronizing the fractional-order hyper-chaotic systems in secure communications.

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“…These operators are widely used in automatic control systems to construct robust fractional controllers like fractional P I or P D , ...etc. [1,2,3,4,5,6].…”

Section: Introductionmentioning

confidence: 99%

Diffusive representation of a fractional control using adaptive partitioning algorithm

BOUDJEHEM

2018

Communications Faculty of Science University of Ankara Series A1Mathematics and Statistics

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Abstract. This article presents optimal fractional control. This control is based on the property of the invariance of a fractional order di¤erential equation. The problem formulation of the used control is expressed by di¤usive representation. The fractional control problem is described in a minimization form, where the global optimum represents the di¤usive realization of the controller. To determine the optimal fractional di¤usive control, an adaptive partitioning algorithm is used. As an application, we have chosen the control of a DC motor with uncertain parameters.

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Robust Fractional Order Controller for Chaotic Systems

Cited by 9 publications

References 20 publications

Fractional order PID controller for the stabilisation of chaotic systems using Takagi-Sugeno fuzzy model

Fractional order PID controller for the stabilisation of chaotic systems using Takagi-Sugeno fuzzy model

A new approach for security of wireless sensor networks based on anti-synchronization of the fractional-order hyper-chaotic system

Diffusive representation of a fractional control using adaptive partitioning algorithm

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