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Abstract

This paper presents the control design framework for the hybrid synchronization (HS) and parameter identification of the 3-Cell Cellular Neural Network. The cellular neural network (CNN) of this kind has increasing practical importance but due to its strong chaotic behavior and the presence of uncertain parameters make it difficult to design a smooth control framework. Sliding mode control (SMC) is very helpful for this kind of environment where the systems are nonlinear and have uncertain parameters and bounded disturbances. However, conventional SMC offers a dangerous chattering phenomenon, which is not acceptable in this scenario. To get chattering-free control, smooth higher-order SMC formulated on the smooth super twisting algorithm (SSTA) is proposed in this article. The stability of the sliding surface is ensured by the Lyapunov stability theory. The convergence of the error system to zero yields hybrid synchronization and the unknown parameters are computed adaptively. Finally, the results of the proposed control technique are compared with the adaptive integral sliding mode control (AISMC). Numerical simulation results validate the performance of the proposed algorithm.
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Authors and Affiliations

Nazam Siddique
1
ORCID: ORCID
Fazal ur Rehman
2
Uzair Raoof
3
Shahid Iqbal
1
Muhammad Rashad
3

  1. University of Gujrat, Gujrat, Pakistan
  2. Capital University of Science and Technology, Islamabad, Pakistan
  3. University of Lahore, Lahore, Pakistan
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Abstract

This paper introduces a fractional-order PD approach (F-oPD) designed to control a large class of dynamical systems known as fractional-order chaotic systems (F-oCSs). The design process involves formulating an optimization problem to determine the parameters of the developed controller while satisfying the desired performance criteria. The stability of the control loop is initially assessed using the Lyapunov’s direct method and the latest stability assumptions for fractional-order systems. Additionally, an optimization algorithm inspired by the flight skills and foraging behavior of hummingbirds, known as the Artificial Hummingbird Algorithm (AHA), is employed as a tool for optimization. To evaluate the effectiveness of the proposed design approach, the fractional-order energy resources demand-supply (Fo-ERDS) hyperchaotic system is utilized as an illustrative example.
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Authors and Affiliations

Ammar Soukkou
1
Yassine Soukkou
2
Sofiane Haddad
1
Mohamed Benghanem
3
Abdelhamid Rabhi
4

  1. Renewable Energy Laboratory, Faculty of Science and Technology, Department of Electronics, University of MSBY Jijel, BP. 98, Ouled Aissa, Jijel, Algeria
  2. Research Center in Industrial Technologies CRTI, P. O. Box. 64, Cheraga 16014, Algiers, Algeria
  3. Physics Department, Faculty of Science, Islamic University of Madinah, Madinah, KSA
  4. Modeling, Information and Systems Laboratory, University of Picardie Jules Verne, Amiens, France.

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