Fixed-Time Output-Constrained Synchronization of Unknown Chaotic Financial Systems Using Neural Learning

Qijia Yao, Hadi Jahanshahi (), Larissa M. Batrancea (), Naif D. Alotaibi and Mircea-Iosif Rus
Additional contact information
Qijia Yao: School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China
Hadi Jahanshahi: Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
Larissa M. Batrancea: Department of Business, Babeş-Bolyai University, 7 Horea Street, 400174 Cluj-Napoca, Romania
Naif D. Alotaibi: Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Mircea-Iosif Rus: National Institute for Research and Development in Constructions, Urbanism and Sustainable Spatial Development “URBAN INCERC”, 117 Calea Floresti, 400524 Cluj-Napoca, Romania

Mathematics, 2022, vol. 10, issue 19, 1-14

Abstract: This article addresses the challenging problem of fixed-time output-constrained synchronization for master–slave chaotic financial systems with unknown parameters and perturbations. A fixed-time neural adaptive control approach is originally proposed with the aid of the barrier Lyapunov function (BLF) and neural network (NN) identification. The BLF is introduced to preserve the synchronization errors always within the predefined output constraints. The NN is adopted to identify the compound unknown item in the synchronization error system. Unlike the conventional NN identification, the concept of indirect NN identification is employed, and only a single adaptive learning parameter is required to be adjusted online. According to the stability argument, the proposed controller can ensure that all error variables in the closed-loop system regulate to the minor residual sets around zero in fixed time. Finally, simulations and comparisons are conducted to verify the efficiency and benefits of the proposed control strategy. It can be concluded from the simulation results that the proposed fixed-time neural adaptive controller is capable of achieving better synchronization performance than the compared linear feedback controller.

Keywords: synchronization; chaotic financial system; fixed-time control; neural network; output constraint (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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