Synchronization of Chaotic Systems: A Generic Nonlinear Integrated Observer-Based Approach

Muhammad Majid Hussain, Muhammad Siddique*, Ziyad M. Almohaimeed, Romaisa Shamshad, Rizwan Akram*, Naeem Aslam

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
30 Downloads (Pure)


The purpose of this research is to study the synchronization of two integrated nonlinear systems with time delay and disturbances. A nonlinear system is a system in which the difference in output is not relative to the difference in input. A new control methodology for synchronization of the two chaotic systems master and slave is recognized by means of the unique integrated chaotic synchronous observer and the integrated chaotic adaptive synchronous observer. The instantaneous approximation states of the master and slave systems are accomplished by means of methods for suggesting observers for every one of the master and slave systems and by the production of error signals between these approximated states. This approximated synchronization error signal and state approximation errors meet at the origin by means of methods involving a particular observer-based feedback control signal to ensure synchronization and state approximation. Using Lyapunov stability theory, adaptive and nonadaptive laws for control systems, and nonlinear properties, the intermingling conditions for state approximation errors and approximated synchronization errors are established as nonlinear matrix inequalities. A solution to the resulting inequality constraints using a two-step linear matrix inequality (LMI)-based approach is introduced, giving essential and adequate conditions to extract values from the controller gain and observer gain matrices. Simulation of the suggested synchronization procedure for FitzHugh–Nagumo neuronal systems is demonstrated to expand the viability of the suggested observer-based control techniques.
Original languageEnglish
Article number4558400
Publication statusPublished - 2 Dec 2021


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