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Loembe Souamy, R.M.D., Abena, J.A., Mankiti Fati, A., Jiang, G.-P., Wang, H.H. and Wen, X.B. (2025) An Enhanced of Synchronization of Chaos to the Applications of the Drive Principle of a Synchronized Responsive Based a Reference Observer of DCSK by Comparing of the Performance between GCS-DCSKI and GCSDCSKII over AWGN Chaotic System. Proceedings Book 26th International Forum on Applied Sciences and Innovation, Bangkok, 11-13 November 2025, 91-124.

has been cited by the following article:

• TITLE: Chaos Synchronization of Lorenz System Using an Observer Backstepping Finite-Time Controller Design

  AUTHORS: Rostand Martialy Davy Loembe Souamy, Guoping Jiang, Chunxia Fan, Honghua Wang, Macaire Ngomo, Clément Hodévèwan Miwadinou, Richard Louis Mpeka, Landry Jean Pierre Gomat, Christian Tathy

  KEYWORDS: Chaotic Lorenz System, Finite-Time Synchronization, Neural Network, Observer Backstepping Controller, Uncertainty

  JOURNAL NAME: Journal of Flow Control, Measurement & Visualization, Vol.13 No.2, July 31, 2026

  ABSTRACT: This paper presents an observer backstepping control strategy to achieve finite-time Chaos Synchronization for uncertain Lorenz system. To compensate for unknown behavior dynamics, radial basis function neural networks (RBFNN) is employed to approximate the uncertain terms system depending on the slave behavior of the system independent of the master system and adaptive laws are derived to update the network weight online within the Lyapunov framework. The authors use Lyapunov stability theory analysis to design an observer controller that ensures the synchronization error converges to zero in a finite-time even with unknown system parameters constants and external disturbances. The effectiveness of the proposed method is demonstrated through numerical simulations.