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A Predefined-Time Robust Neural Dynamics Controller for Projective Synchronization of Second-Order Chaotic Systems
IEEE Transactions on Cybernetics
|February 26, 2026
Summary
This study introduces a novel predefined-time robust neural dynamics controller (PTRNDC) for second-order chaotic systems. The PTRNDC enhances projective synchronization by ensuring swift, robust convergence and enabling secure image encryption.
Area of Science:
- Control Theory
- Chaos Synchronization
- Neural Dynamics
Background:
- Second-order chaotic systems exhibit high state variable coupling, limiting existing synchronization methods.
- Current control schemes for these systems struggle to balance convergence speed and robustness.
Purpose of the Study:
- To develop an advanced controller for robust projective synchronization in second-order chaotic systems.
- To overcome limitations of existing methods regarding convergence and robustness trade-offs.
Main Methods:
- Design of a predefined-time nonsingular terminal sliding mode variable (PTNTSMV) for error coupling control.
- Development of a predefined-time double-integral zeroing neural dynamics (ZNDs) formula using a time-base generator (TBG).
- Rigorous theoretical analysis of stability, convergence, and robustness.
Main Results:
- The proposed predefined-time robust neural dynamics controller (PTRNDC) ensures nonsingularity and convergence.
- The controller achieves swift and robust attainment of the sliding mode surface.
- Simulations confirm the effectiveness and robustness of the PTRNDC for projective synchronization.
Conclusions:
- The PTRNDC effectively addresses challenges in second-order chaotic system synchronization.
- The generated chaotic sequences are successfully applied to image encryption, demonstrating visual distortion properties.
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