Karim Kemih

This paper proposes a novel chaos control approach with adaptive artificial neural network (ANN) compensation for a DFIG-based wind turbine. First, a nonlinear third-order DFIG model is established to characterize the complex dynamic and chaotic behavior of the system. Subsequently, a novel state-feedback control law is developed to stabilize the closed-loop system, suppress the chaotic dynamics, and ensure accurate electrical-speed tracking. To further enhance the tracking performance, an adaptive ANN is introduced as an auxiliary compensation mechanism to learn and compensate online for residual tracking errors without modifying the nominal feedback gains. A Lyapunov-based adaptation law is formulated to guarantee the boundedness of the adaptive parameters and the uniform ultimate boundedness of the tracking error. Simulation results demonstrate that the proposed control scheme effectively suppresses the chaotic dynamics of the DFIG while achieving accurate electrical-speed tracking. Moreover, the integration of the adaptive ANN significantly reduces the residual tracking error compared with the nominal controller, while maintaining the stability and robustness of the closed-loop system.

The dynamics of chaotic memristor-based systems offer promising potential for secure communication. However, existing solutions frequently suffer from drawbacks such as slow synchronization, low key diversity, and poor noise resistance. To overcome these issues, a novel fractional-order chaotic system incorporating a memristor emulator derived from the Shinriki oscillator is proposed. The main contribution lies in the enhanced dynamic complexity and flexibility of the proposed architecture, making it suitable for cryptographic applications. Furthermore, the feasibility of synchronization to ensure secure data transmission is demonstrated through the validation of two strategies: an active control method ensuring asymptotic convergence, and a finite-time control method enabling faster stabilization. The robustness of the scheme is confirmed by simulation results on a color image: χ ² = 253/237/267 (R/G/B); entropy ≈7.993; correlations between adjacent pixels in all directions are close to zero (e.g., −0.0318 vertically); and high number of pixel change rate and unified average changing intensity (e.g., 33.40% and 99.61%, respectively). Peak signal-to-noise ratio analysis shows that resilience to noise and external disturbances is maintained. It is shown that multiple fractional orders further enrich the chaotic behavior, increasing the systems suitability for secure communication in embedded environments. These findings highlight the relevance of fractional-order chaotic memristive systems for lightweight secure transmission applications.

In this article, we investigate shape synchronization within the drive-response framework for a specific class of 2D discrete-time noninteger-order chaotic dynamics. To achieve synchronization between the driver and response systems, we formulate a novel theorem that establishes a specific control law. This control strategy is meticulously developed to synchronize the chaotic attractors's shapes in the driver-response scheme. A rigorous proof of the theorem is provided, demonstrating its theoretical soundness. The effectiveness of the synchronization technique is validated through extensive numerical simulations. The results show that the synchronization error converges asymptotically to zero, with fast convergence speed, confirming the reliability of the control scheme. Moreover, the response system successfully replicates the geometric structure of the drive system's attractor, demonstrating precise shape synchronization. Using a discrete-time fractional-order chaotic system offers significant advantages, including increased dynamical complexity and a broader key space, which enhance the system's potential for secure communication and encryption applications. Furthermore, shape synchronization allows for the preservation of topological features in chaotic signals, making it particularly useful in applications where structural integrity of the transmitted chaotic signal is essential.

This paper introduces a novel chattering-free terminal sliding mode control (SMC) strategy to address chaotic behavior in permanent magnet synchronous generators (PMSG) for offshore wind turbine systems. By integrating an adaptive exponential reaching law with a continuous barrier function, the proposed approach eliminates chattering and ensures robust performance under model uncertainties. The methodology combines adaptive SMC with dynamic switching to estimate and compensates for unknown uncertainties, providing smooth and stable control. Finally, the performance and effectiveness of the proposed approach are compared with those of a previous study.

Controlling variable wind speed turbine (VWT) system based on a doubly-fed induction generator (DFIG) is a challenging task. It requires a control law that is both adaptable and robust enough to handle the complex dynamics of the closed control loop system. Sliding mode control (SMC) is a robust control technology that has shown good performance when employed as a passive fault-tolerant control for wind energy systems. To improve the closed control loop of VWT based on DFIG with the aim of improving energy efficiency, even in presence of nonlinearities and a certain range of bounded parametric uncertainties, whether electrically or mechanically, an adaptive passive fault tolerant control (AP-FTC) based on a self-tuning fractional integral sliding mode control law (ST-FISMC) developed from a novel hyperbolic fractional surface is proposed in this paper. ST-FISMC introduces a nonlinear hyperbolic function into the sliding manifold for self-tuning adaptation of control law, while fractional integral of the control law smooths discontinuous sign function to reduce chattering. Additionally, this work introduces an adaptive observer, developed and proved based on a chosen Lyapunov function. This observer is designed to estimate variations in electrical parameters and stator flux, ensuring sensorless decoupling in indirect field- oriented control (SI-FOC) of DFIG. Lyapunov theory is also used to prove stability of states vectors in closed control loop with presence of bounded parameters uncertainties or external disturbances. Simulation results show that the proposed approach offers better performance in capturing optimal wind energy, as well as the ability to regulate active/reactive power and high resilience in presence of occurring parameter uncertainties or external disturbances.

Recently, digital chaotic communication with limited bandwidth has seized the attention of several researchers. This interest stems from the fact that all physical communication channels are limited in bandwidth for data transmission. In furtherance of this, researchers and investigators in this area have put forward various new communication strategies based on synchronizing and controlling coupled chaotic systems evolving in discrete time. In this article, we present a new robust communication scheme based on the synchronization of two fractional-order discrete-time chaotic systems connected by bandlimited channels. The major contribution is to give a sufficient condition in term of the existing and updated parameters of the incorporated fractional-order system on one hand and the inserted filter coefficients, on the other hand. Once applied for data communications, the currently drawn up synchronization scheme pledges to enhance the transmission robustness. Indeed, the new considered parameters act like novel secret keys which widen the cipher key space. Primary and pertinent simulation curves and results performed on Matlab software portray and illustrate the effectiveness of our approach.

This paper presents a novel approach for secure communication utilizing a chaotic hybrid optical bistable system and chaotic modulation. The proposed crypto system encrypts the message at the transmitter using the chaotic hybrid optical bistable system with decorrelation operation to improve the chaotic sequence's performance. The encoded message is then injected into the dynamics of the chaotic memristor system. At the receiver, the synchronization of the two chaotic systems with passive control and predictive control allows for the recovery of the message through chaotic demodulation. The effectiveness of this approach is demonstrated through numerical simulation using medical images.