Synchronization of chaotic systems via nonlinear control

Control of chaos: Methods and applications in engineering

In this paper, global exponential synchronization stability in an array of linearly diffusively coupled reaction-diffusion neural networks with time-varying delays is investigated by adding impulsive controller to a small fraction of nodes (pinning-impulsive controller). In order to overcome the difficulty resulting from the fact that the impulsive controller affects only the dynamical behaviors of the controlled nodes, a new analysis method is developed. By using the developed method, two known lemmas on stability of delayed functional differential equation with and without impulses, and Lyapunov stability theory, several novel and easily verified synchronization criteria guaranteeing the whole network will be pinned to a homogenous solution are derived. Moreover, the effects of the pinning-impulsive controller and the dynamics of the uncontrolled nodes and the diffusive couplings on the synchronization process are explicitly expressed in the obtained criteria. Our results also show that we can always de...

Synchronization of Coupled Reaction-Diffusion Neural Networks with Time-Varying Delays via Pinning-Impulsive Controller

In this brief, a simple and useful technique for both synchronization and secure communication of chaotic systems is developed. The proposed approach is based on generalized state space observer design for a class of nonlinear systems. By means of regular transformations we show that asymptotic stability is assured under mild conditions. To show accuracy and high performances of the proposed method, the well-known chaotic Lorentz system will be considered as an illustrative example.

Generalized state-space observers for chaotic synchronization and secure communication

The problems and methods of control of chaos, which in the last decade was the subject of intensive studies, were reviewed. The three historically earliest and most actively developing directions of research such as the open-loop control based on periodic system excitation, the method of Poincare map linearization (OGY method), and the method of time-delayed feedback (Pyragas method) were discussed in detail. The basic results obtained within the framework of the traditional linear, nonlinear, and adaptive control, as well as the neural network systems and fuzzy systems were presented. The open problems concerned mostly with support of the methods were formulated. The second part of the review will be devoted to the most interesting applications.

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Control of Chaos: Methods and Applications. I. Methods

In this paper a systematic approach, based on the linear-state-observer design for constructing two chaotically synchronized systems, is developed. The proposed method is then applied to suggest a means to secure communications. The method combines chaotic masking and chaotic modulation, where the information signal is injected into the transmitter and simultaneously transmitted to the receiver. Furthermore, two well-known chaotic systems, Rossler and Lorenz, and a hyperchaotic Rossler system are considered as illustrative examples to demonstrate the effectiveness of the proposed approach.

An observer-based approach for chaotic synchronization with applications to secure communications

A system and method for providing one hardware platform to implement multiple wireless communication standards, services and applications. The kernel oriented macro based software defined radio (SDR) architecture provides a configurable and programmable hardware platform to implement multiple wireless communication standards, services and applications.

Software defined radio (SDR) architecture for wireless digital communication systems

Control and synchronization of discrete-time chaotic systems via variable structure control technique

This paper presents a self-learning fuzzy proportional-integral (PI) controller design for a buck converter. Although a triangle type of membership functions is used, their parameters for fuzzy subsets are deemed unnecessary in the fuzzy control rules design. Knowledge of both the normalization factor in the fuzzification phase and feedback gain of the PI controller is also not required. In addition, a genetic algorithm (GA) optimization technique is proposed to tune the parameters of normalization factors, membership functions and the gain of PI-like controller. The proposed GA-based fuzzy controller is then applied to a buck converter. Simulation results indicate that the output voltage of the closed-loop system can be regulated to a desired reference voltage regardless of the variations in input voltage and of changes in output load.

Genetic algorithm-based self-learning fuzzy PI controller for buck converter

This paper presents the motivation and design of a mini-course to teach hardware implementation of neural networks using high-level synthesis (HLS) in less than four hours for engineering education of intelligent embedded computing. By standing on the shoulders of giants, the combination of the real-world problem, decoding the process of neural networks hardware design and using HLS as hands-on lab, the students are able to not only pick up the underlying concepts of digital system design naturally but also implement a real working neural networks hardware accelerator in person. Thus, the main contribution of the work is to facilitate the engineering education of hardware design more engaging and practical.

Teaching Hardware Implementation of Neural Networks using High-Level Synthesis in Less Than Four Hours for Engineering Education of Intelligent Embedded Computing

Nan-Sheng Huang

Papers

An observer-based approach for chaotic synchronization with applications to secure communications

Software defined radio (SDR) architecture for wireless digital communication systems

Control and synchronization of discrete-time chaotic systems via variable structure control technique

Genetic algorithm-based self-learning fuzzy PI controller for buck converter

Teaching Hardware Implementation of Neural Networks using High-Level Synthesis in Less Than Four Hours for Engineering Education of Intelligent Embedded Computing