Other approaches utilizing the EAs for stabilizing chaotic dynamics have mostly applied the particle swarm optimization (PSO) algorithm and multi-interval gradient method or minimum entropy control technique. This method is very advantageous for the evolutionary computation, due to its amount of easy accessible control parameters, which can be easily tuned by means of EAs. The synthesis of control is inspired by the Pyragas' delayed feedback control technique. Such approach also may overcome the possible sensitivity to initial conditions which may lead to stability issues.
ETDAS CHAOS CONTROL HOW TO
When compared to the aforementioned research, later works show a possibility as to how to generate the entire control law (not only how to optimize several parameters) for the purpose of stabilization of a chaotic system. The initial research was conducted in, whereas was more concerned with the tuning of parameters inside the chaos control technique based on the Pyragas method: extended delay feedback control (ETDAS). The interest about the interconnection between evolutionary techniques and control of chaotic systems is rapidly spreading. Currently, EAs are known as a powerful set of tools for almost any difficult and complex optimization problem. The most popular of these methods are neural networks, evolutionary algorithms (EAs), fuzzy logic, and tools for symbolic regression like genetic programming. The most current methods are mostly based on soft computing, which is a discipline tightly bound to computers, representing a set of methods of special algorithms, belonging to the artificial intelligence paradigm.
Subsequently, the rapid development of methods for stabilizing of nonlinear chaotic dynamics has arisen and many advanced techniques have been applied for chaos control and chaos synchronization including methods from the artificial intelligence field.ĭuring recent years, usage of new intelligent systems in engineering, technology, modeling, computing, and simulations has attracted the attention of researchers worldwide. The first pioneering approach to control chaotic dynamics by means of a simple analytical linearization method was introduced in 1990s by Ott et al. In many engineering applications, one of the most innate tasks is the controlling of highly nonlinear dynamical systems in order to either eliminate or synchronize the chaos.
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