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The main aim of this paper is to develop a PID tuning methodology for a processing plant using Genetic Algorithm. Genetic Algorithm or in short GA is a stochastic algorithm based on principles of natural selection and genetics. Genetic Algorithms (GAs) are a stochastic global search method that mimics the process of natural evolution. Genetic Algorithms have been shown to be capable of locating high...
In this paper a prototype of crude network tanks system is modeled from the first principle, the system is then controlled using the PID (Proportional Integral Derivative) control system, the PID tuning parameters are optimized successfully using Genetic algorithm based on control performance indices (i.e. Mean Square Error (MSE), integral square error (ISE), integral absolute error (IAE), and integrated...
Differential Evolution algorithm has recently emerged as a simple yet very powerful technique for real parameter optimization. This article describes an application of DE for the design of fractional order proportional Integral Derivative controller. FOPID controller parameter are composed of the proportional constant, integral constant, derivative constant, derivative order and integer order, and...
The paper presents two plants which use proportional-integral-derivative (PID) controller for obtaining the desired transient response. Ziegler Nichols (ZN) and Genetic Algorithm (GA) tuning methods are employed on both the plants affected by band-limited white noise, and the optimized parameters (viz. Settling Time and Peak Overshoot) are compared. The efficacy of system optimization by GA is being...
In this paper, a new intelligent method based on hybridization of Elite Genetic Algorithm and Tabu Search (HEGATS) to design optimal fuzzy controllers for multi-input multi-output (MIMO) nonlinear system is proposed. The principle of the proposed method is to find the elitism by Genetic Algorithm and to introduce it in the Tabu Search algorithm as initial solution in order to find the optimal fuzzy...
This study investigates the effectiveness of the genetic algorithm evolved neural network and its application in the drive control systems of electromechanical objects. The methodology adopts a real coded GA strategy using datasets in a series of experiments that evaluate the effects on network performance of different choices of network parameters.
The genetic algorithm with greedy heuristic, initially developed for solving location problems on networks, can be adapted for solving continuous problems such as k-means. However, the efficiency of such algorithm in case of continuous problems does not allow to use it for solving the large-scale problems. In this paper, authors propose a modification to this algorithm which allows such algorithm...
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