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Document Type

Original Study

Keywords

Control Engineering

Abstract

Accurate on-line estimates of critical system states and parameters are needed in a variety of engineering applications, such as condition monitoring, fault diagnosis, and process control. In these and many other applications it is required to estimate a system variable which is not easily accessible for measurement, using only measured system inputs and outputs. The classical identification methods, such as least-square method, are calculus-based search method. They have many drawbacks such as requiring a good initial guess of the parameter and gradient or higher-order derivatives of the objective function are generally required also there is always a possibility to fall into a local minimum. In this paper we develop on-line, robust, efficient, and global optimization identification for parameters estimation based on genetic algorithms. The simulation results show that the proposed algorithm is very fast to find and adapt the estimated parameters.

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