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.