Nonlinear On-line Identification of Dynamic Systems with Restricted Genetic Optimization

This paper presents an improved on-line identification method of non-linear time-varying dynamic systems with linear and non-linear models . This method is based on Genetic algorithms with a new technique to simulate the behaviour of the gradient method without using the concept of derivatives. This method uses an on-line identification algorithm that begins by calculating what ARX model adapts better to the system. Then the algorithm uses the orders and delay of this first model to calculate an OE, ARMAX or NARMAX model. This new model serves as a seed to initialize a Restricted Genetic Optimization, which improves the previous identification and tracks the system in its variations with the time. The main characteristics are: it is fast, robust, it works on-line and identifies nonlinear time-varying systems.