This study proposes a new modeling method for nonlinear systems based on a genetic algorithm (GA). Through this method, the transfer functions of the above unidentified systems can be identified. This method would well help the nonlinear systems to find out their optimal solutions in controls. First, the physical input-output data pairs of the unidentified system should be collected. Next, the coefficients of the first second-order transfer function can be identified by GA. Last, based on the errors between the physical data pair and the first second-order transfer function; similarly the second second-order transfer functions can be identified by GA.
So far as known, electromagnetic flywheel (EF) systems are with many uncertainties, such as nonlinear electromechanical coupling and electromagnetic saturation, etc. They are difficult to accurately modeling by traditional mathematic ways that restrict the control and application of them. In order to confirm the effectiveness and feasibility of this proposed method, an EF system is setup for this study. By assessing the results of calculations and experiments shows that the proposed method is possible to be applied for any nonlinear systems.