Evolutionary identification algorithm for unknown structured mechatronics systems using GA

Soft computing techniques, e.g. neural networks, fuzzy inference, evolutionary computation, and chaos theory, have been applied to a wide variety of control systems in industry because of their control capability and flexibility. They are also powerful to handle the complicated mechatronics systems with various nonlinearities which are difficult to be modeled by mathematical formulas. This paper presents a novel evolutionary algorithm for the identification of unknown structured mechatronics systems using genetic algorithms (GA), where the optimal system mathematical structure and its set of parameters can be determined by means of the optimization ability of GA. The effectiveness of the proposed identification can be verified by experiments using the typical mechanical systems with a velocity controller