GA-Based Identification of Unknown Structured Mechatronics System

Soft computing techniques, e.g., Neural Networks, Fuzzy inference, evolutionary computation, and chaos theory, have been applying 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 non-linearities which are difficult to be modeled by mathematical formulas. This paper presents a novel autonomous algorithm for the identification of unknown structured motion control systems using Genetic Algorithms (GA), where the optimal order of a system polynomial and the optimal set of its coefficients can be determined by means of the optimization ability of the GA. The effectiveness of the proposed identification can be verified by experiments using the typical mechanical systems with velocity controller.