Building knowledge bases of fuzzy controllers: the superiority of intelligent technologies based on soft computing for modeling control of unstable dynamic systems

The paper presents the results of a study of the use of two industrial software products for building knowledge bases of fuzzy controllers for controlling unstable nonlinear dynamic objects: based on ANFIS (built-in Matlab module) and AFM with tools $SCOptKB^{TM}$ - knowledge base optimizer based on soft computing technologies, and $QCOptKB^{TM}$ - knowledge base optimizer based on quantum computing. The results of a comparative analysis of the quality of knowledge bases built using these tools for intelligent control systems are considered. The comparison is realized by modeling intelligent control of typical nonlinear dynamic systems with local, global and partial (in terms of variables) instability characteristic of robotic systems. An effective approach to the design of knowledge bases based on soft computing technology is considered using a number of important thermodynamic and information-entropy control criteria that increase the objectivity of the information contained in the database about the dynamic behavior of the control object.