Industrial robotic intelligent robust control system: applying quantum soft computing technologies and quantum software engineering in unpredicted control situations

The strategy for designing intelligent control systems based on quantum and soft computing technologies is described. The synergetic effect of quantum self-organization of a robust knowledge base, extracted from imperfect knowledge bases of an intelligent fuzzy controller, is presented. The developed technology improves the reliability of intelligent cognitive control systems in unforeseen control situations, for example, with various types of interacting robots. Benchmarks demonstrated the effective implementation of a quantum fuzzy inference circuit as a ready-made programmable algorithmic solution for lower-level control systems embedded in a standard board, demonstrated the quantum superiority of quantum intelligent control of classical control objects, ex-panding the Feynman-Manin thesis. The correct physical interpretation of the process of controlling self-organization at the quantum level is discussed on the basis of quantum information-thermodynamic models of exchange and extraction of quantum (hidden) valuable information from/between classical particle trajectories in the “swarm of interacting particles” model. A new information synergetic effect is demonstrated: a robust knowledge base of a quantum fuzzy controller is created in real time from two unreliable knowledge bases of a fuzzy controller. This effect is purely quantum in nature and uses hidden quantum information extracted from classical states. The main physical and information-thermodynamic aspects of the model of quantum intelligent control of classical control objects are discussed.