The speaker will present the results of the development of intelligent automatic control systems based on artificial neural networks, fuzzy logic, and quantum-like algorithms. MLIT created a specialised robotics testing ground to test technologies on real physical devices. Various manipulators, tracked vehicles, and other objects operating in a non-deterministic environment serve as control objects at the robotic testing ground. Software libraries have been developed that implement genetic and fuzzy controller algorithms and enable interaction between devices. The results obtained at the robotic test site were used to develop an intelligent superstructure over the nitrogen valve control system at the test bench of the superconducting magnets factory in VBLHEP. A program that operates in the Tango Controls environment and performs coordinated control of several valves to stabilise nitrogen pressure in a cryogenic facility was developed. This program provides automatic pressure control in a cryogenic facility without disturbing the already existing control level.

Further work involves the development of a robotic testing ground for testing and implementing new algorithms and approaches to building robust automatic control systems, including those based on soft computing algorithms and quantum fuzzy neural networks.