Professor Sergey Ulyanov: the structure and mechanism of a quantum PID controller based on a quantum decision-making logic

Professor Sergey Victorovich Ulyanov was born in Russia, December 15, 1946, and in 1971 graduated from Moscow Technical University on the specialty “Electro- Mechanical Engineering and Automation Control Systems”. In 1974, he got PhD from the Central Institute of Building Construction (Moscow) on the specialty “Dynamic of Building Construction on Earthquake Excitations”. In 1992, he got State Dr. of Physics and Mathematics Sciences from the Institute of Physical-Technical Problems (Moscow) on the specialty “Quantum and Relativistic Dynamic Control Systems”.

His scientific interests are in AI control systems with timedependent random (variable) structures for complex mechanical systems, intelligent toolkit for robotics, fuzzy wise control, SW/HW of fuzzy controllers, intelligent mechantronics, bio-medical engineering, quantum and relativistic control systems, soft computing , quantum algorithms and quantum soft computing.

Academic activities: He published more than 35 books and 200 papers in periodical journals and proceedings of conferences in different scientific domains. As selection, examples of the books are “Introduction in Relativistic Theory and its Applications to New Technology” (Moscow, 1979); “Problems for Quantum and Relativistic Dynamic Control Systems” (Moscow, 1982); “Statistical Dynamics of Machine Building Constructions” (Moscow, 1977); “Statistical Analysis of Building Construction on Earthquake Excitations” (Moscow, 1977); “Theory of models in Control Systems” (Moscow,1978), “Fuzzy controllers and Intelligent Control Systems “(Moscow,1990,1991 and 1992). “Quantum Information and Quantum Computational Intelligence: Backgrounds and Applied Toolkit of Information Design Technologies,” (Vol. 79 – 86, Italy, 2005).

From 1974 he gave lectures in Moscow University of Electronics, Automatic and Radiotechnique as a Professor; from 1994 – 1997 is professor of University of Electro- Communications (Tokyo, Japan); from 1998 – 2003 as professor of Milan University (Italy).

He is the member of Editorial board in many International Journals as “Soft Computing: A Fusion of Foundations, Methodologies and Applications,” “Journal of Robotics and Mechatronics,” “Journal of Advanced Computational Intelligence,” “Biomedical Engineering,” and etc.; chainman of many sections in International conferences; scientific coordinator of International projects between USA, Italy, Japan and Russia. He is inventor of more than 25 patents (robust intelligent control and quantum soft computing) published in USA, EU, Japan and China.

Industry activities: He developed a practical model of the wall climbing robot for the decontamination of Chernobyl nuclear-power plant, for cleaning, painting, fire-fighting operations, etc.; fuzzy controller for mobile robots and manipulators in the Institute for Problems in Mechanics (Russian Academy of Sciences) and in the University of Electro- Communications (Tokyo, Japan). In the earthquake engineering he developed a new designing technology of building with variable structures. In mechatronics he contributed to the development of a new electro-pneumatic proportional regulator on VLSI for intelligent controllers of the wall climbing robot and suspension of automobile. In biomedical engineering he invented a new apparatus for artificial lung ventilation on the basis of this electro-pneumatic regulator and got patens for it. Together with ST Microelectronics Company (Italy-French) he was developing new software and hardware of interfaces for fuzzy processor and fuzzy controller. The applications of this fuzzy controller are expected to intelligent mobile robot for service use which is developed in the Prof.Yamafuji Laboratory, the University of Electro-Communications (Tokyo, Japan), and apparatus for artificial lung ventilation in the Biomedical Research Institute in Russia. In Yamaha Motor Co., Ltd he was developing toolkit for applications of quantum computing in robust intelligent control system design.

Prof. S.V. Ulyanov was collaborating in AI fuzzy control system and intelligent mechatronics for mobile robots and a robotic unicycle in the Yamafuji & Ulyanov Lab., Department of Mechanical Engineering and Intelligent Systems of the University of Electro-Communications (Tokyo, Japan); and intelligent robust control of car suspension system and robotic motorcycle in Yamaha Motor Co., Ltd.

What main problem in intelligent control systems design you solve?

Main problem in intelligent control systems design is following: how to introduce self-learning, self-adaptation and self-organizing capabilities into the control process that enhanced robustness of developed advanced control system. Many learning schemes based on BP-algorithm or other have been proposed. But for more complicated unpredicted control situations (time delay and noise in sensor system, jumped change of control object model parameters, unpredicted stochastic noises etc.), learning and adaptation methods based on BP-algorithms and other random iteration algorithms doesn’t can supply robust control in global sense.

The complexity of problem increased for the case of integrated control systems with the necessity to design the coordination control of many sub-systems as control objects with different optimization criteria (general problem of System of Engineering Systems). Soft computing methodologies had expanded application areas of FC by adding learning and adaptation features. But still now it is difficult to design “good” and robust intelligent control system, when its operational conditions have to evolve dramatically (aging, sensor failure, sensor’s noises or delay, etc.). Such conditions could be predicted from one hand, but it is difficult to cover such situations by a single FC.

You are an author of quantum soft computing approach. You showed the role and necessity of applying intelligent information technologies development based on computational intelligence toolkits in the task of objective estimation of a general psychophysical state of a human being operator. What is human being emotion in cognitive intelligent robotic control that was developed by your team?

We showed the possibility of quantum / soft computing optimizers of knowledge bases (QSCOptKBTM) as the toolkit of quantum deep machine learning technology implementation in the solution’s search of intelligent cognitive control tasks applied the cognitive helmet as neurointerface. In particular case, we demonstrated the possibility of classifying the mental states of a human being operator in on line with knowledge extraction from electroencephalograms based on SCOptKBTM and QCOptKBTM sophisticated toolkit.

Application of soft computing technologies to identify objective indicators of the psychophysiological state of an examined person was purposed by our team. You proposed the new method of robust self-organized PID controller design based on a quantum fuzzy inference algorithm. You are described structure of information design technology of intelligent self-organized fuzzy controllers based on quantum search algorithm model as quantum fuzzy inference.

The structure design of robust advanced autonomous or integrated control systems for unpredicted control situations is the corner stone of modern control theory and systems. The degree to which a control system deals successfully with above difficulties depends on the intelligent level of advanced control system. We are solving the algorithmic complexity problems in advanced control system design with unconventional sophisticated methods of computational intelligence. Main result of quantum computing is exponential (or quadratic) speed-up in comparison to classical computation and solutions of problems.

The structure and mechanism of a quantum PID controller (QPID) based on a quantum decision-making logic by using two K-gains of classical PID (with constant K-gains) controllers. We applied computational intelligence toolkit as a soft computing technology in learning situations.

How is your design technology implemented?

The ultimate applications of quantum control strategies may include CO as smart macro- and micro-electromechanical systems, intelligent sensor systems (with compressing of redundant data information processing and advanced decision making), intelligent robotics and mechatronics, quantum informatics, computer science, AI security communication and information systems, including quantum algorithm modeling system for robust intelligent control design in nanotechnologies.

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