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Международный журнал Программные продукты и системы

Промышленная роботизированная интеллектуальная робастная система управления: применение технологий квантовых мягких вычислений и квантовой программной инженерии в неопределенных условиях управления

Сергей Викторович Ульянов, S.V. Ulyanov, А.Г. Решетников, Reshetnikov A.G., Дарья Петровна Зрелова, Зрелова Д. П.

Published Mar 3, 2023• 1 citation
Publisher

Abstract

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 readymade 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, expanding 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.

DOI
10.15827/0236-235x.141.026-045

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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 readymade 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, expanding 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.
The 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. The main physical and information-thermodynamic aspects of the model of quantum intelligent control of classical control objects are discussed and described. An approach is considered for constructing reference control models based on new laws of quantum deep machine learning applying Lagrange/Hamilton neural networks. This work develops the approach of self-organized intelligent control, describing the strategy of designing intelligent cognitive control systems based on quantum and soft computing. 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Обсуждается физическая интерпретация процесса управления самоорганизацией на квантовом уровне на основе квантовых информационно-термодинамических моделей обмена и извлечения квантовой (скрытой) ценностной информации из/между классическими траекториями частиц в модели «рой взаимодействующих частиц». Представлены и описываются основные физические и информационно-термодинамические аспекты модели квантового интеллектуального управления классическими объектами управления. Рассматривается подход построения эталонных моделей управления на основе новых законов квантового глубокого машинного обучения с применением квантовых нейронных сетей Лагранжа/Гамильтона. Данная работа развивает подход самоорганизующегося интеллектуального управления, описывая стратегию проектирования интеллектуальных систем когнитивного управления на основе квантовых и мягких вычислений. 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Гарантированное достижение цели управления роботом осуществляется на основе спроектированной интеллектуальной когнитивной системы управления с применением инструментария квантового оптимизатора баз знаний QCOptKBTM, в структуру которого включен квантовый нечеткий вывод – КНВ. Квантовый алгоритм самоорганизации не робастных баз знаний КНВ структурно опирается на синергетические эффекты от скрытой квантовой информации для осуществления реализации оптимального распределения качеств управления. Данная технология позволяет повысить надежность интеллектуальных когнитивных систем управления в ситуациях управления в условиях неопределенности. Примеры продемонстрировали эффективность введения схемы КНВ в качестве готового программируемого алгоритмического решения для встраиваемых интеллектуальных систем управления.
Strategy of intelligent cognitive control systems of ill-defined control objects based on quantum and soft computing presented. Quantum self-organization knowledge base synergetic effect extracted from intelligent fuzzy controller’s imperfect knowledge bases described. That technology improved of robustness of intelligent cognitive control systems in hazard control situations described with different types of robot cooperation. Examples demonstrated the introduction of quantum fuzzy inference gate design as prepared programmable algorithmic solution for board embedded control systems. The 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. The main physical and information-thermodynamic aspects of the model of quantum intelligent control of classical control objects are discussed and described. An approach is considered for constructing reference control models based on new laws of quantum deep machine learning applying neural networks in mega-science project NICA.
From the point view of quantum IT engineering, the definition of the concept and issues of intelligent control of weakly structured and poorly formalized objects with underdetermined/hidden parameters are discussed. The stages of domestic quantum IT software engineering for the design of embedded self-organizing intelligent control systems using the QSCIT (Quantum Soft Computational Intelligence Toolkit) software toolkit are presented. QSCIT is based on industrial knowledge base optimizers (SC&QC)OptKBTM and employs quantum soft computing technologies. A new type of coordinated dual-channel intelligent control of nitrogen pressure and flow valves with hidden feedback is considered in a process with a variable superconducting magnet cooling structure. In this case, the superconducting magnet is considered as a classical control object with a weakly structured, poorly formalized model with underdetermined parameters, information about which is contained in the learning signal measured on the actual object. Quantum technology for studying a highly nonlinear and dynamically unstable autonomous intelligent robot forms the basis of a system simulating the interaction of nitrogen pressure and flow valves, as well as an intelligent self-organizing quantum controller coordinating the control of nitrogen pressure and flow valves in a cryogenic superconducting magnet cooling unit in the NICA mega-science project (JINR). Quantum soft computing and quantum fuzzy inference in the quantum controller structure extract knowledge from the learning signal and, based on the extracted amount of hidden quantum information, generate additional "thermodynamic" control power as an additional information resource for the intelligent controller to perform useful work. These processes are implemented on a classical processor. The generation of this additional information resource is possible due to the existence (experimentally proven in the study) of a quantum synergistic effect of self-organization of imprecise knowledge bases: from a finite number of imprecise knowledge bases, quantum fuzzy inference generates a robust knowledge base in real time. An entropy analysis of the controllers' operation demonstrated the rational use of useful resources by the quantum intelligent controller, necessary to guarantee the achievement of the control goal. The efficiency and quantum advantage of using intelligent self-organizing quantum controllers (embedded in onboard intelligent control systems) to control classical control objects based on quantum soft computing technologies are demonstrated.
The quantum self-organization algorithm model of wise knowledge base design for intelligent fuzzy controllers with required robust level considered. Background of the model is a new model of quantum inference based on quantum genetic algorithm. Quantum genetic algorithm applied on line for the quantum correlation’s type searching between unknown solutions in quantum superposition of imperfect knowledge bases of intelligent controllers designed on soft computing. Disturbance conditions of analytical information-thermodynamic trade-off interrelations between main control quality measures (as new design laws) discussed in Part I. The smart control design with guaranteed achievement of these tradeoff interrelations is main goal for quantum self-organization algorithm of imperfect KB. Sophisticated synergetic quantum information effect in Part I (autonomous robot in unpredicted control situations) and II (swarm robots with imperfect KB exchanging between “master - slaves”) introduced: a new robust smart controller on line designed from responses on unpredicted control situations of any imperfect KB applying quantum hidden information extracted from quantum correlation. Within the toolkit of classical intelligent control, the achievement of the similar synergetic information effect is impossible. Benchmarks of intelligent cognitive robotic control applications considered.
This paper considers the use of the quantum genetic algorithm for automatic selection of the optimum type and kind of correlation in the quantum structure of fuzzy inference.When solving intelligent and cognitive control tasks based on quantum soft computing and the principles of quantum deep machine learning, it is important to choose the type and kind of quantum correlation.It is an additional physical and informational computing resource in the formation of the laws of the time variation of the gains of traditional regulators located at the lower (performing) level of the intelligent control system structure.This approach is essential for the realization of adaptive and self-organizing processes of knowledge bases and guaranteed to achieve the control objectives under contingency control situations.Successful solution of the problem of choosing the type and kind of quantum correlations allows strengthening the successful search for solutions of algorithmically unsolvable problems at the classical control level.A genetic algorithm is a powerful computational intelligence toolkit for random searching of effective solutions for poorly formalized tasks.However, it has a big disadvantage when used on a classic computer: low speed and dependence on the expert's choice of a decision-making space.The paper describes the types of quantum genetic algorithms based on a combination of quantum and classical calculations, and an algorithm consisting only of quantum calculations.In such algorithm, a population can be composed of only one chromosome in a state of superposition.Immersion in the quantum structure of the fuzzy inference quantum genetic algorithm provides a synergistic effect and allows realizing quantum fuzzy inference on a classical processor.The new effect is based on the quantum genetic algorithm extracting information hidden in the classical state laws change over time the gains of traditional regulators on a new unexpected situation control.Such synergistic effect is possible only with end-to-end intellectual information technology of quantum computing and is absent at the classical level of application of the classical computing technology.
On concrete examples from robotics and self-organizing quantum controllers embedded in on-board control systems, the effectiveness and advantage of applying intelligent control systems using soft and quantum computing are shown. The results of the technology application are summarized on the examples of a dynamically unstable autonomous robot and a quantum regulator controlling the nitrogen pressure in the cryogenic cooling system of the superconducting magnet elements of the NICA plant. The possibility of transferring the existing technology to other control objects is shown. Quantum soft computing and quantum fuzzy inference are implemented on a classical processor and modeled on the “Govorun” supercomputer.