scholarly journals Intelligent robust control of redundant smart robotic arm Pt I: Soft computing KB optimizer - deep machine learning IT

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Alena V Nikolaeva ◽  
Sergey Victorovich Ulyanov
Keyword(s):  
Machine Learning ◽  
Control System ◽  
Robust Control ◽  
Control Systems ◽  
Soft Computing ◽  
Intelligent Control ◽  
Control Object ◽  
Robotic Arm ◽  
Control Laws ◽  
Intelligent Control Systems

Redundant robotic arm models as a control object discussed. Background of computational intelligence IT based on soft computing optimizer of knowledge base in smart robotic manipulators introduced. Soft computing optimizer is the toolkit of deep machine learning SW platform with optimal fuzzy neural network structure. The methods for development and design technology of intelligent control systems based on the soft computing optimizer presented in this Part 1 allow one to implement the principle of design an optimal intelligent control systems with a maximum reliability and controllability level of a complex control object under conditions of uncertainty in the source data, and in the presence of stochastic noises of various physical and statistical characters. The knowledge bases formed with the application of a soft computing optimizer produce robust control laws for the schedule of time dependent coefficient gains of conventional PID controllers for a wide range of external perturbations and are maximally insensitive to random variations of the structure of control object. The robustness of control laws is achieved by application a vector fitness function for genetic algorithm, whose one component describes the physical principle of minimum production of generalized entropy both in the control object and the control system, and the other components describe conventional control objective functionals such as minimum control error, etc. The application of soft computing technologies (Part I) for the development a robust intelligent control system that solving the problem of precision positioning redundant (3DOF and 7 DOF) manipulators considered. Application of quantum soft computing in robust intelligent control of smart manipulators in Part II described.

scholarly journals Intelligent robust control system of robotic unicycle based on the end-to-end soft computing technology

2019 ◽  
pp. 58-86
Author(s):  
Sergey Ulyanov ◽  
Viktor Ulyanov ◽  
Andrey Reshetnikov ◽  
Olga Tyatyushkina ◽  
Kazuo Yamafuji
Keyword(s):  
Control System ◽  
Robust Control ◽  
Entropy Production ◽  
Soft Computing ◽  
Intelligent Control ◽  
Fitness Function ◽  
Control Object ◽  
Minimum Entropy ◽  
Intelligent Control System ◽  
Production Calculation

The concept of an intelligent control system for a complex nonlinear biomechanical system of an extension cableless robotic unicycle is considered. A thermodynamic approach to study optimal control processes in complex nonlinear dynamic systems is used. The results of stochastic simulation of a fuzzy intelligent control system for various types of external/ internal excitations for a dynamic, globally unstable control object -extension cableless robotic unicycle based on Soft Computing (Computational Intelligence Toolkit) technology are presented. A new approach to design an intelligent control system based on the principle of the minimum entropy production (minimum of useful resource losses) determination in the movement of the control object and the control system is developed. This determination as a fitness function in the genetic algorithm is used to achieve robust control of a robotic unicycle. An algorithm for entropy production calculation and representation of their relationship with the Lyapunov function (a measure of stochastic robust stability) is described.

A SoC for Intelligent Control Systems

2012 ◽  
Vol 571 ◽  
pp. 514-517 ◽  
Author(s):  
Zheng Xing Zheng ◽  
Guo Min Tang ◽  
Li Min Liu
Keyword(s):  
Control System ◽  
Embedded System ◽  
Control Systems ◽  
Intelligent Control ◽  
Industrial Automation ◽  
Intelligent Controller ◽  
Intelligent Control System ◽  
Intelligent Control Systems ◽  
Software And Hardware

Intelligent control is a new direction of industrial automation. An intelligent control system is composed of algorithm, software and hardware. SoC is one of the best embedded system hardware. SoC may get some new progress for intelligent control. In this paper, intelligent control, SoC and some intelligent controller based on SoC are discussed. The new controller can be the smaller and more reliable.

Soft computing simulation design of intelligent control systems in micro-nano-robotics and mechatronics

2000 ◽  
Vol 4 (3) ◽  
pp. 147-156 ◽  
Author(s):  
S. A. Panfilov ◽  
S. V. Ulyanov ◽  
I. Kurawaki ◽  
V. S. Ulyanov ◽  
L. V. Litvintseva ◽  
...  
Keyword(s):  
Control Systems ◽  
Soft Computing ◽  
Intelligent Control ◽  
Simulation Design ◽  
Intelligent Control Systems ◽  
Computing Simulation

scholarly journals Necessity of intelligent control systems application in experimental nuclear physics complexes (NICA type).PT.I: Problems of unforeseen situations

2020 ◽  
pp. 32-57
Author(s):  
Oleg Brovko ◽  
Alexey Eliseev ◽  
Vladimir Kekelidze ◽  
Vladimir Korenkov ◽  
Dmitry Monahov ◽  
...  
Keyword(s):  
Control System ◽  
Control Systems ◽  
Intelligent Control ◽  
Nuclear Physics ◽  
Fuzzy Controller ◽  
Knowledge Bases ◽  
Level Control ◽  
Intelligent Computing ◽  
Intelligent Control Systems ◽  
End To End

In the article (Part I) the necessity of application of end-to-end quantum technologies of intelligent computing in problems of structure elements control of complex experimental accelerator complex NICA is analyzed. Given a description of possible unforeseen situations, their classification is given to include adjustments to the decision-making application of production rules, logic knowledge bases of intelligent control systems based on emerging information risk increment. In the second part (Part II) the proposed two-level intelligent control system, a physical experimental setup of the NICA complex, in which the lower Executive level is a traditional control system based on expert control using the Tango control system Controls, while the upper (intelligent) level control actions are generated using the methods of quantum end-to-end IT design quantum fuzzy controller. Part II presents the QSCIT (Quantum Soft Computational Intelligence Toolkit), based on soft and quantum computing, to design robust knowledge bases in self-organizing intelligent control systems.

Peculiarities of development intelligent control systems for the production of entomophages

2021 ◽  
pp. 42-50
Author(s):  
V. Lysenko ◽  
◽  
I. Chernova ◽  
Keyword(s):  
Control System ◽  
Knowledge Base ◽  
Control Systems ◽  
Air Temperature ◽  
Intelligent Control ◽  
Semantic Network ◽  
Research Management ◽  
Production Rules ◽  
Intelligent Control System ◽  
Intelligent Control Systems

The article reveals the features of the development of intelligent control systems for the production of entomophages, the relevance of the selected research area is determined. The aim of the study was to substantiate the feasibility of using intelligent control systems in the production of entomophages. Object of research – management process the cultivation of caterpillars Ephestia kuehniella in a specialized box in the production of entomophage Habrobracon hebetor. Research methods – systems approach, data mining, semantic modeling, situational management. A structural diagram of a hybrid intelligent system for controlling the air temperature of a box for growing entomocultures has been developed; knowledge representation model in relation to the construction of a hybrid intelligent control system in the form of a predicate semantic network. The production rules of the knowledge base of the intelligent control system have been developed, which allow to clarify the process of its creation. At the same time, attention is focused on: the sequence of formation of the training sample, which affects the accuracy of control of the air temperature of the box and, accordingly, the costs of electricity; use of ANFIS-editor MATLAB, which developed a self-learning neural network that automatically created a knowledge base regarding dependency the temperature setting of the meter-controller from the error of air temperature regulation. The expediency of using intelligent control systems in the production of entomophages has been scientifically substantiated. Formalized process of creating a hybrid intelligent box air temperature control system for growing entomocultures in the form of a predicate semantic network using production rules. The abovementioned makes it possible to systematize knowledge in relation to the processes of managing the production of entomophages, improve energy efficiency and the level of automation of the production process, which has a positive effect on both the state of the biological component of the object and the economy of the enterprise. The research results were introduced in the laboratory production of the entomophage Habrobrakon hebetor.

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