Soft Computing Testing in Real Industrial Platforms for Process Intelligent Control

2013 ◽  
pp. 221-230 ◽  
Author(s):  
E. Larzabal ◽  
J. A. Cubillos ◽  
M. Larrea ◽  
E. Irigoyen ◽  
J. J. Valera
Keyword(s):  
Soft Computing ◽  
Intelligent Control

scholarly journals Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2617
Author(s):  
Catalin Dumitrescu ◽  
Petrica Ciotirnae ◽  
Constantin Vizitiu
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Intelligent Control ◽  
Fuzzy Controller ◽  
Control Technique ◽  
Distributed Intelligence ◽  
High Level ◽  
The Impact ◽  
Fuzzy Subsets

When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.

Soft computing algorithms for intelligent control of a mobile robot for service use

1997 ◽  
Vol 1 (2) ◽  
pp. 99-106 ◽  
Author(s):  
T. Tanaka ◽  
J. Ohwi ◽  
L. V. Litvintseva ◽  
K. Yamafuji ◽  
S. V. Ulyanov
Keyword(s):  
Mobile Robot ◽  
Soft Computing ◽  
Intelligent Control ◽  
Service Use

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 Intelligent control of mobile robot with redundant manipulator & stereovision: quantum / soft computing toolkit

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Kirill V Koshelev ◽  
Alena V Nikolaeva ◽  
Andrey G Reshetnikov ◽  
Sergey Victorovich Ulyanov
Keyword(s):  
Quantum Computing ◽  
Mobile Robot ◽  
Knowledge Base ◽  
Soft Computing ◽  
Intelligent Control ◽  
Computational Intelligence ◽  
Control Object ◽  
Knowledge Bases ◽  
Self Organization ◽  
Redundant Manipulator

The task of an intelligent control system design applying soft and quantum computational intelligence technologies discussed. An example of a control object as a mobile robot with redundant robotic manipulator and stereovision introduced. Design of robust knowledge bases is performed using a developed computational intelligence – quantum / soft computing toolkit (QC/SCOptKBTM). The knowledge base self-organization process of fuzzy homogeneous regulators through the application of end-to-end IT of quantum computing described. The coordination control between the mobile robot and redundant manipulator with stereovision based on soft computing described. The general design methodology of a generalizing control unit based on the physical laws of quantum computing (quantum information-thermodynamic trade-off of control quality distribution and knowledge base self-organization goal) is considered. The modernization of the pattern recognition system based on stereo vision technology presented. The effectiveness of the proposed methodology is demonstrated in comparison with the structures of control systems based on soft computing for unforeseen control situations with sensor system.

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