Fuzzy logic application for intelligent control of a variable speed drive

1994 ◽  
Vol 9 (4) ◽  
pp. 679-685 ◽  
Author(s):  
Yifan Tang ◽  
Longya Xu
Keyword(s):  
Fuzzy Logic ◽  
Intelligent Control ◽  
Variable Speed ◽  
Variable Speed Drive

DESIGN, SIMULATION AND IMPLEMENTATION MONITORING SYSTEM FOR SYSTEM CONTROL C4 FEEDING PUMP FOR FEED VAPORIZATION IN THE BUTADIENE PLANT

2020 ◽  
Vol 1 (1) ◽  
pp. 31-35
Author(s):  
Fahmi Yunistyawan ◽  
Yunistyawan J Berchmans ◽  
Gembong Baskoro
Keyword(s):  
Motor Control ◽  
Control System ◽  
Field Operator ◽  
System Control ◽  
Variable Speed ◽  
Variable Speed Drive ◽  
Software And Hardware ◽  
Discharge Pressure ◽  
Selection Of

This study implements the auto start control system on an electric motor 3 phase C4Feeding pump when the discharge pressure is low-low (4.3 kg /cm²). The C4 feeding pumpmotor was initially manually operated from the local control station, this was very ineffectiveand inefficient because it still relied on the field operator to operate the pump motor and whenthe plant was in normal operating it is very risk if the field operator late to operate motor then itwill impact to quality of the product, and if the delay time to operate motor is too long then planthave to shut down, therefore improvement is needed in the C4 feeding pump motor controlsystem. In this paper, various types of 3-phase motor control are explained which allow it to beapplied to the C4 feeding pump motor that are on-off, inverter, and variable speed drive andefficient selection of the three systems control of the motor. Software and hardware used in thisthesis work are DCS CENTUM VP Yokogawa.

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.

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