scholarly journals A computer simulator for steel plant electrical arc furnace regulator

2021 ◽  
Author(s):  
Behzad (George) Jorjani
Keyword(s):  
Closed Loop ◽  
Fuzzy Inference ◽  
Open Loop ◽  
Arc Furnace ◽  
Inference System ◽  
Modeling And Control ◽  
Electrical Arc ◽  
Computer Simulator ◽  
Main Component ◽  
And Control

The function of the simulator is to imitate the behavior of the regulator loop, which is the main component of the Electrical Arc Furnace (EAF) control systems. In the past, the use of artificial intelligence methods, and in particular, the Adaptive Neuro Fuzzy Inference System (ANFIS) were successfully applied in the modeling and control of the EAF components individually. This research expands the use of ANFIS in building the full closed loop computer simulator for the three-phase regulator loop. THe ANFIS models inuts and outpus selected for this project were tried for the first time in this research. The simulator components were trained and verified by the use of plant recorded data in the open loop mode. The response of the closed loop simulator was tuned to follow the behavior of the plant EAF. Therefore the simulator works independent of the plant data or operation commands. The developed simulator, then, was used to measure the results of applying new controls in EAF such as fuzzy controllers, without disturbing the actual plant process.

scholarly journals A computer simulator for steel plant electrical arc furnace regulator

2021 ◽  
Author(s):  
Behzad (George) Jorjani
Keyword(s):  
Closed Loop ◽  
Fuzzy Inference ◽  
Open Loop ◽  
Arc Furnace ◽  
Inference System ◽  
Modeling And Control ◽  
Electrical Arc ◽  
Computer Simulator ◽  
Main Component ◽  
And Control

The function of the simulator is to imitate the behavior of the regulator loop, which is the main component of the Electrical Arc Furnace (EAF) control systems. In the past, the use of artificial intelligence methods, and in particular, the Adaptive Neuro Fuzzy Inference System (ANFIS) were successfully applied in the modeling and control of the EAF components individually. This research expands the use of ANFIS in building the full closed loop computer simulator for the three-phase regulator loop. THe ANFIS models inuts and outpus selected for this project were tried for the first time in this research. The simulator components were trained and verified by the use of plant recorded data in the open loop mode. The response of the closed loop simulator was tuned to follow the behavior of the plant EAF. Therefore the simulator works independent of the plant data or operation commands. The developed simulator, then, was used to measure the results of applying new controls in EAF such as fuzzy controllers, without disturbing the actual plant process.

Modeling and Control of an Automotive All-Wheel Drive Clutch as a Piecewise Affine System

2013 ◽  
Vol 136 (1) ◽  
Author(s):  
Shiming Duan ◽  
Jun Ni ◽  
A. Galip Ulsoy
Keyword(s):  
Closed Loop ◽  
Open Loop ◽  
Lyapunov Theory ◽  
Loop System ◽  
Linear Matrix ◽  
Closed Loop System ◽  
Modeling And Control ◽  
Piecewise Affine ◽  
Wheel Drive ◽  
And Control

Piecewise affine (PWA) systems belong to a subclass of switched systems and provide good flexibility and traceability for modeling a variety of nonlinear systems. In this paper, application of the PWA system framework to the modeling and control of an automotive all-wheel drive (AWD) clutch system is presented. The open-loop system is first modeled as a PWA system, followed by the design of a piecewise linear (i.e., switched) feedback controller. The stability of the closed-loop system, including model uncertainty and time delays, is examined using linear matrix inequalities based on Lyapunov theory. Finally, the responses of the closed-loop system under step and sine reference signals and temperature disturbance signals are simulated to illustrate the effectiveness of the design.

scholarly journals Modeling and control of an unstable system using probabilistic fuzzy inference system

2015 ◽  
Vol 25 (3) ◽  
pp. 377-396
Author(s):  
N. Sozhamadevi ◽  
S. Sathiyamoorthy
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Integrated Approach ◽  
Fuzzy Rules ◽  
Unstable System ◽  
Inference System ◽  
Modeling And Control ◽  
Highly Nonlinear ◽  
And Control

Abstract A new type Fuzzy Inference System is proposed, a Probabilistic Fuzzy Inference system which model and minimizes the effects of statistical uncertainties. The blend of two different concepts, degree of truth and probability of truth in a unique framework leads to this new concept. This combination is carried out both in Fuzzy sets and Fuzzy rules, which gives rise to Probabilistic Fuzzy Sets and Probabilistic Fuzzy Rules. Introducing these probabilistic elements, a distinctive probabilistic fuzzy inference system is developed and this involves fuzzification, inference and output processing. This integrated approach accounts for all of the uncertainty like rule uncertainties and measurement uncertainties present in the systems and has led to the design which performs optimally after training. In this paper a Probabilistic Fuzzy Inference System is applied for modeling and control of a highly nonlinear, unstable system and also proved its effectiveness.

Lagrangian D’Alembert Modeled Maneuverable Autonomous Non-Holonomic Mobile Robot Using a Closed Loop PD Controller

2009 ◽  
Author(s):  
E. Georgiou ◽  
J. Dai
Keyword(s):  
Mobile Robot ◽  
Closed Loop ◽  
Ad Hoc ◽  
Search And Rescue ◽  
Pd Controller ◽  
Modeling And Control ◽  
Holonomic Constraints ◽  
And Control ◽  
Holonomic Mobile Robot ◽  
Initial Results

The motivation for this work is to develop a platform for a self-localization device. Such a platform has many applications for the autonomous maneuverable non-holonomic mobile robot classification, which can be used for search and rescue or for inspection devices where the robot has a desired path to follow but because of an unknown terrain, the device requires the ability to make ad-hoc corrections to its movement to reach its desire path. The mobile robot is modeled using Lagrangian d’Alembert’s principle considering all the possible inertias and forces generated, and are controlled by restraining movement based on the holonomic and non-holonomic constraints of the modeled vehicle. The device is controlled by a PD controller based on the vehicle’s holonomic and non-holonomic constraints. An experiment was setup to verify the modeling and control structure’s functionality and the initial results are promising.

Dynamic Modeling and Control of Packing Pressure in Injection Molding

1994 ◽  
Vol 116 (2) ◽  
pp. 244-249 ◽  
Author(s):  
J. Hu ◽  
J. H. Vogel
Keyword(s):  
Injection Molding ◽  
Closed Loop ◽  
Good Control ◽  
Pressure Control ◽  
Physical Parameters ◽  
Closed Loop System ◽  
Modeling And Control ◽  
Packing Pressure ◽  
And Control ◽  
Plastic Injection

A dynamic model of injection molding developed from physical considerations is used to select PID gains for pressure control during the packing phase of thermo-plastic injection molding. The relative importance of various aspects of the model and values for particular physical parameters were identified experimentally. The controller gains were chosen by pole-zero cancellation and root-locus methods, resulting in good control performance. Both open and closed-loop system responses were predicted and verified, with good overall agreement.

Computers: Applications to the Quantification of Human Behavior

1964 ◽  
Vol 6 (6) ◽  
pp. 585-605
Author(s):  
Gustave J. Rath ◽  
William P. Allman
Keyword(s):  
Human Behavior ◽  
Closed Loop ◽  
Data Gathering ◽  
Open Loop ◽  
Behavioral Analysis ◽  
On Line ◽  
Analysis System ◽  
Response Collection ◽  
And Control ◽  
Computing Machines

This paper discusses the use of computing machines in the biological and social sciences, namely the ultilization of computerized behavior analysis systems in the quantification of human behavior. Only systems of which living human organisms are a part are considered. Some specific functional uses of computers for stimulus preparation and presentation, response collection, and apparatus scheduling and control are presented. All of these functions may be performed by automated systems characterized by the amount of experimental integration and control performed by the computer. Systems types include on-line open-loop, on-line closed loop single or multiple purpose, and off-line. The multiple-man, multiple-purpose system which permits numerous automated investigations upon different source subjects to occur simultaneously is highlighted as the culmination of current automated behavioral analysis systems. But the possibility of behavioral scientists “tapping” into operating systems is presented as possibly having revolutionary consequences with respect to the data gathering of human behaviour. Finally, a general automated behavioral analysis system schematic assists in discussing current advantages, potential advances, and impending limitations of contributions of computers to the quantification of human behavior.

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