Adaptive Control of CNC Machining Parameters Based on Fuzzy Control Theory

Abstract Traditional virtual synchronous generators (VSG) control inverters. Inverter output frequency characteristic of the virtual inertia (J) and virtual damping (D) coefficient, and the virtual parameters need to be modified and adjusted according to the purpose. To solve this problem, this paper proposes a virtual parameter adaptive control strategy based on fuzzy control theory to adjust the frequency characteristics of VSG. MATLAB/Simulink is used to build a simulation model to verify the correctness of the proposed fuzzy control theory’s adaptive virtual parameter theory.

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Smart Adaptive CNC Machining - State of the Art

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.657.859 ◽

2014 ◽

Vol 657 ◽

pp. 859-863 ◽

Cited By ~ 2

Author(s):

Anton Mircea Vasiloni ◽

Mircea Viorel Dragoi

Keyword(s):

Adaptive Control ◽

Condition Monitoring ◽

Cnc Machining ◽

Plant Production ◽

Machining Parameters ◽

Chatter Vibration ◽

New Directions ◽

Optimum Utilization ◽

Smart Machining

Condition monitoring is becoming popular in industry because of its efficient role in detecting potential failures. The use of condition monitoring techniques will generally improve plant production availability and reduce downtime cost. A reliable adaptive control system can prevent downtime of the machine or avoid unwanted conditions such as chatter vibration, excessive tool wear by allowing the optimum utilization of the tool life. To ensure the quality of machining products, reduce the machining costs and increase the machining efficiency, it is necessary to adjust the machining parameters in real time. A survey of actual researches is presented in this paper in purpose to define new directions of improvement of adaptive control towards smart machining systems.

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Intelligent Monitoring of Taijiquan Exercise Based on Fuzzy Control Theory

Microprocessors and Microsystems ◽

10.1016/j.micpro.2021.103859 ◽

2021 ◽

pp. 103859

Author(s):

Shengnan Lu ◽

Shilong Wang

Keyword(s):

Fuzzy Control ◽

Control Theory ◽

Intelligent Monitoring

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Fuzzy control based on genetic algorithm in intelligent psychology teaching system

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189827 ◽

2021 ◽

pp. 1-9

Author(s):

Xiaojia Pang ◽

Yuwen Ning

Keyword(s):

Genetic Algorithm ◽

Fuzzy Control ◽

Control Theory ◽

Classroom Teaching ◽

Improved Genetic Algorithm ◽

Group Function ◽

Teaching Process ◽

Teaching Management ◽

Teaching System ◽

Volume Group

The advancement of science has made computer technology and the education industry more and more closely related, and the development of intelligent teaching systems has also opened a new path for classroom teaching. This paper studies the application of fuzzy control based on genetic algorithms in the intelligent psychology teaching system. Facing the complicated variables in the teaching process, the improved genetic algorithm can better realize dynamic teaching decisions through fuzzy control. This article aims to improve the quality of psychology classroom teaching, and develops an intelligent psychology teaching system based on the fuzzy control theory of genetic algorithm. Combined with the current development of fuzzy control theory, the problems existing in the intelligent teaching system are studied and analyzed, and they have been optimized and improved. This paper proposes a control algorithm based on a teaching management system. The algorithm can implement fuzzy control on student models, knowledge organization structure, intelligent test papers and teaching decision-making. While restoring the real teaching process, it can better realize teaching students in accordance with their aptitude and improve teaching. The intelligence of the system. According to the system test data, the proportions of the difficulty of the system’s automatic test paper are 30.1%, 51.6%, 18.3%, which are in line with the designer’s set expectation of 3 : 5:2, which shows the improved genetic algorithm. It can realize the intelligent volume group function very well.

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