Synthesis of Automatic Motion Control Systems of an AUV Based on Fuzzy Logic Methods with Neural Network Setting of Parameters

2020 ◽  
pp. 755-763
Author(s):  
Van Tuan Pham ◽  
Thi Ha Nguyen
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Control Systems ◽  
Motion Control

Deadzone compensation in motion control systems using adaptive fuzzy logic control

1999 ◽  
Vol 7 (6) ◽  
pp. 731-742 ◽  
Author(s):  
F.L. Lewis ◽  
Woo Kam Tim ◽  
Li-Zin Wang ◽  
Z.X. Li
Keyword(s):  
Fuzzy Logic ◽  
Control Systems ◽  
Motion Control ◽  
Fuzzy Logic Control ◽  
Adaptive Fuzzy ◽  
Logic Control ◽  
Adaptive Fuzzy Logic

Control Systems in Manufacturing

1994 ◽  
Author(s):  
Mruthyunjaya S. Telagi ◽  
Athamaram H. Soni
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Control Systems ◽  
Research Trends ◽  
Future Research ◽  
Cerebellar Model Articulation Controller ◽  
Manufacturing Environment ◽  
The Future ◽  
Planar Robot

Abstract This paper reviews different control methodologies applied in manufacturing environment. Since comparatively newer control methodologies like — Neural networks, Fuzzy logic, and Cerebellar model articulation controller have gained more research interests in recent years, they have been dealt in more detail. With this, we have presented application of neural network for endeffector positioning of three degree planar robot and results have been evaluated. Finally the future research trends in these areas have been discussed.

scholarly journals Investigation of the Possibility of Building a Neural Fuzzy Logic Controller with Discrete Terms for Controlling and Automating Oil and Gas Engineering Facilities

2021 ◽  
Vol 19 (3) ◽  
pp. 105-110
Author(s):  
A. M. Sagdatullin ◽  
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Control Systems ◽  
Fuzzy Logic Controller ◽  
Oil And Gas ◽  
Fuzzy Controller ◽  
Neuro Fuzzy ◽  
Classical Control

The issue of increasing the efficiency of functioning of classical control systems for technological processes and objects of oil and gas engineering is investigated. The relevance of this topic lies in the need to improve the quality of the control systems for the production and transportation of oil and gas. The purpose of the scientific work is to develop a neuro-fuzzy logic controller with discrete terms for the control and automation of pumping units and pumping stations. It is noted that fuzzy logic, neural network algorithms, together with control methods based on adaptation and synthesis of control objects, make it possible to learn the automation system and work under conditions of uncertainty. Methods for constructing classical control systems are studied, the advantages and disadvantages of fuzzy controllers, as the main control system, are analyzed. A method for constructing a control system based on a neuro-fuzzy controller with discrete terms in conditions of uncertainty and dynamic parameters of the process is proposed. The positive features of the proposed regulator include a combination of fuzzy reasoning about a technological object and mathematical predictive models, a fuzzy control system gains the possibility of subjective description based on neural network structures, as well as adaptation to the characteristics of the object. The graph of dependence for the term-set of the controlled parameter on the degree of membership is presented. A possible implementation of tracking the triggering of one of the rules of the neuro-fuzzy system in the format of functional block diagrams is presented. The process of forming an expert knowledge base in a neuro-fuzzy control system is considered. For analysis, a graph of the dependence of the output parameter values is shown. According to the results obtained, the deviation of the values for the model and the real process does not exceed 18%, which allows us to speak of a fairly stable operation of the neuro-fuzzy controller in automatic control systems.

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