A hierarchical fuzzy logic controller with a velocity modifier for restructurable flight control systems

1995 ◽  
Author(s):  
Howard Ho ◽  
S Balakrishnan
Keyword(s):  
Fuzzy Logic ◽  
Control Systems ◽  
Flight Control ◽  
Fuzzy Logic Controller

Professor Ebrahim Mamdani Memorial Issue

2012 ◽  
Vol 16 (5) ◽  
pp. 591-591
Author(s):  
Yoshinori Arai ◽  
Toshihiko Watanabe
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Control ◽  
Control Systems ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Special Issue ◽  
Fuzzy Algorithms ◽  
Dynamic Plant ◽  
Memorial Issue ◽  
The Many

On February 22, 2010, Prof. Ebrahim H. Mamdani who devised Mamdani fuzzy inference has passed away. His work in fuzzy inference, which rapidly paved the way to its practical use, helped disseminate Prof. Lotfi Zadehfs fuzzy logic and the development of fuzzy research. Prof. Mamdanifs two papers on fuzzy inference ? gApplication of fuzzy algorithms for control of simple dynamic planth (Proc. IEE, Vol.121, No. 12, pp. 1585-1588, 1974) and gAn experiment in linguistic synthesis with a fuzzy logic controllerh (Int. J. of Man-Machine Studies, Vol.7, No.1, pp. 1-13, 1975) with S. Assilian ? enabled fuzzy inference technology to develop dramatically both indicatively and indirectly to where it has been applied, including fuzzy control systems. This special issue honors Prof. Mamdani for his invaluable efforts in these and many other fields. We have asked for submissions by researchers influenced by Prof. Mamdanifs achievements, including his work in fuzzy inference, and have narrowed down to one review and seven full papers. The review by Hirosato Seki and Kai Meng Tay provides an incisive overview on the many aspects of fuzzy inference that Prof. Mamdani brought to light. Prof. Mamdanifs fuzzy inference has become a deterministic technology that can be chosen naturally and that will continue to be influential and survivable well into the future.

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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