From Classical Control to Fuzzy Logic Control

2007 ◽  
pp. 1-15
Author(s):  
Charles P. Coleman
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Logic Control ◽  
Classical Control

Implementation of a Fuzzy Logic Control Strategy on a Harvester’s Controller Based on MATLAB Environment

2019 ◽  
Vol 33 (13) ◽  
pp. 1959043
Author(s):  
Wei Li ◽  
Jingqian Wen ◽  
Qing Jiang ◽  
Liangtu Song ◽  
Zhengyong Zhang
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Nonlinear Process ◽  
C Language ◽  
Logic Control ◽  
Cleaning System ◽  
Complete Set ◽  
Classical Control ◽  
Upper Computer

Due to the nonlinear process of grain harvesting, there is no precise mathematical model to describe the behavior of the cleaning system of a harvester. Both the classical control and modern control methods cannot fulfil the requirements. Owing to this, the intelligent control algorithm was proposed, and the fuzzy logic control (FLC) method is a type of this. At present, most FLC algorithms are proposed in a MATLAB environment. However, the control problems in reality are controlled by microcomputer controllers with different chips. The control language of the microcomputer controller is usually written in C language. It is impossible to directly migrate the algorithm between these two different languages. Therefore, it is an important issue to transplant the FLC algorithm procedure written by MATLAB to the microcomputer controller. To realize the above target, we have built a complete set of control systems for our harvester’s cleaning system based on an upper computer and an STM32 core-chip controller. By means of combining FLC theory and expert knowledge, we adopted an improved FLC algorithm for the cleaning system, which is mounted in our self-designed combine harvester. Through this scheme, we have realized the objective of migrating the FLC algorithm from a MATLAB environment to the controller. The results of the experiment show that our method is reliable.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

A contribution to the identification in fuzzy-logic control

1990 ◽  
Vol 55 (4) ◽  
pp. 951-963 ◽  
Author(s):  
Josef Vrba ◽  
Ywetta Purová
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Automatic Generation ◽  
Linguistic Variable ◽  
Fuzzy Subset ◽  
Input Output ◽  
Logic Control ◽  
Correlation Tables

A linguistic identification of a system controlled by a fuzzy-logic controller is presented. The information about the behaviour of the system, concentrated in time-series, is analyzed from the point of its description by linguistic variable and fuzzy subset as its quantifier. The partial input/output relation and its strength is expressed by a sort of correlation tables and coefficients. The principles of automatic generation of model statements are presented as well.

Sign in / Sign up

Export Citation Format

Share Document