scholarly journals Fuzzy Control Using Piecewise Linear Membership Functions Based on Knowledge of Tuning PID Controller.

1998 ◽  
Vol 64 (619) ◽  
pp. 925-931 ◽  
Author(s):  
Kenichiro HAYASHI ◽  
Akifumi OTSUBO ◽  
Shuta MURAKAMI ◽  
Mikio MAEDA
Keyword(s):  
Fuzzy Control ◽  
Pid Controller ◽  
Piecewise Linear ◽  
Membership Functions

Fuzzy Control Using Piecewise Linear Membership Functions Based on Knowledge of Tuning a PID Controller

2001 ◽  
Vol 5 (1) ◽  
pp. 71-77 ◽  
Author(s):  
Kenichiro Hayashi ◽  
◽  
Akifumi Otsubo ◽  
Kazuhiko Shiranita
Keyword(s):  
Fuzzy Control ◽  
Pid Controller ◽  
High Speed ◽  
Fuzzy Controller ◽  
Piecewise Linear ◽  
Membership Functions ◽  
Trial And Error ◽  
Tuning Parameters ◽  
Control Rules ◽  
Set Up

Tuning control parameters of a fuzzy controller depends on trial-and-error. How this can be accomplished efficiently is an important subject in fuzzy control that should be investigated. We propose a method in which membership functions of a fuzzy controller can be simply set up using the knowledge of tuning parameters in a conventional PID controller. In this method, fuzzy control is realized as follows: Piecewise linear membership functions determined using the knowledge of tuning parameters in a PID controller are set up in antecedent parts of four fuzzy control rules having simple structures. Then the simplified inference method that enables high-speed inference is applied to fuzzy control rules.

Induction Heating Furnace Temperature Control Based on the Fuzzy PID

2012 ◽  
Vol 217-219 ◽  
pp. 2463-2466 ◽  
Author(s):  
Xue Gang Hou ◽  
Cheng Long Wang
Keyword(s):  
Fuzzy Control ◽  
Temperature Control ◽  
Induction Heating ◽  
Pid Control ◽  
Pid Controller ◽  
Heating Furnace ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Furnace Temperature ◽  
Run Up

Induction heating furnace temperature control is a complex nonlinear hysteretic inertial process, it's difficult to obtain an accurate mathematical model because the temperature and disturb from outside is complicated. The normal PID control algorithm is hard to satisfy the standards of control. The fuzzy PID controller provided in this article is a combination between fuzzy control and the traditional PID control. The Fuzzy control theory is used to setting the ratio, the integral and the differential coefficient of the PID control. In the run-up stage, rapidity is guaranteed, overstrike and the steady-state error is up to the mustard. An example indicates that fuzzy PID control is superior to the normal PID controller.

EXPLICIT FUNCTIONS OF FUZZY CONTROL SYSTEMS

1996 ◽  
Vol 04 (06) ◽  
pp. 515-535 ◽  
Author(s):  
LÁSZLÓ T. KÓCZY ◽  
MICHIO SUGENO
Keyword(s):  
Computational Complexity ◽  
Fuzzy Control ◽  
Control Systems ◽  
Mathematical Description ◽  
Membership Functions ◽  
Mathematical Functions ◽  
Low Computational Complexity ◽  
Special Cases ◽  
Single Input ◽  
Explicit Formulae

Fuzzy control systems have proved their applicability in many areas. Their user-friend-liness and transparency certainly belong to their main advantages, and these two enable developing and tuning such controllers easily, without knowing their exact mathematical description. Nevertheless, it is of interest to know, what mathematical functions hide behind a set of fuzzy rules and an inference machine. For practical purposes it is necessary to consider real, implementable fuzzy control systems with reasonably low computational complexity. This paper discusses the problem of what types of functions are generated by realistic fuzzy control systems. In the paper the explicit formulae of the transference functions for practically important special cases are determined, controllers having rules with triangular and trapezoidal membership functions, and crisp consequents. Here we restrict our investigations to rules with a single input.

DISPATCHING ALGORITHMS FOR THE PRODUCTION UNITS WORK

2020 ◽  
pp. 88-95
Author(s):  
G.B. BURDO ◽  
Keyword(s):  
Fuzzy Control ◽  
Control Algorithm ◽  
Control Method ◽  
Machine Building ◽  
Small Scale ◽  
Mechanical Processing ◽  
Membership Functions ◽  
Fuzzy Variables ◽  
Input And Output ◽  
Algorithm Implementation

Presents the approach to dispatching the work of mechanical processing technological divisions of machine-building single and small-scale multi-product production plants. Algorithms for technological processes dispatching based on the fuzzy control method are shown. Input and output fuzzy variables are defined and their membership functions are shown. The control algorithm is given in the form of fuzzy rules. An example of the algorithm implementation is shown.

A New Fuzzy Inference Method for Symbolic Stability Analysis of Multigranular Intelligent Control System

2000 ◽  
Vol 12 (6) ◽  
pp. 664-674
Author(s):  
Hidehiro Yamamoto ◽  
◽  
Takeshi Furuhashi
Keyword(s):  
Control System ◽  
Stability Analysis ◽  
Fuzzy Control ◽  
Control Systems ◽  
Fuzzy Inference ◽  
Continuous System ◽  
Asymptotically Stable ◽  
Membership Functions ◽  
Symbolic System ◽  
Inference Method

Fuzzy inference has a multigranular architecture consisting of symbols and continuous values, and has worked well to incorporate experts' know-how into fuzzy controls. Stability analysis of fuzzy control systems is one of the main topics of fuzzy control. A recently proposed stability analysis method on the symbolic level opened the door to the design of stable fuzzy controller using symbols. However the validity of the stability analysis in the symbolic system is not guaranteed in the continuous system. To guarantee this validity, a nonseparate condition has been introduced. If the fuzzy control system is asymptotically stable in the symbolic system and the system satisfies the nonseparate condition, the continuous system is also asymptotically stable. However this condition is too conservative. The new condition called a relaxed nonseparate condition has been proposed and the class of control systems with guaranteed discretization has been expanded. However the relaxed condition has been applicable only to controf systems having symmetric membership functions. This paper presents a new fuzzy inference method that makes the relaxed condition applicable to fuzzy control systems with asymmetric membership functions. Simulations are done to demonstrate the effectiveness of the new fuzzy inference method. The proof of the expansion of the relaxed nonseparate condition is also given.

Application of Fuzzy PID Control in Sluice Control

2012 ◽  
Vol 241-244 ◽  
pp. 1248-1254
Author(s):  
Feng Chen Huang ◽  
Hui Feng ◽  
Zhen Li Ma ◽  
Xin Hui Yin ◽  
Xue Wen Wu
Keyword(s):  
Fuzzy Control ◽  
Pid Control ◽  
Information Integration ◽  
Pid Controller ◽  
Fuzzy Pid ◽  
Proportional Integral Derivative ◽  
Fuzzy Pid Control ◽  
Tuning Method ◽  
Fuzzy Pid Controller ◽  
Self Tuning

Fuzzy control, based on traditional Proportional-Integral-Derivative (PID) control, is used to improve the management of a hydro-junction’s sluice scheduling. In this study, we combined the PID and Fuzzy control theories and determined the PID parameters of the fuzzy self-tuning method of a hydro-junction’s sluice. A fuzzy self-tuning PID controller and its algorithm were designed. In hydro-junction sluice control, the Fuzzy PID controller can modify PID parameters in real-time, resulting in a more dynamic response. The application of the fuzzy self-tuning PID controller in the CiHuai River project information integration system yielded very good results.

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