MODELING THE SYNTHESIS OF TAKAGI — SUGENO — KANG FUZZY CONTROLLERS IN SOME CONTROL SYSTEMS

2021 ◽  
Vol 7 (2) ◽  
pp. 147-169
Author(s):  
Irina V. Kulikova
Keyword(s):  
Genetic Algorithms ◽  
Control System ◽  
Fuzzy Controller ◽  
Industrial Society ◽  
Control Action ◽  
Effective Control ◽  
Control Parameters ◽  
Membership Functions ◽  
Fuzzy Controllers ◽  
Takagi Sugeno

Modern challenges in a post-industrial society require further development of management systems for complex technical and technological phenomena and processes. Effective control of an object is possible if a controller, or a fuzzy controller, correctly generates the required control action. Recently, fuzzy controllers have been very popular. Fuzzy logical statements in this case help considering various nonlinear relationships. The synthesis of the fuzzy controller parameters allows for more efficient operation of the control system. A possible option for obtaining the best set of parameters for a fuzzy controller is the use of genetic algorithms for its synthesis. The use of genetic algorithms for the fuzzy controllers synthesis can lead to the fact that the elements of its parameters array will change in such a way that an incorrect value of one or more elements will occur. This situation leads to impossibility of composing membership functions for the terms of the variables of the fuzzy controller. Incorrect value formation is excluded by constructing a limited functional dependency. This paper proposes a mathematical model of the parameters of the term-set of variables of a fuzzy controller of the Takagi — Sugeno — Kang type of the zero and first orders. The authors disclose the content of the conditions and conclusions of the rule base for the fuzzy controller of the above type. As a result of the simulation, some operations of the genetic algorithm are implemented using a random number generator. Graphical models of the membership functions of the input variables of the fuzzy controller of the type under consideration clearly illustrate the occurrence of all parameters in their range of possible values. A description of the control system operation with two control parameters and one control action at the specified values of the control parameters is presented.

Takagi-Sugeno Fuzzy Load-Following Control of Nuclear Reactors Based on Reactor Point Kinetics Equations

2014 ◽  
Author(s):  
Xiuchun Luan ◽  
Jie Zhou ◽  
Yu Zhai
Keyword(s):  
Control System ◽  
Linear Models ◽  
Fuzzy Controller ◽  
Fuzzy Model ◽  
Linear Matrix ◽  
Neutron Density ◽  
Load Following ◽  
Wide Range ◽  
Takagi Sugeno ◽  
Operating Points

A state differential feedback control system based Takagi-Sugeno (T-S) fuzzy model is designed for load-following operation of nonlinear nuclear reactor whose operating points vary within a wide range. Linear models are first derived from the original nonlinear model on several operating points. Next the fuzzy controller is designed via using the parallel distributed compensation (PDC) scheme with the relative neutron density at the equilibrium point as the premise variable. Last the stability analysis is given by means of linear matrix inequality (LMI) approach, thus the control system is guaranteed to be stable within a large range. The simulation results demonstrate that the control system works well over a wide region of operation.

scholarly journals Development and Application of a Fuzzy Control System for a Lead-Acid Battery Bank Connected to a DC Microgrid

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Juan José Martínez ◽  
José Alfredo Padilla-Medina ◽  
Sergio Cano-Andrade ◽  
Agustín Sancen ◽  
Juan Prado ◽  
...  
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Controller ◽  
Discharge Process ◽  
Membership Functions ◽  
Response Dynamics ◽  
Dc Microgrid ◽  
Fuzzy Control System ◽  
Dc Bus ◽  
Lead Acid

This study presents the development and application of a fuzzy control system (FCS) for the control of the charge and discharge process for a bank of batteries connected to a DC microgrid (DC-MG). The DC-MG runs on a maximum power of 1 kW with a 190 V DC bus using two photovoltaic systems of 0.6 kW each, a 1 kW bidirectional DC-AC converter to interconnect the DC-MG with the grid, a bank of 115 Ah to 120 V lead-acid batteries, and a general management system used to define the operating status of the FCS. This FCS uses a multiplexed fuzzy controller, normalizing the controller’s inputs and outputs in each operating status. The design of the fuzzy controller is based on a Mamdani inference system with AND-type fuzzy rules. The input and output variables have two trapezoidal membership functions and three triangular membership functions. LabVIEW and the NI myRIO-1900 embedded design device were used to implement the FCS. Results show the stability of the DC bus of the microgrid when the bank of batteries is in the charging and discharging process, with the bus stabilized in a range of 190 V ± 5%, thus demonstrating short response times to perturbations considering the microgrid’s response dynamics.

Robust H∞ Fuzzy Control for Nonlinear Networked Control System and its Application

2014 ◽  
Vol 1022 ◽  
pp. 406-410
Author(s):  
Qing Feng Wang ◽  
Hong Bo Wang
Keyword(s):  
Control System ◽  
Fuzzy Controller ◽  
Fuzzy Model ◽  
Networked Control System ◽  
Networked Control ◽  
Design System ◽  
Membership Functions ◽  
Pendulum System ◽  
Inverted Pendulum System ◽  
Nonlinear Networked Control System

The paper studies the effect of networks with transmission delay in the feedback loop of a nonlinear networked control system (NCS). The nonlinear system is modeled by a T-S fuzzy model, and transmission delays are modeled by a finite state Markov process. The fuzzy controller’s membership functions can be different from the plant’s. The membership functions of the plant and the fuzzy controller are incorporated into the controller design. System performance is measured via an norm from disturbance to error and the controller is computed by sum of square approach. Finally we applied these results to an inverted pendulum system. Theoretical analysis and simulation results show that the control strategy studied in this paper is effective and feasible.

scholarly journals Tuning of a TS Fuzzy Output Regulator Using the Steepest Descent Approach and ANFIS

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Ricardo Tapia-Herrera ◽  
Jesús Alberto Meda-Campaña ◽  
Samuel Alcántara-Montes ◽  
Tonatiuh Hernández-Cortés ◽  
Lizbeth Salgado-Conrado
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Descent Method ◽  
Output Regulation ◽  
Steepest Descent ◽  
Steepest Descent Method ◽  
Membership Functions ◽  
Inference System ◽  
Fuzzy Membership Functions ◽  
Takagi Sugeno

The exact output regulation problem for Takagi-Sugeno (TS) fuzzy models, designed from linear local subsystems, may have a solution if input matrices are the same for every local linear subsystem. Unfortunately, such a condition is difficult to accomplish in general. Therefore, in this work, an adaptive network-based fuzzy inference system (ANFIS) is integrated into the fuzzy controller in order to obtain the optimal fuzzy membership functions yielding adequate combination of the local regulators such that the output regulation error in steady-state is reduced, avoiding in this way the aforementioned condition. In comparison with the steepest descent method employed for tuning fuzzy controllers, ANFIS approximates the mappings between local regulators with membership functions which are not necessary known functions as Gaussian bell (gbell), sigmoidal, and triangular membership functions. Due to the structure of the fuzzy controller, Levenberg-Marquardt method is employed during the training of ANFIS.

scholarly journals The Fuzzy Robust Anti-Sway Crane Control System

2009 ◽  
Vol 9-10 (1) ◽  
pp. 87-98
Author(s):  
Jarosław Smoczek ◽  
Janusz Szpytko
Keyword(s):  
Control System ◽  
Fuzzy Controller ◽  
Pole Placement ◽  
Laboratory Model ◽  
Robust Controller ◽  
Minimal Set ◽  
Inference System ◽  
Crane Control ◽  
Takagi Sugeno ◽  
Operating Points

The Fuzzy Robust Anti-Sway Crane Control SystemThe paper presents the pole placement approach to solve problem of conventional, based of proportional-derivative controllers, as well as robust, based of fuzzy controller, anti-sway crane control. The methods of robust gain-scheduling crane control system and selecting minimal set of operating points were shown. The fuzzy robust controller, based of Takagi-Sugeno-Kang inference system, was presented, as well as results of experiments, carried out using laboratory model of an overhead traveling crane, were shown in the paper.

Application of Fuzzy H∞ Control via T–S Fuzzy Models for Nonlinear Time-Delay Systems

2003 ◽  
Vol 12 (02) ◽  
pp. 117-137 ◽  
Author(s):  
Feng-Hsiag Hsiao ◽  
Wei-Ling Chiang
Keyword(s):  
Time Delay ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Direct Method ◽  
Delay System ◽  
Delay Systems ◽  
Time Delay Systems ◽  
Fuzzy Controllers ◽  
Fuzzy Models ◽  
Takagi Sugeno

This paper deals with the problem of stability analysis and stabilization via Takagi-Sugeno (T-S) fuzzy models for nonlinear time-delay systems. First, Takagi-Sugeno (T-S) fuzzy models and some stability results are recalled. To design fuzzy controllers, nonlinear time-delay systems are represented by Takagi-Sugeno fuzzy models. The concept of parallel-distributed compensation (PDC) is employed to determine structures of fuzzy controllers from the T-S fuzzy models. LMI-based design problems are defined and employed to find feedback gains of fuzzy controller and common positive definite matrices P satisfying stability a delay-dependent stability criterion derived in terms of Lyapunov direct method. Based on the control scheme and this criterion, a fuzzy controller is then designed via the technique of PDC to stabilize the nonlinear time-delay system and the H∞ control performance is achieved in the mean time. Finally, the proposed controller design method is demonstrated through numerical simulations on the chaotic and resonant systems.

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