Application Of Fuzzy Logic Controller Research Articles

Control of a nonlinear continuous bioreactor with bifurcation by a type-2 fuzzy logic controller

The object of this paper is the application of a type-2 fuzzy logic controller to a nonlinear system that presents bifurcations. A bifurcation can cause instability in the system or can create new working conditions which, although stable, are unacceptable. The only practical solution for an efficient control is the use of high performance controllers that take into account the uncertainties of the process. A type-2 fuzzy logic controller is tested by simulation on a nonlinear bioreactor system that is characterized by a transcritical bifurcation. Simulation results show the validity of the proposed controllers in preventing the system from reaching bifurcation and instable or undesirable stable conditions.

Hierarchical fuzzy controllers for an astronomical telescope tracking

The paper presents an application of fuzzy logic controller (FLC) with hierarchically structured rule base for a two-link direct drive Celestron telescope. The hierarchical fuzzy logic controller (HFLC) is implemented, as nonlinear blocks used in a control system, for supervision of PID controller for position tracking of the telescope driven by electric motors. The HFLC is composed of two FLC stages connected in cascade. The input variables, for the first stage, of the HFLC are the position error and the rate of change in the position error. Then the output from the first stage and the position error integral are used as input variables for the second stage of the HFLC PID. The simulation results of the proposed HFLC PID, when the telescope is subjected to a unit step in reference position, are compared with the PID controller. The dynamic responses of the HFLC PID improve the rise time, damping factor and settling time compared with the PID controller. Also, the proposed techniques, hierarchical fuzzy PID controller, reduce the computation time due to reduction of rule base. The simulation results show the effectiveness of the proposed HFLC PID controller scheme as a promising technique.

Application of genetic algorithm-based fuzzy logic control in wire transport system of wire-EDM machine

Wire tension as well as wire feed should be controlled tightly for the geometry and corner accuracy of wire-EDM. In this paper, a closed-loop wire tension control system for Micro-Wire-EDM is presented to guarantee a smooth wire transport and a constant tension value. In order to keep smooth wire transportation and avoid wire breakage during wire feeding, the reel roller is modified and the clip reel is removed from the wire transport mechanism. A genetic algorithm-based fuzzy logic controller is proposed to investigate the dynamic performance of the closed-loop wire tension control system. Experimental results demonstrate that the developed wire transport system can result in satisfactory transient response, steady-state response and robustness. The proposed genetic algorithm-based fuzzy logic controller can obtain faster transient response and smaller steady-state error than a PI controller.

Application of type-2 fuzzy logic controller to an induction motor drive with seven-level diode-clamped inverter and controlled infeed

This paper describes a new control method for the integrated of six current rectifiers—seven-level diode-clamped inverter feeding induction motor considering dc-link capacitors voltage balancing problem. The proposed controller uses type-2 fuzzy systems to compensate the fluctuations of capacitors voltage, draw a sinusoidal line current with nearly unity power factor, and ensure the motor speed control. The performance of dc-link voltages control is evaluated in comparison to the conventional PI control scheme. The overall system and the control algorithm are presented and a set of simulations is carried out in order to prove the good performances of the proposed solution.

An Investigation Into the Application of Fuzzy Logic Control to Industrial Gas Turbines

This paper investigates the feasibility benefits of applying fuzzy logic control (FLC) strategies for use with industrial gas turbines. Our main objective is to investigate different designs methods, design implement an FLC strategy, plant simulation in a test environment optimize the FLC, conduct tests to compare the FLC with conventional controls in scenarios relevant to the application. We have designed, implemented, and tested our simulation to the exhaust temperature control problem of a gas turbine problem. The FLC, plant simulation, existing control configuration, and integrated test environment were developed in Java. Heuristic methods were used to optimize the FLC, which proved time consuming. The paper illustrates that while implementation of the FLC is feasible, it requires more effort than the conventional controls examined.

Automatic generation control of multi‐area power system using fuzzy logic controller

AbstractThis paper presents an application of Fuzzy Logic Controller (FLC) with triangular and gauss membership functions to study Automatic Generation Control (AGC) of a four‐area interconnected power system. Out of the four areas three have steam turbines and one has hydro turbine. All steam turbines in the system contain the reheat effect of non‐linearity and hydro turbine contains upper and lower constraints for generation rate. The result proves that the performance of FLC with Gaussian membership function is better than that of conventional controller. Copyright © 2007 John Wiley & Sons, Ltd.

A novel fuzzy logic controller for STATCOM to improve power system stability

This paper presents an application of Single-input Fuzzy Logic Controller (SFLC) to determine the control signal of a Static Compensator (STATCOM) synchronous for improvement of power system stability. This compensation scheme is relevant to Flexible AC Transmission systems (FACTSs) technology which is used worldwide to improve system dynamic performance. STATCOM improves the damping of electromechanical oscillations when used in transmission systems. The SFLC uses only one input variable which is called as signed distance. The SFLC has the advantages of reduced number of rules. Thus, generation, estimation and tuning of control rules are much easier while comparing with the existing conventional Fuzzy Logic Controllers (FLCs). The proposed control method is applied to control the AC and DC bus voltage of a STATCOM connected at a load bus in a Single Machine Infinite Bus (SMIB) System and also to improve the power system stability.

TS-Fuzzy-Controlled Active Power Filter for Load Compensation

This paper describes the application of Takagi-Sugeno (TS)-type fuzzy logic controller to a three-phase shunt active power filter for the power-quality improvement and reactive power compensation required by a nonlinear load. The advantage of fuzzy logic control is that it does not require a mathematical model of the system. The application of the Mamdani-type fuzzy logic controller to a three-phase shunt active power filter was investigated earlier but it has the limitation of a larger number of fuzzy sets and rules. Therefore, it needs to optimize a large number of coefficients, which increases the complexity of the controller. On the other hand, TS fuzzy controllers are quite general in that they use arbitrary input fuzzy sets, any type of fuzzy logic, and the general defuzzifier. Moreover, the TS fuzzy controller could be designed by using a lower number of rules and classes. Further, in this paper, the hysteresis current control mode of operation is implemented for pulsewidth-modulation switching signal generation. Computer simulation results show that the dynamic behavior of the TS fuzzy controller is better than the conventional proportional-integral (PI) controller and is found to be more robust to changes in load and other system parameters compared to the conventional PI controller.

Pictool: Integrated Hardware and Software for Teaching/Learning FLC using Microcontrollers

Abstract In this paper, an educational tool named PICTooI for FLC (Fuzzy Logic Controller) applications using PIC (Peripheral Interface Controller) microcontrollers is presented for cost-effective teaching and learning. PICTooI is based on reconfigurable computing technology, targeting its use in different laboratory based FLC applications, with minimal modifications of the hardware components. The tool is designed to help the students improve their thorough understanding of FLC and PIC microcontrollers. PICTooI was incorporated seven different exercises, which include hazdware and software, to allow the student to interact directly with the microcontrollers. Students practice using the tool, interpret and draw conclusions related to the microcontroller architecture and FLC by using the given examples. The tool as a teaching aid was very successful according to feedback from the students and the instructors.

Energy Saving in a Wastewater Treatment Process: an Application of Fuzzy Logic Control

Many uncertain factors affect the operation of Wastewater Treatment Plants. Due to the complexity of biological wastewater treatment processes, classical methods show significant difficulties when trying to control them automatically. Consequently soft computing techniques and, specifically, fuzzy logic appears to be a good candidate for controlling these ill-defined, time-varying and non-linear systems. This paper describes the development and implementation of a Fuzzy Logic Controller to regulate the aeration in the Taradell Wastewater Treatment Plant. The main goal of this control process is to save energy without decreasing the quality of the effluent discharged. The fuzzy controller integrates the information coming from two different signals: the Dissolved Oxygen and Oxidation-Reduction Potential values. The simulation results proved that fuzzy logic is a good tool for controlling the aeration of the wastewater treatment plant. The results obtained show that energy savings of more than 10% can be achieved using aeration fuzzy control and at the same time still keeping the good removal levels.

Nonlinear Function Controller: A Simple Alternative to Fuzzy Logic Controller for a Power Electronic Converter

Recently, there has been an increase in the application of fuzzy logic controllers (FLCs) for control of power electronic converters. Due to the FLCs' complex algorithm, their realization often calls for a compromise between cost and performance. In this paper, it is shown that the rule table of most of the two-input FLCs used with power converters can be approximated into a single nonlinearity. This allows the controller to be easily realized using simple, fast, and inexpensive analog circuits. The simplified nonlinear function controller (NLFC) developed in this manner is shown to be equivalent in performance to the original FLC through simulations. The NLFC concept is then applied to PI-FLC, a type of FLC popular in power converter control applications. This results in the PI-FLC being replaced by a simple nonlinear PI controller (NPIC). Using this simplification, the design of NPICs to obtain good dynamic performance in power converters is explained. An example design of NPIC for controlling a dc-dc boost power converter is presented. Experimental results are also presented to demonstrate the superior dynamic performance of the converter with NPIC versus that of a linear-PI controller.

MATLAB-based GUIs for fuzzy logic controller design and applications to PMDC motor and AVR control

An attractive and instructive approach for teaching basis of fuzzy logic-based controller design steps and effects of properties of the controller to control system by using MATLAB environment is presented in this paper. All design stages are developed in MATLAB's graphical user interface (GUI). Source codes used for both controller and to define systems to be controlled are written in separated script files using MATLAB language. Maximum and minimum values of the parameters that belong to membership functions (MFs) and rule base table are determined by using step responses and error graphics of the systems. Two different examples are given to demonstrate the usefulness of the developed tool and to visualize effect of changes of fuzzy logic-based controller parameters. © 2005 Wiley Periodicals, Inc. Comput Appl Eng Educ 13: 10–25, 2005; Published online in Wiley InterScience (www.interscience.wiley.com); DOI 10.1002/cae.20026

Genetic fuzzy logic controller: an iterative evolution algorithm with new encoding method

Logic rules and membership functions are two key components of a fuzzy logic controller (FLC). If only one component is learned, the other one is often set subjectively thus can reduce the applicability of FLC. If both components are learned simultaneously, a very long chromosome is often needed thus may deteriorate the learning performance. To avoid these shortcomings, this paper employs genetic algorithms to learn both logic rules and membership functions sequentially. We propose a bi-level iterative evolution algorithm in selecting the logic rules and tuning the membership functions for a genetic fuzzy logic controller (GFLC). The upper level is to solve the composition of logic rules using the membership functions tuned by the lower level. The lower level is to determine the shape of membership functions using the logic rules learned from the upper level. We also propose a new encoding method for tuning the membership functions to overcome the problem of too many constraints. Our proposed GFLC model is compared with other similar GFLC, artificial neural network and fuzzy neural network models, which are trained and validated by the same examples with theoretical and field-observed car-following behaviors. The results reveal that our proposed GFLC has outperformed.

Stator flux optimised Direct Torque Control system for induction motors

Direct Torque Control (DTC) has, over the years, become one of the most popular methods of control for induction motors (IM) drive systems. There still remain, however, many disadvantages with this method of control, which have to be overcome. In the present paper we, therefore, consider a method of improving conventional DTC by reducing the undesirable torque ripple that can occur in the output torque. This new method, which is fully described in the paper, also has the additional advantage of maintaining the switching frequency of the power switching devices constant. The average torque ripple reduction achieved by this new method of control can be greater than 50%. This significant reduction in torque ripple is realised by the introduction of a two state modulation process between the active selected state from conventional DTC and an additional null state. The control of the two state modulation process is achieved by the application of an adaptive Fuzzy Logic controller (FLC). Additionally, a novel flux optimiser strategy is also introduced into the new DTC system, which together with the two state modulation process further reduces the torque ripple. Simulation and experimental results for the drive system presented are included in the paper and it is shown that not only good agreement exists between both sets of results but they also support the claims made in the paper.

Adapted Fuzzy Controller for Astronomical Telescope Tracking

This paper presents a novel application of fuzzy logic (FL) controller driven by an adaptive fuzzy set (AFS) for position tracking of the telescope driven by electric motor. Also, the proposed FL controller, driven by AFS, is compared with a classical FL control, driven by a static fuzzy set (SFS). Both FL controllers algorithm use the position error and its rate of change as an input vector. The mathematical model of the telescope driven by electric motor is highly nonlinear differential equations. Therefore the use of the artificial intelligent controller, such as FL is much better than the conventional controller, to cover a wide range of operating conditions. So, the output of FL control is utilized to force the electric drives, of the telescope, to satisfy a perfect matching of the predefined desired position of the telescope arms. Both of FL controllers, using AFS and SFS, are simulated and tested when the system is subjected to a step change in reference value. In addition, these simulation results are compared with the conventional Proportional-Derivative (PD) controller, driven by fixed gain. The proposed FL, using an adaptive fuzzy set, improve the dynamic response of the overall system by improving the damping coefficient and decreasing the rise time and settling time compared with other two controllers.

Fuzzy PID Control of a Five DOF Robot Arm

This paper studies the application of fuzzy logic control on a five degrees of freedom (DOF) robot arm, the Maker 100 of U.S. Robots. The elaboration of the fuzzy control laws is based on two structures of coupled rules fuzzy PID controllers. The fuzzy PID controllers are numerically simulated and the simulation results confirm the success of the fuzzy PID control in trajectory tracking problems. Seeking a performance optimization, a systematic study of the choice of tuning parameters of the controllers is done. The success of the proposed fuzzy control law is again affirmed by a comparative evaluation with respect to the computed torque control method and the direct adaptive control method, the last two controls being also numerically implemented using the same dynamic model of the robot arm.

Application of fuzzy logic controller to active power filters

In this paper, the way to use one of the most powerful problem-solving methodologies, fuzzy logic, to enhance the quality of the power system is described. To prove the power of fuzzy logic for the modeling of nonlinear systems, the modeling of active power filters with a fuzzy logic based control strategy is presented as a case study and its capability to reject reactive power eliminate current harmonic components is simulated by MATLAB. The advantages in using fuzzy logic as emphasized in the case study are its simple application to various types of systems, high performance, lower cost, and higher productivity.

Initial Experiments on Fuzzy Control for Nuclear Reactor Operations at the Belgian Reactor 1

The application of fuzzy logic control (FLC) in the domain of the nuclear industry presents a tremendous challenge. The main reason for this is the public awareness of the risks of nuclear reactors and the very strict safety regulations in force for nuclear power plants. The very same regulations prevent a researcher from quickly introducing novel control methods into this field. On the other hand, the application of FLC has, despite the ominous sound of the word “fuzzy” to nuclear engineers, a number of very desirable advantages over classical control, e.g., its robustness and the capability to include human experience into the controller. In this paper an FLC for controlling the power level of a nuclear reactor is described. The study is intended to assess the applicability of FLC in this domain. The final goal is to develop an optimized and intrinsically safe controller. After reviewing some available literature on FLC in nuclear reactors, an FLC is proposed and first tested by comparing it with the classical controller of the Belgian reactor 1 (BR1). In the next step the BR1 at the Belgian Nuclear Research Center (SCK·CEN) was used as a test bed to implement a programmable logic controller-based hardware controller. The BR1 reactor is internationally regarded as a nuclear calibration reference. It therefore provides an excellent environment for this type of experiment because over the years considerable knowledge of the static and dynamic properties of the reactor has been accumulated. The project (1995-1999) aimed at investigating the added value and technical limits of FLC for nuclear reactor operations. The progress made in these experiments including closed-loop experiments are presented and discussed in this paper.

PC Based Educational Tool for a Switched Reluctance Drive with Fuzzy Logic

This paper introduces a PC based educational tool for a switched reluctance drive (SRD) with fuzzy logic which is prepared for undergraduate and graduate level students. The paper first describes how the switched reluctance drive system works. Then details are given of how to estimate the flux linkage by using fuzzy set theory. Finally an application of fuzzy logic control to an SRM drive is presented.

A boiler-turbine system control using a fuzzy auto-regressive moving average (farma) model

This paper presents an application of an online self-organizing fuzzy logic controller to a boiler-turbine system of a fossil power plant. The control rules and the membership functions of the proposed fuzzy logic controller are generated automatically without using a plant model. A boiler-turbine system is described as a multi-input multioutput (MIMO) nonlinear system in this paper. Then, three single-loop fuzzy logic controllers are designed independently. Simulation shows robust results for various kinds of electric load demand changes and parameter variations of boiler-turbine system.