Application Of Fuzzy Logic Controller Research Articles

A SURVEY OF STABILITY OF FUZZY LOGIC CONTROL WITH AEROSPACE APPLICATIONS

During recent years considerable efforts have been devoted to guarantee the stability of fuzzy logic control (FLC). Several stability analysis methods have been established, and stable control designs have been introduced. This is important to pave the way for the use of FLC in applications were no risks should be run. Aerospace is a clear example of a field where control must be sure. This paper is motivated by an ESA research project on stability of FLC concerning, in particular, aerospace potential applications. The paper present a review of the research on FLC stability, both in time and frequency domains, including also several alternatives of stable control design methods. In addition, there is a section about FLC applications in aerospace.

Control of a flotation column using fuzzy logic inference

This paper describes a fuzzy controller that was developed for the control, in real time, of three main variables of the column flotation process. Column flotation is a solid–solid separation process based on the physical and chemical properties of mineral particle surfaces. It is applied to the concentration of low grade ores and to ores that require very fine grinding to achieve mineral liberation. The economic exploitation of the process, guaranteeing the desired final product characteristics from a feed of varying composition, requires a stable operation, which is accomplished through the automatic control of some key process variables. The difficulties encountered in the development of process controllers for this process are related to the lack of reliable measuring devices for the process variables involved, with the non-existence of mathematical dynamical models that can be used in a controller synthesis and with its non-linear and time-varying behaviour. The process and the problems related with its control are briefly explained and the main reasons for fuzzy logic control application are discussed. The real time control system and the fuzzy controller structure and parameters are presented. A general overview of the pilot plant where the experimental work was performed is presented. Finally, results of the experimental work, carried out with the two-phase system (air and water), are summarised and the conclusions are drawn.

Application of Fuzzy Logic Control to Tea Rolling Process

This paper presents real-time fuzzy logic control for press cap roller used in tlle rolling process. The system is installed at tea processing facilities in Tea Research Institute, Gambung, Indonesia. Fuzzy logic control methodology is applied to achieve the desired temperature in press cap roller machineused in black tea rolling process. Mamdani-type fuzzy logic control strategy is implemented in software running on PC

Application of Self-Tuning Fuzzy Logic Controller on Process Control

An advanced self-tuning fuzzy logic controller (STFC) is reported in this paper. The STFC is basically an FLC but with the addition of performance measure and inference step functions. The performance measure is used to evaluate the system response and the inference step is used to apply reasoning that base on a set of fuzzy “meta rules”. The key function of the STFC is to provide automatic tuning of input scaling factors to the fuzzy logic controller. After simulation, the algorithm of the STFC is tested on the PT326 Process Trainer. The performance of the PID, FLC and STFC are compared and analyzed.

Fuzzy control of current in thyristor DC drive

In the paper an application of fuzzy logic controller (FLC) for control of thyristor DC drive is presented. During synthesis of the FLC a robustness against variation of structure of the current control plant was taken into account. Comparison between the fuzzy control system and an ordinary digital control system, carried out by simulation method, proved that FLC as a robust controller gives better performance in the range where non‐linearity and parameter variation is observed. The simulation results were confirmed by the laboratory experiment.

Filling System Fuzzy Control of a Packaging Machinery

This paper concerns with a practical application of fuzzy logic control to a foodstuff packaging machinery. The fuzzy control is applied to the packages filling system, which sets the amount of foodstuff product in the packages.The paper details the structure of the fuzzy controller as well as some experimental results obatained from test performed at Tetra Brick laboratory in Modena. Results from those testa show good performance of the fuzzy controlled filling systems.

Fuzzy logic control for the isomerized hop pellets production

This paper presents an application of fuzzy logic control to isomerized hop pellet production. The hop is very important in the brewing industries because a hop of high quality provides a good aroma to the beer and gives consistency to the foam. The isomerized pellets in this case study, are produced by warming stabilized pellets to approximately 50°C and holding them for one–two weeks. The alpha acids are almost completely isomerized under these conditions. The fuzzy controller was shown to have a high degree of robustness producing good results such that the quality of the product is guaranteed. A comparison with other control approaches widely used in the industry is performed using a nonlinear computer model.

Adaptive fuzzy reference model control of multi-machine power systems

This paper is concerned with the application of an adaptive fuzzy logic controller to both single and multi-machine power system simulations. Significant reductions in the amount of required calculations have been made which allow for a very computationally efficient design. A novel on-line training algorithm has been developed to reduce the error between the plant output and a reference model output. The controller is shown to provide excellent regulation for various disturbances over a wide range of operating conditions. Multi-machine analysis shows the ability of the fuzzy logic controller to damp multi-modal oscillations much more effectively than conventional algorithms.

Controlling the power output of a nuclear reactor with fuzzy logic

The application of fuzzy logic control (FLC) in the domain of 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 the present paper we describe an FLC for controlling the power level of a nuclear reactor. The study is intended to assess the applicability of FLC in this domain. The final goal is to develop an optimised and intrinsically safe controller. After reviewing the available literature on FLC in nuclear reactors, an FLC is proposed and first tested by comparing it with the classical controller of BR1 (Belgium's first research reactor). In the next step the BR1 at the Belgian Nuclear Research Centre (SCK · CEN) was used as a test bed to implement a PLC-based hardware controller. The BR1 reactor is internationally regarded as a nuclear calibration reference. It therefore provides an excellent environment for this type of experiments, because over the years considerable knowledge of the static and dynamic properties of the reactor has been accumulated. The progress made in these experiments will be presented and discussed.

Application of fuzzy logic control for continuous casting mold level control

This paper deals with the problem of molten metal level control in continuous casting. Under normal circumstances, proportional integral derivative (PID) control performs quite well, but abnormal conditions (in particular nozzle clogging/unclogging) require manual intervention. Indeed, when the flow of matter into the mold increases suddenly, the PID controller is not always able to prevent large level variations that can even lead to mold overflow. So, a fuzzy controller has been designed using the expert knowledge of the operators for controlling the process during disturbed phases. The paper discusses both the design of the fuzzy logic controller and its integration with the PID in a global control architecture. Results from simulation and successful online implementation are presented.

A CONJUGATE GRADIENT TECHNIQUE FOR OPTIMAL FUZZY LOGIC CONTROL OF HIGHLY COMPLEX DYNAMIC SYSTEM

The paper presents a novel approach for the application of adaptive fuzzy logic control' algorithm. The aim of this adaptive system is to minimize an objective function in the output error via varying the center of each label. The model inependency characteristics of the fuzzy controller allows simple calculation and low implementation cost which are highly recommended features in control industry. A conjugate gradient technique is applied in this paper to inqiiinize the error in least square sense. A real time nonlinear dynamic system is used to ensure the effectiveness of tAe proposed control scheme.

Book review: Fuzzy logic and Neuro Fuzzy Applications Explained by Constantin von Altrock (Prentice Hall 1995)

Fuzzy Logic and News Fuzzy Applications Explained presents three major topics: (1) an intuitive introduction to the theory of fuzzy sets, fuzzy logic and fuzzy control; (2) a plethora of engineering applications of fuzzy logic and fuzzy logic-based control, and (3) an introduction to fuzzyTECH TM , a software system for development of fuzzy logic-based controllers from INFORM Corporation, Aachen, Germany. A demonstration version of FuzzyTECH with an example is included on 2 diskettes (PC version only).

Extended algorithm of a fuzzy‐set based power‐system stabilizer with genetic algorithm timing

AbstractThe application of fuzzy logic controllers to improve power‐system damping behaviour has received some attention in the literature during the last few years. The paper presents an approach in which besides speed and its first derivative, the second derivative is applied to the Fuzzy‐set based power‐system stabilizer (PSS), so that a three‐dimensional space with improved behaviour is formed. The parameters are then optimized by the new searching technique, the genetic algorithm. Excellent digital simulation results are demonstrated for a single machine infinite bus‐bar system.

Application of a self-organizing fuzzy logic controller to nuclear steam generator level control

In this paper, the self-organizing fuzzy logic controller is investigated for the water level control of a steam generator. In comparison with conventional fuzzy logic controllers, this controller performs the control task with no initial control rules; instead, it creates control rules and tunes input membership functions based on the performance criteria as the control behavior develops, and also modifies its control structure when uncertain disturbance is suspected. Selected tuning parameters of the self-organizing fuzzy logic controller are updated on-line in the learning algorithm, by a gradient descent method. This control algorithm is applied to the water level control of a steam generator model developed by Irving et al. The computer simulation results confirm the good performance of this control algorithm for all power ranges. This control algorithm can be expected to be used for the automatic control of a feedwater control system in a nuclear power plant with digital instrumentation and control systems.

A fuzzy set theory based control of superconductive magnetic energy storage unit to improve power system dynamic performance

At present fuzzy logic control is receiving increasing emphasis in process control applications. The paper describes the application of fuzzy logic control in a power system that uses a 12-pulse bridge converter associated with superconductive magnetic energy storage (SMES) unit. The fuzzy control is used in both the frequency and voltage control loops, replacing the conventional control method. The control algorithms have been developed in detail and simulation results are presented. These results clearly indicate the superior performance of fuzzy control during the dynamic period of energy transfer between the power system and SMES unit.

Application of Neural and Fuzzy Logic Controllers to Control Selected Mechanical Systems

This article recalls the idea of neural controllers with application to the control of mechanical systems. Basic structures of such controllers are suggested, and the results of simulations comparing their performance to that of conventional and fuzzy logic controllers are shown. The experiments indicate that the performance of the proposed neural controllers is satisfactory.

Smart oil recovery

Fuzzy-logic-based expert systems assist Brazilian engineers with the drilling, pumping, and processing of offshore oil, cutting costs without compromising safety. The authors discuss these applications of fuzzy logic control.

Robustness Study of an On-Line Application of a Self-Learning Fuzzy Logic Controller

A difficult problem in fuzzy logic is the development of a rule-base that is both complete and consistent. A Self-Learning Fuzzy Logic Control (SLFLC) methodology offers a possible solution. A robustness study is presented to illustrate the performance of the SLFLC by demonstrating its ability to generate a consistent set of rules to a predetermined criteria and by evaluating its transient performance over a variety of tests. The SLFLC has been applied to a laboratory liquid-level process. The on-line results presented show thai even with limited knowledge of the process, the self-learning procedure is able to yield a satisfactory performance with a degree of robustness and with high repeatability.

Adaptive fuzzy power control for CDMA mobile radio systems

Introduces a new application of fuzzy-logic control (FLC) to power control in a DS/CDMA cellular system over mobile fading radio channels. The conventional feedback power control algorithms allow the base station to send a power command to raise/lower each user transmitting signal power level by a fixed power step and then keep the received powers almost equal. The fixed-step approach is actually an integral control whose power increment is determined according to the bang-bang-like control policy. However, this control scheme suffers from several disadvantages. To tackle these difficulties, a fuzzy proportional-plus-integral (PI) control, whose input variables are the received power error and error change, is introduced to determine each user's transmitting power in order to maintain simultaneously all users' signal power received at the base station nearly equal and to achieve better system stability and control performance. The derivation of the fuzzy PI control has been carried out by analyzing both the closed-loop steady state behavior and transient response of the system with a priori knowledge of the dynamics of the CDMA mobile fading channels. In fuzzy control, linguistic descriptions of actions in controlling a process are represented as fuzzy rules. This fuzzy-rule base is used by an inference mechanism in conjunction with some knowledge of the states of process in order to determine control actions. These actions would lead to the fast rise time, minimum overshoot, and small root-mean-squared (rms) tracking error. Additional advantages of fuzzy PI control over conventional control theories are discussed.

Control of a Fluidized Bed Combustor Using Fuzzy Logic

Application of fuzzy logic control to a fluidized bed combustor (FBC) is examined. Major aspects of fuzzy control are reviewed, and design of a fuzzy controller for the FBC is described. Selected experimental results are presented, and performance of the fuzzy controller is evaluated through comparisons to results from classical PI control of the combustor.