Fuzzy Logic Controller (fuzzy + logic_controller)

Distribution by Scientific Domains


Selected Abstracts


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

EUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 3 2008
P. Subbaraj
Abstract This 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. [source]


Fuzzy logic controller in a packaging plant

PACKAGING TECHNOLOGY AND SCIENCE, Issue 1 2003
Marcello Braglia
Abstract This paper deals with the analysis of a controller used to synchronize two parallel belt conveyors in a packaging plant. A first conveyor carries the products, while the second delivers the packages. The insertion is obtained by a proper mechanical action. The control system is based on a ,hybrid' fuzzy logic controller, whose parameters are optimized by using an advanced ,operational' genetic algorithm. ,Hybrid' means that a conventional fuzzy logic controller is integrated with a set of special rules needed to solve particular situations characterizing the system. An important constraint is given, since the physical structure of the existing control system is to be kept unchanged. It is shown that the controller efficiently governs the belt conveyors when: (a) the distances between goods and the relative packages become higher than a certain value; (b) the performance of the electrical engine deteriorates during working time; and also (c) interference phenomena occur between consecutive good-package couples. Copyright 2003 John Wiley & Sons, Ltd. [source]


A real-time computer-controlled simulator: For control systems

COMPUTER APPLICATIONS IN ENGINEERING EDUCATION, Issue 2 2008
I. H. Altas
Abstract A real-time simulator to accompany automatic control system courses is introduced. The design and realization methods and processes are discussed. The simulator is basically a computer-controlled system that implements the developed user friendly virtual interface software to control the speed of a small size DC motor. The virtual interface includes digital implementation models of classical proportional, integral, derivative, and all combinations of them as well as a fuzzy logic controller. The user is able to select and adjust the parameters of any desired controller that is defined and represented virtually. 2008 Wiley Periodicals, Inc. Comput Appl Eng Educ 16: 115,126, 2008; Published online in Wiley InterScience (www.interscience.wiley.com); DOI 10.1002/cae.20130 [source]


Applying fuzzy logic and genetic algorithms to enhance the efficacy of the PID controller in buffer overflow elimination for better channel response timeliness over the Internet

CONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 7 2006
Wilfred W. K. Lin
Abstract In this paper two novel intelligent buffer overflow controllers: the fuzzy logic controller (FLC) and the genetic algorithm controller (GAC) are proposed. In the FLC the extant algorithmic PID controller (PIDC) model, which combines the proportional (P), derivative (D) and integral (I) control elements, is augmented with fuzzy logic for higher control precision. The fuzzy logic divides the PIDC control domain into finer control regions. Every region is then defined either by a fuzzy rule or a ,don't care' state. The GAC combines the PIDC model with the genetic algorithm, which manipulates the parametric values of the PIDC as genes in a chromosome. The FLC and GAC operations are based on the objective function . The principle is that the controller should adaptively maintain the safety margin around the chosen reference point (represent by the ,0' of ) at runtime. The preliminary experimental results for the FLC and GAC prototypes indicate that they are both more effective and precise than the PIDC. After repeated timing analyses with the Intel's VTune Performer Analyzer, it was confirmed that the FLC can better support real-time computing than the GAC because of its shorter execution time and faster convergence without any buffer overflow. Copyright 2005 John Wiley & Sons, Ltd. [source]


Solving resource constrained multiple project scheduling problems by random key-based genetic algorithm

ELECTRONICS & COMMUNICATIONS IN JAPAN, Issue 8 2009
Ikutaro Okada
Abstract In this paper, we propose a hybrid genetic algorithm with fuzzy logic controller (flc-rkGA) to solve the resource-constrained multiple project scheduling problem (rc-mPSP) which is well known as an NP-hard problem and the objective in this paper is to minimize total complete time in the project. It is difficult to treat the rc-mPSP problems with traditional optimization techniques. The new approach proposed is based on the hybrid genetic algorithm (flc-rkGA) with fuzzy logic controller (FLC) and random-key encoding. For these rc-mPSP problems, we demonstrate that the proposed flc-rkGA to solve the rc-mPSP problem yields better results than several heuristic genetic algorithms presented in the computation result. 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 92(8): 25,35, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10101 [source]


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

EUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 3 2008
P. Subbaraj
Abstract This 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. [source]


A fuzzy approach to active usage parameter control in IEEE 802.11b wireless networks

EXPERT SYSTEMS, Issue 5 2004
David Soud
Abstract: Usage parameter control (UPC) provides support for quality of service across heterogeneous networks. For the network operator UPC assists in limiting network usage through traffic shaping, to prevent unacceptable delay. Traditional methods to apply UPC involve the generic cell rate algorithm or ,leaky bucket' algorithm, now commonly implemented in asynchronous transmission mode networks. This paper proposes a novel form of UPC for 802.11b wireless networks. The method proposed measures the rate of individual network flows to actively manage link utilization using a fuzzy logic controller (FLC). The FLC monitors the flow rate and adjusts the sending transmissions to stabilize flows as close to the optimum desired rate as possible. Imposing UPC and using the FLC within a packet switched TCP network enforces cooperation between competing streams of traffic. After carrying out experiments within a wireless network, the results obtained significantly improve upon a ,best effort' service. [source]


Experimental modelling and intelligent control of a wood-drying kiln

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 8 2001
Givon Chuen Kee Yan
Abstract Proper control of the wood-drying kiln is crucial in ensuring satisfactory quality of dried wood and in minimizing drying time. This paper presents the development, implementation, and evaluation of a control system for a lumber drying kiln process incorporating sensory feedback from in-wood moisture content sensors and intelligent control such that the moisture content of lumber will reach and stabilize at the desired set point without operator interference. The drying process is difficult to model and control due to complex dynamic nonlinearities, coupling effects among key variables, and process disturbances caused by the variation of lumber sizes, species, and environmental factors. Through system identification scheme using experimental data and recursive least-squares algorithm for parameter estimation, appropriate models are developed for simulation purpose and controller design. Two different control methodologies are employed and compared: a conventional proportional-integral-derivative (PID) controller and a direct fuzzy logic controller (FLC), and system performance is evaluated through simulations. The developed control system is then implemented in a downscaled industrial kiln located at the Innovation Centre of National Research Council (NRC) of Canada. This experimental set-up is equipped with a variety of sensors, including thermocouples for temperature feedback, an air velocity transmitter for measuring airflow speed in the plenum, relative humidity sensors for measuring the relative humidity inside the kiln, and in-wood moisture content sensors for measuring the moisture content of the wood pieces. For comparison, extensive experimental studies are carried out on-line using the two controllers, and the results are evaluated to tune the controller parameters to achieve good performance in the wood-drying kiln. The combination of conventional control with the intelligent control promises improved performance. The control system developed in this study may be applied in industrial wood-drying kilns, with a clear potential for improved quality and increased speed of drying. Copyright 2001 John Wiley & Sons, Ltd. [source]


How to optimize the TS-fuzzy knowledge base to achieve desired performances: Accuracy and robustness

OPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 1 2008
A. Soukkou
Abstract Designing an effective criterion/learning to find the best rule and optimal structure is a major problem in the design process of fuzzy neural controller. In this paper, we introduce a new robust model of Takagi Sugeno fuzzy logic controller. A hybrid learning algorithm, called hybrid approach to fuzzy supervised learning (HAFSL), which combines the genetic algorithm (GA) and gradient descent technique (GD) is proposed for constructing an efficient and robust fuzzy neural network controller (FNNC). Two phases of design and learning process are presented in this work. A GA is used for finding near optimal structure/parameters of the FNNC that minimizes the number of rules (initialization procedure). The second stage of learning algorithm uses the backpropagation algorithm based on GD method to fine tune the consequent parameters of the controller. The genes of chromosome are arranged into two parts, the first part contains the control genes (the certainty factors) and the second part contains the parameters genes that representing the fuzzy knowledge base. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. The performances of the HAFSL are compared to these found by the traditional PI with genetic optimization (GA-PI). Simulations demonstrate that the proposed HAFSL and GA-PI algorithms have good generalization capabilities and robustness on the water bath temperature control system. Copyright 2007 John Wiley & Sons, Ltd. [source]


Fuzzy logic controller in a packaging plant

PACKAGING TECHNOLOGY AND SCIENCE, Issue 1 2003
Marcello Braglia
Abstract This paper deals with the analysis of a controller used to synchronize two parallel belt conveyors in a packaging plant. A first conveyor carries the products, while the second delivers the packages. The insertion is obtained by a proper mechanical action. The control system is based on a ,hybrid' fuzzy logic controller, whose parameters are optimized by using an advanced ,operational' genetic algorithm. ,Hybrid' means that a conventional fuzzy logic controller is integrated with a set of special rules needed to solve particular situations characterizing the system. An important constraint is given, since the physical structure of the existing control system is to be kept unchanged. It is shown that the controller efficiently governs the belt conveyors when: (a) the distances between goods and the relative packages become higher than a certain value; (b) the performance of the electrical engine deteriorates during working time; and also (c) interference phenomena occur between consecutive good-package couples. Copyright 2003 John Wiley & Sons, Ltd. [source]


Designing a belt conveyor controller in a bottling plant using fuzzy logic and genetic algorithms

PACKAGING TECHNOLOGY AND SCIENCE, Issue 6 2001
Marcello Braglia
Abstract This paper presents an industrial case study relevant to a fuzzy logic controller designed via a properly developed genetic algorithm. We consider an example of a fuzzy logic-based industrial process-controller. In particular, we deal with the problem of controlling the speed of a belt conveyor for glass containers in a bottling plant. The primary objective of the controller is to guarantee the continuous feed to the filling station, in the presence of frequent gaps between bottles. The secondary objective is to reduce the impact speed between arriving bottles and those standing in the queue, in order to reduce the plant noise. High-performance parameters of the fuzzy controller are found by a properly developed genetic algorithm. The results provided by Monte Carlo simulations demonstrate that, with such controllers, it is possible to achieve both the objectives mentioned above. Copyright 2001 John Wiley & Sons, Ltd. [source]


Load control of ball mill by a high precision sampling fuzzy logic controller with self-optimizing

ASIAN JOURNAL OF CONTROL, Issue 6 2008
Hui Cao
Abstract A self-optimizing, high precision sampling fuzzy logic controller for keeping a ball mill circuit working stably and efficiently is proposed in this paper. The controller is based on fuzzy logic control strategy, and a fuzzy interpolation algorithm is presented to improve the control precision. The final output of the controller is calculated through the interpolation calculation of the observation and its neighboring antecedents, and the interpolation weight coefficients are obtained according to a fuzzy inference algorithm. In the proposed controller, the sampling control strategy is used to deal with a large delay time and a controller set value which can be adjusted by a self-optimizing algorithm, which can overcome the time-varying characteristic. Simulation results verify that the controller can control the ball mill circuit effectively and have higher control quality. Field service results also verify that the controller can successfully optimize the control of ball mill circuit. Copyright 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source]