Dissertations / Theses on the topic 'Controller of fuzzy logic'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Controller of fuzzy logic.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
García, Z. Yohn E. "Fuzzy logic in process control : a new fuzzy logic controller and an improved fuzzy-internal model controller." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001552.
Full textGarcÃa, Z. Yohn E. "Fuzzy logic in process control: A new fuzzy logic controller and an improved fuzzy-internal model controller." Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/2529.
Full textMenon, Vinay. "Fuzzy logic controller for an artificial heart." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq32405.pdf.
Full textVijeh, Nader. "Microprocessor engineering aspects of a self-organizing fuzzy-logic controller." Thesis, University of Exeter, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.328485.
Full textMohan, Ashwin. "A fuzzy controller developed in RSLogix 5000 using ladder logic and function blocks implemented on a Control Logix PLC /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p1420941.
Full textJiang, Xiaowen. "A fuzzy logic controller for intestinal levodopa infusion in Parkinson’s disease." Thesis, Högskolan Dalarna, Datateknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4727.
Full textSheng, Lan. "Fuzzy logic controller synthesis for electro-mechanical systems with nonlinear friction." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0001/MQ35526.pdf.
Full textHu, Yanting. "Advanced control system for stand-alone diesel engine driven-permanent magnet generator sets." Thesis, De Montfort University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366632.
Full textTam, Kin Seng. "Intelligent power factor controller with new measuring method and fuzzy logic control." Thesis, University of Macau, 1998. http://umaclib3.umac.mo/record=b1447758.
Full textAlmardy, Mohamed. "Design of fuzzy logic controller for the cathodic protection of underground pipelines." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0007/MQ43133.pdf.
Full textVick, Andrew W. "Genetic Fuzzy Controller for a Gas Turbine Fuel System." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1291053513.
Full textKhor, Jeen Ghee. "An intelligent controller for synchronous generators." Thesis, De Montfort University, 1999. http://hdl.handle.net/2086/4125.
Full textButt, Casey Benjamin. "Simplified fuzzy logic controller based vector control of an interior permanent magnet motor /." Internet access available to MUN users only, 2003. http://collections.mun.ca/u?/theses,155545.
Full textBirkin, Philip. "A novel dual surface type-2 fuzzy logic controller for a micro robot." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/12544/.
Full textBennett, W. E. "Construction equipment emerging technologies: fuzzy logic controllers." Thesis, Monterey, California. Naval Postgraduate School, 1995. http://hdl.handle.net/10945/25784.
Full textLin, Yu-Jen. "Design of fuzzy logic controllers for FACTS." Thesis, University of Strathclyde, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366675.
Full textChrysanthakopoulos, Georgios. "A fuzzy-logic autonomous agent, applied as a supervisory controller in a simulated environment /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/6044.
Full textLIN, GAO-RONG, and 林高榮. "Self-learning fuzzy logic controller." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/83666544770829326164.
Full textWANG, CHIEN HSIUNG, and 王建雄. "Design Logic For A Fuzzy Controller." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/28379518443566104259.
Full text國立臺北科技大學
生產系統工程與管理研究所
90
A fuzzy controller usually uses a database (referred to as “fuzzy knowledge base”) to characterize its control logic for a given system to control. The control logic is always described piecewisely based on the system characteristics. As compared with conventional controllers such as PD,PI or PID controller , the fuzzy controllers present better results according to the reports given by the previous investigators. The governing factors affecting a fuzzy controller, as reported ,includes its designs on fuzzifcation 、membership function、fuzzy knowledge base 、inference engine and defuzzification. Among these factors, the designs of the fuzzy knowledge base and membership function are considered to be the most important factors. Many previous studies claim that their fuzzy knowledge base and membership function are created by trial and error, their resulting fuzzy controllers give relative obvious errors. The attempt of this work is to present a logic concept by which a fuzzy controller is designed. An analysis of the resulting fuzzy controller will also be discussed . Hopefully, the results of this work will be able to provide a clue to the person who is working in this area. keyword：fuzzy control、input and output design、steady-state error、phase-plane trajectory.
Huang, Yuan-Sheng, and 黃淵聖. "The Self-Tuning Fuzzy Logic Controller." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/50644510191069177876.
Full text國立臺灣大學
化學工程學系
85
This article aims to present a systematic methodology for on-line tuning of a fuzzy logic controller. First, we introduce the basic structure and inference mechanisms of the crisp-type fuzzy logic controller in use. The initial values of the fuzzy logic controller parameters are given by choosing equally-spaced triangles as the input membership functions, and simple control rule mapping as the control rules. Then a method of evaluating process response performance is proposed according to three characteristics --- percentage overshoot, rising time and oscillation amplitude. The performance evaluation will decide whether the tuning procedure is necessary or not. This evaluation will also be used as a stopping criterion in each stage of the subsequent tuning work. Parameters being tuned can be divided into two parts：scaling factors and control rules. Linquistic tuning guides for scaling factors are inferred from the analogy of the fuzzy logic controller and the conventional PI controller. The control rules being fired will be further adjusted according to the discrepancy between the true response and the target response, while the later is determined by the above-mentioned response characteristics. Some numerical examples are supplied to demonstrate the adequacy and efficiency of the proposed self-tuning fuzzy logic controller.
Oliveira, Tiago. "Advanced Fuzzy Logic Heat Pump Controller." Dissertação, 2013. http://hdl.handle.net/10216/73307.
Full textFerreira, Carlos Daniel Dias. "Advanced Fuzzy Logic Heat-Pump Controller." Dissertação, 2014. https://repositorio-aberto.up.pt/handle/10216/88661.
Full textOliveira, Tiago Caetano Neves da Silva. "Advanced Fuzzy Logic Heat Pump Controller." Dissertação, 2013. https://repositorio-aberto.up.pt/handle/10216/68027.
Full text賴建華. "Adaptive Genetic Fuzzy Logic Signal Controller." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/07086395182199808192.
Full text國立交通大學
交通運輸研究所
91
Genetic fuzzy logic controller (GFLC) can overcome the drawbacks of conventional fuzzy logic controller (FLC) which has to subjectively set the logic rules and membership functions. Thus, GFLC can greatly enhance the applicability of FLC. This thesis attempts to construct an adaptive genetic fuzzy logic control model for an isolated intersection signal timing control. This GFLC model uses traffic flow and queue length as state variables and extended green time (EGT) as control variable. The intersection total delay, estimated by fluid approximation method, is used to evaluate the control performance. At the moment of ending of a minimum green time or of the previous EGT inference, the GFLC model is activated for another inference of EGT. If the value of EGT is zero or a maximum green time is reached, then the signal switches to the competing direction. Based on three flow volumes (low, medium and high) and two traffic patterns (uniform and varying arrivals), a total of six scenarios are designed and compared with the Webster pre-timed signal control model to verify the robustness of this GFLC model. In order to further validate the control performance of the GFLC model, a fully enumerative method is employed to solve, respectively, for the optimal single timing plan (only one set of signal timing for a given traffic condition during the simulation period) and for the optimal multiple timing plans (several sets of signal timings depending on varying traffic patterns). The scenario analysis shows that GFLC model can reduce total delay by 0~13.1% in comparison with the Webster’s model. Under uniform arrivals, the total delay of GFLC model is slightly higher than the optimal single timing plan by 0~5.83%. Under varying traffic patterns, the total delay for GFLC model is 0.81~4.68% less than the optimal single timing plan but 1.78~13.5％ higher than the optimal multiple timing plans. It indicates that the proposed GFLC model has better control performance than the Webster model and the optimal single timing plan under varying traffic patterns. However, the GFLC model is inferior to the optimal single timing plan under uniform arrivals as well as the optimal multiple timing plans under varying traffic patterns. It suggests that our proposed GFLC model can still be improved, which deserves to be explored. To validate the applicability of our GFLC model, a field study at the signalized intersection of Zhong-Zheng Road and Wen-Lin Road in Taipei City is conducted. The results show that the total delay of GFLC model is respectively 19% and 16% less than that of the current timing plan and Webster’s model.
Ferreira, Carlos Daniel Dias. "Advanced Fuzzy Logic Heat-Pump Controller." Master's thesis, 2014. https://repositorio-aberto.up.pt/handle/10216/88661.
Full textOliveira, Tiago Caetano Neves da Silva. "Advanced Fuzzy Logic Heat Pump Controller." Master's thesis, 2013. https://repositorio-aberto.up.pt/handle/10216/68027.
Full textJIAN, YUAN-ZHEN, and 簡源震. "Adaptive fuzzy logic controller using neural networks." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/67432838151022219149.
Full textXu, Shun-Tang, and 許順鏜. "Fuzzy logic controller as a neural network." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/76502445639528045442.
Full textXU, SHUN-TANG, and 許順鏜. "Fuzzy logic controller as a neural network." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/60162101651486012092.
Full textLiou, Sheng-Huei, and 劉盛輝. "The Design of Combined Fuzzy Logic Controller." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/36883986692710618433.
Full text淡江大學
資訊工程研究所
82
According to the obtaining of the control singal, it is seen that the conventional fuzzy controller is often either fuzzy proportional-derivative ( FPD ) or fuzzy proportional-integral ( FPI ) type. Generally, a FPD controller ( FPDC ) yields atransient performance, while a FPI controller( FPIC ) usually yields a better steady state performance. However, it is difficult to simultaneously take into account both transient and steady state performances in these conventional fuzzyers. Thus, we propose a combined fuzzy controller( CFC ) which combines FPDC with FPIC, so that we can simultaneously get better response in both transient and steady states. The simulation results appear that the transient response of the CFC is faster than that of the FPDC, and the steady stater of the CFC is also smaller than that of the EPIC. But,7 or 8 parameters in this CFC to try, and it takes much time to find out these parameters. So we use genetic algorithm ( GA ) to find these parameters. By this method, we can not only reduce the time needed to find the parameters, but also get a better CFC by using these new parameters.
LIN, ZHI-DA, and 林志達. "Design of adaptive intellectual fuzzy logic controller." Thesis, 1989. http://ndltd.ncl.edu.tw/handle/16036188206064432498.
Full text國立臺灣大學
化學工程研究所
77
Abstract This theis aima at studing the design and implementation methods for an Adaptive Intellectual Fuzzy Logic Controller (AIFLC). The AIFIC is designed to imitate the judging and control actions of the expert operator .Different linguistic control rules are proposed in various steps of feed- back process control (e.g. serve control,regulatory control,contraints). In the AIFLC,most design variables are given directly to simplify the tun- ing procedures.Only the scaling factors of the fuzzy variables are consid- ered performance-relevant.Some simulated examples are given to illuustrate the performance and its robustness of the AIFLC. The applicability of the AIFIC is also demonstrated experimentally on a partially simulated CSTR. 本文之主旨在於探討智慧型模糊控制器之設計與執行問題。本文依迴饋控制各階段（ 伺服控制、調節控制、限制控制等）之特性，分別設計適用之口語化控制規則及其執 行方法，以達成擬人化之控制功能。文中將設計變數（如模糊變數之隸屬度函數，控 制規則等）區分為次要與必要等類，並固定次要變數，只保留少數主要變數（模糊變 數之量化因子）供使用者依應用程序特性調諧，以大量減少傳統模糊控制器中需設計 之變數過多的缺點。所設計模糊控制器之性能及其軔性以模擬例題測試，均能獲得驗 証。此控制器並經設計成以個人電腦為基礎之可攜式控制器，經試用於部份模擬連續 攪拌反應槽之溫度控制，顯示出其實用性。
Xiao, Shun-Zhi, and 蕭順治. "Design for Fuzzy Logic Current Controller of PMSM." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/02841134502639530558.
Full text中原大學
電機工程研究所
90
Investigated in this thesis, the possibility of using Fuzzy-PI controller in current control of Permanent Magnet Synchronous Motor (PMSM). The parameters of PMSM will change slightly with different input levels. And fuzzy control can achieve good output response even when the plant is unknown. Thus, a fuzzy controller is used to adjust the error of the input signal of the original PI controller. As for the optimization of the proposed controller, the 5×5 consequent variables of the control rule table, the vertexes at the base of the triangular-shaped membership functions and the initial setting of the parameters of the PI controller are all included in searching parameters, which are chosen by an adaptive genetic algorithm (AGA). The use of AGA can avoid falling into local optimum and speed up the convergence. Simulations through Matlab show that Fuzzy-PI controller posses high robustness and low sensitivity. Furthermore, the Fuzzy-PI controller optimized by AGA can follow current command quickly and assure the best performance according to their fitness functions.
Huang, Chun-Yueh, and 黃俊岳. "Design, Implementation and Application of Fuzzy Logic Controller." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/61083370625994091190.
Full text國立成功大學
電機工程學系
85
In this dissertation, the design, implementation, and application of the fuzzy logic controller are studied. Regarding the design methodology, we proposed a novel fuzzy tree approach to design the fuzzy logic controller. The fuzzy log ic controller designed with this method has the following advantages: 1) the r ule extraction process can be done by one-pass; 2) the search spaces for fuzzy inference can be largely reduced; 3) the n-dimensional matrix operations in c onventional compositional rule of inference can be simplified as one-dimension al operation. In order to verify the proposed method, the fuzzy tree approach has been successfully applied to design the color reproduction system. In addition, considering the hardware implementation of the fuzzy logic contro ller, we propose a set of basic current-mode fuzzy logic circuits, including a programmable membership function generator, a fuzzification unit, a multi-inp ut maximum/minimum circuit, and a defuzzification unit, to construct the fuzzy logic controller. By means of these basic circuits, we also design and implem ent a fast and efficient current-mode array based fuzzy logic controller, whic h is suitable for real-time processing and has high inference speed. To develop further, we applied the adjustment of the membership function of fuzz y set through fuzzy linguistic hedge circuits to design the adaptive fuzzy log ic controller. These fuzzy linguistic hedge circuits, containing absolutely, v ery, much more, more, plus, minus, more or less, slightly, and contrast intens ification operations, have been fabricated and verified in a 0.8(m CMOS proces s. In the future, a real-time adaptive fuzzy logic controller, which uses the linguistic hedge to modify the membership function, can be developed by combin ing the fuzzy logic controller with the proposed hedge circuits.
Shee, Fa-Yia, and 徐發義. "An Adaptive Fuzzy Logic Controller with Reinforcement Learning." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/72857937695007986656.
Full textChang, Wen-Bin, and 張文賓. "Neural-Network-Based Linear Combination Fuzzy Logic Controller." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/37314354910006924949.
Full text國立臺灣科技大學
工程技術研究所
81
To design a fuzzy logic controller without depending on control engineering and expert's experience, we implemented Sugeno's fuzzy logic control rule on multi- layer feedforward neural network. This neural network can be trained by backpropa- gation learning algorithm, so the parameters of fuzzy logic controller such as parameters of membership function and para- meters of consequence part of rules are designed by learning. The advantages of this research method are (1) design a fuzzy logic controller without depending on expert's experience (2) the trained fuzzy logic controller has strong noise resistant. A fuzzy car running example is presented to illusrate the per- formance and applicability of the proposed neural network.
Wang, Ching-Huie, and 王欽輝. "The Design Tool of the Fuzzy Logic Controller." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/78452214756798608094.
Full text大同工學院
資訊工程研究所
81
Fuzzy logic control systems may perform better than conventional control systems when the system under control is complex, nonlinear, or subject to uncertainty. In its natural, the fuzzy logic controller is a knowledge based system that requires the incorporation of expertise pertinent to the specific applications. The development process thus requires a tool capable of executing the expert rules. This thesis proposes an architecture for the fuzzy inference engine that acts as the kernel in the inference process and also facilitates in the analysis process. The objective of this design is to provide flexibility and convenience to the system development. The designed inference engine is implemented.Test run examples are also given to illustrate the capability of the implemented system.
鄭朝鴻. "Fuzzy Logic Based Controller For Orchards Mobile Robot." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/62427567592591059754.
Full text建國科技大學
自動化工程系暨機電光系統研究所
101
This thesis is designed to improve the traditional orchard transporter manipulative design, system fuzzy theory in BASIC Commander ® single board computer controller design architecture that combines sensor on the color resolution, ultrasonic sensors, electronic compass allows orchards mobile robot with independent direction, the carriage may want to distinguish themselves the fruits of what belongs to the fruit and automatic carriage parked at, in order to facilitate handling fruit sales, improve transport efficiency. The experiments prove that the system design is feasible, future applications extended to farmers harvest transportation, improve farmers in work under adverse environmental efficiency and transport convenience.
Tian, Yun Xiang, and 田雲翔. "A fuzzy logic controller design using genetic algorithm." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/91415832927118411648.
Full textAmbre, Mandar Kwan Bing Woon. "A design methodology for the implementation of fuzzy logic traffic controller using programmable gate array /." 2004. http://etd.lib.fsu.edu/theses/available/etd-04122004-164143.
Full textAdvisor: Dr. Bing Kwan, Florida State University, College of Engineering, Dept. of Electrical and Computer Engineering. Title and description from dissertation home page (viewed June 16, 2004). Includes bibliographical references.
"Adaptive fuzzy logic steering controller for a Steckel mill." Thesis, 2009. http://hdl.handle.net/10210/2164.
Full textColumbus Stainless, a subsidiary of Acerinox, manufactures stainless steel in their plant located in Middelburg, South Africa. During the hot rolling operation the steel is rolled on a 4-high finishing mill where strip movement perpendicular to the rolling direction occurs. This movement is undesirable because it causes inferior product quality and may also lead to downtime if the strip moves past the edge of the rolls. In the past the operator made adjustments to the relative alignment of the rolls in the mill in an attempt to limit the sideways movement of the strip. In order to improve product quality and production throughput, the manual action of adjusting the parallelism of the rolls was replaced with an automatic steering control system. Analysis of the process revealed that several variables have an impact on the way the strip reacts to changes in the alignment of rolls in the mill. An adaptive fuzzy logic control system was designed and implemented in the real time control system of the mill. During commissioning the system did not have an adverse effect on production and all initial project criteria were met, as was stipulated in Section 1.4 of this document. The control system improved the strip movement by an average of 11% on various products rolled. Based on production data, the system potentially prevented two coils from leaving the rolls during the month long evaluation period and saved 40 minutes of production time. If the savings in material losses and the potential gain in production time are added the possible anticipated monetary saving is estimated to be about 24 million Rand a year.
Chan-HongChao and 趙全鋐. "Design and Application of Type-1 Fuzzy Logic Controller and Interval Type-2 Fuzzy Immune Controller." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/55092220871610339589.
Full text國立成功大學
電機工程學系碩博士班
98
This dissertation focuses on the applications of type-1 fuzzy controller and the development of a new kind of fuzzy controller, the interval type-2 fuzzy immune controller. Firstly, an omni-directional vision-based control scheme for the car-like mobile robot (CLMR) is presented. From the image information, one can estimate the position of the CLMR in the parking space and figure out a feasible reference path. Then, we propose a fuzzy logical control to manipulate the steering wheel such that it can execute parallel-parking missions. On the other hand, an image processing approach for real-time target tracking and obstacle avoidance for mobile robot navigation in an indoor environment using stereo vision is also proposed. Several image processing techniques are combined to find the target and obstacles. Then one can compute the angular position of the detected target and obstacle related to the mobile robot. The stereo vision system is utilized to calculate the relative distance of the target and obstacle from the mobile robot. According to the distance, one can determine the relationship of the target and obstacle to the mobile robot. The best target tracking path and obstacle avoidance path can be determined by different behavior modes. Therefore, the mobile robot plans a collision-free and successful track target to complete the patrol routine. Moreover, a novel interval type-2 fuzzy immune control (IT2FIC) for linear and nonlinear discrete systems is presented. The controller is designed by integration of the interval type-2 fuzzy logic control (IT2FLC) and immune feedback control law. The type-2 fuzzy logic system is adopted to approximate the undetermined nonlinear function of the immune system. In order to reduce the computational loads of the type-reduction process, singleton type membership function with uncertain width and a new algorithm is proposed for type-reduction with a geometric analysis. The simulation results of the discrete linear system and the inverted pendulum system demonstrate that the proposed IT2FIC can obtain the best tracking performance among the type-1 fuzzy logic control (T1FLC), the IT2FIC, and the type-1 fuzzy immune control (T1FIC).
Chien, Tsun-I., and 簡尊彞. "An FPGA implementation of Self-learning fuzzy logic controller." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/82384797671837391411.
Full text國立成功大學
工程科學系
88
Abstract Since Prof. Zadeh proposed the fuzzy set theory in 1965, the fuzzy theory has been applied to a variety of engineer fields, especially in control systems. In order to overcome the drawbacks of try-and-error in designing conventional controllers for unknown systems, a self-learning fuzzy logic controller base on a novel searching method associated with a genetic algorithm has been proposed in this thesis. The proposed searching method can save the searching time by eliminating the searched chromosome parameters with bad system performance at early searching process. Besides, this self-learning fuzzy logic controller can be implemented by using FPGAs. In addition, a random number generator in FPGAs is also derived. Furthermore, simulation results are also given to confirm the validity of the proposed controllers.
Jian, Chih-Yuan, and 簡志遠. "Fuzzy Logic Controller Design for Active Power Factor Correction." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/r4xsbx.
Full text國立臺北科技大學
電機工程系所
99
With the rapid development of electronics industry, the need for switching power converters has increased significantly. Therefore, increasing the energy efficiency of electronic products is a very important topic. Because the electrical equipment has nonlinear power components, it makes unnecessary power consumption. However, the purpose of power factor correction is to make the phase of input current in phase with the input voltage. Then the load will be equivalent to the pure resistive load, and the power only provides real power we need. Power factor correction can be classified as passive and active. The passive power factor correction is to use the passive components to compensate for the phase such as inductors, capacitors, etc. But we cannot arbitrarily change compensation components we need with changes in loading. Therefore, passive power factor correction has its disadvantage. In this thesis, we use active power factor correction to switch elements and compensate for the phase. Because the power conversion circuits are nonlinear systems, we propose a fuzzy logic controller to replace the traditional PI controller. Without detail mathematical derivation of the system, the proposed method will be able to achieve power factor correction and output voltage regulation purposes. After comparing with traditional controller, the transient response of power factor corrector with fuzzy controller is faster than traditional one in the same condition of power factor.
Lien, Kuo-Chin, and 連國欽. "THE PERFORMANCE IMPROVEMENT OF FUZZY LOGIC CONTROLLER BY CMAC." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/11466639593995661904.
Full text大同大學
電機工程研究所
90
Fuzzy logic controllers (FLCs) have been widely applied recently. In particular, FLCs are very effective techniques for complicated control processes. They can easily approximate what human experts perform well under some ill-defined environments. In fact, the knowledge base that human experts build is the most important factor to the performance of the FLCs. To slove this problem, we adopt the Cerebellar Model Arithmetic Controller (CMAC) to reduce the error of the FLCs in control application. CMAC network imitates the human cerebellum, storing information in different layer. There are three advantages of CMAC for nonlinear function approximation: (1) the faster convergence, (2) the better accuracy, and (3) without knowing the model of the system in advance. When applied in the control aspect, CMAC can accomplish fast learning and fast convergence by the look-up table method of CMAC without knowing the mathematical model of controlled plant. Simulation results show that the proposed structure is better than the ill-defined FLCs.
Yang, Dih Hua, and 楊迪華. "Design of Fuzzy Logic Controller Based on Numerical Data." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/71654492371302016364.
Full textHsieh, Chung-Tyan, and 謝中天. "The Design and Analysis of the Fuzzy Logic Controller." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/40737517065404109330.
Full textChen, Szu-Han, and 陳思翰. "Fuzzy Logic Controlled Lithium-Ion Battery Equalization Controller Using Zero-Ripple Switching Operation." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/68250612226681006560.
Full text輔仁大學
電子工程學系
94
An intelligent battery equalization scheme based on fuzzy logic control (FLC) is presented to adaptively design the proposed fuzzy logic control battery equalization controller (FLC-BEC). Consequently, it can effectively reduce the equalization time. The proposed individual cell equalization (ICE) scheme is an improved topology of zero ripple Cûk converter. The purpose of ripple free input and output current not only can reduce the conducted electromagnetic interference (EMI), but also can maintain safe operation for the circuit topology. A state space approach is adopted to inspect the stability of the proposed FLC-BEC. Matlab/Simulink simulation and experimental results can be used to verify the stability of FLC-BEC. In this article, an 8-bit micro-controller unit (MCU) is used to accomplish the FLC-BEC, which can reduce volume and complexity of the hardware used for the FLC-BEC.
Navale, Rahul Laxman. "Development of an adaptive fuzzy logic controller for HVAC system /." 2006.
Find full textWang, Yun-Ching, and 王雲慶. "A Genetic Fuzzy Logic Controller-based Adaptive Ramp Metering System." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/74429443654188589565.
Full text逢甲大學
交通工程與管理所
93
Ramp metering might be the most popular and effective strategy for freeway traffic control. Thus, the researches and practices regarding ramp metering have been conducted and implemented over thirty years. Especially, due to the break-through development of fuzzy logic controller (FLC), many researches explore the applicability and performance of FLC ramp metering. However, most of these studies developed the FLC ramp-metering strategy by subjectively setting the membership functions of linguistic variables and selecting the inference rules. As a result, the performance and applicability of these models is negatively affected. Thus, hundreds of studies employed genetic algorithms to design an adaptive fuzzy logic controller (FLC) by learning from examples, namely genetic fuzzy logic controller (GFLRC). GFLC models can not only avoid the subjective settings of membership functions and inference rules, but also enhance the performance of control. Based on that, this paper attempts to develop and validate a genetic fuzzy logic ramp-metering controller (GFLRC). The present paper proposes two GFLRC strategies: independent and integrated. The independent GFLRC strategy uses mainline average traveling speed and on-ramp queuing length as two state variables, metering rate as control variable. For considering the effects of upstream metering traffic, the integrated GFLRC strategy uses an additional state variable, the metering rate of upstream ramp, based on the independent strategy. The performance of the models is measured by the total travel time of all users during the control period under given traffic flow rates. Besides, the techniques of macroscopic traffic simulation and fluid approximation are adopted to simulate and compute the performances. For investigating the applicability and control performance of proposed models, studies on an exemplified example with four interchanges and field case with six interchanges (the northern network of the Taiwan first freeway) are conducted. Five-minute flow rates are simulated for the exemplified example or collected for the field case, respectively. In corresponding to the interval of traffic flow data, the decision horizon of the models is also set as five minutes. The performance of without ramp-metering, FLRC model and optimal single ramp metering strategies are also conducted and analyzed for the comparisons. The results of case study on exemplified example shows that independent and integrated GFLRC can curtail the total travel time by 6.93% and 7.80%, respectively; while the proposed optimal single ramp metering strategy can reduce the total travel time by 4.81% and 7.22%, the FLRC model can reduce the total travel time only by 0.45% at most, comparing with the strategy of without ramp-metering. The field study also concludes the similar results. We also find that the GFLRC models perform better in heavy traffic than in light traffic. At last, we also analyze the unfairness of users caused by implementing these ramp-metering strategies. The results shows FLRC model is rather fairness, while the independent-GFLRC is the most unfairness.
Tsao, Ju-Ying, and 曹如瑩. "Linguistic Hedges and Genetic Algorithms on Fuzzy Logic COntroller Design." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/30660883491682096306.
Full text國立成功大學
電機工程學系
86
In this thesis, a fuzzy logic controller, called as linguistic hedge fuzzy logiccontroller, designed by the linguistic hedges and genetic algorithms is proposed.The linguistic hedge is a fuzzy operation applied to adjust the membership functionsof the fuzzy sets. If the control results fail to acquire the system requirements, the control objective can still be achieved by adjusting the membership functions of the fuzzy sets or the control rules. The proposed linguistic hedge fuzzy logic controller takes advantage of dynamically modifying the membership functions of a ×3-rule fuzzy logic controller system to achieve a similar performance that a 7×7-rule fuzzy logic controller system possesses. Therefore, the computational complexity is dramatically reduced due to the less number of rules required. Furthermore, a modified genetic algorithm is used to search the optimal linguistic hedge combination. Finally, three well-known control systems ─the truck backer- upper system, the inverted pendulum system, and the nonlinear plant model system ─are used to demonstrate the feasibility of the linguistic hedge fuzzy logic controller.