An improved stable adaptive fuzzy control method
Kurt Fischle; D. Schroder
1999-01-01
Stable adaptive fuzzy control is a self-tuning concept for fuzzy controllers that uses a Lyapunov-based learning algorithm, thus guaranteeing stability of the system plant-controller-learning algorithm and convergence of the plant output to a given reference signal. In the paper, two new methods for stable adaptive fuzzy control are presented. The first method is an extension of an existing concept: it
Stable adaptive fuzzy control of nonlinear systems
Li-Xin Wang
1993-01-01
A direct adaptive fuzzy controller that does not require an accurate mathematical model of the system under control, is capable of incorporating fuzzy if-then control rules directly into the controllers, and guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded is developed. The specific formula for the bounds is provided,
A direct fuzzy model reference adaptive control
NASA Astrophysics Data System (ADS)
Wang, An; Fan, Wenxia
2006-11-01
A direct fuzzy model reference adaptive control is proposed in this paper. The designed controller employs direct feedback linearization, coupled with a pseudo control variable to linearize the nonlinear system. A fuzzy adaptive compensator based on universal approximation is designed to cancel the system pseudo error, disturbance and system interconnection. Furthermore, a dynamic compensator is designed to stabilize the system. The proposed algorithm is proved by Lyapunov stability theory to be asymptotically stable. Simulation results are given to demonstrate that the designed system has perfect tracking performance.
Genetic algorithms in adaptive fuzzy control
NASA Technical Reports Server (NTRS)
Karr, C. Lucas; Harper, Tony R.
1992-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.
Adaptive fuzzy mixed H2\\/H? attitude control of spacecraft
Bor-Sen Chen; Charng-Shi Wu; Ying-Wen Jan
2000-01-01
This paper presents an adaptive fuzzy mixed H2\\/H? attitude control of nonlinear spacecraft systems with unknown or uncertain inertia matrix and external disturbances. Using an adaptive fuzzy approximation method, an uncertain nonlinear model is estimated. Then, by a mixed H2 and H? attitude control design, the effect of external disturbance and fuzzy approximation error on spacecraft attitude can be restrained
FPGA based real-time adaptive fuzzy logic controller
Aws Abu-Khudhair; Radu Muresan; Simon X. Yang
2010-01-01
Fuzzy logic based control systems provide a simple and efficient method to control highly complex and imprecise systems. However, the lack of a simple hardware design that is capable of modifying the fuzzy controller's parameters to adapt for any changes in the operation environment, or behavior of the plant system limits the applicability of fuzzy based control systems in the
Stable adaptive fuzzy controllers with application to inverted pendulum tracking
Li-Xin Wang
1996-01-01
An adaptive fuzzy controller is constructed from a set of fuzzy IF-THEN rules whose parameters are adjusted on-line according to some adaptation law for the purpose of controlling the plant to track a given-trajectory. In this paper, two adaptive fuzzy controllers are designed based on the Lyapunov synthesis approach. We require that the final closed-loop system must be globally stable
Design of adaptive fuzzy sliding mode for nonlinear system control
Sinn-Cheng Lin; Yung-Yaw Chen
1994-01-01
An adaptive fuzzy sliding mode controller (AFSMC) is proposed. The parameters of the membership functions in the fuzzy rule base are changed according to some adaptive algorithm for the purpose of controlling the system state to hit a user-defined sliding surface and then slide along it. The initial IF-THEN rules in the AFSMC can be randomly selected or roughly given
Boiler drum level controlled by fuzzy self-adapting PID
Yue Wei-jie; Liu Yong-xin
2009-01-01
The tuning of parameters for traditional PID controllers was based on the mathematical model of the object and some control rules, which was difficult to adapt to complicated and variable control systems. Fuzzy control technology was applied to design a two-input and three-output self-adapting fuzzy PID controller to control the water level of boilers drum. The designed controller was simulated
Low speed control of a DC motor driving a mechanical system with fuzzy adaptive compensation
Hyun, Dongyoon
1997-01-01
A fuzzy adaptive feedforward control scheme in conjunction with classical feedback control is proposed for the low speed control of DC motors driving mechanical systems in the presence of friction. In the fuzzy adaptive scheme, a fuzzy logic based...
Low speed control of a DC motor driving a mechanical system with fuzzy adaptive compensation
Hyun, Dongyoon
1997-01-01
A fuzzy adaptive feedforward control scheme in conjunction with classical feedback control is proposed for the low speed control of DC motors driving mechanical systems in the presence of friction. In the fuzzy adaptive scheme, a fuzzy logic based...
Application of adaptive fuzzy controller in intelligent greenhouse control system
Shihua Li; Shiyan liu; Limei Ju
2008-01-01
In this paper, the author introduces a new technique. Adopt adaptive fuzzy controller to greenhouse to achieve the greenhouse control of environmental factors. Firstly, the general structure design of the system was introduced. Secondly, descript the hardware designing, and determine the systempsilas structure and working principle. It was with emphasis on the design of an intelligent controller, software programming of
Stable adaptive control using fuzzy systems and neural networks
J. T. Spooner; K. M. Passino
1996-01-01
Stable direct and indirect adaptive controllers are presented, which use Takagi-Sugeno fuzzy systems, conventional fuzzy systems, or a class of neural networks to provide asymptotic tracking of a reference signal for a class of continuous-time nonlinear plants with poorly understood dynamics. The indirect adaptive scheme allows for the inclusion of a priori knowledge about the plant dynamics in terms of
Variable universe adaptive fuzzy control on the quadruple inverted pendulum
Hongxing Li; Miao Zhihong; Wang Jiayin
2002-01-01
This paper focuses on the control problem of the quadruple inverted pendulum by variable universe adaptive fuzzy control.\\u000a First, the mathematical model on the quadruple inverted pendulum is described and its controllability is versified. Then,\\u000a an efficient controller on the quadruple inverted pendulum is designed by using variable universe adaptive fuzzy control theory.\\u000a Finally the simulation of the quadruple inverted
Maximum Power Point Tracking Control for Photovoltaic System Using Adaptive Neuro-Fuzzy
Paris-Sud XI, Université de
Maximum Power Point Tracking Control for Photovoltaic System Using Adaptive Neuro- Fuzzy "ANFIS Adaptive Neuro- Fuzzy "ANFIS". The PV array has an optimum operating point to generate maximum power conventional controller like Adaptive Neuro-Fuzzy "ANFIS" and fuzzy logic controller is proposed and simulated
Adaptive Fuzzy Control of a Direct Drive Motor
NASA Technical Reports Server (NTRS)
Medina, E.; Kim, Y. T.; Akbaradeh-T., M. -R.
1997-01-01
This paper presents a state feedback adaptive control method for position and velocity control of a direct drive motor. The proposed control scheme allows for integrating heuristic knowledge with mathematical knowledge of a system. It performs well even when mathematical model of the system is poorly understood. The controller consists of an adaptive fuzzy controller and a supervisory controller. The supervisory controller requires only knowledge of the upper bound and lower bound of the system parameters. The fuzzy controller is based on fuzzy basis functions and states of the system. The adaptation law is derived based on the Lyapunov function which ensures that the state of the system asymptotically approaches zero. The proposed controller is applied to a direct drive motor with payload and parameter uncertainty, and the effectiveness is verified by simulation results.
Enhanced adaptive fuzzy sliding mode control for uncertain nonlinear systems
NASA Astrophysics Data System (ADS)
Roopaei, Mehdi; Zolghadri, Mansoor; Meshksar, Sina
2009-09-01
In this article, a novel Adaptive Fuzzy Sliding Mode Control (AFSMC) methodology is proposed based on the integration of Sliding Mode Control (SMC) and Adaptive Fuzzy Control (AFC). Making use of the SMC design framework, we propose two fuzzy systems to be used as reaching and equivalent parts of the SMC. In this way, we make use of the fuzzy logic to handle uncertainty/disturbance in the design of the equivalent part and provide a chattering free control for the design of the reaching part. To construct the equivalent control law, an adaptive fuzzy inference engine is used to approximate the unknown parts of the system. To get rid of the chattering, a fuzzy logic model is assigned for reaching control law, which acting like the saturation function technique. The main advantage of our proposed methodology is that the structure of the system is unknown and no knowledge of the bounds of parameters, uncertainties and external disturbance are required in advance. Using Lyapunov stability theory and Barbalat's lemma, the closed-loop system is proved to be stable and convergence properties of the system is assured. Simulation examples are presented to verify the effectiveness of the method. Results are compared with some other methods proposed in the past research.
Adaptive fuzzy control of ship autopilots with uncertain nonlinear systems
Yansheng Yang; Changjiu Zhou
2004-01-01
This paper presents a novel adaptive fuzzy control for ship autopilots with uncertain system and gain nonlinear functions, which are all the unstructured (or non-repeatable) state-dependent unknown nonlinear functions. The Takagi-Sugeno type fuzzy logic systems are used to approximate uncertain functions and the algorithm is proposed by use of the idea of changing supply functions. The closed-loop system is proven
Generating fuzzy rules for the acceleration control of an adaptive cruise control system
R. Holve; P. Protzel; K. Naab
1996-01-01
A procedure for the data-driven generation of fuzzy rules is described, which was used in the development of an adaptive fuzzy controller to assist a driver in vehicle speed and distance control. The driver remains in the control loop of the ACC (adaptive cruise control) system through haptical feedback via the accelerator pedal. Thus, the control of the pedal behavior
A. Hazzabl; M. Zerbo; I. K. Bousserhane; P. Sicard
2006-01-01
Control of an induction motor using fuzzy gain adaptation of PI controller (adaptive FLC-PI) is presented. Fuzzy rules are utilized on-line to determine the controller parameters based on tracking error and its first time derivative. Simulation and experimental results of the proposed scheme show good performances compared to the PI controller with fixed parameters
Fault-tolerant switched reluctance motor drive using adaptive fuzzy logic controller
Sayeed Mir; Mohammad S. Islam; Tomy Sebastian; Iqbal Husain
2004-01-01
An adaptive fuzzy controller has been designed to develop a high-performance fault-tolerant switched reluctance motor (SRM) drive. The fuzzy controller continuously adapts its properties to regulate the machine torque as desired by the drive system even under fault conditions. The adaptation of the fuzzy membership functions results in extended conduction period and increased peak current of the healthy phases to
Chia-Feng Juang; Chao-Hsin Hsu
2005-01-01
Online adaptive temperature control by field-programmable gate array (FPGA) - implemented adaptive recurrent fuzzy controller (ARFC) chip is proposed in this paper. The RFC is realized according to the structure of Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network. Direct inverse control configuration is used. To design RFC offline, evolutionary fuzzy controller using the hybrid of the Simplex method and particle swarm optimization
Fuzzy Adaptive Control for Intelligent Autonomous Space Exploration Problems
NASA Technical Reports Server (NTRS)
Esogbue, Augustine O.
1998-01-01
The principal objective of the research reported here is the re-design, analysis and optimization of our newly developed neural network fuzzy adaptive controller model for complex processes capable of learning fuzzy control rules using process data and improving its control through on-line adaption. The learned improvement is according to a performance objective function that provides evaluative feedback; this performance objective is broadly defined to meet long-range goals over time. Although fuzzy control had proven effective for complex, nonlinear, imprecisely-defined processes for which standard models and controls are either inefficient, impractical or cannot be derived, the state of the art prior to our work showed that procedures for deriving fuzzy control, however, were mostly ad hoc heuristics. The learning ability of neural networks was exploited to systematically derive fuzzy control and permit on-line adaption and in the process optimize control. The operation of neural networks integrates very naturally with fuzzy logic. The neural networks which were designed and tested using simulation software and simulated data, followed by realistic industrial data were reconfigured for application on several platforms as well as for the employment of improved algorithms. The statistical procedures of the learning process were investigated and evaluated with standard statistical procedures (such as ANOVA, graphical analysis of residuals, etc.). The computational advantage of dynamic programming-like methods of optimal control was used to permit on-line fuzzy adaptive control. Tests for the consistency, completeness and interaction of the control rules were applied. Comparisons to other methods and controllers were made so as to identify the major advantages of the resulting controller model. Several specific modifications and extensions were made to the original controller. Additional modifications and explorations have been proposed for further study. Some of these are in progress in our laboratory while others await additional support. All of these enhancements will improve the attractiveness of the controller as an effective tool for the on line control of an array of complex process environments.
Adaptive Fuzzy Control Technology for Automatic Oil Drilling System
Qingjie Zhao; Fasheng Wang; Wei Wang; Hongbin Deng
2007-01-01
The paper proposes an adaptive fuzzy control method for the new oil rigs of ZJ30DB series with AC frequency converters. The assistant motor and its gearing work as executing system. Considering the time-delay, time-variability and nonlinearity of controlled objects, we design a double-loop control system in order to control the drill pressure tending to an expected value. Because it is
Neural and Fuzzy Adaptive Control of Induction Motor Drives
Bensalem, Y. [Research Unit of Modelisation, Analyse, Command of Systems MACS (Tunisia); Sbita, L.; Abdelkrim, M. N. [6029 Universite High School of Engineering-Gabes-Tunisia (Tunisia)
2008-06-12
This paper proposes an adaptive neural network speed control scheme for an induction motor (IM) drive. The proposed scheme consists of an adaptive neural network identifier (ANNI) and an adaptive neural network controller (ANNC). For learning the quoted neural networks, a back propagation algorithm was used to automatically adjust the weights of the ANNI and ANNC in order to minimize the performance functions. Here, the ANNI can quickly estimate the plant parameters and the ANNC is used to provide on-line identification of the command and to produce a control force, such that the motor speed can accurately track the reference command. By combining artificial neural network techniques with fuzzy logic concept, a neural and fuzzy adaptive control scheme is developed. Fuzzy logic was used for the adaptation of the neural controller to improve the robustness of the generated command. The developed method is robust to load torque disturbance and the speed target variations when it ensures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the IM designed controller.
Wei-yen Wang; Mei-lang Chan; Tsu-tian Lee; Cheng-hsin Liu
2000-01-01
In this paper, an adaptive fuzzy controller for strict-feedback canonical nonlinear systems is proposed. The completely unknown nonlinearities and disturbances of the systems are considered. Since fuzzy logic systems can uniformly approximate nonlinear continuous functions to arbitrary accuracy, the adaptive fuzzy control theory is employed to derive the control law for the strict-feedback system with unknown nonlinear functions and disturbances.
Fuzzy control interoperability for adaptive domotic framework
G. Acampora; V. Loia
2004-01-01
The evolution of microprocessor industry, combined with the reduction on cost and increase of efficiency, arises new scenario for ubiquitous computing where humans trigger seamlessly activities and tasks using unusual (often imperceptible) interfaces according to physical space and context. Many problems have to be faced: adaptively, hybrid control strategies, system (hardware) integration, and ubiquitous networking access. This paper presents an
Controlling chaos using Takagi Sugeno fuzzy model and adaptive adjustment
NASA Astrophysics Data System (ADS)
Zheng, Yong-Ai
2006-11-01
In this paper, an approach to the control of continuous-time chaotic systems is proposed using the Takagi-Sugeno (TS) fuzzy model and adaptive adjustment. Sufficient conditions are derived to guarantee chaos control from Lyapunov stability theory. The proposed approach offers a systematic design procedure for stabilizing a large class of chaotic systems in the literature about chaos research. The simulation results on Rössler's system verify the effectiveness of the proposed methods.
PWM predictive current control strategy based on self-adaptive intelligent fuzzy PID controller
Hui Wang; Juan Lu; Qi Yang; Jian Zhou
2004-01-01
This paper presents a PWM predictive current control strategy using self-adaptive intelligent fuzzy PID controller. PI parameter of the controller is on-line adjusted using fuzzy logic. Simulation shows that the controller based on the new strategy can speed the system response and improve system stability greatly.
Ahmed Rubaai; Abdul Ofoli; Donatus Cobbinah
2005-01-01
An embedded hybrid adaptive fuzzy control structure is implemented for trajectory tracking control of a brushless servo drive system (BSDS). The control structure employs a fuzzy logic controller incorporating an H? tracking controller via an actual acceleration feedback signal. The fuzzy logic controller is equipped with an adaptive law-based Lyapunov synthesis approach to compensate for system uncertainty and random changes
Fuzzy Soft-Switching Law of an Adaptive Sliding Mode Controller for Induction Motor Speed Control
A. Hazzabl; I. K. Bousserhane; P. Sicard
2006-01-01
An adaptive fuzzy sliding mode control based on soft-switching law is defined to control the speed of an induction motor. A sliding-mode control (SMC) system with proportional plus integral control action that generates an equivalent control action is investigated. A simple adaptive algorithm is utilized to generate soft-switching parameters of the equivalent control action, resulting into a fuzzy sliding mode
Adaptive Process Control with Fuzzy Logic and Genetic Algorithms
NASA Technical Reports Server (NTRS)
Karr, C. L.
1993-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Adaptive process control using fuzzy logic and genetic algorithms
NASA Technical Reports Server (NTRS)
Karr, C. L.
1993-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Andreasen, Søren Juhl
Methanol Reformer System Modeling and Control using an Adaptive Neuro-Fuzzy Inference System Adaptive Neuro-Fuzzy Inference Systems (ANFIS). Figure 2 shows the general structure of the ANFIS approach. ANFIS is a neuro-fuzzy modeling approach which uses linguistic variables and parameters which
Adaptive fuzzy-based tracking control for nonlinear SISO systems via VSS and H? approaches
Yeong-Chan Chang
2001-01-01
An adaptive fuzzy-based tracking control equipped with VSS and H ? control algorithms is proposed for nonlinear SISO systems involving plant uncertainties and external disturbances. Both well-defined VSS indirect and direct adaptive fuzzy-based H? control schemes are developed. In order to compensate the effect of the approximation error via the adaptive fuzzy system on the H? tracking control, a modified
Switched reluctance motor control via fuzzy adaptive systems
Donald S. Reay; Mehran Mirkazemi-Moud; Tim C. Green; Barry W. Williams
1995-01-01
This article presents the application of fuzzy adaptive systems to the problem of torque ripple reduction in a switched reluctance motor. Conventional methods for torque linearization and decoupling are reviewed briefly, as is the previous application, by the authors, of neural network based techniques. A solution based on the use of fuzzy adaptive systems is then described. Experimental measurements of
Adaptive fuzzy PID temperature control system based on OPC and modbus\\/TCP protocol
Qingbao Huang; Qianzhong She; Xiaofeng Lin
2010-01-01
In this paper, an adaptive fuzzy PID controller is designed to improve the control performance of the resistance furnace, and then is applied to a remote temperature control system based on modbus\\/TCP industrial Ethernet via OPC communication. The adaptive PID controller is developed by the fuzzy toolbox in Matlab. The data acquisition and devices driving are realized by PCAuto, a
Adaptive Fuzzy Logic Control of Permanent Magnet Synchronous Machines With Nonlinear Friction
Hicham Chaoui; Pierre Sicard
2012-01-01
In this paper, an adaptive fuzzy control scheme is in- troduced for permanent magnet synchronous machines (PMSMs). The adaptive control strategy consists of a Lyapunov stability- based fuzzy speed controller that capitalizes on the machine's in- verse model to achieve accurate tracking with unknown nonlinear system dynamics. As such, robustness to modeling and param- etric uncertainties is achieved. Moreover, no
Fuzzy adaptive control for nonlinear systems. Real-time implementation for a robot wrist
Reda Boukezzoula; Sylvie Galichet; Laurent Foulloy
2001-01-01
In this paper, an adaptive Takagi-Sugeno fuzzy controller for continuous nonlinear systems is proposed. The controller is designed under the constraint that only the output of the plant is measured. A state observer is thus introduced in the global control structure. The nonlinear plant is approximated with a Takagi-Sugeno fuzzy system whose parameters are adjusted via adaptive laws. Based on
Observer-based robust adaptive fuzzy tracking control in robot arms
Yongfu Wang; Xiaoling Huang; Lijie Zhao; Tianyou Chai
2004-01-01
In this paper, an observer-based robust adaptive fuzzy tracking control for rigid robotic systems is presented with plant unknown. It is assumed that only the joint angular positions are measured, the joint angular velocities are estimated via a fuzzy observer. First, we design a nonlinear observer based on fuzzy basis functions (FBF) to estimate the joint angular velocities in which
Fuzzy Longitudinal Controller Design and Experimentation for Adaptive Cruise Control and Stop&Go
Ching-Chih Tsai; Shih-Min Hsieh; Chien-Tzu Chen
2010-01-01
This paper presents a fuzzy longitudinal control system with car-following speed ranging from 0 to 120 km\\/h, thereby achieving\\u000a the main functions of both adaptive cruise control (ACC) and Stop&Go control. A fuzzy longitudinal controller is synthesized\\u000a by inputting the difference of the actual relative distance and the safe distance obtained from the preceding vehicle, and\\u000a the relative speed, and then
M. B. Djukanovic; M. S. Calovic; B. V. Vesovic; D. J. Sobajic
1997-01-01
This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients,
Torque ripple minimization in switched reluctance motors using adaptive fuzzy control
Sayeed Mir; Malik Elbuluk; Iqbal Husain
1997-01-01
An adaptive fuzzy control scheme for torque ripple minimization of switched reluctance machines (SRM) is presented. The fuzzy parameters are initially chosen randomly and then adjusted to optimize the control. The controller produces smooth torque upto the motor base speed. The torque is generated over the maximum positive torque producing region of a phase. This increases the torque density and
NASA Astrophysics Data System (ADS)
Mousavi, Seyyed Hossein; Noroozi, Navid; Safavi, Ali Akbar; Ebadat, Afrooz
2011-09-01
This paper proposes an observer based self-structuring robust adaptive fuzzy wave-net (FWN) controller for a class of nonlinear uncertain multi-input multi-output systems. The control signal is comprised of two parts. The first part arises from an adaptive fuzzy wave-net based controller that approximates the system structural uncertainties. The second part comes from a robust H? based controller that is used to attenuate the effect of function approximation error and disturbance. Moreover, a new self structuring algorithm is proposed to determine the location of basis functions. Simulation results are provided for a two DOF robot to show the effectiveness of the proposed method.
Adaptive fuzzy sliding mode controller for two cascaded tanks level control
Nawaporn Waurajitti; Jongkol Ngamwiwit; Yotin Prempraneerach
2000-01-01
This paper proposes an indirect adaptive fuzzy sliding mode controller for a nonlinear plant. Fast convergence of the controlled system is first obtained from the indirect approach by roughly estimating the nonlinear functions of the plant. Then the sliding mode control approach with boundary layer is used to compensate the deteriorated system performance due to the model mismatch and unmodeled
NASA Astrophysics Data System (ADS)
Hwang, Eun-Ju; Hyun, Chang-Ho; Kim, Euntai; Park, Mignon
2009-05-01
This Letter presents fuzzy model-based robust tracking control for the adaptive synchronization of uncertain chaotic systems. Fuzzy model and adaptive algorithm are employed to present the unknown chaotic systems. H and sliding mode control are combined to construct a robust tracking controller. The incorporated H controller can attenuate the external disturbance and approximation error to any prescribed level. The proposed scheme guarantees that all the variables are bounded and the tracking error is compensated.
S. P. Moustakidis; G. A. Rovithakis; J. B. Theocharis
2006-01-01
An adaptive neuro-fuzzy controller is proposed in this paper to deal with the problem of tracking nonlinear affine in the control dynamical systems with unknown nonlinearities. The plant is described by means of a Takagi-Sugeno fuzzy model, including dynamic fuzzy rules of generalized form, where the local submodels are realized through nonlinear input-output mappings. Instead of modelling the plant dynamics
Adaptive fuzzy logic position control of a Stepper motor with Extended Kalman Filter
V Bindu; A Unnikrishnan; R Gopikakumari
2012-01-01
The present paper proposes an adaptive fuzzy logic control (AFLC) for the position control of a Stepper motor. The weights used for combining the fuzzy rules are also updated, using the least mean square algorithm. The paper also demonstrates a Kalman filter for the estimation of motor parameters like speed and flux vector position. The estimation is ensured to be
Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles
Sefer Kurnaz; Omer Cetin; Okyay Kaynak
2010-01-01
In this paper, an ANFIS (adaptive neuro-fuzzy inference system) based autonomous flight controller for UAVs (unmanned aerial vehicles) is described. To control the position of the UAV in three dimensional space as altitude and longitude–latitude location, three fuzzy logic modules are developed. These adjust the pitch angle, the roll angle and the throttle position of the UAV so that its
Fuzzy rule-based model reference adaptive control of permanent magnet synchronous motor drive
Z. Kovacic; Stjepan Bogdan; P. Crnosija
1993-01-01
The paper presents a model reference adaptive control scheme using fuzzy logic adaptation mechanism to generate additional signal to the system input. The proposed method has been applied to the linearized model of the angular speed control system of a vector controlled chopper-fed permanent magnet synchronous motor (PMSM) drive. Simulation results indicate that a good adaptation is achieved even for
Adaptive Neuro-fuzzy Control System by RBF and GRNN Neural Networks
Teo Lian Seng; Marzuki Khalid; Rubiyah Yusof; Sigeru Omatu
1998-01-01
Recently, adaptive control systems utilizing artificial intelligent techniques are being actively investigated in many applications. Neural networks with their powerful learning capability are being sought as the basis for many adaptive control systems where on-line adaptation can be implemented. Fuzzy logic on the other hand have been proven to be rather popular in many control system applications providing a rule-base
Observer-based adaptive fuzzy-neural control for unknown nonlinear dynamical systems
Yih-guang Leu; Tsu-tian Lee; Wei-yen Wang
1999-01-01
In this paper, an observer-based adaptive fuzzy-neural controller for a class of unknown nonlinear dynamical systems is developed. The observer-based output feedback control law and update law to tune on-line the weighting factors of the adaptive fuzzy-neural controller are derived. The total states of the nonlinear system are not assumed to be available for measurement. Also, the unknown nonlinearities of
Li Li; Fuchun Sun
2007-01-01
In this paper, we first present a series of dynamic TS fuzzy subsystems to approximate a nonlinear singularly perturbed system.\\u000a Then the reference model with same fuzzy sets is established. To make the states of the closed-loop system follow those of\\u000a the reference model, a controller including of neuro-fuzzy adaptive and linear feedback term is designed. The linear feedback\\u000a parameters
Adaptive fuzzy-VSS control for the spindle motor of CD-ROM systems
Gwo-Ruey Yu; Shun-Qin Yang
2002-01-01
The design method of combining variable structure system (VSS) with fuzzy theory is presented in this paper, which is applied to the speed control and responses improvement of a spindle motor of CD-ROM drives. The exogenous disturbance of the motor is estimated by means of the adaptive fuzzy logic system. The input voltage of the spindle motor could be adjusted
Robust adaptive fuzzy control for a class of uncertain servomechanism systems
Udom Komin; Suthee Phoojaruenchanachai; Somchai Chatratana; Suwat Kuntanapreeda
2004-01-01
In this paper an adaptive fuzzy control technique is presented for a class of uncertain mechanical systems. The uncertainties are mainly caused by mechanical friction and unbalanced load. The fuzzy logic system is employed to compensate the effect of the friction and the unbalanced load. The technique does not require a complete mathematical model or a priori knowledge about the
Adaptive fuzzy fault-tolerant control for unknown nonlinear systems with disturbances
Ping Li; Guang-hong Yang
2008-01-01
A class of unknown nonlinear systems subject to uncertain actuator faults and external disturbances will be studied in this paper with the help of fuzzy approximation theory. Using backstepping technique, a novel adaptive fuzzy control approach is proposed to accommodate the uncertain actuator faults during operation and deal with the external disturbances. The considered faults are modeled as both loss
Meriem Benbrahim; Najib Essounbouli; Abdelaziz Hamzaoui; Ammar Betta
2010-01-01
A class of MIMO unknown nonlinear systems subject to uncertain actuator faults and external disturbances is studied in this paper using fuzzy approximation theory and sliding modes. Using the universal approximation theorem, an adaptive fuzzy control law is proposed to accommodate the uncertain actuator faults during operation. The considered faults are modeled as both loss of effectiveness and lock-in-place (stuck
Adaptive Controller for T-S Fuzzy Model with Reconstruction Error
NASA Astrophysics Data System (ADS)
Han, Hugang
In this paper, the reconstruction error between the real system to be controlled and its T-S fuzzy model is considered, and fuzzy approximator is employed to cope with the reconstruction error. As a result, it reaches an adaptive controller that has two parts: one is obtained by solving certain linear matrix inequalities (LMIs) (fixed part) and another one is acquired by the fuzzy approximator in which the related parameters are tuned by adaptive law (variable part). The proposed controller can guarantee the control state to converge and uniformly bounded while maintaining all the signals involved stable. Also, the convergence in terms of relaxing the LMIs conservatism is discussed. An inverted pendulum is provided to demonstrate the effectiveness of the proposed adaptive fuzzy controller.
Son Kuswadi; Akiko Takahashi; Aki Ohnishi; Mitsuji Sampei; Shigeki Nakaura
2002-01-01
This paper addresses the control method of one linear actuator hopping robot in a plane by using feedback error learning scheme. Adaptive fuzzy network was used in this scheme, and backpropagation techniques utilized as learning algorithm. The robot consists of a body and a leg, which are in contact with a sufficiently wide horizontal ground; both are fixed rigidly. We
The research on boiler drum water level control system based on self-adaptive fuzzy-PID
Liang Chen; Cuizhu Wang; Yang Yu; Yawei Zhao
2010-01-01
The control theory of self-adaptable fuzzy-PID is expounded in this paper, the dynamic characteristic and system structure of boiler drum water level control system is introduced also. The self-adaptable fuzzy-PID control method is applied in automatic control of boiler drum water level, under the situation of interaction and no interaction, the simulation research is done to PID and self-adaptable fuzzy-PID,
Adaptive Neural Network Fuzzy Inference Controller Using Predictive Evolutionary Tuning
Gordon K. Lee; Edward Grant
2007-01-01
Abstract - The design of intelligent controllers for nonlinear systems continues to ,be a ,challenging problem, particularly when the system is uncertain or the environment noisy. A nonparametric approach which has gained success is to employ a neural network to learn about the unknown plant and fuzzy inference to compensate for the uncertainty (GANFIS control). Inherent in the design of
Ping Li; Guang-Hong Yang
2008-01-01
In this paper, an adaptive fuzzy approach is proposed to deal with output regulation of unknown nonlinear systems with actuator failures. The actuator failures under consideration can be lock-in-place or\\/and loss of effectiveness. Based on fuzzy logic systems (FLS) equipped with adaptive algorithms to approximate the nonlinear system functions and the occurred failures together, a fault tolerant control law is
Performance improvement of a microbial fuel cell based on adaptive fuzzy control.
Fan, Liping; Li, Chong; Boshnakov, Kosta
2014-05-01
Microbial fuel cells have been obtaining more and more attention with the associated abilities of continuous electrical power supply and wastewater treatment. Because of its complicated reaction mechanism and its inherent characteristics of time varying, uncertainty, strong coupling and nonlinearity, there are complex control challenges in microbial fuel cells. In this paper, an adaptive fuzzy control scheme is proposed for the microbial fuel cell system to achieve constant voltage output under different loads. A main fuzzy controller is used to track the set value, and an auxiliary fuzzy controller is applied to adjust the factors of the main controller. Simulation results show that the output voltage can track the given value well. The proposed adaptive fuzzy controller can give better steady-state behavior and faster response, and it improves the running performance of the microbial fuel cell. PMID:24816708
Study on application of adaptive fuzzy control and neural network in the automatic leveling system
NASA Astrophysics Data System (ADS)
Xu, Xiping; Zhao, Zizhao; Lan, Weiyong; Sha, Lei; Qian, Cheng
2015-04-01
This paper discusses the adaptive fuzzy control and neural network BP algorithm in large flat automatic leveling control system application. The purpose is to develop a measurement system with a flat quick leveling, Make the installation on the leveling system of measurement with tablet, to be able to achieve a level in precision measurement work quickly, improve the efficiency of the precision measurement. This paper focuses on the automatic leveling system analysis based on fuzzy controller, Use of the method of combining fuzzy controller and BP neural network, using BP algorithm improve the experience rules .Construct an adaptive fuzzy control system. Meanwhile the learning rate of the BP algorithm has also been run-rate adjusted to accelerate convergence. The simulation results show that the proposed control method can effectively improve the leveling precision of automatic leveling system and shorten the time of leveling.
PID-Type Fuzzy Control for Anti-Lock Brake Systems with Parameter Adaptation
Chih-Keng Chen; Ming-Chang Shih
2004-01-01
In this research, a platform is built to accomplish a series of experiments to control the Antilock Brake System (ABS). A commercial ABS module controlled by a controller is installed and tested on the platform. The vehicle and tire models are deduced and simulated by a personal computer for real time control. An adaptive PID-type fuzzy control scheme is used.
Teresa Orlowska-Kowalska; Mateusz Dybkowski; Krzysztof Szabat
2010-01-01
In this paper, the concept of a model reference adaptive control of a sensorless induction motor (IM) drive with elastic joint is proposed. An adaptive speed controller uses fuzzy neural network equipped with an additional option for online tuning of its chosen parameters. A sliding-mode neuro-fuzzy controller is used as the speed controller, whose connective weights are trained online according
Application of adaptive fuzzy control technology to pressure control of a pressurizer
Ben-Kun Yang; Xin-Qian Bian; Wei-Lai Guo
2005-01-01
A pressurizer is one of important equipment in a pressurized water reactor plant. It is used to maintain the pressure of primary\\u000a coolant within allowed range because the sharp change of coolant pressure affects the security of reactor, therefor, the study\\u000a of pressurizer’s pressure control methods is very important. In this paper, an adaptive fuzzy controller is presented for\\u000a pressure
NASA Astrophysics Data System (ADS)
Hu, Hongtao; Jing, Zhongliang; Hu, Shiqiang
2006-12-01
A novel adaptive algorithm for tracking maneuvering targets is proposed. The algorithm is implemented with fuzzy-controlled current statistic model adaptive filtering and unscented transformation. A fuzzy system allows the filter to tune the magnitude of maximum accelerations to adapt to different target maneuvers, and unscented transformation can effectively handle nonlinear system. A bearing-only tracking scenario simulation results show the proposed algorithm has a robust advantage over a wide range of maneuvers and overcomes the shortcoming of the traditional current statistic model and adaptive filtering algorithm.
NASA Astrophysics Data System (ADS)
Wu, Zhenhui; Dong, Chaoyang
2006-11-01
Because of nonlinearity and strong coupling of reaction-jet and aerodynamics compound control missile, a missile autopilot design method based on adaptive fuzzy sliding mode control (AFSMC) is proposed in this paper. The universal approximation ability of adaptive fuzzy system is used to approximate the nonlinear function in missile dynamics equation during the flight of high angle of attack. And because the sliding mode control is robustness to external disturbance strongly, the sliding mode surface of the error system is constructed to overcome the influence of approximation error and external disturbance so that the actual overload can track the maneuvering command with high precision. Simulation results show that the missile autopilot designed in this paper not only can track large overload command with higher precision than traditional method, but also is robust to model uncertainty and external disturbance strongly.
NASA Astrophysics Data System (ADS)
Wang, Li-Ming; Tang, Yong-Guang; Chai, Yong-Quan; Wu, Feng
2014-10-01
An adaptive fuzzy sliding mode strategy is developed for the generalized projective synchronization of a fractional-order chaotic system, where the slave system is not necessarily known in advance. Based on the designed adaptive update laws and the linear feedback method, the adaptive fuzzy sliding controllers are proposed via the fuzzy design, and the strength of the designed controllers can be adaptively adjusted according to the external disturbances. Based on the Lyapunov stability theorem, the stability and the robustness of the controlled system are proved theoretically. Numerical simulations further support the theoretical results of the paper and demonstrate the efficiency of the proposed method. Moreover, it is revealed that the proposed method allows us to manipulate arbitrarily the response dynamics of the slave system by adjusting the desired scaling factor ?i and the desired translating factor ?i, which may be used in a channel-independent chaotic secure communication.
Robust adaptive sliding-mode control using fuzzy modeling for an inverted-pendulum system
Chaio-Shiung Chen; Wen-Liang Chen
1998-01-01
In this paper, a new robust adaptive control architecture is proposed for operation of an inverted-pendulum mechanical system. The architecture employs a fuzzy system to adaptively compensate for the plant nonlinearities and forces the inverted pendulum to track a prescribed reference model. When matching with the model occurs, the pendulum will be stabilized at an upright position and the cart
CONTROL OF THE PENICILLIN PRODUCTION WITH ADAPTIVE IMC USING FUZZY NEURAL NETWORKS
M. J. Araúzo Bravo; E. Gómez Sánchez; J. M. Cano Izquierdo; J. López Coronado; M. J. López Nieto; A. Collados de la Vieja
This paper introduces the use adaptation in IMC strategy for the control of a simulated penicillin plant. The plant model and control modules are built using FasBack neuro-fuzzy system, featuring fast stable learning guided by matching and error minimisation and good identification performance. Control results show good general performance both in the nominal case and in the presence of noise.
Reda Boukezzoula; Sylvie Galichet; Laurent Foulloy
2004-01-01
This paper examines the tracking control problem for a class of feedback linearizable nonlinear systems for which there is no available analytic model. Based on the ability of fuzzy systems to approximate any nonlinear mapping, the unknown nonlinear system is represented by a Takagi-Sugeno (TS) fuzzy system. First, we represent the nonlinear plant with an adaptive TS fuzzy system, where
Adaptive control based on genetic algorithm and fuzzy tuning for unknown systems with time-delay
Hongxing Li; Bingzhang Luo
2008-01-01
Considering unknown systems with time-delay, a neural network approach for on-line parameter estimation is presented. The unknown steady-state gain and time-delay of the systems are estimated on-line by Adaline network and then used to modify parameters of Smith predictor in real-time. An adaptive neuron controller based on fuzzy tuning and genetic algorithm is designed in this paper. Fuzzy rules are
Backstepping adaptive fuzzy control of uncertain nonlinear systems against actuator faults
Ping Li; Guanghong Yang
2009-01-01
A class of unknown nonlinear systems subject to uncertain actuator faults and external disturbances will be studied in this\\u000a paper with the help of fuzzy approximation theory. Using backstepping technique, a novel adaptive fuzzy control approach is\\u000a proposed to accommodate the uncertain actuator faults during operation and deal with the external disturbances though the\\u000a systems cannot be linearized by feedback.
Robust adaptive fuzzy control (RAFC) for ship steering with uncertain nonlinear systems
Yansheng yang; Bo Jiang
2004-01-01
A novel robust adaptive fuzzy control algorithm is presented for ship steering with an uncertain system and a gain function, which are all the unstructured (or non-repeatable) state-dependent unknown nonlinear functions. The Takagi-Sugeno type fuzzy logic systems are used to approximate uncertain functions and the RAFC algorithm is designed by the use of input-to-state stability (ISS) approach and small gain
Fuzzy logic based adaptive cruise control with guaranteed string stability
Sang-Jin Ko; Ju-Jang Lee
2007-01-01
Recently, many topics in intelligent transportation system (ITS) are researched. One of the topics is adaptive cruise control (ACC). The adaptive cruise control systems should be designed such that string stability can be guaranteed in addition to that every vehicle in a string of ACC vehicles which use the same control law must track any bounded acceleration and velocity of
Networked flexible spacecraft attitude maneuver based on adaptive fuzzy sliding mode control
Chaoyang Dong; Lijie Xu; Yu Chen; Qing Wang
2009-01-01
For the attitude stabilization of networked flexible spacecraft during large angle slew maneuver, a novel type of adaptive fuzzy sliding mode control (AFSMC) method for solving the dynamic model with network-induced delay, nonlinear and uncertain parameters is proposed in this paper. A novel and systematic sliding mode control (SMC) scheme, which integrates a time-advanced nonlinear predictor, is proposed to compensate
Song, Zhankui; Sun, Kaibiao
2014-01-01
A novel adaptive backstepping sliding mode control (ABSMC) law with fuzzy monitoring strategy is proposed for the tracking-control of a kind of nonlinear mechanical system. The proposed ABSMC scheme combining the sliding mode control and backstepping technique ensure that the occurrence of the sliding motion in finite-time and the trajectory of tracking-error converge to equilibrium point. To obtain a better perturbation rejection property, an adaptive control law is employed to compensate the lumped perturbation. Furthermore, we introduce fuzzy monitoring strategy to improve adaptive capacity and soften the control signal. The convergence and stability of the proposed control scheme are proved by using Lyaponov's method. Finally, numerical simulations demonstrate the effectiveness of the proposed control scheme. PMID:24059943
Adaptive Neuro-Fuzzy Inference System PID controller for SG water level of nuclear power plant
Xue-Kui Wang; Xu-Hong Yang; Gang Liu; Hong Qian
2009-01-01
In a nuclear power plant, the water level in the steam generator (SG) is one of main causes that shutdown the reactor, this problem has been of great concern for many years as the SG is a highly nonlinear system showing inverse response dynamics. For controlling the SG water level at a certain range, adaptive neuro-fuzzy inference system (ANFIS) PID
Dongkyoung Chwa
2012-01-01
Unlike most works based on pure nonholonomic constraint, this paper proposes a fuzzy adaptive tracking control method for wheeled mobile robots, where unknown slippage occurs and violates the nonholononomic constraint in the form of state-dependent kinematic and dynamic disturbances. These disturbances degrade tracking performance significantly and, therefore, should be compensated. To this end, the kinematics with state-dependent disturbances are rigorously
Adaptive Load Shedding via Fuzzy Control in Data Stream Management Systems
Kang, Kyoung-Don
Adaptive Load Shedding via Fuzzy Control in Data Stream Management Systems Can Basaran, Kyoung:msuzer@harran.edu.tr Abstract--Data stream management systems (DSMS) aim to process massive data streams in a timely fashion to large bursts in data stream arrivals and data-dependent query executions. To avoid over- loads, we
Intelligent fuzzy supervisory control for distillation columns
Santhanam, Srinivasan
1993-01-01
for dynamically adapting the models to achieve tight composition control. Simple control techniques do not exist for model adaptation in MIMO systems. This thesis will outline a fuzzy supervisory controller based on fuzzy logic and show that control performance...
Design of adaptive fuzzy wavelet neural sliding mode controller for uncertain nonlinear systems.
Shahriari kahkeshi, Maryam; Sheikholeslam, Farid; Zekri, Maryam
2013-05-01
This paper proposes novel adaptive fuzzy wavelet neural sliding mode controller (AFWN-SMC) for a class of uncertain nonlinear systems. The main contribution of this paper is to design smooth sliding mode control (SMC) for a class of high-order nonlinear systems while the structure of the system is unknown and no prior knowledge about uncertainty is available. The proposed scheme composed of an Adaptive Fuzzy Wavelet Neural Controller (AFWNC) to construct equivalent control term and an Adaptive Proportional-Integral (A-PI) controller for implementing switching term to provide smooth control input. Asymptotical stability of the closed loop system is guaranteed, using the Lyapunov direct method. To show the efficiency of the proposed scheme, some numerical examples are provided. To validate the results obtained by proposed approach, some other methods are adopted from the literature and applied for comparison. Simulation results show superiority and capability of the proposed controller to improve the steady state performance and transient response specifications by using less numbers of fuzzy rules and on-line adaptive parameters in comparison to other methods. Furthermore, control effort has considerably decreased and chattering phenomenon has been completely removed. PMID:23453235
Adaptive IMC using fuzzy neural networks for the control on non linear systems
E. Gómez Sánchez; J. M. Cano Izquierdo; M. J. Araúzo Bravo; Y. A. Dimitriadis; J. López Coronado
This paper introduces the use of FasBack neuro-fuzzy system for the identification and control of non linear MIMO plants within IMC scheme. FasBack presents fast stable learning guided by matching and error minimisation, and presents good MIMO identification performance. Emphasis is made on the on-line adaptive capability of FasBack that can be used to develop adaptive IMC strategies, which are
NASA Astrophysics Data System (ADS)
Yang, Yueneng; Wu, Jie; Zheng, Wei
2013-04-01
This paper presents a novel approach for station-keeping control of a stratospheric airship platform in the presence of parametric uncertainty and external disturbance. First, conceptual design of the stratospheric airship platform is introduced, including the target mission, configuration, energy sources, propeller and payload. Second, the dynamics model of the airship platform is presented, and the mathematical model of its horizontal motion is derived. Third, a fuzzy adaptive backstepping control approach is proposed to develop the station-keeping control system for the simplified horizontal motion. The backstepping controller is designed assuming that the airship model is accurately known, and a fuzzy adaptive algorithm is used to approximate the uncertainty of the airship model. The stability of the closed-loop control system is proven via the Lyapunov theorem. Finally, simulation results illustrate the effectiveness and robustness of the proposed control approach.
Lin, F J; Wai, R J; Lin, H H
1999-01-01
In this study an adaptive fuzzy-neural-network controller (AFNNC) is proposed to control a rotary traveling wave-type ultrasonic motor (USM) drive system. The USM is derived by a newly designed, high frequency, two-phase voltage source inverter using two inductances and two capacitances (LLCC) resonant technique. Then, because the dynamic characteristics of the USM are complicated and the motor parameters are time varying, an AFNNC is proposed to control the rotor position of the USM. In the proposed controller, the USM drive system is identified by a fuzzy-neural-network identifier (FNNI) to provide the sensitivity information of the drive system to an adaptive controller. The backpropagation algorithm is used to train the FNNI on line. Moreover, to guarantee the convergence of identification and tracking errors, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the FNNI and the optimal learning rate of the adaptive controller. In addition, the effectiveness of the adaptive fuzzy-neural-network (AFNN) controlled USM drive system is demonstrated by some experimental results. PMID:18238472
Fuzzy control interoperability and scalability for adaptive domotic framework
Giovanni Acampora; Vincenzo Loia
2005-01-01
The evolution of the microprocessor industry, combined with the reduction on cost and increase of efficiency, gives rise to new scenario for ubiquitous computing where humans trigger seamlessly activities and tasks using unusual (often imperceptible) interfaces according to physical space and context. Many problems must be faced: adaptivity, hybrid control strategies, system (hardware) integration, and ubiquitous networking access. In this
Adaptive Fuzzy Hysteresis Band Current Controller for Four-Wire Shunt Active Filter
NASA Astrophysics Data System (ADS)
Hamoudi, F.; Chaghi, A.; Amimeur, H.; Merabet, E.
2008-06-01
This paper presents an adaptive fuzzy hysteresis band current controller for four-wire shunt active power filters to eliminate harmonics and to compensate reactive power in distribution systems in order to keep currents at the point of common coupling sinusoidal and in phase with the corresponding voltage and the cancel neutral current. The conventional hysteresis band known for its robustness and its advantage in current controlled applications is adapted with a fuzzy logic controller to change the bandwidth according to the operating point in order to keep the frequency modulation at tolerable limits. The algorithm used to identify the reference currents is based on the synchronous reference frame theory (dq?). Finally, simulation results using Matlab/Simulink are given to validate the proposed control.
Fuzzy-rule-based Adaptive Resource Control for Information Sharing in P2P Networks
NASA Astrophysics Data System (ADS)
Wu, Zhengping; Wu, Hao
With more and more peer-to-peer (P2P) technologies available for online collaboration and information sharing, people can launch more and more collaborative work in online social networks with friends, colleagues, and even strangers. Without face-to-face interactions, the question of who can be trusted and then share information with becomes a big concern of a user in these online social networks. This paper introduces an adaptive control service using fuzzy logic in preference definition for P2P information sharing control, and designs a novel decision-making mechanism using formal fuzzy rules and reasoning mechanisms adjusting P2P information sharing status following individual users' preferences. Applications of this adaptive control service into different information sharing environments show that this service can provide a convenient and accurate P2P information sharing control for individual users in P2P networks.
NASA Astrophysics Data System (ADS)
Zheng, Yongai; Nian, Yibei; Wang, Dejin
2010-12-01
In this Letter, a kind of novel model, called the generalized Takagi-Sugeno (T-S) fuzzy model, is first developed by extending the conventional T-S fuzzy model. Then, a simple but efficient method to control fractional order chaotic systems is proposed using the generalized T-S fuzzy model and adaptive adjustment mechanism (AAM). Sufficient conditions are derived to guarantee chaos control from the stability criterion of linear fractional order systems. The proposed approach offers a systematic design procedure for stabilizing a large class of fractional order chaotic systems from the literature about chaos research. The effectiveness of the approach is tested on fractional order Rössler system and fractional order Lorenz system.
Adaptive fuzzy-neural-network control for induction spindle motor drive
Faa-Jeng Lin; Rong-Jong Wai
2002-01-01
An induction spindle motor drive using synchronous pulse-width modulation (PWM) and dead-time compensatory techniques with an adaptive fuzzy-neural-network controller (AFNNC) is proposed in this study for advanced spindle motor applications. First, the operating principles of a new synchronous PWM technique and the circuit of dead-time compensator are described in detail. Then, since the control characteristics and motor parameters for high-speed-operated
Flight test results of the fuzzy logic adaptive controller-helicopter (FLAC-H)
Robert L. Wade; Gregory W. Walker
1996-01-01
The fuzzy logic adaptive controller for helicopters (FLAC-H) demonstration is a cooperative effort between the US Army Simulation, Training, and Instrumentation Command (STRICOM), the US Army Aviation and Troop Command, and the US Army Missile Command to demonstrate a low-cost drone control system for both full-scale and sub-scale helicopters. FLAC-H was demonstrated on one of STRICOM's fleet of full-scale rotary-winged
Observer-Based Direct Adaptive Fuzzy-Neural Control for Anti-lock Braking Systems
Guan-Ming Chen; Wei-Yen Wang; Tsu-Tian Lee; C. W. Tao
2006-01-01
In this paper, an observer-based direct adaptive fuzzy-neural controller (ODAFNC) for an anti-lock braking system (ABS) is developed under the con- straint that only the system output, i.e., the wheel slip ratio, is measurable. The main control strategy is to force the wheel slip ratio to well track the optimal value, which may vary with the environment. The observer-based output
Synchronization of two different chaotic systems using novel adaptive fuzzy sliding mode control.
Roopaei, M; Jahromi, M Zolghadri
2008-09-01
In this paper, an adaptive fuzzy sliding mode control (AFSMC) scheme is proposed for the synchronization of two chaotic nonlinear systems in the presence of uncertainties and external disturbance. To design the reaching phase of the sliding mode control (SMC), a fuzzy controller is used. This will reduce the chattering and improve the robustness. An AFSMC is used (as an equivalent control part of the SMC) to approximate the unknown parts of the uncertain chaotic systems. Although the above schemes have been proposed in the past as separate stand-alone control schemes, in this paper, we integrate these methods to propose an effective control scheme having the benefits of each. The stability analysis for the proposed control scheme is provided and simulation examples are presented to verify the effectiveness of the method. PMID:19045471
Synchronization of two different chaotic systems using novel adaptive fuzzy sliding mode control
NASA Astrophysics Data System (ADS)
Roopaei, M.; Zolghadri Jahromi, M.
2008-09-01
In this paper, an adaptive fuzzy sliding mode control (AFSMC) scheme is proposed for the synchronization of two chaotic nonlinear systems in the presence of uncertainties and external disturbance. To design the reaching phase of the sliding mode control (SMC), a fuzzy controller is used. This will reduce the chattering and improve the robustness. An AFSMC is used (as an equivalent control part of the SMC) to approximate the unknown parts of the uncertain chaotic systems. Although the above schemes have been proposed in the past as separate stand-alone control schemes, in this paper, we integrate these methods to propose an effective control scheme having the benefits of each. The stability analysis for the proposed control scheme is provided and simulation examples are presented to verify the effectiveness of the method.
Guo Wencheng; Shi Wuxi; Guo Lijin
2009-01-01
A direct adaptive fuzzy control scheme is developed for a class of nonlinear discrete-time systems. In this scheme, the fuzzy logic system is used to design controller directly, and the parameters are adjusted by time-varying dead-zone, which its size is adjusted adaptively with the estimated bounds on the approximation error. The proposed design scheme guarantees that all the signals in
Adaptive Control of TwoAxis Motion Control System Using Interval Type2 Fuzzy Neural Network
Faa-Jeng Lin; Po-Huan Chou
2009-01-01
An interval type-2 fuzzy neural network (IT2FNN) control system is proposed for the precision control of a two-axis motion control system in this paper. The adopted two-axis motion control system is composed of two permanent-magnet linear synchronous motors. In the proposed IT2FNN control system, an IT2FNN, which combines the merits of an interval type-2 fuzzy logic system and a neural
Ali Azadeh; Najme Neshat; Afsaneh Kazemi; Mortezza Saberi
In this paper, adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and partial least squares (PLS)\\u000a approaches are applied to predictive control of a drying process. In the proposed approaches, the PLS analysis is used to\\u000a pre-process actual data and to provide the necessary background to apply ANN and ANFIS approaches. A reasonable section of\\u000a this study is assigned
Tsung-chih Lin; Chi-hsu Wang; Han-leih Liu
2004-01-01
Fuzzy control is a model free approach, i.e., it does not require a mathematical model of the system under control. An observer-based indirect adaptive fuzzy neural tracking control equipped with VSS and H? control algorithms is developed for nonlinear SISO systems involving plant uncertainties and external disturbances. Three important control methods, i.e., adaptive fuzzy neural control scheme, VSS control design
Simulation of traffic flow and control using conventional, fuzzy, and adaptive methods
Bisset, K.R.; Kelsey, R.L.
1992-01-01
This paper describes the graphical simulation of a traffic environment. The environment includes streets leading to an intersection, the intersection, vehicle traffic, and signal lights in the intersection controlled by different methods. The simulation allows for the study of parameters affecting traffic environments and the study of different control strategies for traffic signal lights, including conventional, fuzzy, and adaptive control methods. Realistic traffic environments are simulated including a cross intersection, with one or more lanes of traffic in each direction, with and without turn lanes. Vehicle traffic patterns are a mixture of cars going straight and making right or left turns. The free velocities of vehicles follow a normal distribution with a mean of the posted'' speed limit. Actual velocities depend on such factors as the proximity and velocity of surrounding traffic, approaches to intersections, and human response time. The simulation proves the be a useful tool for evaluating controller methods. Preliminary results show that larger quantities of traffic are handled'' by fuzzy control methods then by conventional control methods. Also, the average time spent waiting in traffic decreases with the use of fuzzy control versus conventional control.
Simulation of traffic flow and control using conventional, fuzzy, and adaptive methods
Bisset, K.R.; Kelsey, R.L.
1992-06-01
This paper describes the graphical simulation of a traffic environment. The environment includes streets leading to an intersection, the intersection, vehicle traffic, and signal lights in the intersection controlled by different methods. The simulation allows for the study of parameters affecting traffic environments and the study of different control strategies for traffic signal lights, including conventional, fuzzy, and adaptive control methods. Realistic traffic environments are simulated including a cross intersection, with one or more lanes of traffic in each direction, with and without turn lanes. Vehicle traffic patterns are a mixture of cars going straight and making right or left turns. The free velocities of vehicles follow a normal distribution with a mean of the ``posted`` speed limit. Actual velocities depend on such factors as the proximity and velocity of surrounding traffic, approaches to intersections, and human response time. The simulation proves the be a useful tool for evaluating controller methods. Preliminary results show that larger quantities of traffic are ``handled`` by fuzzy control methods then by conventional control methods. Also, the average time spent waiting in traffic decreases with the use of fuzzy control versus conventional control.
Research on fuzzy robust adaptive unscented particle filtering
Yi Gao; Shesheng Gao
2011-01-01
This paper present a new fuzzy robust adaptive Unscented particle filtering method based on the fuzzy control theory. This method absorbs the advantages of the fuzzy control theory, the robust adaptive filtering and the Unscented particle filtering. Using the influence of the gross errors in the observation vectors on the state vector parameters to obtain the robust adaptive Unscented particle
Adaptive fuzzy controller for thermal comfort inside the air-conditioned automobile chamber
Tong, L.; Yu, B.; Chen, Z.; Yang, K.
1999-07-01
In order to meet the passengers' demand for thermal comfort, the adaptive fuzzy logic control design methodology is applied for the automobile airconditioner system. In accordance with the theory of air flow and heat transfer, the air temperature field inside the airconditioned automobile chamber is simulated by a set of simplified half-empirical formula. Then, instead of PMV (Predicted Mean Vote) criterion, RIV (Real Individual Vote) criterion is adopted as the base of the control for passengers' thermal comfort. The proposed controller is applied to the air temperature regulation at the individual passenger position. The control procedure is based on partitioning the state space of the system into cell-groups and fuzzily quantificating the state space into these cells. When the system model has some parameter perturbation, the controller can also adjust its control parameters to compensate for the perturbation and maintain the good performance. The learning procedure shows its ideal effect in both computer simulation and experiments. The final results demonstrate the ideal performance of this adaptive fuzzy controller.
Disturbance road adaptive driving control of power-assisted wheelchair using fuzzy inference.
Seki, Hirokazu; Kiso, Atsushi
2011-01-01
This paper describes a novel driving control scheme of electric power-assisted wheelchairs for assistive driving on various large disturbance roads. The "electric power-assisted wheelchair" which assists the driving force by electric motors is expected to be widely used as a mobility support system for elderly people and disabled people; however, there are lots of large disturbance roads such as uphill roads and rough roads and operators need to row the hand-rims with the larger power load on such roads in order to obtain the enough driving velocity. For example the wheelchair might move backward on uphill roads due to the driving torque shortage. Therefore this study proposes a fuzzy algorithm based adaptive control scheme in order to realize the assistive driving without the operator's power load on large disturbance roads. The proposed fuzzy rules are designed from the driving distance information and the control parameters are inferred by the fuzzy algorithm. The assisted torque can be adjusted so that the enough distance and velocity are kept even on large disturbance roads. Driving experimental results are provided to verify the effectiveness of the proposed control system. PMID:22254627
Sheldon S. L. Chang; Lofti A. Zadeh
1972-01-01
A fuzzy mapping from X to Y is a fuzzy set on X ?? Y. The concept is extended to fuzzy mappings of fuzzy sets on X to Y, fuzzy function and its inverse, fuzzy parametric functions, fuzzy observation, and control. Set theoretical relations are obtained for fuzzy mappings, fuzzy functions, and fuzzy parametric functions. It is shown that under
PID-Type Fuzzy Control for Anti-Lock Brake Systems with Parameter Adaptation
NASA Astrophysics Data System (ADS)
Chen, Chih-Keng; Shih, Ming-Chang
In this research, a platform is built to accomplish a series of experiments to control the Antilock Brake System (ABS). A commercial ABS module controlled by a controller is installed and tested on the platform. The vehicle and tire models are deduced and simulated by a personal computer for real time control. An adaptive PID-type fuzzy control scheme is used. Two on-off conversion methods: pulse width modulation (PWM) and conditional on-off, are used to control the solenoid valves in the ABS module. With the pressure signal feedbacks in the caliper, vehicle dynamics and wheel speeds are computed during braking. Road surface conditions, vehicle weight and control schemes are varied in the experiments to study braking properties.
Modeling and control compensation of nonlinear friction using adaptive fuzzy systems
NASA Astrophysics Data System (ADS)
Wang, Y. F.; Wang, D. H.; Chai, T. Y.
2009-11-01
System performance in terms of control accuracy and stability is usually negatively affected by friction occurrences in mechanical systems. Thus, it is important to model the friction properly so that it can be used in controller design. This paper employs adaptive fuzzy systems to approximate unknown nonlinear friction functions, and applies the estimation of friction in proportional-derivative (PD) control law to enhance the control performance. On the basis of Lyapunov stability theory, a bound of tracking errors of the closed-loop control system is derived. Techniques proposed in this paper have been applied to a typical motion control system for simulation studies. The results obtained demonstrate that our proposed method in this paper has good potential in controlling many mechanical systems with unknown nonlinear friction.
Adaptive Fuzzy Controller for Efficiency Optimization of Induction Motors
Durval de Almeida Souza; W. C. P. de Aragao Filho; G. C. D. Sousa
2007-01-01
This paper introduces a new technique for efficiency optimization of adjustable-speed drives, with an emphasis on vector-controlled induction motor drives. The technique combines two distinct control methods, namely, online search of the optimal operating point and a model-based efficiency control. For a given operating condition, which is characterized by a given speed and load torque, a search controller (SC); based
Adaptive fuzzy model based predictive control of nuclear steam generators
H. Eliasi; H. Davilu; M. B. Menhaj
2007-01-01
Poor control of U-tube steam generators (UTSG) in a nuclear power plant can lead to frequent reactor shutdowns or damage of turbine blades. The dynamics of steam generator vary as power level changes. There is, therefore, a need to systematically design a suitable controller for all power levels. In this paper, we employ the concepts of both predictive control and
Networked flexible spacecraft attitude maneuver based on adaptive fuzzy sliding mode control
NASA Astrophysics Data System (ADS)
Dong, Chaoyang; Xu, Lijie; Chen, Yu; Wang, Qing
2009-12-01
For the attitude stabilization of networked flexible spacecraft during large angle slew maneuver, a novel type of adaptive fuzzy sliding mode control (AFSMC) method for solving the dynamic model with network-induced delay, nonlinear and uncertain parameters is proposed in this paper. A novel and systematic sliding mode control (SMC) scheme, which integrates a time-advanced nonlinear predictor, is proposed to compensate for the network-induced delay and to overcome the negative effect of uncertainties. Then, an adaptive fuzzy system is used to approximate the strong coupling nonlinear dynamics between rigid hub and flexible appendages. Following that, the designed adaptive algorithms are developed in the sense of the Lyapunov stability theorem, so that system-tracking stability can be guaranteed. Finally, simulation results show that, with the application of the proposed method, not only high-precision attitude stabilization of flexible spacecraft is achieved, but also the elastic vibration of flexible spacecraft during maneuver is suppressed effectively, and the system is robust against system uncertainties, network-induced delays and any outer disturbances.
Adaptive Fault-Tolerant Tracking Control of Near-Space Vehicle Using Takagi-Sugeno Fuzzy Models
Bin Jiang; Zhifeng Gao; Peng Shi; Yufei Xu
2010-01-01
Based on the adaptive-control technique, this paper deals with the problem of fault-tolerant tracking control for near-space-vehicle (NSV) attitude dynamics. First, Takagi-Sugeno (T-S) fuzzy models are used to describe the NSV attitude dynamics; then, an actuator-fault model is developed. Next, an adaptive fault-tolerant tracking-control scheme is proposed based on the online estimation of actuator faults, in which a compensation control
Adaptive Fuzzy Controller for Output Power Maximization of Induction Generators
Durval A. Souza; José A. Dominguez Navarro; Jesús Sallán Arasanz
This note presents a new technique for efficiency optimization of induction generators working at variable speed and load.\\u000a The technique combines two distinct control methods, namely, on-line search of the optimal operating point and a model based\\u000a efficiency control. For a given operating condition, characterized by a given turbine speed (?T) and electric torque (Te), the search control is implemented
Wang, Shun-Yuan; Tseng, Chwan-Lu; Lin, Shou-Chuang; Chiu, Chun-Jung; Chou, Jen-Hsiang
2015-01-01
This paper presents the implementation of an adaptive supervisory sliding fuzzy cerebellar model articulation controller (FCMAC) in the speed sensorless vector control of an induction motor (IM) drive system. The proposed adaptive supervisory sliding FCMAC comprised a supervisory controller, integral sliding surface, and an adaptive FCMAC. The integral sliding surface was employed to eliminate steady-state errors and enhance the responsiveness of the system. The adaptive FCMAC incorporated an FCMAC with a compensating controller to perform a desired control action. The proposed controller was derived using the Lyapunov approach, which guarantees learning-error convergence. The implementation of three intelligent control schemes—the adaptive supervisory sliding FCMAC, adaptive sliding FCMAC, and adaptive sliding CMAC—were experimentally investigated under various conditions in a realistic sensorless vector-controlled IM drive system. The root mean square error (RMSE) was used as a performance index to evaluate the experimental results of each control scheme. The analysis results indicated that the proposed adaptive supervisory sliding FCMAC substantially improved the system performance compared with the other control schemes. PMID:25815450
Pengfei Li; Xinping Yan; Bo Yang; Huabin Wang
2010-01-01
The bearingless permanent magnet-types synchronous motor is nonlinear and coupling complex system. On the basis of the full formula which express the coupling between suspension and rotor torque, parameters of the motor are discussed to the operation speed and suspension performance. Meanwhile, aiming at the robust control problem, fuzzy adaptive sliding mode control theory is applied. Throughout the simulation, the
Direct adaptive fuzzy control for a two-link space robot
Steve Ulrich; Jurek Z. Sasiadek
2011-01-01
strategy [5] uses a robust fuzzy for manipulators designed to guarantee both global stability and performance. The proposed design was compared with a standard nonlinear robust controller and simulation results showed that the proposed fuzzy control scheme yields slightly superior tracking performance when friction effects and disturbances are considered. Also, a control scheme whose structure is composed by a sectorial
Fuzzy systems in instrumentation: fuzzy control
Emil M. Petriu; Graham Eatherley
1995-01-01
This paper presents a short overview of fuzzy control and discusses fuzzy partition in comparison with classical A\\/D and dither quantification. A fuzzy controller for docking a model truck and trailer is finally presented
Kayacan, Erkan; Kayacan, Erdal; Ramon, Herman; Saeys, Wouter
2012-07-01
As a model is only an abstraction of the real system, unmodeled dynamics, parameter variations, and disturbances can result in poor performance of a conventional controller based on this model. In such cases, a conventional controller cannot remain well tuned. This paper presents the control of a spherical rolling robot by using an adaptive neuro-fuzzy controller in combination with a sliding-mode control (SMC)-theory-based learning algorithm. The proposed control structure consists of a neuro-fuzzy network and a conventional controller which is used to guarantee the asymptotic stability of the system in a compact space. The parameter updating rules of the neuro-fuzzy system using SMC theory are derived, and the stability of the learning is proven using a Lyapunov function. The simulation results show that the control scheme with the proposed SMC-theory-based learning algorithm is able to not only eliminate the steady-state error but also improve the transient response performance of the spherical rolling robot without knowing its dynamic equations. PMID:22773047
Ren C. Luo; Tse Min Chen; Zu Hung Hsiao; Chi-yang Hu
1999-01-01
Many potential accidents due to direct control of electrical wheelchair may be occurred by unreasonable control in various internal or external environmental conditions, such as the uncertainties of the front caster wheels and the unbalanced friction condition between wheels and floor. This paper presents a grey-fuzzy decision-making (GFD) algorithm based on grey prediction theory and fuzzy logic theory. The GFD
Wilamowski, Bogdan Maciej
in an Adaptive Neuro Fuzzy Inference System M. Onder Efe Bogazici University, Electrical and Electronic@ieee.org Abstract: Adaptive neuro-fuzzy inference systems exhibit both the numeric power of neural networks et al [4] propose an Adaptive Neuro Fuzzy Inference System (ANFIS), in which polynomials are used
Adaptive fuzzy system for 3-D vision
NASA Technical Reports Server (NTRS)
Mitra, Sunanda
1993-01-01
An adaptive fuzzy system using the concept of the Adaptive Resonance Theory (ART) type neural network architecture and incorporating fuzzy c-means (FCM) system equations for reclassification of cluster centers was developed. The Adaptive Fuzzy Leader Clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from Fuzzy c-Means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data, and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets. The hybrid neuro-fuzzy AFLC algorithm will enhance analysis of a number of difficult recognition and control problems involved with Tethered Satellite Systems and on-orbit space shuttle attitude controller.
Chen, Hung-Yi; Liang, Jin-Wei; Wu, Jia-Wei
2013-01-01
This paper presents an intelligent control strategy to overcome nonlinear and time-varying characteristics of a diaphragm-type pneumatic vibration isolator (PVI) system. By combining an adaptive rule with fuzzy and sliding-mode control, the method has online learning ability when it faces the system's nonlinear and time-varying behaviors during an active vibration control process. Since the proposed scheme has a simple structure, it is easy to implement. To validate the proposed scheme, a composite control which adopts both chamber pressure and payload velocity as feedback signal is implemented. During experimental investigations, sinusoidal excitation at resonance and random-like signal are input on a floor base to simulate ground vibration. Performances obtained from the proposed scheme are compared with those obtained from passive system and PID scheme to illustrate the effectiveness of the proposed intelligent control. PMID:23820746
A new class of adaptive fuzzy control systems applied in an industrial thermal vacuum process
J. E. Araujo Filho; Sandra A. Sandri; Elbert E. N. Macau
2001-01-01
A feasible solution to the problem of controlling thermal vacuum chambers automatically to satisfy testing requirements in the space sector is considered. The design of appropriate controllers is not a trivial task due to intrinsic time delay and changing dynamics related to variable operating conditions in thermal vacuum tests. The fuzzy reference gain-scheduling control approach (FRGS) is a concept that
Adaptive fuzzy command acquisition with reinforcement learning
Chin-Teng Lin; Ming-Chih Kan
1998-01-01
Proposes a four-layered adaptive fuzzy command acquisition network (AFCAN) for adaptively acquiring fuzzy command via interactions with the user or environment. It can catch the intended information from a sentence (command) given in natural language with fuzzy predicates. The intended information includes a meaningful semantic action and the fuzzy linguistic information of that action. The proposed AFCAN has three important
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.
1992-01-01
Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.
Da, F
2000-01-01
A new type controller, fuzzy neural networks sliding mode controller (FNNSMC), is developed for a class of large-scale systems with unknown bounds of high-order interconnections and disturbances. Although sliding mode control is simple and insensitive to uncertainties and disturbances, there are two main problems in the sliding mode controller (SMC): control input chattering and the assumption of known bounds of uncertainties and disturbances. The FNNSMC, which incorporates the fuzzy neural networks (FNNs) and the SMC, can eliminate the chattering by using the continuous output of the FNN to replace the "discontinuous" sign term in the SMC. The bounds of uncertainties and disturbances are also not required in the FNNSMC design. Two examples are presented to support the validity of the new controller. The simulation results show that the FNNSMC is robuster than the SMC. PMID:18249871
Adaptive Neuro-Fuzzy Inference System Based Autonomous Flight Control of Unmanned Air Vehicles
Sefer Kurnaz; Okyay Kaynak; Ekrem Konakoglu
2007-01-01
This paper proposes ANFIS logic based autonomous flight controller for UAVs (unmanned aerial vehicles). Three fuzzy logic\\u000a modules are developed for the control of the altitude, the speed, and the roll angle, through which the altitude and the latitude-longitude\\u000a of the air vehicle is controlled. The implementation framework utilizes MATLAB’s standard configuration and the Aerosim Aeronautical\\u000a Simulation Block Set which
A Fuzzy Adaptive Differential Evolution Algorithm
Junhong Liu; Jouni Lampinen
2005-01-01
The differential evolution algorithm is a floating-point encoded evolutionary algorithm for global optimization over continuous spaces. The algorithm has so far used empirically chosen values for its search parameters that are kept fixed through an optimization process. The objective of this paper is to introduce a new version of the Differential Evolution algorithm with adaptive control parameters – the fuzzy
NASA Astrophysics Data System (ADS)
Boudana, Djamel; Nezli, Lazhari; Tlemçani, Abdelhalim; Mahmoudi, Mohand Oulhadj; Tadjine, Mohamed
2012-05-01
The double star synchronous machine (DSSM) is widely used for high power traction drives. It possesses several advantages over the conventional three phase machine. To reduce the torque ripple the DSSM are supplied with source voltage inverter (VSI). The model of the system DSSM-VSI is high order, multivariable and nonlinear. Further, big harmonic currents are generated. The aim of this paper is to develop a new direct torque adaptive fuzzy logic control in order to control DSSM and minimize the harmonics currents. Simulations results are given to show the effectiveness of our approach.
Design of a neuro-fuzzy controller for speed control applied to AC servo motor
Sang Hoon Kim; Lark Kyo Kim
2001-01-01
In this study, a neuro-fuzzy controller which has the characteristic of fuzzy control and an artificial neural network is designed. A fuzzy rule to be applied is automatically selected by the allocated neurons. The neurons correspond to fuzzy rules that are created by an expert. To adapt the more precise modeling, error backpropagation learning of adjusting the link-weight of fuzzy
Stable auto-tuning of adaptive fuzzy\\/neural controllers for nonlinear discrete-time systems
Hazem N. Nounou; Kevin M. Passino
2004-01-01
In direct adaptive control, the adaptation mechanism attempts to adjust a parameterized nonlinear controller to approximate an ideal controller. In the indirect case, however, we approximate parts of the plant dynamics that are used by a feedback controller to cancel the system nonlinearities. In both cases, \\
Adaptive Nonlinear Control Using TSK-Type Recurrent Fuzzy Neural Network System
Ching-hung Lee; Ming-hui Chiu
2007-01-01
This paper presents a TSK-type recurrent fuzzy neural network (TRFNN) system and hybrid algorithm to control nonlinear uncertain\\u000a systems. The TRFNN is modified from the RFNN to obtain generalization and fast convergence rate. The consequent part is replaced\\u000a by linear combination of input variables and the internal variable- fire strength is feedforward to output to increase the\\u000a network ability. Besides,
Freeway on-ramp metering using fuzzy immune PID controller
Xinrong Liang; Yanxiu Wei
2008-01-01
Aiming at the nonlinear and time variable characteristics of freeway traffic system, a fuzzy immune PID controller is designed and applied to freeway ramp metering based on the biological immune feedback regulating law and the adaptive ability of fuzzy inference. The freeway traffic flow model is firstly built. Then the principle of immune controller is formulated. Combined fuzzy inference with
Fuzzy logic in control systems: fuzzy logic controller. I
C. C. Lee
1990-01-01
The fuzzy logic controller (FLC) provides a means of converting a linguistic control strategy. A survey of the FLC is presented, and a general methodology for constructing an FLC and assessing its performance is described. In particular, attention is given to fuzzification and defuzzification strategies, the derivation of the database and fuzzy control rules, the definition of fuzzy implication, and
Adaptive fuzzy synchronization of discrete-time chaotic systems
Nastaran Vasegh; Vahid Johari Majd
2006-01-01
This paper presents a fuzzy model-based adaptive approach to synchronize two different discrete-time chaotic systems. Takagi–Sugeno (TS) fuzzy model is employed to represent the chaotic drive and response systems. Since the parameters of the drive system are assumed unknown, an adaptive law is derived to estimate its unknown parameters. Then, a control law is proposed to stabilize the error dynamics.
Wallace M. Bess; Aline S. de Paul; Marcelo A. Savi
2009-01-01
Chaos control may be understood as the use of tiny perturbations for the stabilization of unstable periodic orbits embedded in a chaotic attractor. The idea that chaotic behavior may be controlled by small perturbations of physical parameters allows this kind of behav- ior to be desirable in different applications. In this work, chaos control is performed employing a variable structure
The control of indoor thermal comfort conditions: introducing a fuzzy adaptive controller
Francesco Calvino; Maria La Gennusa; Gianfranco Rizzo; Gianluca Scaccianoce
2004-01-01
The control and the monitoring of indoor thermal conditions represents a pre-eminent task with the aim of ensuring suitable working and living spaces to people. Especially in industrialised countries, in fact, several rules and standards have been recently released in order of providing technicians with suitable design tools and effective indexes and parameters for the checking of the indoor microclimate.
Hybrid Fuzzy Model Reference Learning Control for Missile Autopilot Design
This paper presents the application of hybrid direct adaptive fuzzy control techniques to a missile autopilot design. The proposed control structure is to augment existing conventional controller with a fuzzy model reference learning controller (FMRLC). The conventional control law is chosen to stabilize the missile system and to provide approximate control. A feature of this scheme is that the FMRLC
A Fuzzy Neural Network Control System Based on Embedded System
Xiaoying Meng; Yuening Tang; Jia He
2007-01-01
This paper present a fuzzy neural network control system based on embedded system, which is at the base of research on the fuzzy neural network and the embedded technology. The fuzzy neural network is an adaptive neural network whose parameters can be corrected by learning algorithms automatically. The embedded system consists of s3c44box with ARM core and a real time
FPGA-Based Speed Control IC for PMSM Drive With Adaptive Fuzzy Control
Ying-Shieh Kung; Ming-Hung Tsai
2007-01-01
The new generation of field programmable gate array (FPGA) technologies enables an embedded processor intellectual property (IP) and an application IP to be integrated into a system-on-a-programmable-chip (SoPC) developing environment. Therefore, this study presents a speed control integrated circuit (IC) for permanent magnet synchronous motor (PMSM) drive under this SoPC environment. First, the mathematic model of PMSM is defined and
A Fuzzy Immune PID Controller for Electronic Throttle
Lifeng Chen; Ran Chen
2009-01-01
Due to the influence of nonlinear dynamic characteristic of electronic throttle, time delay, external disturbance and parameter variation, the opening of electronic throttle is hard to control precisely. A fuzzy immune adaptive PID control is presented based on the immune feedback regulating law and the adaptive ability of fuzzy logic ratiocination. The model of the electronic throttle system is built
An FPGA-Based Adaptive Fuzzy Coprocessor
Antonio Di Stefano; Costantino Giaconia
2005-01-01
\\u000a The architecture of a general purpose fuzzy logic coprocessor and its implementation on an FPGA based System on Chip is described.\\u000a Thanks to its ability to support a fast dynamic reconfiguration of all its parameters, it is suitable for implementing adaptive\\u000a fuzzy logic algorithms, or for the execution of different fuzzy algorithms in a time sharing fashion. The high throughput
Designing a fuzzy model by adaptive macroevolution genetic algorithms
Yo-Ping Huang; Sheng-Fang Wang
2000-01-01
In this paper the adaptive macroevolution genetic algorithms are proposed to identify three different types of fuzzy models. Several newly established techniques, such as adaptive choice function and macroevolution, are adopted into the simple genetic algorithms to improve the optimization capability. The genetic algorithms used here are controlled to retain the best solution in the population until a better one
Fuzzy model reference learning control for cargo ship steering
J. R. Layne; K. M. Passino
1993-01-01
The use of a learning control system to maintain adequate performance of a cargo ship autopilot when there are process disturbances or variations is examined. The objective is to make an initial assessment of what advantages a fuzzy learning control approach has over conventional adaptive control approaches. The simulation results indicate that the fuzzy model reference learning controller (FMRLC) has
K. Zhao; B. R. Upadhyaya
2005-01-01
An adaptive fuzzy inference causal graph is presented as an integrated approach for fault detection and isolation of field devices including sensors, actuators, and controllers in nuclear power plants. In this approach, nuclear plant systems are represented as a causal graph consisting of individual process variables connected with adaptive fuzzy inference system models. The adaptive fuzzy inference system models generated
A Car-Steering Model Based on an Adaptive Neuro-Fuzzy Controller
NASA Astrophysics Data System (ADS)
Amor, Mohamed Anis Ben; Oda, Takeshi; Watanabe, Shigeyoshi
This paper is concerned with the development of a car-steering model for traffic simulation. Our focus in this paper is to propose a model of the steering behavior of a human driver for different driving scenarios. These scenarios are modeled in a unified framework using the idea of target position. The proposed approach deals with the driver’s approximation and decision-making mechanisms in tracking a target position by means of fuzzy set theory. The main novelty in this paper lies in the development of a learning algorithm that has the intention to imitate the driver’s self-learning from his driving experience and to mimic his maneuvers on the steering wheel, using linear networks as local approximators in the corresponding fuzzy areas. Results obtained from the simulation of an obstacle avoidance scenario show the capability of the model to carry out a human-like behavior with emphasis on learned skills.
Tuning of a neuro-fuzzy controller by genetic algorithm
Teo Lian Seng; Marzuki Bin Khalid; Rubiyah Yusof
1999-01-01
Due to their powerful optimization property, genetic algorithms (GAs) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems. This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be tuned simultaneously by GA. The structure of the controller is based on the Radial Basis Function neural network (RBF) with Gaussian
Application of Optimal Fuzzy PID Controller Design: PI Control for Nonlinear Induction Motor
Jingwei Xu; Xin Feng; B. Mirafzal; Nabeel A. Demerdash
2006-01-01
We present in this paper a novel method (called PID design through numerical optimization) for the design of adaptive fuzzy PID controllers to achieve optimal control performance. By applying the numerical optimization, the fuzzy PID design problem is transferred to a numerical optimization problem. First, a fuzzy parameter tuner is built to generate initial PID parameters, including positions and shapes
Fuzzy logic in control systems: Fuzzy logic controller. I, II
NASA Technical Reports Server (NTRS)
Lee, Chuen Chien
1990-01-01
Recent advances in the theory and applications of fuzzy-logic controllers (FLCs) are examined in an analytical review. The fundamental principles of fuzzy sets and fuzzy logic are recalled; the basic FLC components (fuzzification and defuzzification interfaces, knowledge base, and decision-making logic) are described; and the advantages of FLCs for incorporating expert knowledge into a control system are indicated. Particular attention is given to fuzzy implication functions, the interpretation of sentence connectives (and, also), compositional operators, and inference mechanisms. Applications discussed include the FLC-guided automobile developed by Sugeno and Nishida (1985), FLC hardware systems, FLCs for subway trains and ship-loading cranes, fuzzy-logic chips, and fuzzy computers.
Fuzzy adaptive Kalman filtering for INS\\/GPS data fusion
J. Z. Sasiadek; Q. Wang; M. B. Zeremba
2000-01-01
Presents a method for sensor fusion based on adaptive fuzzy Kalman filtering. The method is applied in fusing position signals from Global Positioning Systems (GPS) and inertial navigation systems (INS) for autonomous mobile vehicles. The presented method has been validated in a 3-D environment and is of particular importance for guidance, navigation, and control of flying vehicles. The extended Kalman
Extending Fuzzy System Concepts for Control of a Vitrification Melter
Whitehouse, J.C. [Westinghouse Savannah River Company, AIKEN, SC (United States); Sorgel, W. [Clemson University, Clemson, SC (United States); Garrison, A. [Clemson University, Clemson, SC (United States); Schalkoff, R.J. [Clemson University, Clemson, SC (United States)
1995-08-16
Fuzzy systems provide a mathematical framework to capture uncertainty. The complete description of real, complex systems or situations often requires far more detail and information than could ever be obtained (or understood). Fuzzy approaches are an alternative technology for both system control and information processing and management. In this paper, we present the design of a fuzzy control system for a melter used in the vitrification of hazardous waste. Design issues, especially those related to melter shutdown and obtaining smooth control surfaces, are addressed. Several extensions to commonly-applied fuzzy techniques, notably adaptive defuzzification and modified rule structures are developed.
Fuzzy system for adaptive network routing
NASA Astrophysics Data System (ADS)
Pasupuleti, Ajay; Mathew, Athimootil V.; Shenoy, Nirmala; Dianat, Soheil A.
2002-06-01
In this paper we propose an adaptive routing using a fuzzy system. The traffic in the network is re-routed to nodes, which are less congested, or have spare capacity. Based on a set of fuzzy rules, link cost is dynamically assigned depending upon the present condition of the network. Distance vector algorithm, which is one of the shortest path routing algorithms is used to build the routing tables at each node in the network. The proposed fuzzy system determines the link cost given the present congestion situation measured via the delays experienced in the network and the offered load on the network. Delay in the links, was estimated by the time taken for the test packets to travel from the node to its neighbors. The delay information collected by the test packets and the number of packets waiting in the queue, are the two inputs to the fuzzy system. The output of the fuzzy system is the link cost. This algorithm was applied on a simulated NSFNET, the USA backbone, as well as to another test network with a different topology. Robustness and optimality of the proposed fuzzy system was tested by simulating various types of load patterns on these networks. Simulation studies showed that the performance of the fuzzy system was very close to or better than the best performance of the composite metric under different load conditions and topologies.
Robust adaptive fuzzy observer design in robot arms
Y. F. Wang; T. Y. Chai
2004-01-01
In this paper, a robust adaptive fuzzy observer based on fuzzy basis functions (FBF) for rigid robotic systems is presented with plant unknown. It is assumed that only the joint angular positions are measured, the joint angular velocities are estimated via a fuzzy observer, we design a nonlinear observer based on fuzzy basis functions (FBF) to estimate the joint angular
Adaptive Fuzzy Systems in Computational Intelligence
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.
1996-01-01
In recent years, the interest in computational intelligence techniques, which currently includes neural networks, fuzzy systems, and evolutionary programming, has grown significantly and a number of their applications have been developed in the government and industry. In future, an essential element in these systems will be fuzzy systems that can learn from experience by using neural network in refining their performances. The GARIC architecture, introduced earlier, is an example of a fuzzy reinforcement learning system which has been applied in several control domains such as cart-pole balancing, simulation of to Space Shuttle orbital operations, and tether control. A number of examples from GARIC's applications in these domains will be demonstrated.
Knowledge-based adaptive fuzzy control of drum level in a boiler system
Hugh F. VanLandingham; Nishith D. Tripathi
1996-01-01
A boiler system is an integral component of a thermal power plant, and control of the water level in the drum of the boiler system is a critical operational consideration. For the drum level control, a 3-element proportional-integral-derivative (PID) control is a popular conventional approach. This scheme works satisfactorily in the absence of any process disturbances. However, when there are
Fuzzy controlled parallel PSO to solving large practical economic dispatch
Belkacem Mahdad; K. Srairi; T. Bouktir; M. El Benbouzid
2010-01-01
This paper proposes a version of fuzzy controlled parallel particle swarm optimization approach based decomposed network (FCP-PSO) to solve large nonconvex economic dispatch problems. The proposed approach combines practical experience extracted from global database formulated in fuzzy rules to adjust dynamically the three parameters associated to PSO mechanism search. The adaptive PSO executed in parallel based in decomposed network procedure
Adaptive Fuzzy Systems for Multichannel Signal Processing
Plataniotis, Konstantinos N.
Adaptive Fuzzy Systems for Multichannel Signal Processing KONSTANTINOS N. PLATANIOTIS, MEMBER, IEEE Processing multichannel signals using digital signal process- ing techniques has received increased attention beginning in this area and 2) to provide a review for the reader who may be well versed in signal processing
Asish K Mukhopadhyay; Sajal Saha
2010-01-01
This paper propose a novel call admission control (CAC) scheme that considers real-time and non-real-time nature of a call before allocating resources for its progress in a network. The scheme accumulates the benefits of different CAC schemes for best optimization of the available resources. It has combined the three major schemes- reserve guard channel, buffer based and prioritization. Application of
Fuzzy support vector machines for adaptive Morse code recognition.
Yang, Cheng-Hong; Jin, Li-Cheng; Chuang, Li-Yeh
2006-11-01
Morse code is now being harnessed for use in rehabilitation applications of augmentative-alternative communication and assistive technology, facilitating mobility, environmental control and adapted worksite access. In this paper, Morse code is selected as a communication adaptive device for persons who suffer from muscle atrophy, cerebral palsy or other severe handicaps. A stable typing rate is strictly required for Morse code to be effective as a communication tool. Therefore, an adaptive automatic recognition method with a high recognition rate is needed. The proposed system uses both fuzzy support vector machines and the variable-degree variable-step-size least-mean-square algorithm to achieve these objectives. We apply fuzzy memberships to each point, and provide different contributions to the decision learning function for support vector machines. Statistical analyses demonstrated that the proposed method elicited a higher recognition rate than other algorithms in the literature. PMID:16807054
EL-SIM: a Development Environment for Neuro-Fuzzy Intelligent Controllers
Reyneri, Leonardo
EL-SIM: a Development Environment for Neuro-Fuzzy Intelligent Controllers M. Chiaberge , G. Di Bene are commonly integrated into the neuro-fuzzy ap- proach, which has proven well adapted to non- linear control- tectures by appropriately combining and opti- mizing them. However, although the neuro-fuzzy approach alone
Fuzzy model-based adaptive synchronization of time-delayed chaotic systems
Nastaran Vasegh; Vahid Johari Majd
2009-01-01
In this paper, fuzzy model-based synchronization of a class of first order chaotic systems described by delayed-differential equations is addressed. To design the fuzzy controller, the chaotic system is modeled by Takagi–Sugeno fuzzy system considering the properties of the nonlinear part of the system. Assuming that the parameters of the chaotic system are unknown, an adaptive law is derived to
Learning fuzzy logic control system
NASA Technical Reports Server (NTRS)
Lung, Leung Kam
1994-01-01
The performance of the Learning Fuzzy Logic Control System (LFLCS), developed in this thesis, has been evaluated. The Learning Fuzzy Logic Controller (LFLC) learns to control the motor by learning the set of teaching values that are generated by a classical PI controller. It is assumed that the classical PI controller is tuned to minimize the error of a position control system of the D.C. motor. The Learning Fuzzy Logic Controller developed in this thesis is a multi-input single-output network. Training of the Learning Fuzzy Logic Controller is implemented off-line. Upon completion of the training process (using Supervised Learning, and Unsupervised Learning), the LFLC replaces the classical PI controller. In this thesis, a closed loop position control system of a D.C. motor using the LFLC is implemented. The primary focus is on the learning capabilities of the Learning Fuzzy Logic Controller. The learning includes symbolic representation of the Input Linguistic Nodes set and Output Linguistic Notes set. In addition, we investigate the knowledge-based representation for the network. As part of the design process, we implement a digital computer simulation of the LFLCS. The computer simulation program is written in 'C' computer language, and it is implemented in DOS platform. The LFLCS, designed in this thesis, has been developed on a IBM compatible 486-DX2 66 computer. First, the performance of the Learning Fuzzy Logic Controller is evaluated by comparing the angular shaft position of the D.C. motor controlled by a conventional PI controller and that controlled by the LFLC. Second, the symbolic representation of the LFLC and the knowledge-based representation for the network are investigated by observing the parameters of the Fuzzy Logic membership functions and the links at each layer of the LFLC. While there are some limitations of application with this approach, the result of the simulation shows that the LFLC is able to control the angular shaft position of the D.C. motor. Furthermore, the LFLC has better performance in rise time, settling time and steady state error than to the conventional PI controller. This abstract accurately represents the content of the candidate's thesis. I recommend its publication.
Advance of Systematic Design Methods on Fuzzy Control
Zhang, J.; Chen, Y.
2006-01-01
method, and fuzzy predictive control of a time-delayed process. This paper will briefly introduce previous research results. Key words: HVAC system; fuzzification; fuzzy logic inference; self-organizing fuzzy control; fuzzy predictive control 1... space to fuzzy set of fuzzy inference space. In order to meet the requirement of different fuzzy logic inference for fuzzification results, various fuzzification methods produces different results, such as fuzzy vector and single membership value[16...
Temperature control with a neural fuzzy inference network
Chin-teng Lin; Chia-feng Juang; Chung-ping Li
1999-01-01
Although multilayered backpropagation neural networks (BPNN's) have demonstrated high potential in the nonconventional branch of adaptive control, their long training time usually discourages their applications in industry. Moreover, when they are trained on-line to adapt to plant variations, the over-tuned phenomenon usually occurs. To overcome the weakness of the BPNN, in this paper we propose a neural fuzzy inference network
ANFIS: adaptive-network-based fuzzy inference system
Jyh-Shing Roger Jang
1993-01-01
The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation,
Nguyen Van Tiem; Vo Huy Hoan
The anti-lock braking system (ABS) is an important component of a complex steering system for the modern automobiles. Most of the controllers available on the market are table-based on-off controlling principle. All automobiles of latest type are fitted with an ABS controller, which aims to maintain a specified tire slip for each wheel during braking. This paper proposes two models
Hani Hagras; Victor Callaghan; Martin Colley; Graham Clarke
2003-01-01
In this paper, we describe a new application domain for intelligent autonomous systems - Intelligent Buildings (IB). In doing so we present a novel approach to the implementation of IB agents based on a hierarchical fuzzy genetic multi embedded-agent architecture comprising a low-level behaviour based reactive layer whose outputs are co-ordinated in a fuzzy way according to deliberative plans. The
One-step ahead predictive fuzzy controller
Predrag D. Vukovic
2001-01-01
A new type of predictive fuzzy relational controller is proposed. The system dynamics is described by a fuzzy relational model, developed on the basis of the input–output description of the process behavior in an open-loop identification experiment. The desired process behavior is defined by a set of fuzzy criteria induced either by a reference model or by general technical requirements.
D Kolokotsa; D Tsiavos; G. S Stavrakakis; K Kalaitzakis; E Antonidakis
2001-01-01
The aim of this paper is to present and evaluate control strategies for adjustment and preservation of air quality, thermal and visual comfort for buildings’ occupants while, simultaneously, energy consumption reduction is achieved. Fuzzy PID, fuzzy PD and adaptive fuzzy PD control methods are applied. The inputs to any controller are: the PMV index affecting thermal comfort, the CO2 concentration
Lei Zhu; Yongling Fu; Jingquan Zhao; Dong Guo
2009-01-01
Based on the cabin pressure regulating system characters of the nonlinear, larger inertia and time varying parameter, the arithmetic of fuzzy gain scheduling was proposed in this paper. The control parameters can be optimized globally using the controller based on the fuzzy gain scheduling because the fuzzy inference was used to changes the parameters of the controller online to adapt
Fuzzy logic based robotic controller
NASA Technical Reports Server (NTRS)
Attia, F.; Upadhyaya, M.
1994-01-01
Existing Proportional-Integral-Derivative (PID) robotic controllers rely on an inverse kinematic model to convert user-specified cartesian trajectory coordinates to joint variables. These joints experience friction, stiction, and gear backlash effects. Due to lack of proper linearization of these effects, modern control theory based on state space methods cannot provide adequate control for robotic systems. In the presence of loads, the dynamic behavior of robotic systems is complex and nonlinear, especially where mathematical modeling is evaluated for real-time operators. Fuzzy Logic Control is a fast emerging alternative to conventional control systems in situations where it may not be feasible to formulate an analytical model of the complex system. Fuzzy logic techniques track a user-defined trajectory without having the host computer to explicitly solve the nonlinear inverse kinematic equations. The goal is to provide a rule-based approach, which is closer to human reasoning. The approach used expresses end-point error, location of manipulator joints, and proximity to obstacles as fuzzy variables. The resulting decisions are based upon linguistic and non-numerical information. This paper presents a solution to the conventional robot controller which is independent of computationally intensive kinematic equations. Computer simulation results of this approach as obtained from software implementation are also discussed.
Fuzzy logic applied to reboiler temperature control
B. Coeyman; J. B. Bowles
1996-01-01
This paper is a case study of applying the Fisher-Rosemount Systems' intelligent fuzzy logic controller (IFLC) to a production distillation column reboiler temperature controller. A performance comparison between the original proportional, integral, and derivative (PID) controller and the IFLC is illustrated. The goals established for trying the fuzzy logic controller were to eliminate the temperature overshoot, reduce fuel and air
Fuzzy decision and control, the Bayes context
Painter, John H.
1993-12-15
This paper shows how it is that fuzzy control may be viewed as a particular kind of stochastic (Bayesian) control. With the Bayes approach, fuzzy control may be viewed as an ensembled-average control, where the average is taken over a set...
Fuzzy regulator design for wind turbine yaw control.
Theodoropoulos, Stefanos; Kandris, Dionisis; Samarakou, Maria; Koulouras, Grigorios
2014-01-01
This paper proposes the development of an advanced fuzzy logic controller which aims to perform intelligent automatic control of the yaw movement of wind turbines. The specific fuzzy controller takes into account both the wind velocity and the acceptable yaw error correlation in order to achieve maximum performance efficacy. In this way, the proposed yaw control system is remarkably adaptive to the existing conditions. In this way, the wind turbine is enabled to retain its power output close to its nominal value and at the same time preserve its yaw system from pointless movement. Thorough simulation tests evaluate the proposed system effectiveness. PMID:24693237
Adaptive fuzzy c-shells clustering and detection of ellipses
Rajesh N. Dave; Kurra Bhaswan
1992-01-01
Several generalizations of the fuzzy c-shells (FCS) algorithm are presented for characterizing and detecting clusters that are hyperellipsoidal shells. An earlier generalization, the adaptive fuzzy c-shells (AFCS) algorithm, is examined in detail and is found to have global convergence problems when the shapes to be detected are partial. New formulations are considered wherein the norm inducing matrix in the distance
A feedback Adaptive fuzzy Petri net model for context reasoning
Saiping Wen; Jian Ye; Zhenmin Zhu
2010-01-01
As an improved model of fuzzy Petri net, adaptive Petri net (AFPN) has got the learning ability from neural network. But AFPN still depends on offline training data, while actual environment is so complex, vague and changeful that AFPN seems slightly inadequate. This paper proposes an approach based on fuzzy logic and feedback theory to improve AFPN. The approach introduces
Multistage fuzzy load frequency control using PSO
H. Shayeghi; A. Jalili; H. A. Shayanfar
2008-01-01
In this paper, a particle swarm optimization (PSO) based multi-stage fuzzy (PSOMSF) controller is proposed for solution of the load frequency control (LFC) problem in a restructured power system that operate under deregulation based on the bilateral policy scheme. In this strategy the control is tuned on line from the knowledge base and fuzzy inference, which request fewer sources and
Effective optimization for fuzzy model predictive control
Stanimir Mollov; Robert Babuska; János Abonyi; Henk B. Verbruggen
2004-01-01
This paper addresses the optimization in fuzzy model predictive control. When the prediction model is a nonlinear fuzzy model, nonconvex, time-consuming optimization is necessary, with no guarantee of finding an optimal solution. A possible way around this problem is to linearize the fuzzy model at the current operating point and use linear predictive control (i.e., quadratic programming). For long-range predictive
High performance induction motor drive using fuzzy self-tuning hybrid fuzzy controller
A. Saghafinia; H. Wooi Ping
2010-01-01
A fuzzy self-tuning hybrid fuzzy controller for indirect field oriented control (IFOC) induction motor drives will be presented in this paper. The developed hybrid fuzzy control law consists of proportional-integral (PI) control at steady state, PI-type fuzzy logic control (FLC) at transient state, a simple switching mechanism between steady and transient states, and fuzzy self-tuning for tuning of their coefficients
A fuzzy classifier system for process control
NASA Technical Reports Server (NTRS)
Karr, C. L.; Phillips, J. C.
1994-01-01
A fuzzy classifier system that discovers rules for controlling a mathematical model of a pH titration system was developed by researchers at the U.S. Bureau of Mines (USBM). Fuzzy classifier systems successfully combine the strengths of learning classifier systems and fuzzy logic controllers. Learning classifier systems resemble familiar production rule-based systems, but they represent their IF-THEN rules by strings of characters rather than in the traditional linguistic terms. Fuzzy logic is a tool that allows for the incorporation of abstract concepts into rule based-systems, thereby allowing the rules to resemble the familiar 'rules-of-thumb' commonly used by humans when solving difficult process control and reasoning problems. Like learning classifier systems, fuzzy classifier systems employ a genetic algorithm to explore and sample new rules for manipulating the problem environment. Like fuzzy logic controllers, fuzzy classifier systems encapsulate knowledge in the form of production rules. The results presented in this paper demonstrate the ability of fuzzy classifier systems to generate a fuzzy logic-based process control system.
An adaptive ordered fuzzy time series with application to FOREX
Majid Bahrepour; Mohammad R. Akbarzadeh-Totonchi; Mahdi Yaghoobi; Mohammad-B. Naghibi Sistani
2011-01-01
An adaptive ordered fuzzy time series is proposed that employs an adaptive order selection algorithm for composing the rule structure and partitions the universe of discourse into unequal intervals based on a fast self-organising strategy. The automatic order selection of FTS as well as the adaptive partitioning of each interval in the universe of discourse is shown to greatly affect
Refining fuzzy logic controllers with machine learning
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.
1994-01-01
In this paper, we describe the GARIC (Generalized Approximate Reasoning-Based Intelligent Control) architecture, which learns from its past performance and modifies the labels in the fuzzy rules to improve performance. It uses fuzzy reinforcement learning which is a hybrid method of fuzzy logic and reinforcement learning. This technology can simplify and automate the application of fuzzy logic control to a variety of systems. GARIC has been applied in simulation studies of the Space Shuttle rendezvous and docking experiments. It has the potential of being applied in other aerospace systems as well as in consumer products such as appliances, cameras, and cars.
Robust Fuzzy Controllers Using FPGAs
NASA Technical Reports Server (NTRS)
Monroe, Author Gene S., Jr.
2007-01-01
Electro-mechanical device controllers typically come in one of three forms, proportional (P), Proportional Derivative (PD), and Proportional Integral Derivative (PID). Two methods of control are discussed in this paper; they are (1) the classical technique that requires an in-depth mathematical use of poles and zeros, and (2) the fuzzy logic (FL) technique that is similar to the way humans think and make decisions. FL controllers are used in multiple industries; examples include control engineering, computer vision, pattern recognition, statistics, and data analysis. Presented is a study on the development of a PD motor controller written in very high speed hardware description language (VHDL), and implemented in FL. Four distinct abstractions compose the FL controller, they are the fuzzifier, the rule-base, the fuzzy inference system (FIS), and the defuzzifier. FL is similar to, but different from, Boolean logic; where the output value may be equal to 0 or 1, but it could also be equal to any decimal value between them. This controller is unique because of its VHDL implementation, which uses integer mathematics. To compensate for VHDL's inability to synthesis floating point numbers, a scale factor equal to 10(sup (N/4) is utilized; where N is equal to data word size. The scaling factor shifts the decimal digits to the left of the decimal point for increased precision. PD controllers are ideal for use with servo motors, where position control is effective. This paper discusses control methods for motion-base platforms where a constant velocity equivalent to a spectral resolution of 0.25 cm(exp -1) is required; however, the control capability of this controller extends to various other platforms.
Fuzzy decision and control, the Bayes context
Painter, John H.
1993-12-15
modified are amalgamated to obtain a single control function, in the step called Combination. Finally, a single, unique control input value is obtained from the modified control density, in the step called Defuzzification. In fuzzy logic, there are two...
A fuzzy logic controller for autonomous vehicle control
Vinson, Yale Patrick
1995-01-01
This thesis presents a feasibility study for the use of fuzzy logic control to solve the autonomous vehicle following problem. After developing the original vehicle following system, the applicability of fuzzy logic to the problem is demonstrated...
Optimal fuzzy controller design: local concept approach
Shinq-Jen Wu; Chin-Teng Lin
2000-01-01
In this paper, we present a global optimal and stable fuzzy controller design method for both continuous- and discrete-time fuzzy systems under both finite and infinite horizons. First, a sufficient condition is proposed which indicates that the global optimal effect can be achieved by the fuzzily combined local optimal controllers. Based on this sufficient condition, we derive a local concept
Research on fuzzy control for steam generator water level
Wei Peng; Da-fa Zhang
2010-01-01
In order to overcome shortcomings of the traditional PID controller for nuclear steam generator water level, we proposed a fuzzy controller by using fuzzy reasoning. By summing up the experience of skilled operators, we gave a set of fuzzy control rules, and determined some important control parameters. To verify the effectiveness of fuzzy controller, a simulation for a steam generator
Inverter air-conditioning control system using PID fuzzy controller
Jiang Jing; Zhang Xuesong
2011-01-01
In this paper, temperature fuzzy controller was designed in the inverter cold and hot air-condition. The control system for inverter air conditioner was divided indoor machine and the outdoor machine. Frequency control was realized by design of hardware circuit and two-dimensional temperature controller was selected in the fuzzy control program. PID fuzzy controller suitable for real-time control was proposed. Finally,
Systematic methods for the design of a class of fuzzy logic controllers
NASA Astrophysics Data System (ADS)
Yasin, Saad Yaser
2002-09-01
Fuzzy logic control, a relatively new branch of control, can be used effectively whenever conventional control techniques become inapplicable or impractical. Various attempts have been made to create a generalized fuzzy control system and to formulate an analytically based fuzzy control law. In this study, two methods, the left and right parameterization method and the normalized spline-base membership function method, were utilized for formulating analytical fuzzy control laws in important practical control applications. The first model was used to design an idle speed controller, while the second was used to control an inverted control problem. The results of both showed that a fuzzy logic control system based on the developed models could be used effectively to control highly nonlinear and complex systems. This study also investigated the application of fuzzy control in areas not fully utilizing fuzzy logic control. Three important practical applications pertaining to the automotive industries were studied. The first automotive-related application was the idle speed of spark ignition engines, using two fuzzy control methods: (1) left and right parameterization, and (2) fuzzy clustering techniques and experimental data. The simulation and experimental results showed that a conventional controller-like performance fuzzy controller could be designed based only on experimental data and intuitive knowledge of the system. In the second application, the automotive cruise control problem, a fuzzy control model was developed using parameters adaptive Proportional plus Integral plus Derivative (PID)-type fuzzy logic controller. Results were comparable to those using linearized conventional PID and linear quadratic regulator (LQR) controllers and, in certain cases and conditions, the developed controller outperformed the conventional PID and LQR controllers. The third application involved the air/fuel ratio control problem, using fuzzy clustering techniques, experimental data, and a conversion algorithm, to develop a fuzzy-based control algorithm. Results were similar to those obtained by recently published conventional control based studies. The influence of the fuzzy inference operators and parameters on performance and stability of the fuzzy logic controller was studied Results indicated that, the selections of certain parameters or combinations of parameters, affect greatly the performance and stability of the fuzzy controller. Diagnostic guidelines used to tune or change certain factors or parameters to improve controller performance were developed based on knowledge gained from conventional control methods and knowledge gained from the experimental and the simulation results of this study.
A fuzzy control design case: The fuzzy PLL
NASA Technical Reports Server (NTRS)
Teodorescu, H. N.; Bogdan, I.
1992-01-01
The aim of this paper is to present a typical fuzzy control design case. The analyzed controlled systems are the phase-locked loops (PLL's)--classic systems realized in both analogic and digital technology. The crisp PLL devices are well known.
Improvement on fuzzy controller design techniques
NASA Technical Reports Server (NTRS)
Wang, Paul P.
1993-01-01
This paper addresses three main issues, which are somewhat interrelated. The first issue deals with the classification or types of fuzzy controllers. Careful examination of the fuzzy controllers designed by various engineers reveals distinctive classes of fuzzy controllers. Classification is believed to be helpful from different perspectives. The second issue deals with the design according to specifications, experiments related to the tuning of fuzzy controllers, according to the specification, will be discussed. General design procedure, hopefully, can be outlined in order to ease the burden of a design engineer. The third issue deals with the simplicity and limitation of the rule-based IF-THEN logical statements. The methodology of fuzzy-constraint network is proposed here as an alternative to the design practice at present. It is our belief that predicate calculus and the first order logic possess much more expressive power.
Lucas, Simon M.
demonstrates the concept of adaptive cruise control and vehicle follow- ing where by a safe distance with alternative controllers. 1 Introduction Adaptive Cruise Control (ACC) extends the concept of cruise controlAn Integrated Stereo Vision and Fuzzy Logic Controller for Following Vehicles in an Unstructured
Optimizing a fuzzy logic controller for reactive navigation
NASA Astrophysics Data System (ADS)
Castellano, G.; Stella, Ettore; Attolico, Giovanni; Distante, Arcangelo
1997-01-01
Low-level navigation for autonomous vehicles can be accomplished efficiently by a behavioral-based approach that involves the simultaneous execution of independent sub-tasks seen as primitive behaviors. Each behavior maps sensory data into control commands in a reactive way, with no need of internal representations. A useful tool for realizing such a direct mapping is fuzzy logic, that allows the production of control rules by either manual programming or automatic learning. In prospect of implementing an articulated control system including all the low-level behaviors of navigation, this paper focuses on the problem of obtaining an efficient and robust fuzzy controller performing a single behavior and presents a method for minimizing the number of rules of a fuzzy controller developed for driving a TRC Labmate based vehicle along the wall on its right-hand side. Fuzzy rules, that map ultrasonic sensor readings onto steering velocity values, are learned automatically from training data collected during operator-driven runs of the vehicle. In addition, we address the problem of defining an appropriate performance function, that may be useful for evaluating the influence of the rule base reduction on the overall behavior of the vehicle during navigation, but also for estimating the quality of a control rule, in order to adapt rules on- line. Results of an experimental comparison between the original fuzzy wall-follower and its optimized version are reported.
HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems
Jaesoo Kim; Nikola K. Kasabov
1999-01-01
This paper proposes an adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input–output fuzzy membership functions can be optimally tuned from training examples by a hybrid
Fuzzy control of pH using genetic algorithms
Charles L. Karr; Edward J. Gentry
1993-01-01
Abstruct- Establishing suitable control of pH, a requirement in a number of mineral and chemical industries, poses a difficult problem because of inherent nonlinearities and frequently changing process dynamics. Researchers at the U.S. Bureau of Mines have developed a technique for producing adaptive fuzzy logic controllers (FLC’s) that are capable of effectively managing such systems. In this technique, a genetic
Adaptive fuzzy logic-based velocity observer for servo motor drives
Feng-Chieh Lin; Sheng-Ming Yang
2003-01-01
As the position transducers commonly used in industry do not inherently measure an instantaneous velocity, signal processing is generally required to improve the accuracy of velocity estimation at each sampling instant. This estimated signal is then used as the velocity feedback for the velocity loop control in servo motor drives. In this paper, an adaptive fuzzy logic-based observer is proposed
Learning and tuning fuzzy logic controllers through reinforcements
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.; Khedkar, Pratap
1992-01-01
A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.
Learning and tuning fuzzy logic controllers through reinforcements
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.; Khedkar, Pratap
1992-01-01
This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.
Terminology and concepts of control and Fuzzy Logic
NASA Technical Reports Server (NTRS)
Aldridge, Jack; Lea, Robert; Jani, Yashvant; Weiss, Jonathan
1990-01-01
Viewgraphs on terminology and concepts of control and fuzzy logic are presented. Topics covered include: control systems; issues in the design of a control system; state space control for inverted pendulum; proportional-integral-derivative (PID) controller; fuzzy controller; and fuzzy rule processing.
Universal fuzzy models and universal fuzzy controllers for discrete-time nonlinear systems.
Gao, Qing; Feng, Gang; Dong, Daoyi; Liu, Lu
2015-05-01
This paper investigates the problems of universal fuzzy model and universal fuzzy controller for discrete-time nonaffine nonlinear systems (NNSs). It is shown that a kind of generalized T-S fuzzy model is the universal fuzzy model for discrete-time NNSs satisfying a sufficient condition. The results on universal fuzzy controllers are presented for two classes of discrete-time stabilizable NNSs. Constructive procedures are provided to construct the model reference fuzzy controllers. The simulation example of an inverted pendulum is presented to illustrate the effectiveness and advantages of the proposed method. These results significantly extend the approach for potential applications in solving complex engineering problems. PMID:25137736
Controlling Force Based on Radial Fuzzy Functions in High-Speed Machining Processes
Rodolfo Haber-guerra; Rodolfo Haber-haber; José R. Alique
2005-01-01
\\u000a This paper addresses the development of a new control strategy to regulate cutting force in a high-speed machining process.\\u000a Fuzzy basis functions (FBF), on the basis of L.X.Wang’s approach, serve as basement for designing and implementing adaptive\\u000a fuzzy control system in an open computerized numerical control (CNC). The controller uses cutting force measured from a dynamometric\\u000a platform, and mathematically processed
Decentralized fuzzy control of multiple nonholonomic vehicles
Driessen, B.J.; Feddema, J.T.; Kwok, K.S.
1997-09-01
This work considers the problem of controlling multiple nonholonomic vehicles so that they converge to a scent source without colliding with each other. Since the control is to be implemented on simple 8-bit microcontrollers, fuzzy control rules are used to simplify a linear quadratic regulator control design. The inputs to the fuzzy controllers for each vehicle are the (noisy) direction to the source, the distance to the closest neighbor vehicle, and the direction to the closest vehicle. These directions are discretized into four values: Forward, Behind, Left, and Right, and the distance into three values: Near, Far, Gone. The values of the control at these discrete values are obtained based on the collision-avoidance repulsive forces and the change of variables that reduces the motion control problem of each nonholonomic vehicle to a nonsingular one with two degrees of freedom, instead of three. A fuzzy inference system is used to obtain control values for inputs between the small number of discrete input values. Simulation results are provided which demonstrate that the fuzzy control law performs well compared to the exact controller. In fact, the fuzzy controller demonstrates improved robustness to noise.
Paris-Sud XI, Université de
IEEE Trans Med Imaging . Author manuscript Page /1 21 A fuzzy locally adaptive Bayesian as well as the previously proposed fuzzy hidden Markov chains (FHMC) and the Fuzzy C-Means (FCM ; Computer Simulation ; Fuzzy Logic ; Humans ; Image Processing, Computer-Assisted ; methods ; Markov Chains
Prediction of conductivity by adaptive neuro-fuzzy model.
Akbarzadeh, S; Arof, A K; Ramesh, S; Khanmirzaei, M H; Nor, R M
2014-01-01
Electrochemical impedance spectroscopy (EIS) is a key method for the characterizing the ionic and electronic conductivity of materials. One of the requirements of this technique is a model to forecast conductivity in preliminary experiments. The aim of this paper is to examine the prediction of conductivity by neuro-fuzzy inference with basic experimental factors such as temperature, frequency, thickness of the film and weight percentage of salt. In order to provide the optimal sets of fuzzy logic rule bases, the grid partition fuzzy inference method was applied. The validation of the model was tested by four random data sets. To evaluate the validity of the model, eleven statistical features were examined. Statistical analysis of the results clearly shows that modeling with an adaptive neuro-fuzzy is powerful enough for the prediction of conductivity. PMID:24658582
Prediction of Conductivity by Adaptive Neuro-Fuzzy Model
Akbarzadeh, S.; Arof, A. K.; Ramesh, S.; Khanmirzaei, M. H.; Nor, R. M.
2014-01-01
Electrochemical impedance spectroscopy (EIS) is a key method for the characterizing the ionic and electronic conductivity of materials. One of the requirements of this technique is a model to forecast conductivity in preliminary experiments. The aim of this paper is to examine the prediction of conductivity by neuro-fuzzy inference with basic experimental factors such as temperature, frequency, thickness of the film and weight percentage of salt. In order to provide the optimal sets of fuzzy logic rule bases, the grid partition fuzzy inference method was applied. The validation of the model was tested by four random data sets. To evaluate the validity of the model, eleven statistical features were examined. Statistical analysis of the results clearly shows that modeling with an adaptive neuro-fuzzy is powerful enough for the prediction of conductivity. PMID:24658582
The Utilization of Fuzzy Control in Energy Saving Control System of Water Source Heat Pump
Li Li; Liu Xiang-long; Chen Xiao; Li Ming; Lin Han-zhu
2009-01-01
This paper introduces that water source heat pump is successfully used in Xiangtan city. The PID fuzzy control method of water source heat pump is brought forward for the reasons of complicacy of water source heat pump system, plenty of controlled variables, nonlinear, time delay characteristic and bad adaptability. This method can get high precision, rapid dynamic response and work
Fuzzy-PI Damping Control for Hydraulic Crane Tip
Yong Yang
2008-01-01
Simple and effective control strategies are very important for hydraulic crane systems. In this paper, a fuzzy-PI compound control which integrates fuzzy control (FC) with an improved feedforward proportional control based on fuzzy tuning and a segmented integral control, is proposed to adjust the tip damping of a hydraulic crane system under load dynamics. FC is responsible for the stable
Intelligent control based on fuzzy logic and neural net theory
NASA Technical Reports Server (NTRS)
Lee, Chuen-Chien
1991-01-01
In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.
Fuzzy logic attitude control for Cassini spacecraft
Richard Y. Chiang; Jyh-Shing Roger Jang
1994-01-01
A fuzzy logic attitude controller has been developed for Cassini spacecraft. Feedback control issues such as tracking capability, thruster on\\/off time and cycle have been investigated and compared with conventional bang\\/bang control. A discrete nonlinear simulation was set up to assess the system performance with different controllers
Fuzzy logic control of an automotive engine
George Vachtsevanos; Shehu S. Farinwata; Dimitrios K. Pirovolou
1993-01-01
A systematic fuzzy logic control design method for control of automotive engine idling speed is discussed. The method uses the direct intelligent control paradigm. The procedure is based on partitioning of the state space into small rectangles called cell groups, and quantization of the states and the available controls into finite levels or bins. Membership functions are then assigned for
Nonlinear system control with fuzzy logic design
Chu Kwong Chak; Gang Feng
1994-01-01
Presents a control design method for nonlinear systems. In this method a set of control laws is designed based on the linear models about some points of state space of nonlinear systems. The controller output is the result of applying fuzzy logic theory to manipulate the given set of control laws. By this approach, the authors ensure good performance as
T. S. Mahmoud; Mohammed H. Marhaban; Tang S. Hong; Sokchoo Ng
2009-01-01
In this paper, adaptive neural fuzzy inference system (ANFIS) and fuzzy subtractive clustering method (FSCM) were used to solve non-linearity, trajectory, and interaction problems of twin rotor MIMO system (TRMS). Basically, four fuzzy logic controllers (FLC) have been proposed to match the control objectives on TRMS. The four FLCs are considered as high consumers of memory and processing time relatively.
Fuzzy Rule Reduction and Tuning of Fuzzy Logic Controllers for a HVAC System
Granada, Universidad de
Fuzzy Rule Reduction and Tuning of Fuzzy Logic Controllers for a HVAC System R. Alcal´a, J. Alcal, Ventilating and Air Conditioning (HVAC) Systems are equip- ments usually implemented for maintaining satisfactory comfort conditions in build- ings. The design of Fuzzy Logic Controllers (FLCs) for HVAC Systems
Comparison between the performance of two classes of fuzzy controllers
NASA Technical Reports Server (NTRS)
Janabi, T. H.; Sultan, L. H.
1992-01-01
This paper presents an application comparison between two classes of fuzzy controllers: the Clearness Transformation Fuzzy Controller (CTFC) and the CRI-based Fuzzy Controller. The comparison is performed by studying the application of the controllers to simulation examples of nonlinear systems. The CTFC is a new approach for the organization of fuzzy controllers based on a cognitive model of parameter driven control, the notion of fuzzy patterns to represent fuzzy knowledge and the Clearness Transformation Rule of Inference (CTRI) for approximate reasoning. The approach facilitates the implementation of the basic modules of the controller: the fuzzifier, defuzzifier, and the control protocol in a rule-based architecture. The CTRI scheme for approximate reasoning does not require the formation of fuzzy relation matrices yielding improved performance in comparison with the traditional organization of fuzzy controllers.
Image Segmentation Based on Adaptive Fuzzy-C-Means Clustering
Mohamed Walid Ayech; Karim El Kalti; Béchir el Ayeb
2010-01-01
The clustering method “Fuzzy-C-Means” (FCM) is widely used in image segmentation. However, the major drawback of this method is its sensitivity to the noise. In this paper, we propose a variant of this method which aims at resolving this problem. Our approach is based on an adaptive distance which is calculated according to the spatial position of the pixel in
Some remarks on adaptive neuro-fuzzy systems
Romeo Ortega; Genie Informatique
1995-01-01
Makes three remarks concerning adaptive implementations of neural networks and fuzzy systems. First, the author brings to the readers attention the fact that the potential power of these systems as function approximators is lost when, as done in recently published work, the adjustable parameters are only the linear combination weights of the basis functions. Second, the author shows that the
Fuzzy Logic Control for a Two Tanks Hydraulic System Model
Juan Anzurez; Luis Alberto Torres; Isidro Ignacio Lazaro
2011-01-01
This paper presents a Fuzzy Logic Control design for a two tanks hydraulic system model. The fuzzy logic control algorithm performance was tested for different references as well as perturbation scenarios. The fuzzy logic control design proves its superiority when compared to classical control algorithms because of its inherent characteristics to deal with non-lineal systems. It bases its functioning in
FLoCAD: Fuzzy Logic Controlled Video Streaming with Congestion Avoidance using Delay and Packet Loss
E. Jammeh; M. Fleury; M. Ghanbari
Accuracy in determining congestion level is a big factor in designing efficient rate adaptive networked multimedia. This paper proposes measuring one-way delay of video packets to serve as incipient network congestion indicators. The delay is potentially used in conjunction with packet loss rate as input to a fuzzy logic controller. The controller efficiently adapts the sending rate of the application
Toward intelligent machining: hierarchical fuzzy control for the end milling process
R. E. Haber; C. R. Peres; A. Alique; S. Ros; C. Gonzalez; J. R. Alique
1998-01-01
The difficulties in implementing adaptive and other advanced control schemes in industrial machining processes have encouraged researchers to combine the utilization of one hierarchical level, a fuzzy control algorithm, and robust sensing systems. The main idea of this paper deals with self-regulating controllers (SRCs). The control signal's scaling factor (output scaling factor) is self-regulated during the control process, and it
A transductive neuro-fuzzy controller: application to a drilling process.
Gajate, Agustín; Haber, Rodolfo E; Vega, Pastora I; Alique, José R
2010-07-01
Recently, new neuro-fuzzy inference algorithms have been developed to deal with the time-varying behavior and uncertainty of many complex systems. This paper presents the design and application of a novel transductive neuro-fuzzy inference method to control force in a high-performance drilling process. The main goal is to study, analyze, and verify the behavior of a transductive neuro-fuzzy inference system for controlling this complex process, specifically addressing the dynamic modeling, computational efficiency, and viability of the real-time application of this algorithm as well as assessing the topology of the neuro-fuzzy system (e.g., number of clusters, number of rules). A transductive reasoning method is used to create local neuro-fuzzy models for each input/output data set in a case study. The direct and inverse dynamics of a complex process are modeled using this strategy. The synergies among fuzzy, neural, and transductive strategies are then exploited to deal with process complexity and uncertainty through the application of the neuro-fuzzy models within an internal model control (IMC) scheme. A comparative study is made of the adaptive neuro-fuzzy inference system (ANFIS) and the suggested method inspired in a transductive neuro-fuzzy inference strategy. The two neuro-fuzzy strategies are evaluated in a real drilling force control problem. The experimental results demonstrated that the transductive neuro-fuzzy control system provides a good transient response (without overshoot) and better error-based performance indices than the ANFIS-based control system. In particular, the IMC system based on a transductive neuro-fuzzy inference approach reduces the influence of the increase in cutting force that occurs as the drill depth increases, reducing the risk of rapid tool wear and catastrophic tool breakage. PMID:20659865
Neural-Network-Based Fuzzy Logic Control and Decision System
Chin-teng Lin; C. S. George Lee
1991-01-01
A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed. This connectionist model, in the form of feedforward multilayer net, combines the idea of fuzzy logic controller and neural-network structure and learning abilities into an integrated neural-network-based fuzzy logic control and decision system. A fuzzy logic control decision network is constructed automatically by learning the training
Fuzzy control of a double-inverted pendulum
Fuyan Cheng; Guomin Zhong; Youshan Li; Zhengming Xu
1996-01-01
A high-accuracy and high-resolution fuzzy controller is designed to stabilize a double-inverted pendulum at an upright position successfully. A new idea of dealing with multivariate systems is described. The composition coefficient is gained by combining the fuzzy control theory with the optimal control theory. The fuzzy control rules of a double-inverted pendulum are given and a powerful fuzzy decision way
Fuzzy sliding-mode control for a Mini-UAV
Fu-Kuang Yeh; Ching-Mu Chen; Jian-Ji Huang
2010-01-01
This work addresses a fuzzy sliding-mode controller, which is mainly composed of the sliding mode controller and the fuzzy inference mechanism, for a mini unmanned air vehicle (UAV) with propellers to follow the predetermined trajectory. In this paper, a sliding-mode controller with a sliding surface is designed. And a fuzzy sliding-mode controller is proposed, such that a simple fuzzy inference
Guan-Chyun Hsieh; Liang-Rui Chen; Kuo-Shun Huang
1999-01-01
A fuzzy-controlled active state-of-charge controller (FC-ASCC) is presented to adaptively program the charging behavior of the Li-ion battery. The proposed FC-ASCC can provide two charging modes (SM and CM) instead of the usual CV mode for improving the charging trajectory of the Li-ion battery. A fuzzy-controlled strategy is built with a set of membership functions to prescribe the charging process
Fuzzy logic control of a switched reluctance motor drive
Silverio Bolognani; Mauro Zigliotto
1996-01-01
The paper deals with the fuzzy logic control of a switched reluctance motor (SRM) drive. The fundamentals of the fuzzy logic are first illustrated, pointing out the aspects related to the control under consideration. A fuzzy logic controller (FLC) of the motor speed is then designed and simulated. The results show that the use of an FLC in the speed
FPGA Implementation of Embedded Fuzzy Controllers for Robotic Applications
Santiago Sánchez-Solano; Alejandro J. Cabrera; Iluminada Baturone; Francisco J. Moreno-Velo; María Brox
2007-01-01
Fuzzy-logic-based inference techniques provide efficient solutions for control problems in classical and emerging applications. However, the lack of specific design tools and systematic approaches for hardware implementation of complex fuzzy controllers limits the applicability of these techniques in modern microelectronics products. This paper discusses a design strategy that eases the implementation of embedded fuzzy controllers as systems on programmable chips.
Autonomous navigation system using a fuzzy adaptive nonlinear H? filter.
Outamazirt, Fariz; Li, Fu; Yan, Lin; Nemra, Abdelkrim
2014-01-01
Although nonlinear H? (NH?) filters offer good performance without requiring assumptions concerning the characteristics of process and/or measurement noises, they still require additional tuning parameters that remain fixed and that need to be determined through trial and error. To address issues associated with NH? filters, a new SINS/GPS sensor fusion scheme known as the Fuzzy Adaptive Nonlinear H? (FANH?) filter is proposed for the Unmanned Aerial Vehicle (UAV) localization problem. Based on a real-time Fuzzy Inference System (FIS), the FANH? filter continually adjusts the higher order of the Taylor development thorough adaptive bounds and adaptive disturbance attenuation , which significantly increases the UAV localization performance. The results obtained using the FANH? navigation filter are compared to the NH? navigation filter results and are validated using a 3D UAV flight scenario. The comparison proves the efficiency and robustness of the UAV localization process using the FANH? filter. PMID:25244587
Autonomous Navigation System Using a Fuzzy Adaptive Nonlinear H? Filter
Outamazirt, Fariz; Li, Fu; Yan, Lin; Nemra, Abdelkrim
2014-01-01
Although nonlinear H? (NH?) filters offer good performance without requiring assumptions concerning the characteristics of process and/or measurement noises, they still require additional tuning parameters that remain fixed and that need to be determined through trial and error. To address issues associated with NH? filters, a new SINS/GPS sensor fusion scheme known as the Fuzzy Adaptive Nonlinear H? (FANH?) filter is proposed for the Unmanned Aerial Vehicle (UAV) localization problem. Based on a real-time Fuzzy Inference System (FIS), the FANH? filter continually adjusts the higher order of the Taylor development thorough adaptive bounds (?i) and adaptive disturbance attenuation (?), which significantly increases the UAV localization performance. The results obtained using the FANH? navigation filter are compared to the NH? navigation filter results and are validated using a 3D UAV flight scenario. The comparison proves the efficiency and robustness of the UAV localization process using the FANH? filter. PMID:25244587
Fuzzy Sequential Control Based On Petri Nets
J. C. Pascal; R. Valette; D. Andreu
1992-01-01
Programmable Logic Controllers are able to directly implement control sequences specified by means of standard languages such as Grafcet or formal models such as Petri nets. In case of simple regulation problems it could be of particular interest to introduce a notion of fuzzy events. Such an event corresponds to a continuous evolution from one state to another and results
Fuzzy Controller for a Gas Turbine Plant
T. R. Rangaswamy; J. Shanmugam; T. Thyagarajan
2006-01-01
A novel control for a gas turbine plant using fuzzy scheme is proposed. At the inlet, the compressor in the gas turbine plant is equipped with an adjustable inlet guide vane (IGV), which is controlling the airflow to the entire turbine at all load conditions. At any load change the IGV staggering angle is adjusted such, that the change in
Intelligent fuzzy controller of a quadrotor
Matilde Santos; Victoria López; Franciso Morata
2010-01-01
The aim of this work is to describe an intelligent system based on fuzzy logic that is developed to control a quadrotor. A quadrotor is a helicopter with four rotors, that make the vehicle more stable but more complex to model and to control. The quadrotor has been used as a testing platform in the last years for various universities
Robust fuzzy control for a mobile robot
BASIL M. AL-HADITHI; F. Matia; A. Jimenez
2008-01-01
The paper proposes a stable tracking fuzzy controller for a mobile robot (MR). Input to the vehicle are the reference position (xr, yr, thetas). The main objective is to propose a control algorithm to guide the robot from an initial position to a predefined target. One of the difficulties of this problem lies in the fact that the velocities posses
Constructing a fuzzy logic controller using evolutionary Q-learning
Min-Soeng Kim; Ju-Jang Lee
2000-01-01
This paper proposes an evolutionary Q-learning algorithm for the design of a fuzzy logic controller. By defining Q-values as a functional value of state and each fuzzy logic controller, Q-learning is easily applied to the group of fuzzy logic controllers. An evolutionary algorithm which uses Q-values for the evaluation of the fitness value is proposed to extract the best fuzzy
Andon V. Topalov; Erdal Kayacan; Yesim Oniz; Okyay Kaynak
2009-01-01
A neuro-fuzzy adaptive control approach for nonlinear systems with model uncertainties is proposed. The implemented control scheme consists of a proportional plus derivative controller that is provided both to guarantee global asymptotic stability in compact space and as an inverse reference model of the response of the controlled system. Its output is used as an error signal by an on-line
Genetic algorithm based fuzzy control of spacecraft autonomous rendezvous
NASA Technical Reports Server (NTRS)
Karr, C. L.; Freeman, L. M.; Meredith, D. L.
1990-01-01
The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy logic based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective decision. High performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.
Research of irrigation control system based on fuzzy neural network
Guifen Chen; Lisong Yue
2011-01-01
Saving water is vital for water-saving irrigation project. The paper proposed an intelligent irrigation control system and combined fuzzy system and neural network to intelligent control system. Aimed at water-saving irrigation technology, the new method not only can offset the deficiency of neural network in processing the fuzzy data, but also can effectively resolve the disability of fuzzy logic on
A model reference control structure using a fuzzy neural network
Yie-Chien Chen; Ching-Cheng Teng
1995-01-01
In this paper, we present a design method for a model reference control structure using a fuzzy neural network. We study a simple fuzzy-logic based neural network system. Knowledge of rules is explicitly encoded in the weights of the proposed network and inferences are executed efficiently at high rate. Two fuzzy neural networks are utilized in the control structure. One
Position control of a servopneumatic system using fuzzy compensation
Sathyanarayana, Sreenivas
2000-01-01
The position control of a servopneumatic system in the presence of stick-slip type of friction is investigated. A cost effective, model-free fuzzy compensation scheme is proposed. The fuzzy compensation scheme compensates for the friction force...
A fuzzy set theory based control of a phase-controlled converter DC machine drive
G. C. D. Sousa; Bimal K. Bose
1994-01-01
Fuzzy logic or fuzzy set theory is getting increasing emphasis in process control applications. The paper describes application of fuzzy logic in a speed control system that uses a phase-controlled bridge converter and a separately excited DC machine. The fuzzy control is used to linearize the transfer characteristics of the converter in discontinuous conduction mode occurring at light load and\\/or
Simulating Human Lifting Motions Using Fuzzy-Logic Control
Xingda Qu; Maury A. Nussbaum
2009-01-01
Human motion simulation is an ill-posed problem. In order to predict unique lifting motion trajectories, a motion simulation model based on fuzzy-logic control is presented. The human body was represented by a 2-D five-segment model, and the neural controller was specified by fuzzy logic. Fuzzy rules were defined with their antecedent part describing the fuzzy variables of scaled positional error
Composite Fuzzy Control of Nonlinear Singularly Perturbed Systems
Tzuu-hseng S. Li; Kuo-jung Lin
2007-01-01
This paper presents the composite fuzzy control to stabilize the nonlinear singularly perturbed (NSP) systems with guaranteed Hinfin control performance. We use the Takagi-Sugeno (T-S) fuzzy model to construct the singularly perturbed fuzzy (SPF) systems. The corresponding fuzzy slow and fast subsystems of the original SPF system are also obtained. At first, a set of common positive-define matrices and the
H2 state feedback control for fuzzy singularly perturbed systems
Huaping Liu; Fuchun Sun; Kezhong He; Zengqi Sun
2003-01-01
This study introduces an H2 fuzzy state feedback control design method for nonlinear singularly perturbed systems. First, the Takagi-Sugeno fuzzy model is employed to approximate a nonlinear singularly perturbed system. Next, based on the fuzzy singularly perturbed model, a fuzzy PDC ?-independent controller is developed to achieve the H2 performance for small enough ?. By the proposed two-stage procedure, the
A Three-Domain Fuzzy Process Control System
Han-Xiong Li; Zhang Xx; Sy Li
2006-01-01
A three-domain fuzzy logic control (3D FLC) is presented for controlling the distributed parameter system (DPS). Different to the traditional FLC, the 3D FLC deals with three-dimensional fuzzy set (3D fuzzy set), i.e. the traditional fuzzy set plus spatial dimension. The proposed FLC still consists of defuzzification, rule inference, and defuzzification. Different to the traditional FLC, the 3D FLC can
Paris-Sud XI, Université de
Proton Exchange Membrane Fuel Cell degradation prediction based on Adaptive Neuro Fuzzy Inference Management, Time-series prediction, Adaptive Neuro-Fuzzy Inference System. [*] Corresponding author, silvasan nominal operating condition of a PEM fuel cell stack. It proposes a methodology based on Adaptive Neuro
Adaptive Neuro-Fuzzy Extended Kalman Filtering for Robot Localization
Havangi, Ramazan; Teshnehlab, Mohammad
2010-01-01
Extended Kalman Filter (EKF) has been a popular approach to localization a mobile robot. However, the performance of the EKF and the quality of the estimation depends on the correct a priori knowledge of process and measurement noise covariance matrices (Qk and Rk, respectively). Imprecise knowledge of these statistics can cause significant degradation in performance. This paper proposed the development of an Adaptive Neuro- Fuzzy Extended Kalman Filtering (ANFEKF) for localization of robot. The Adaptive Neuro-Fuzzy attempts to estimate the elements of Qk and Rk matrices of the EKF algorithm, at each sampling instant when measurement update step is carried out. The ANFIS supervises the performance of the EKF with the aim of reducing the mismatch between the theoretical and actual covariance of the innovation sequences. The free parameters of ANFIS are trained using the steepest gradient descent (SD) to minimize the differences of the actual value of the covariance of the residual with its theoretical value as...
NASA Technical Reports Server (NTRS)
Sultan, Labib; Janabi, Talib
1992-01-01
This paper analyses the internal operation of fuzzy logic controllers as referenced to the human cognitive tasks of control and decision making. Two goals are targeted. The first goal focuses on the cognitive interpretation of the mechanisms employed in the current design of fuzzy logic controllers. This analysis helps to create a ground to explore the potential of enhancing the functional intelligence of fuzzy controllers. The second goal is to outline the features of a new class of fuzzy controllers, the Clearness Transformation Fuzzy Logic Controller (CT-FLC), whereby some new concepts are advanced to qualify fuzzy controllers as 'cognitive devices' rather than 'expert system devices'. The operation of the CT-FLC, as a fuzzy pattern processing controller, is explored, simulated, and evaluated.
Design of fuzzy control systems with guaranteed stability
G. Feng; S. G. Cao; N. W. Rees; C. K. Chak
1997-01-01
This paper addresses the analysis and design of fuzzy control systems. The fuzzy systems are represented by a family of local state space models with aggregation. The controller is designed by considering each local state feedback controller and a compensating controller. The compensating controller is based on the well-known variable structure control theory. It is shown that this controller guarantees
Fuzzy Control/Space Station automation
NASA Technical Reports Server (NTRS)
Gersh, Mark
1990-01-01
Viewgraphs on fuzzy control/space station automation are presented. Topics covered include: Space Station Freedom (SSF); SSF evolution; factors pointing to automation & robotics (A&R); astronaut office inputs concerning A&R; flight system automation and ground operations applications; transition definition program; and advanced automation software tools.
Structure identification of generalized adaptive neuro-fuzzy inference systems
Mohammad Fazle Azeem; Madasu Hanmandlu; Nesar Ahmad
2003-01-01
This paper presents a method to identify the structure of generalized adaptive neuro-fuzzy inference systems (GANFISs). The structure of GANFIS consists of a number of generalized radial basis function (GRBF) units. The radial basis functions are irregularly distributed in the form of hyper-patches in the input-output space. The minimum number of GRBF units is selected based on a heuristic using
Feature Identification for Fuzzy Logic Based Adaptive Kalman Filtering
Abhik Mukherjee; Partha Pratim Adhikari; Prasanta Kumar Nandi
2002-01-01
Standard approaches to fuzzy logic based adaptive Kalman filter use features based on adjustment of noise statistics according\\u000a to performance of plant and sensor noise sources. Availability of this information is limited to specific domains. Here the\\u000a Kalman gain computed by conventional Kalman filter is modified using online estimate of measurement residuals which is always\\u000a available. Arguments are given for
Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm
NASA Technical Reports Server (NTRS)
Mitra, Sunanda; Pemmaraju, Surya
1992-01-01
Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.
Fuzzy Gain Scheduling Control of a Stepper Motor Driving a Flexible Rotor
S. F. Rezeka; N. M. Elsodany; N. A. Maharem
2010-01-01
Stepping motors are widely used in robotics and in the numerical control of machine tools to perform high precision positioning operations. The classical closed-loop control of the stepper motor does not respond properly to the system variations unless adaptive technique is used. In this paper, the feasibility of fuzzy gain scheduling control for stepping motor driving flexible rotor has been
Adaptive Neuro-Fuzzy Inference System for mid term prognostic error stabilization
Paris-Sud XI, Université de
Adaptive Neuro-Fuzzy Inference System for mid term prognostic error stabilization Otilia DRAGOMIR prediction errors appears to be essential. For that purpose a neuro-fuzzy predictor based on the ANFIS model is proposed to perform prognostic. Keywords: prognostic, neuro-fuzzy system, ANFIS, error of prediction. 1
Gail A. Carpenter; Stephen Grossberg; David B. Rosen
1991-01-01
A Fuzzy Adaptive Resonance Theory (ART) model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns
A Fuzzy Locally Adaptive Bayesian Segmentation Approach for Volume Determination in PET
Mathieu Hatt; Catherine Cheze-Le Rest; Alexandre Turzo; Christian Roux; Dimitris Visvikis
2009-01-01
Accurate volume estimation in positron emission tomography (PET) is crucial for different oncology applications. The objective of our study was to develop a new fuzzy locally adaptive Bayesian (FLAB) segmentation for automatic lesion volume delineation. FLAB was compared with a threshold approach as well as the previously proposed fuzzy hidden Markov chains (FHMC) and the fuzzy C-Means (FCM) algorithms. The
Classification of a large anticancer data set by Adaptive Fuzzy Partition
Nadáge Piclin; Marco Pintore; Christophe Wechman; Jacques R. Chrétien
2004-01-01
An Adaptive Fuzzy Partition (AFP) algorithm, derived from Fuzzy Logic concepts, was used to classify an anticancer data set, including about 1300 compounds subdivided into eight mechanisms of action. AFP classification builds relationships between molecular descriptors and bio-activities by dynamically dividing the descriptor hyperspace into a set of fuzzy subspaces. These subspaces are described by simple linguistic rules, from which
Dynamic knowledge inference and learning under adaptive fuzzy Petri net framework
Xiaoou Li; Wen Yu; Felipe Lara-rosano
2000-01-01
Since knowledge in an expert system is vague and modified frequently, expert systems are fuzzy and dynamic. It is very important to design a dynamic knowledge inference framework which is adjustable according to knowledge variation as human cognition and thinking. A generalized fuzzy Petri net model, called adaptive fuzzy Petri net (AFPN), is proposed with this object in mind. AFPN
Simple Fuzzy control Structure for a Lateral Missile Control Problem
Nabil Aouf; C. A. Rabbath; M. Lauzon
2003-01-01
The paper proposes a bank-to-turn lateral missile autopilot technique that is based on a simple fuzzy control structure. Numerical simulations demonstrate that the proposed approach provides satisfactory performance over the entire nonlinear missile flight envelope.
Introduction to n-adaptive fuzzy models to analyze public opinion on AIDS
Dr. W. B. Vasantha Kandasamy; Dr. Florentin Smarandache
2006-02-18
There are many fuzzy models like Fuzzy matrices, Fuzzy Cognitive Maps, Fuzzy relational Maps, Fuzzy Associative Memories, Bidirectional Associative memories and so on. But almost all these models can give only one sided solution like hidden pattern or a resultant output vector dependent on the input vector depending in the problem at hand. So for the first time we have defined a n-adaptive fuzzy model which can view or analyze the problem in n ways (n >=2) Though we have defined these n- adaptive fuzzy models theorectically we are not in a position to get a n-adaptive fuzzy model for n > 2 for practical real world problems. The highlight of this model is its capacity to analyze the same problem in different ways thereby arriving at various solutions that mirror multiple perspectives. We have used the 2-adaptive fuzzy model having the two fuzzy models, fuzzy matrices model and BAMs viz. model to analyze the views of public about HIV/ AIDS disease, patient and the awareness program. This book has five chapters and 6 appendices. The first chapter just recalls the definition of four fuzzy models used in this book and gives illustration of some of them. Chapter two introduces the new n-adaptive fuzzy models. Chapter three uses for the first time 2 adaptive fuzzy models to study psychological and sociological problems about HIV/AIDS. Chapter four gives an outline of the interviews. Chapter five gives the suggestions and conclusion based on our study. Of the 6 appendices four of them are C-program made to make the working of the fuzzy model simple.
Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong
2015-01-01
In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896
Fuzzy logic control of a nitrogen laser
NASA Astrophysics Data System (ADS)
Tam, Siu Chung; Tan, Siong-Chai; Neo, Wah-Peng; Foong, Sze-Chern; Chan, Choon-Hao; Ho, Anthony T.; Chua, Hock-Chuan; Lee, Sing
2001-02-01
Traditionally, the stability of the output of a laser is controlled through scientific means or by a simple feedback loop. For multiinput multioutput control and for medium- to high-power lasers, however, these control schemes may break down. We report on the use of a fuzzy logic control scheme to improve the stability of a pulsed nitrogen laser. Specifically, the nitrogen laser is modeled as a two-input two-output system. The two input parameters are the discharge voltage (V) and nitrogen pressure (P), and the two output parameters are the pulse energy (E) and pulse width (PW). The performance of the fuzzy logic controller is compared with a decoupled two-channel PID (proportional+integral+derivative) controller. In our experiment, the long-term stabilities of the open-loop system are 1.82% root mean square (rms) for pulse energy and 4.58% rms for pulse width. The PID controller achieves better performance with long-term stabilities of 1.46% rms for pulse energy and 4.46% rms for pulse width. The fuzzy logic controller performs the best with long-term stabilities of 1.02% rms for pulse energy and 4.24% rms for pulse width, respectively.
A fuzzy logic controller for boiler systems in power plants
Heidar A. Malki; Guanrong Chen
1996-01-01
This paper describes a fuzzy proportional-integral (PI) controller and its application in boilers control for power plants. First, the fuzzy PI controller is briefly described. Then, some real data simulations for boilers control in power plants are shown to demonstrate its advantages for controlling the temperature of the generator and the boiler feed tank level over the conventional PI controllers
Bitaraf, Maryam
2012-07-16
...................................................................................... 16 2.1 Fuzzy Logic Control Method ............................................................... 16 2.2 Optimal Controller ............................................................................... 17 2.3 Simple Adaptive Control... ..................................................... 1 Figure 1.2 Nihon-Kagaku-Miraikan, the Tokyo National Museum of Emerging Science and Innovation, Japan ............................................................. 8 Figure 2.1 General architecture of a fuzzy logic controller...
Fuzzy Compensator Using RGA for TRMS Control
Jih-gau Juang; Wen-kai Liu
2006-01-01
\\u000a This paper presents a new approach using fuzzy compensator and PID controller to an experimental propeller setup which is\\u000a called the twin rotor multi-input multi-output system (TRMS). Some previous works ignored the interactions between two axes\\u000a and the controller being designed in horizontal or vertical direction separately. The goal of this study is to stabilize the\\u000a TRMS in significant cross
Development of a Fuzzy Expert system based on PCS7 and FuzzyControl++ Cement Mill control
L. Hayet Mouss; Sonia Benaicha
The basic idea of this work was to study the application of expert systems and fuzzy logic in the field of diagnostic and industrial maintenance. For this, a fuzzy expert system designed, developed and simulated in Ain Touta cement society in Batna in the East of Algeria. Dedicated to control cement mill. The application of fuzzy logic and expert systems
Maximum entropy approach to fuzzy control
NASA Technical Reports Server (NTRS)
Ramer, Arthur; Kreinovich, Vladik YA.
1992-01-01
For the same expert knowledge, if one uses different &- and V-operations in a fuzzy control methodology, one ends up with different control strategies. Each choice of these operations restricts the set of possible control strategies. Since a wrong choice can lead to a low quality control, it is reasonable to try to loose as few possibilities as possible. This idea is formalized and it is shown that it leads to the choice of min(a + b,1) for V and min(a,b) for &. This choice was tried on NASA Shuttle simulator; it leads to a maximally stable control.
Autonomous mobile target tracking system based on grey-fuzzy control algorithm
Ren C. Luo; Tse Min Chen
2000-01-01
This paper presents a new position-based tracking system for autonomous mobile target tracking task. A grey-fuzzy controller (GFC) is developed for motion control of the tracker, in which dynamics models of the target and tracker are not required a priori. The target detection is based on the adaptive visual detector (AVD), which can online adjust the histogram model based on
Ilham N. Huseyinov
\\u000a In this study, based on fuzzy linguistic modelling and fuzzy multi level granulation an adaptation strategy to cognitive\\/learning\\u000a styles is presented. Fuzzy if-then rules are utilized to adaptively map cognitive\\/learning styles of users to their information\\u000a navigation and presentation preferences through natural language expressions. The important implications of this approach\\u000a are that, first, uncertain and vague information is handled; second,
Design of a stable fuzzy controller for an articulated vehicle.
Tanaka, K; Kosaki, T
1997-01-01
This paper presents a backward movement control of an articulated vehicle via a model-based fuzzy control technique. A nonlinear dynamic model of the articulated vehicle is represented by a Takagi-Sugeno fuzzy model. The concept of parallel distributed compensation is employed to design a fuzzy controller from the Takagi-Sugeno fuzzy model of the articulated vehicle. Stability of the designed fuzzy control system is guaranteed via Lyapunov approach. The stability conditions are characterized in terms of linear matrix inequalities since the stability analysis is reduced to a problem of finding a common Lyapunov function for a set of Lyapunov inequalities. Simulation results and experimental results show that the designed fuzzy controller effectively achieves the backward movement control of the articulated vehicle. PMID:18255895
Adaptive fuzzy systems for multichannel signal processing
KONSTANTINOS N. PLATANIOTIS; DIMITRIOS ANDROUTSOS; ANASTASIOS N. VENETSANOPOULOS
1999-01-01
Processing multichannel signals using digital signal processing techniques has received increased attention lately due to its importance in applications such as multimedia technologies and telecommunications. The objective of this paper is twofold: 1) to introduce adaptive filtering techniques to the reader who is just beginning in this area and 2) to provide a review for the reader who may be
Yannis A. Tolias; Stavros M. Panas
1998-01-01
We present an adaptive fuzzy clustering scheme for image segmentation, the adaptive fuzzy clustering\\/segmentation (AFCS) algorithm. In AFCS, the nonstationary nature of images is taken into account by modifying the prototype vectors as functions of the sample location in the image. The inherent high interpixel correlation is modeled using neighborhood information. A multiresolution model is utilized for estimating the spatially
Xiaodong Liu; Qingling Zhang
2003-01-01
The problems of relaxed quadratic stability conditions, fuzzy observer designs and H? controller designs for T-S fuzzy systems have been studied. First new stability conditions are obtained by relaxing the stability conditions derived in previous papers. Secondly, new fuzzy observers based on the relaxed stability conditions for the T-S fuzzy systems have been proposed. Thirdly two sufficient LMI conditions, which
Power stabilization of nuclear research reactor via fuzzy controllers
H. M. Emara; A. Elsadat; A. Bahgat; M. Sultan
2002-01-01
Power stabilization is a critical issue in nuclear reactors. A conventional PD controller is currently used in Egypt's second testing research reactor (ETRR-2). This controller is designed neglecting the nonlinear nature of the plant. In this paper, two fuzzy controllers are used instead of the conventional PD. The first fuzzy controller has a static rule set, while the second one
Fuzzy logic based Congestion control Andreas Pitsillides1
Pitsillides, Andreas
Fuzzy logic based Congestion control Andreas Pitsillides1 , Ahmet Sekercioglu2 1 Department: ASekerci@swin.edu.au ABSTRACT: It is generally accepted that the problem of network congestion control propose a fuzzy based congestion control approach to address the congestion control problem
Multivariable circle criterion: stable fuzzy control of a milling process
G. Schmitt-Braess; R. E. Haber Guerra; R. H. Haber; A. Alique
2002-01-01
Finding for a complex control plant an analytical model that is detailed enough for controller design is often hard or even impossible. In such cases the regulation of the system can be performed by knowledge-based fuzzy control. Under the assumption that there exists a locally valid linear description for the plant the stability problem of the nonlinear fuzzy control-loop can
Fuzzy decision-making in turbine engine controls
Charles D. McCurry; Richard Mgaya; Saleh Zein-Sabatto
2009-01-01
The design of a multi-level fuzzy-logic control system for the control of turbine engines using type-1 fuzzy-logic is presented in this paper. A Matlab-Simulink model of an advanced turbine engine was used to test and validate the performance of the developed fuzzy control system. The model provided comprehensive and complex model of a turbine engine. As with most modeling of
Using Fuzzy Logic in Automated Vehicle Control
José Eugenio Naranjo; Miguel Ángel Sotelo; Carlos González; Ricardo García; Teresa De Pedro
2007-01-01
Automated versions of a mass-produced vehicle use fuzzy logic techniques to both address common challenges and incorporate human procedural knowledge into the vehicle control algorithms. In-vehicle computing has been largely relegated to auxiliary tasks such as regulating cabin temperature, opening doors, and monitoring fuel, oil, and battery-charge levels. However, computers are increasingly assuming driving-related tasks in some commercial models. Among
A fuzzy logic controller for aircraft flight control
Lawrence I. Larkin
1984-01-01
This paper describes a model of an autopilot controller based on fuzzy algorithms. The controller maneuvers an aircraft from level flight into a final-approach flight path and maintains the aircraft along the glide path until just before touchdown. To evaluate the performance and effectiveness of the model, the aircraft response to controller actions is simulated using flight simulation techniques. The
A fuzzy logic supervisor for PID control of unknown systems
Robert P. Copeland; Kuldip S. Rattan
1994-01-01
The design of a fuzzy logic supervisor for proportional plus integral plus derivative control of unknown systems is carried out in this paper. The PID controllers are designed using Ziegler-Nichols tuning rules. Since the Ziegler-Nichols method provides nominal parameter values for the PID controller, the desired system response is not realized without additional tuning. A fuzzy supervisor is proposed to
Fuzzy logic assisted manual control of joystick operated hydraulic crane
Esa NIEMELA; Tapio VIRVALO
1994-01-01
Fuzzy logic has been applied widely in various closed-loop control systems. In the case of a hydraulic mobile crane, the operator often has many mechanical manual valve lever arms to handle simultaneously. In this paper, it is shown how by using a fuzzy logic controller, the operator's task of controlling independent booms can be reduced using an electrically operated joystick
Fuzzy model-based predictive control by instantaneous linearization
János Abonyi; Lajos Nagy; Ferenc Szeifert
2001-01-01
Abstract This paper investigates the application of the product - sum crisp type fuzzy model linearization technique and the multistep predictive control strategy for the construction of a model - based predictive fuzzy controller A model - based predictive controller is based on a local linear approximate model generated by using the proposed linearization technique that linearizes the product -
Traction control algorithm based on fuzzy PID and PWM modulation
LiBo Chao; Liang Chu; Yang Ou; WenBo Lu
2011-01-01
in this paper, a traction control algorithm based on fuzzy PID and modulation of pump based on PWM is proposed. The target brake pressure needed to be exerted on the driving wheel is determined by a fuzzy PID controller from the difference between the target wheel speeds and actual wheel speeds and is fulfilled by PWM control of hydraulic pump
Fuzzy logic control for switched reluctance motor drive
Hao Chen; Dong Zhang; Zi-Yue Cong; Zhi-Feng Zhang
2002-01-01
This paper introduces the control strategy of the angle position for the switched reluctance motor drive based on fuzzy logic. The hardware of the prototype, such as the structure of the switched reluctance motor and the main circuit of the four-phase asymmetric bridge power converter are introduced. The principles of the fuzzy logic control are given. The input control parameters,
Self-Learning Fuzzy Controllers Based on Temporal Back Propagation
J. sr. Jang
1992-01-01
This paper presents a generalized control strategy that enhances fuzzy controllers with selflearningcapability for achieving prescribed control objectives in a near-optimal manner. Thismethodology, termed temporal back propagation, is model-insensitive in the sense that it candeal with plants that can be represented in a piecewise differentiable format, such as differenceequations, neural networks, GMDH, fuzzy models, etc. Regardless of the numbers of
Bias Field Estimation and Adaptive Segmentation of MRI Data Using a Modi ed Fuzzy C-Means Algorithm
Farag, Aly A.
Bias Field Estimation and Adaptive Segmentation of MRI Data Using a Modi ed Fuzzy C-Means Algorithm for adap- tive fuzzy segmentation of MRI data and estimation of intensity inhomogeneities using fuzzy logic by modifying the objective function of the standard fuzzy c-means FCM algo- rithm to compensate
SaFIN: a self-adaptive fuzzy inference network.
Tung, Sau Wai; Quek, Chai; Guan, Cuntai
2011-12-01
There are generally two approaches to the design of a neural fuzzy system: 1) design by human experts, and 2) design through a self-organization of the numerical training data. While the former approach is highly subjective, the latter is commonly plagued by one or more of the following major problems: 1) an inconsistent rulebase; 2) the need for prior knowledge such as the number of clusters to be computed; 3) heuristically designed knowledge acquisition methodologies; and 4) the stability-plasticity tradeoff of the system. This paper presents a novel self-organizing neural fuzzy system, named Self-Adaptive Fuzzy Inference Network (SaFIN), to address the aforementioned deficiencies. The proposed SaFIN model employs a new clustering technique referred to as categorical learning-induced partitioning (CLIP), which draws inspiration from the behavioral category learning process demonstrated by humans. By employing the one-pass CLIP, SaFIN is able to incorporate new clusters in each input-output dimension when the existing clusters are not able to give a satisfactory representation of the incoming training data. This not only avoids the need for prior knowledge regarding the number of clusters needed for each input-output dimension, but also allows SaFIN the flexibility to incorporate new knowledge with old knowledge in the system. In addition, the self-automated rule formation mechanism proposed within SaFIN ensures that it obtains a consistent resultant rulebase. Subsequently, the proposed SaFIN model is employed in a series of benchmark simulations to demonstrate its efficiency as a self-organizing neural fuzzy system, and excellent performances have been achieved. PMID:22020678
Fuzzy Economizer control using a Prolog-C inference engine
Belur, Raghuveer R.
1993-01-01
. . . . 1 1 . . . . 1 3 . . . . 14 . . . . 16 . . . . 17 III SOFTWARE DEVELOPMENT. . . . . . . 20 3. 1 Fuzzy Logic 3. 2 Prolog. 3. 2. 1 Facts . 3. 2. 2 Questions. . 3. 2. 3 Variables 3. 2. 4 Conjunction . . 3. 2. 5 Rules 3. 3 Prolog and C 3. 4.... 8. Cold deck schematic Fig. 9. Mixing box schematic diagram. Fig. 10. Temperature based economizer control . Fig. 11. Architecture of a fuzzy logic controller . Fig. 12. Fuzzy sets for temperature Fig. 13. Crisp temperature set Fig. 14. Prolog...
A Type2 Fuzzy Switching Control System for Biped Robots
Zhi Liu; Yun Zhang; Yaonan Wang
2007-01-01
In this paper, a type-2 fuzzy switching control system is proposed for a biped robot, which includes switched nonlinear system modeling, type-2 fuzzy control system design, and a type-2 fuzzy modeling algorithm. A new switched system model is proposed to represent the continuous-time dynamic and discrete-event dynamic of a walking biped as a whole, which is helpful to analyze the
A QoS-guaranteed fuzzy channel allocation controller for hierarchical cellular systems
Kuen-Rong Lot; Chung-Ju Chang; C. Bernard ShungS
1999-01-01
In this paper, we propose a fuzzy channel allocation controller (FCAC) for hierarchical cellular systems. FCAC is designed to contain base-station interface module, resource estimator, performance evaluator, and fuzzy channel allocation processor (FCAP). FCAP is a two-layer fuzzy logic controller that consists of a fuzzy admission threshold estimator in the first layer and a fuzzy channel allocator in the second
Fuzzy hypercubes: linguistic learning\\/reasoning systems for intelligent control and identification
Hoon Kang; George Vachtsevanos
1991-01-01
The authors introduce a tool for intelligent control and identification. A robust and reliable learning and reasoning mechanism is addressed based on fuzzy set theory and fuzzy associative memories. The mechanism stores a priori an initial knowledge base via approximate learning and utilizes this information for identification and control via fuzzy inferencing. This processor is called a fuzzy hypercube. Fuzzy
Fuzzy symbolic sensors Fuzzy symbolic sensors
Paris-Sud XI, Université de
symbolic sensors, are particularly adapted when working with control systems which use artificial measurement, range finding sensor, colour matching sensor. 1. Introduction Nowadays, system control by usualFuzzy symbolic sensors 1 Fuzzy symbolic sensors from concept to applications Gilles Mauris, Eric
Coordinated signal control for arterial intersections using fuzzy logic
NASA Astrophysics Data System (ADS)
Kermanian, Davood; Zare, Assef; Balochian, Saeed
2013-09-01
Every day growth of the vehicles has become one of the biggest problems of urbanism especially in major cities. This can waste people's time, increase the fuel consumption, air pollution, and increase the density of cars and vehicles. Fuzzy controllers have been widely used in many consumer products and industrial applications with success over the past two decades. This article proposes a comprehensive model of urban traffic network using state space equations and then using Fuzzy Logic Tool Box and SIMULINK Program MATLAB a fuzzy controller in order to optimize and coordinate signal control at two intersections at an arterial road. The fuzzy controller decides to extend, early cut or terminate a signal phase and phase sequence to ensure smooth flow of traffic with minimal waiting time and length of queue. Results show that the performance of the proposed traffic controller at novel fuzzy model is better that of conventional controllers under normal and abnormal traffic conditions.
An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller
ERIC Educational Resources Information Center
Mamdani, E. H.; Assilian, S.
1975-01-01
This paper describes an experiment on the "linguistic" synthesis of a controller for a model industrial plant (a steam engine). Fuzzy logic is used to convert heuristic control rules stated by a human operator into an automatic control strategy. (Author)
Control of the position DC servo motor by fuzzy logic
N. Khongkoom; A. Kanchanathep; S. Nopnakeepong; S. Tanuthong; S. Tunyasrirut; R. Kagawa
2000-01-01
Presents a fuzzy logic controller for controlling the position of a DC servo motor. The position of the angle location is limited at -? to ? radian. The fuzzy inference engine uses angle position from an angular position sensor, and angular velocity. The DC servo motor revolves the circle of axis at the number of revolutions corresponding to the binding
NASA Astrophysics Data System (ADS)
Winner, Hermann; Danner, Bernd; Steinle, Joachim
Mit Adaptive Cruise Control, abgekürzt ACC, wird eine Fahrgeschwindigkeitsregelung bezeichnet, die sich an die Verkehrssituation anpasst. Synonyme Bezeichnungen sind Aktive Geschwindigkeitsregelung, Automatische Distanzregelung oder Abstandsregeltempomat. Im englischen Sprachraum fnden sich die weiteren Bezeichnungen Active Cruise Control, Automatic Cruise Control oder Autonomous Intelligent Cruise Control. Als markengeschützte Bezeichnungen sind Distronic und Automatische Distanz-Regelung (ADR) eingetragen.
Law, Bonnie N.F.
, and N. F. Law, Member, IEEE Abstract--An image segmentation algorithm based on adaptive fuzzy c-means444 IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 13, NO. 4, AUGUST 2005 Image Segmentation Based the effectiveness of the proposed algorithm. Index Terms--Fuzzy clustering, image segmentation, prototype adaptation
Self-learning fuzzy controllers based on temporal back propagation
NASA Technical Reports Server (NTRS)
Jang, Jyh-Shing R.
1992-01-01
This paper presents a generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner. This methodology, termed temporal back propagation, is model-insensitive in the sense that it can deal with plants that can be represented in a piecewise-differentiable format, such as difference equations, neural networks, GMDH structures, and fuzzy models. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy if-then rules if human experts, or automatically derive the fuzzy if-then rules obtained from human experts are not available. The inverted pendulum system is employed as a test-bed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller.
Xian-Min Ma; Yan-Yun Zhao
2006-01-01
A novel approach is proposed to design an optimal fuzzy controller via particle swarm optimization in this paper. The main idea of the proposed method is to find the suitable fuzzy controller parameters according to the performance index of the system error while the disturbance and induction motor parameter variation are happening. The optimal set of fuzzy controller parameters is
A simple direct-torque neuro-fuzzy control of PWM-inverter-fed induction motor drive
Pawel Z. Grabowski; Marian P. Kazmierkowski; Bimal K. Bose; Frede Blaabjerg
2000-01-01
In this paper, the concept and implementation of a new simple direct-torque neuro-fuzzy control (DTNFC) scheme for pulsewidth-modulation-inverter-fed induction motor drive are presented. An adaptive neuro-fuzzy inference system is applied to achieve high-performance decoupled flux and torque control. The theoretical principle and tuning procedure of this method are discussed. A 3 kW induction motor experimental system with digital signal processor
Adaptive neuro-fuzzy inference system for prediction of water level in reservoir
Fi-John Chang; Ya-Ting Chang
2006-01-01
Accurate prediction of the water level in a reservoir is crucial to optimizing the management of water resources. A neuro-fuzzy hybrid approach was used to construct a water level forecasting system during flood periods. In particular, we used the adaptive network-based fuzzy inference system (ANFIS) to build a prediction model for reservoir management. To illustrate the applicability and capability of
Prince, Jerry L.
IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 18, NO. 9, SEPTEMBER 1999 737 Adaptive Fuzzy Segmentation of Magnetic Resonance Images Dzung L. Pham, Student Member, IEEE, and Jerry L. Prince,* Member, IEEE Abstract--An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three
Fuzzy predictive control for nitrogen removal in biological wastewater treatment.
Marsili-Libelli, S; Giunti, L
2002-01-01
Whenever the carbon/nitrogen ratio of a domestic wastewater is too low, full denitrification is difficult to obtain and an additional source of organic carbon has to be provided. Since loading conditions may vary appreciably over the diurnal cycle, depending on the weather and sewage conditions, dosing should be controlled by an adaptive regulator to keep into account the time-varying process dynamics. A fuzzy predictive controller is proposed in this paper and its performance is tested through numerical simulations. The new aspects brought forward are the use of an improved model for denitrification, the use of benchmark (i.e. thoroughly tested and standardised) input files and the conclusion about regulator performance in overall plant performance, in terms of carbon saving and discharge compliance. PMID:11936655
Fuzzy attitude control for a nanosatellite in leo orbit
NASA Astrophysics Data System (ADS)
Calvo, Daniel; Laverón-Simavilla, Ana; Lapuerta, Victoria; Aviles, Taisir
Fuzzy logic controllers are flexible and simple, suitable for small satellites Attitude Determination and Control Subsystems (ADCS). In this work, a tailored fuzzy controller is designed for a nanosatellite and is compared with a traditional Proportional Integrative Derivative (PID) controller. Both control methodologies are compared within the same specific mission. The orbit height varies along the mission from injection at around 380 km down to a 200 km height orbit, and the mission requires pointing accuracy over the whole time. Due to both the requirements imposed by such a low orbit, and the limitations in the power available for the attitude control, a robust and efficient ADCS is required. For these reasons a fuzzy logic controller is implemented as the brain of the ADCS and its performance and efficiency are compared to a traditional PID. The fuzzy controller is designed in three separated controllers, each one acting on one of the Euler angles of the satellite in an orbital frame. The fuzzy memberships are constructed taking into account the mission requirements, the physical properties of the satellite and the expected performances. Both methodologies, fuzzy and PID, are fine-tuned using an automated procedure to grant maximum efficiency with fixed performances. Finally both methods are probed in different environments to test their characteristics. The simulations show that the fuzzy controller is much more efficient (up to 65% less power required) in single maneuvers, achieving similar, or even better, precision than the PID. The accuracy and efficiency improvement of the fuzzy controller increase with orbit height because the environmental disturbances decrease, approaching the ideal scenario. A brief mission description is depicted as well as the design process of both ADCS controllers. Finally the validation process and the results obtained during the simulations are described. Those results show that the fuzzy logic methodology is valid for small satellites' missions benefiting from a well-developed artificial intelligence theory.
Self-tuning Fuzzy Control Method Based on the Trajectory Performance of the Phase Plane
Zhang, J.; Chen, Y.; Xiong, J.
2006-01-01
) pointed out that fuzzy controller is similar to a sliding mode variable structure controller theoretically. In order to increase the control performance of fuzzy control, Hwan-Rong Lin and Wen-June Wang combine the sliding mode control and fuzzy logic....Y. and M.J. Chung. Systematic design and stability analysis of a fuzzy logic controller[J]. Fuzzy Sets and Systems, 1995, 72(3):271-298. [3] Filev D.P. and R.R. Yager. On the analysis of fuzzy logic controllers[J]. Fuzzy Sets and Systems, 1994, 68...
Fuzzy control of a hand rehabilitation robot to optimize the exercise speed in passive working mode.
Baniasad, Mina Arab; Akbar, Mohammad; Alasty, Aria; Farahmand, Farzam
2011-01-01
The robotic rehabilitation devices can undertake the difficult physical therapy tasks and provide improved treatment procedures for post stroke patients. During passive working mode, the speed of the exercise needs to be controlled continuously by the robot to avoid excessive injurious torques. We designed a fuzzy controller for a hand rehabilitation robot to adjust the exercise speed by considering the wrist angle and joint resistive torque, measured continuously, and the patient's general condition, determined by the therapist. With a set of rules based on an expert therapist experience, the fuzzy system could adapt effectively to the neuromuscular conditions of the patient's paretic hand. Preliminary clinical tests revealed that the fuzzy controller produced a smooth motion with no sudden change of the speed that could cause pain and activate the muscle reflexive mechanism. This improves the recovery procedure and promotes the robot's performance for wide clinical usage. PMID:21335755
A fuzzy call admission control scheme in wireless networks
Yufeng Ma; Shenguang Gong; Xiulin Hu; Yunyu Zhang
2007-01-01
Scarcity of the spectrum resource and mobility of users make quality of service (QoS) provision a critical issue in wireless networks. This paper presents a fuzzy call admission control scheme to meet the requirement of the QoS. A performance measure is formed as a weighted linear function of new call and handoff call blocking probabilities. Simulation compares the proposed fuzzy
A fuzzy-controlled Hooke-Jeeves optimization algorithm
Deepak Sankar Somasundaram; Mohamed B. Trabia
2011-01-01
This article presents an approach to enhance the Hooke-Jeeves optimization algorithm through the use of fuzzy logic. The Hooke-Jeeves algorithm, similar to many other optimization algorithms, uses predetermined fixed parameters. These parameters do not depend on the objective function values in the current search region. In the proposed algorithm, several fuzzy logic controllers are integrated at the various stages of
Fuzzy logic control of a solar power plant
Francisco R. Rubio; Manuel Berenguel; Eduardo F. Camacho
1995-01-01
This paper presents an application of fuzzy logic control to the distributed collector field of a solar power plant. The major characteristic of a solar power plant is that the primary energy source, solar radiation, cannot be manipulated. Solar radiation varies throughout the day, causing changes in plant dynamics and strong perturbations in the process. A special subclass of fuzzy
Fuzzy TBT control of multi-stage flash desalination plants
A. Ismail; E. Abu-Khousa
1996-01-01
The paper describes the application of fuzzy algorithms to the control of top brine temperature (TBT) of multi-stage flash (MSF) seawater desalination plants. A linearized model of a six inputs and six outputs 18-stage MSF plant is considered in this study. The transfer function matrix of this model is subjected to interaction between the loops. The design of fuzzy logic
Application of a self-organizing fuzzy logic controller to nuclear steam generator level control
Gee Yong Park; Poong Hyun Seong
1997-01-01
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
NASA Astrophysics Data System (ADS)
Sugisaka, Masanori; Mbaïtiga, Zacharie
There exist several problems in the control of vehicle brake including the development of control logic for anti-lock braking system (ABS), base-braking and intelligent braking. Here we study the intelligent braking control where we seek to develop a controller that can ensure that the braking torque commended by the driver will be achieved. In particular, we develop, a new PID Fuzzy controller (PIDFC) based on parallel operation of PI Fuzzy and PD Fuzzy control. Two fuzzy rule bases are constructed by separating the linguistic control rule for PID Fuzzy control into two parts: The first part is e-?e and the second part is ?2e-?e respectively. Then two Fuzzy controls employing these rules bases individually are synthesized and run in parallel. The incremental control input is determined by taking weighted mean of the outputs of two Fuzzy controls. The result, which proves the merit of the proposed method are compared to those found in the previous research.
Prediction of oral bioavailability by adaptive fuzzy partitioning.
Pintore, Marco; van de Waterbeemd, Han; Piclin, Nadège; Chrétien, Jacques R
2003-04-01
An adaptive fuzzy partition (AFP) algorithm was applied on two bioavailability data sets subdivided into four ranges of activity. A large set of molecular descriptors was tested and the most relevant parameters were selected with help of a procedure based on genetic algorithm concepts and stepwise method. After building several AFP models on a training set, the best ones were able to predict correctly 75% of the validation set compounds. Furthermore, an improvement of about 15% in the validation results was got, on the same data set, as regard to other prediction methods. The importance to work with data sets including a large molecular diversity, and to use tools able to manage it, was also shown. The prediction power was increased up to 25% employing a data set with a better-optimised molecular diversity. PMID:12750031
Fuzzy logic control: a knowledge-based system perspective
NASA Astrophysics Data System (ADS)
Bonissone, Piero P.; Chiang, Kenneth H.
1993-12-01
We view fuzzy logic control technology as a high level language in which we can efficiently define and synthesize non-linear controllers for a given process. We contrast fuzzy proportional integral (PI) controllers with conventional PI and 2D sliding mode controllers. Then we compare the development of fuzzy logic controllers (FLC) with that of knowledge-based system (KBS) applications. We decompose the comparison into reasoning tasks (representation, inference, and control) and application tasks (acquisition, development, validation, compilation and deployment). After reviewing the reasoning tasks, we focus on the compilation of fuzzy rule bases into fast access lookup tables. These tables can be used by a simplified run-time engine to determine the FLC's crisp output for a given input. Finally we illustrate the application of FLC technology in a hierarchical architecture to control a complex power plant for heavy vehicles.
A fuzzy-controlled Kalman filter applied to stereo-visual tracking schemes
Santiago Aja-fernández; Carlos Alberola-lópez; Juan Ruiz-alzola
2003-01-01
In this paper, the authors propose a fuzzy-controlled Kalman filtering scheme applied to stereo visual tracking. Two control levels have been designed: first, a fuzzy methodology allows the filter to fine tune to actual conditions by estimating the plant noise covariance matrix in every time instant. Second, a fuzzy control stage based on a fuzzy feedback system is used to
Hermann Winner; Bernd Danner; Joachim Steinle
2009-01-01
Mit Adaptive Cruise Control, abgekürzt ACC, wird eine Fahrgeschwindigkeitsregelung bezeichnet, die sich an die Verkehrssituation\\u000a anpasst. Synonyme Bezeichnungen sind Aktive Geschwindigkeitsregelung, Automatische Distanzregelung oder Abstandsregeltempomat.\\u000a Im englischen Sprachraum fnden sich die weiteren Bezeichnungen Active Cruise Control, Automatic Cruise Control oder Autonomous\\u000a Intelligent Cruise Control. Als markengeschützte Bezeichnungen sind Distronic und Automatische Distanz-Regelung (ADR) eingetragen.
Adaptive neuro-fuzzy inference system for real-time monitoring of integrated-constructed wetlands.
Dzakpasu, Mawuli; Scholz, Miklas; McCarthy, Valerie; Jordan, Siobhán; Sani, Abdulkadir
2015-01-01
Monitoring large-scale treatment wetlands is costly and time-consuming, but required by regulators. Some analytical results are available only after 5 days or even longer. Thus, adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the effluent concentrations of 5-day biochemical oxygen demand (BOD5) and NH4-N from a full-scale integrated constructed wetland (ICW) treating domestic wastewater. The ANFIS models were developed and validated with a 4-year data set from the ICW system. Cost-effective, quicker and easier to measure variables were selected as the possible predictors based on their goodness of correlation with the outputs. A self-organizing neural network was applied to extract the most relevant input variables from all the possible input variables. Fuzzy subtractive clustering was used to identify the architecture of the ANFIS models and to optimize fuzzy rules, overall, improving the network performance. According to the findings, ANFIS could predict the effluent quality variation quite strongly. Effluent BOD5 and NH4-N concentrations were predicted relatively accurately by other effluent water quality parameters, which can be measured within a few hours. The simulated effluent BOD5 and NH4-N concentrations well fitted the measured concentrations, which was also supported by relatively low mean squared error. Thus, ANFIS can be useful for real-time monitoring and control of ICW systems. PMID:25607665
Kok Chew Lee; Peter Gardner
2006-01-01
This paper describes an adaptive digital predistorter (ADP) for RF power amplifier (PA) linearization using an adaptive neuro-fuzzy inference system (ANFIS). The ANFIS predistorter (PD) employs the advantage of real-time modeling of the PA's responses in determining the PD's functions. The amplitude and phase corrections for the PD are represented in an easy-to-understand fuzzy if-then rule, while the parameters involved
Self-adaptive neuro-fuzzy inference systems for classification applications
Jeen-Shing Wang; C. S. George Lee
2002-01-01
This paper presents a self-adaptive neuro-fuzzy inference system (SANFIS) that is capable of self-adapting and self-organizing its internal structure to acquire a parsimonious rule-base for interpreting the embedded knowledge of a system from the given training data set. A connectionist topology of fuzzy basis functions with their universal approximation capability is served as a fundamental SANFIS architecture that provides an
Adaptive fuzzy moving K-means clustering algorithm for image segmentation
Nor Mat Isa; Samy Salamah; Umi Ngah
2009-01-01
Image segmentation remains one of the major challenges in image analysis. Many segmentation algorithms have been developed for various applications. Unsatisfactory results have been encountered in some cases, for many existing segmentation algorithms. In this paper, we introduce three modified versions of the conventional moving k-means clustering algorithm called the fuzzy moving k-means, adaptive moving k-means and adaptive fuzzy moving
IMPLEMENTING ADAPTIVE DRIVING SYSTEMS FOR INTELLIGENT VEHICLES BY USING NEURO-FUZZY NETWORKS
Y T Lin; F.-Y. Wang; P B Mirchandani; Long Wu; Z X Wang; Chris Yeo; Michael Do
2001-01-01
The application of supervised learning to train an intelligent vehicle with a neuro-fuzzy controller to mimic the driving behavior of a human driver is discussed. An initial fuzzy control system for vehicle driving was set up on the basis of general human driving experiences, and its control rules were modified to fit the driving behavior of an individual driver. This
Approach to Synchronization Control of Magnetic Bearings Using Fuzzy Logic
NASA Technical Reports Server (NTRS)
Yang, Li-Farn
1996-01-01
This paper presents a fuzzy-logic approach to the synthesis of synchronization control for magnetically suspended rotor system. The synchronization control enables a whirling rotor to undergo synchronous motion along the magnetic bearing axes; thereby avoiding the gyroscopic effect that degrade the stability of rotor systems when spinning at high speed. The control system features a fuzzy controller acting on the magnetic bearing device, in which the fuzzy inference system trained through fuzzy rules to minimize the differential errors between four bearing axes so that an error along one bearing axis can affect the overall control loop for the motion synchronization. Numerical simulations of synchronization control for the magnetically suspended rotor system are presented to show the effectiveness of the present approach.
Fuzzy logic applications to expert systems and control
NASA Technical Reports Server (NTRS)
Lea, Robert N.; Jani, Yashvant
1991-01-01
A considerable amount of work on the development of fuzzy logic algorithms and application to space related control problems has been done at the Johnson Space Center (JSC) over the past few years. Particularly, guidance control systems for space vehicles during proximity operations, learning systems utilizing neural networks, control of data processing during rendezvous navigation, collision avoidance algorithms, camera tracking controllers, and tether controllers have been developed utilizing fuzzy logic technology. Several other areas in which fuzzy sets and related concepts are being considered at JSC are diagnostic systems, control of robot arms, pattern recognition, and image processing. It has become evident, based on the commercial applications of fuzzy technology in Japan and China during the last few years, that this technology should be exploited by the government as well as private industry for energy savings.
Fuzzy support vector machines for adaptive Morse code recognition
Cheng-Hong Yang; Li-Cheng Jin; Li-Yeh Chuang
2006-01-01
Morse code is now being harnessed for use in rehabilitation applications of augmentative–alternative communication and assistive technology, facilitating mobility, environmental control and adapted worksite access. In this paper, Morse code is selected as a communication adaptive device for persons who suffer from muscle atrophy, cerebral palsy or other severe handicaps. A stable typing rate is strictly required for Morse code
NASA Astrophysics Data System (ADS)
Zhang, Mei; Zheng, Meng; Li, Yanqiu
2013-12-01
A variable universe fuzzy PID algorithm is designed to control the misalignment of the lithography projection optics to meet the requirement of high image quality. This paper first simulates the alignment of Schwarzschild objective designed by us. Secondly, the variable universe fuzzy PID control is introduced to feed back the misalignment of Schwarzschild objective to the control system to drive the stage which holds the objective. So the position can be adjusted automatically. This feedback scheme can adjust the variables' universe self-adaptively by using fuzzy rules so that the concrete function and parameters of the contraction-expansion factor are not necessary. Finally, the proposed approach is demonstrated by simulations. The results show that, variable universe fuzzy PID method exhibits better performance in both improving response speed and decreasing overshoot compared to conventional PID and fuzzy PID control methods. In addition, the interference signal can be effectively restrained. It is concluded that this method can improve the dynamic and static properties of system and meet the requirement of fast response.
Application of Fuzzy PID Control in Marine Hydraulic Crane
Zhonghui Luo; Yuzhong Li; Qijun Xiao
2010-01-01
\\u000a This paper has proposed the application of fuzzy PID control strategy in marine hydraulic crane. This crane has been applied\\u000a in ships operating in the ocean, featuring the function of heave compensation. This paper presents the software and hardware\\u000a structure of this control system, as well as the fuzzy PID control algorithm whose validity has been approved by emulation\\u000a and
Embedded fuzzy-control system for machining processes
R. E. Haber; J. R. Alique; A. Alique; J. Hernández; R. Uribe-Etxebarria
2003-01-01
In this paper a fuzzy-control system has been designed, implemented and embedded in an open CNC. The integration process, design steps and results of applying an embedded fuzzy-control system are shown through the example of real machining operations. The controller uses internal CNC signals (i.e. spindle-motor current) that are gathered and mathematically processed by means of an integrated application. The
Neuro-fuzzy control of an MDOF building with a magnetorheological damper using acceleration feedback
Schurter, Kyle Christopher
2000-01-01
Parameter specification of a fuzzy inference system (HS) with the aid of artificial neural networks allows the creation of complex, multi-dimensional models that are computationally efficient and numerically robust. An adaptive neuro-fuzzy inference...
Motor flux minimization controller based on fuzzy logic control for DTC AC drives
Eleftheria S. Sergaki
2010-01-01
This work presents a real time optimum flux adjustment fuzzy controller is cooperated with Direct Torque Control (DTC) adjustable speed drive (ASD) for ac Induction motors, in order to achieve the motor drive loss minimization while meeting the load and speed demands. The proposed control scheme includes a DTC controller, a speed controller and an optimum flux adjustment fuzzy control
Simulation of vehicle stability control system using fuzzy PI control method
You Dei Li; Wei Liu; Jing Li; Zhi Min Ma; Jia Cai Zhang
2005-01-01
In this study, vehicle stability control system is studied by using linear two degrees of freedom vehicle model. Vehicle stability control strategy is based on the direct yaw moment control that adopts the fuzzy PI controller. Compared with conventional PI control, the fuzzy PI control can regulate the proportional and integral parameters and improve the responds of system. The direct
Reliable LQ fuzzy control for nonlinear discrete-time systems via LMIs
Huai-ning Wu
2004-01-01
This paper studies the reliable linear quadratic (LQ) fuzzy regulator problem for nonlinear discrete-time systems with actuator faults. The Takagi-Sugeno fuzzy model is employed to represent a nonlinear system. A sufficient condition expressed in linear matrix inequality (LMI) terms for the existence of reliable guaranteed cost (GC) fuzzy controllers is obtained. The fuzzy controller directly obtained from the LMI solutions
An Advanced Control Method for ABS Fuzzy Control System
Jing-ming Zhang; Bao-yu Song; Gang Sun
2008-01-01
The purpose of an antilock braking system (ABS) is to prevent wheel lockup under heavy braking conditions on any type of road surface. In this study, multifactor input, dphi\\/ds and (dphi\\/ds)\\/dt, are proposed in the fuzzy controller. This new method not only offers a better braking performance, but also improves the pedal pushing feeling. At last, the design is simulated
Nonlinear rescaling of control values simplifies fuzzy control
NASA Technical Reports Server (NTRS)
Vanlangingham, H.; Tsoukkas, A.; Kreinovich, V.; Quintana, C.
1993-01-01
Traditional control theory is well-developed mainly for linear control situations. In non-linear cases there is no general method of generating a good control, so we have to rely on the ability of the experts (operators) to control them. If we want to automate their control, we must acquire their knowledge and translate it into a precise control strategy. The experts' knowledge is usually represented in non-numeric terms, namely, in terms of uncertain statements of the type 'if the obstacle is straight ahead, the distance to it is small, and the velocity of the car is medium, press the brakes hard'. Fuzzy control is a methodology that translates such statements into precise formulas for control. The necessary first step of this strategy consists of assigning membership functions to all the terms that the expert uses in his rules (in our sample phrase these words are 'small', 'medium', and 'hard'). The appropriate choice of a membership function can drastically improve the quality of a fuzzy control. In the simplest cases, we can take the functions whose domains have equally spaced endpoints. Because of that, many software packages for fuzzy control are based on this choice of membership functions. This choice is not very efficient in more complicated cases. Therefore, methods have been developed that use neural networks or generic algorithms to 'tune' membership functions. But this tuning takes lots of time (for example, several thousands iterations are typical for neural networks). In some cases there are evident physical reasons why equally space domains do not work: e.g., if the control variable u is always positive (i.e., if we control temperature in a reactor), then negative values (that are generated by equal spacing) simply make no sense. In this case it sounds reasonable to choose another scale u' = f(u) to represent u, so that equal spacing will work fine for u'. In the present paper we formulate the problem of finding the best rescaling function, solve this problem, and show (on a real-life example) that after an optimal rescaling, the un-tuned fuzzy control can be as good as the best state-of-art traditional non-linear controls.
Automatic Rule Tuning of a Fuzzy Logic Controller Using Particle Swarm Optimisation
Gu Fang; Ngai Ming Kwok; Dalong Wang
2010-01-01
\\u000a While fuzzy logic controllers (FLCs) are developed to exploit human expert knowledge in designing control systems, the actual\\u000a establishment of fuzzy rules and tuning of fuzzy membership functions are usually a time consuming exercise. In this paper\\u000a a technique, based on the particle swarm optimisation (PSO), is employed to automatically tune the fuzzy rules of a Mamdani-type\\u000a of fuzzy controller.
TS Fuzzy Control of Magnetic Levitation Systems Using QEA
Gwo-Ruey Yu; Yu-Jie Huang
2009-01-01
This paper proposed the design of T-S fuzzy control for magnetic levitation systems. The maglev systems are linearized at the equilibrium point first. Then the error state equations are derived and the proportional integral (PI) controller is applied to eliminate the steady-state tracking error. The nonlinear dynamic equations of the magnetic levitation systems are represented by a T-S fuzzy model.
Adaptive sequential controller
El-Sharkawi, Mohamed A. (Renton, WA); Xing, Jian (Seattle, WA); Butler, Nicholas G. (Newberg, OR); Rodriguez, Alonso (Pasadena, CA)
1994-01-01
An adaptive sequential controller (50/50') for controlling a circuit breaker (52) or other switching device to substantially eliminate transients on a distribution line caused by closing and opening the circuit breaker. The device adaptively compensates for changes in the response time of the circuit breaker due to aging and environmental effects. A potential transformer (70) provides a reference signal corresponding to the zero crossing of the voltage waveform, and a phase shift comparator circuit (96) compares the reference signal to the time at which any transient was produced when the circuit breaker closed, producing a signal indicative of the adaptive adjustment that should be made. Similarly, in controlling the opening of the circuit breaker, a current transformer (88) provides a reference signal that is compared against the time at which any transient is detected when the circuit breaker last opened. An adaptive adjustment circuit (102) produces a compensation time that is appropriately modified to account for changes in the circuit breaker response, including the effect of ambient conditions and aging. When next opened or closed, the circuit breaker is activated at an appropriately compensated time, so that it closes when the voltage crosses zero and opens when the current crosses zero, minimizing any transients on the distribution line. Phase angle can be used to control the opening of the circuit breaker relative to the reference signal provided by the potential transformer.
I. Bar-Kana; A. Guez
1989-01-01
A neuromorphic computing architecture for adaptive control of a class of nonlinear systems is presented. Starting with some prior assumptions about stabilizability of the plants, a stable unsupervised architecture is obtained. It is a parallel distributed architecture, and it is shown that it provides bounded tracking and asymptotic regulation, following a class of teacher models. The feasibility of the method
On methods for improving performance of PI-type fuzzy logic controllers
Jihong Lee
1993-01-01
To improve limitations of fuzzy PI controller especially when applied to high order systems, we propose two types of fuzzy logic controllers that take out appropriate amounts of accumulated control input according to fuzzily described situations in addition to the incremental control input calculated by conventional fuzzy PI controllers. The structures of the proposed controller were motivated by the problems
Neuro-fuzzy controller to navigate an unmanned vehicle.
Selma, Boumediene; Chouraqui, Samira
2013-12-01
A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Fuzzy Logic Controller can very well describe the desired system behavior with simple "if-then" relations owing the designer to derive "if-then" rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, an artificial neural network fuzzy inference system (ANFIS) controller is described and implemented to navigate the autonomous vehicle. Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous methods like Artificial Neural Network (ANN). PMID:23705105
Signal Control for Oversaturated Intersections Using Fuzzy Logic
Lin Zhang; Panos D. Prevedouros
ABSTRACT The fuzzy logic controller (FLC) presented in this paper simulates the control logic of experienced human traffic controllers such as police officers who,supersede signal controls at over -saturated intersections during special events. Given real -time traffic information, the FLC controller decides on whether to
G. C. D. Sousa; Bimal K. Bose; Kyung S. Kim
1993-01-01
Slip gain tuning of indirect vector controlled induction motor drive has been a subject of intense research interest in recent years. The paper proposes the fuzzy logic based online tuning of slip gain using the standard model reference adaptive control (MRAC) technique. MRAC methods based on reactive power and D-axis voltage are combined together with a weighting factor that is
NASA Astrophysics Data System (ADS)
Reif, Konrad
Die adaptive Fahrgeschwindigkeitsregelung (ACC, Adaptive Cruise Control) ist eine Weiterentwicklung der konventionellen Fahrgeschwindigkeitsregelung, die eine konstante Fahrgeschwindigkeit einstellt. ACC überwacht mittels eines Radarsensors den Bereich vor dem Fahrzeug und passt die Geschwindigkeit den Gegebenheiten an. ACC reagiert auf langsamer vorausfahrende oder einscherende Fahrzeuge mit einer Reduzierung der Geschwindigkeit, sodass der vorgeschriebene Mindestabstand zum vorausfahrenden Fahrzeug nicht unterschritten wird. Hierzu greift ACC in Antrieb und Bremse ein. Sobald das vorausfahrende Fahrzeug beschleunigt oder die Spur verlässt, regelt ACC die Geschwindigkeit wieder auf die vorgegebene Sollgeschwindigkeit ein (Bild 1). ACC steht somit für eine Geschwindigkeitsregelung, die sich dem vorausfahrenden Verkehr anpasst.
Fuzzy control of parabolic antenna with backlash compensation
NASA Astrophysics Data System (ADS)
Ahmed, Mohammed; Noor, Samsul Bahari B. Mohd
2015-05-01
A fuzzy logic based controller (FLC) was proposed for position control of a parabolic dish antenna system with the major aim of eradicating the effect backlash disturbance which may be present in the system. The disturbance is nonlinear and is capable of generating steady state positional errors. Simulation results obtained using SIMULINK/MATLAB 2012a were compared with those obtained when the controller was proportional-derivative controller (PDC). The fuzzy controller portrays that it has the capability of reducing the noise due to backlash and possibly others more than the proportional-derivative controller.
Implementation of a new fuzzy vector control of induction motor.
Rafa, Souad; Larabi, Abdelkader; Barazane, Linda; Manceur, Malik; Essounbouli, Najib; Hamzaoui, Abdelaziz
2014-05-01
The aim of this paper is to present a new approach to control an induction motor using type-1 fuzzy logic. The induction motor has a nonlinear model, uncertain and strongly coupled. The vector control technique, which is based on the inverse model of the induction motors, solves the coupling problem. Unfortunately, in practice this is not checked because of model uncertainties. Indeed, the presence of the uncertainties led us to use human expertise such as the fuzzy logic techniques. In order to maintain the decoupling and to overcome the problem of the sensitivity to the parametric variations, the field-oriented control is replaced by a new block control. The simulation results show that the both control schemes provide in their basic configuration, comparable performances regarding the decoupling. However, the fuzzy vector control provides the insensitivity to the parametric variations compared to the classical one. The fuzzy vector control scheme is successfully implemented in real-time using a digital signal processor board dSPACE 1104. The efficiency of this technique is verified as well as experimentally at different dynamic operating conditions such as sudden loads change, parameter variations, speed changes, etc. The fuzzy vector control is found to be a best control for application in an induction motor. PMID:24629620
Fuzzy system tuned PI controller for a benchmark drum-boiler model
Kai-Pong Cheung; Li-Xin Wang
1998-01-01
In this paper, we design fuzzy systems to tune the parameters of the PI controllers online and apply them to a benchmark drum-boiler model. We compare the pure PI controllers with the fuzzy system tuned PI controllers for setpoint tracking and disturbance rejection. The fuzzy system tuned PI controllers show better performance than the pure PI controllers
Fuzzy fractional order sliding mode controller for nonlinear systems
NASA Astrophysics Data System (ADS)
Delavari, H.; Ghaderi, R.; Ranjbar, A.; Momani, S.
2010-04-01
In this paper, an intelligent robust fractional surface sliding mode control for a nonlinear system is studied. At first a sliding PD surface is designed and then, a fractional form of these networks PD?, is proposed. Fast reaching velocity into the switching hyperplane in the hitting phase and little chattering phenomena in the sliding phase is desired. To reduce the chattering phenomenon in sliding mode control (SMC), a fuzzy logic controller is used to replace the discontinuity in the signum function at the reaching phase in the sliding mode control. For the problem of determining and optimizing the parameters of fuzzy sliding mode controller (FSMC), genetic algorithm (GA) is used. Finally, the performance and the significance of the controlled system two case studies (robot manipulator and coupled tanks) are investigated under variation in system parameters and also in presence of an external disturbance. The simulation results signify performance of genetic-based fuzzy fractional sliding mode controller.
Intelligent fuzzy immune PID controller design for multivariable process control system
Zheng Li
2010-01-01
Based on biological immune principle and fuzzy theory, this paper presents an intelligent fuzzy immune PID control scheme to solve the control difficulties of industry process with multi-variables. The least square algorithm was used for offline optimization to form immune feedback control system. The application on cement rotary kiln control was discussed in detail as an example. The rotary kiln
Power Network Control Using Neuro-Fuzzy Term-Rewriting Adam Steele Ashley Morris
Steele, Adam
Power Network Control Using Neuro-Fuzzy Term-Rewriting Adam Steele Ashley Morris DePaul University in a uniform fashion. We will show how implementing a neuro- fuzzy solution will not only provide a more and Simulation, Fuzzy CLIPS, Neuro-Fuzzy Introduction Power networks are a classic example of a large-scale re
Intelligent Network Control Using Neuro-Fuzzy Term-Rewriting Adam Steele Ashley Morris
Steele, Adam
Intelligent Network Control Using Neuro-Fuzzy Term-Rewriting Adam Steele Ashley Morris De in a uniform fashion. We will show how implementing a neuro- fuzzy solution will not only provide a more and Simulation, Fuzzy CLIPS, Neuro-Fuzzy Introduction Power networks are a classic example of a large-scale re
Modeling and control of carbon monoxide concentration using a neuro-fuzzy technique
Kazuo Tanaka; Manabu Sano; Hiroyuki Watanabe
1995-01-01
Modeling and control of carbon monoxide (CO) concentration using a neuro-fuzzy technique are discussed. A self-organizing fuzzy identification algorithm (SOFIA) for identifying complex systems such as CO concentration is proposed. The main purpose of SOFIA is to reduce the computational requirement for identifying a fuzzy model. In particular, the authors simplify a procedure for finding the optimal structure of fuzzy
A QoS-Guaranteed Fuzzy Channel Allocation Controller for Hierarchical Cellular Systems
Chung-Ju Chang; Cooper Chang; C. Bernard Shung
2000-01-01
This paper proposes a fuzzy channel allocation controller (FCAC) for hierarchical cellular systems. The FCAC mainly contains a fuzzy channel allocation processor (FCAP) which is designed to be in a two-layer architecture that consists of a fuzzy admission threshold estimator in the first layer and a fuzzy channel allocator in the second layer. The FCAP chooses the handoff failure probability,
A QoS-guaranteed fuzzy channel allocation controller for hierarchical cellular systems
Kuen-Rong Lo; Chung-Ju Chang; C. Chang; C. B. Shung
2000-01-01
This paper proposes a fuzzy channel allocation controller (FCAC) for hierarchical cellular systems. The FCAC mainly contains a fuzzy channel allocation processor (FCAP) which is designed to be in a two-layer architecture that consists of a fuzzy admission threshold estimator in the first layer and a fuzzy channel allocator in the second layer. The FCAP chooses the handoff failure probability,
M. Hanna; A. Buck; R. Smith
1995-01-01
The paper presents a new mathematical definition for fuzzy timed Petri nets. It describes how synergistic fuzzy timed Petri nets can be employed for modelling, monitoring and control the product quality in a CNC milling machining centre. The synergistic technique utilises two fuzzy timed Petri nets and fuzzy timed Petri net combiner in order to identify the quality of surface
Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators
Jean Bosco Mbede; Xinhan Huang; Min Wang
2003-01-01
A new robust neuro-fuzzy controller for autonomous and intelligent robot manipulators in dynamic and partially known environments containing moving obstacles is presented. The navigation is based on a fuzzy technique for the idea of artificial potential fields (APFs) using analytic harmonic functions. Unlike the fuzzy technique, the development of APFs is computationally intensive. A computationally efficient processing scheme for fuzzy
Design of fuzzy controller for induction heating using DSP
Chin-Hsing Cheng
2010-01-01
This paper presents the development of fuzzy controller for induction heating temperature control. Induction heating coil can be controlled by single phase sinusoidal pulse width modulation (SPWM) digital signal processor (DSP)-based inverter. DSP is used to store the required commands for generating the necessary waveforms to control the frequency of the inverter through proper design of switching pulses. The SPWM
Fuzzy Logic Decoupled Lateral Control for General Aviation Airplanes
NASA Technical Reports Server (NTRS)
Duerksen, Noel
1997-01-01
It has been hypothesized that a human pilot uses the same set of generic skills to control a wide variety of aircraft. If this is true, then it should be possible to construct an electronic controller which embodies this generic skill set such that it can successfully control different airplanes without being matched to a specific airplane. In an attempt to create such a system, a fuzzy logic controller was devised to control aileron or roll spoiler position. This controller was used to control bank angle for both a piston powered single engine aileron equipped airplane simulation and a business jet simulation which used spoilers for primary roll control. Overspeed, stall and overbank protection were incorporated in the form of expert systems supervisors and weighted fuzzy rules. It was found that by using the artificial intelligence techniques of fuzzy logic and expert systems, a generic lateral controller could be successfully used on two general aviation aircraft types that have very different characteristics. These controllers worked for both airplanes over their entire flight envelopes. The controllers for both airplanes were identical except for airplane specific limits (maximum allowable airspeed, throttle ]ever travel, etc.). This research validated the fact that the same fuzzy logic based controller can control two very different general aviation airplanes. It also developed the basic controller architecture and specific control parameters required for such a general controller.
Fuzzy controller synthesis for an inverted pendulum system
S. Yurkovich; M. Widjaja
1996-01-01
A frequently discussed issue in the use of fuzzy systems for control design is related to the ad hoc nature by which controller synthesis is performed, where incorporation of the designer's knowledge into the synthesis procedure is often not straightforward. This paper describes a controller synthesis procedure based on the idea of expanding the usable region of a linear control
Reinforcement Ant Optimized Fuzzy Controller for Mobile-Robot Wall-Following Control
Chia-Feng Juang; Chia-Hung Hsu
2009-01-01
This paper proposes a reinforcement ant optimized fuzzy controller (FC) design method, called RAOFC, and applies it to wheeled-mobile-robot wall-following control under reinforcement learning environments. The inputs to the designed FC are range-finding sonar sensors, and the controller output is a robot steering angle. The antecedent part in each fuzzy rule uses interval type-2 fuzzy sets in order to increase
Fuzzy Control Strategy of Sub-mini Underwater Robots in Rectifying Control
Yuyi Zhai; Liang Liu; Lei Wang; Zhenbang Gong
2009-01-01
In this paper, fuzzy control strategy is used to improve the stability of sub-mini underwater robots. The sub-mini underwater robots' fuzzy control method in the horizontal plane is mainly discussed here. Based on the language description of the model and according to the closed-loop system's dynamic model, the fuzzy controller language is educed with the inversion of the language model.
A stable self-organizing fuzzy controller for robotic motion control
Shiuh-Jer Huang; Ji-Shin Lee
2000-01-01
It is well known that robotic manipulators are highly nonlinear coupling dynamic systems. It is difficult to establish an appropriate mathematical model for the design of a model-based controller. Although fuzzy logic control has a model-free feature, it still needs time-consuming work for the rules bank and fuzzy parameters adjustment. In this paper, a stable self-organizing fuzzy controller (SOFC) is
An adaptive neuro-fuzzy system for automatic image segmentation and edge detection
Victor Boskovitz; Hugo Guterman
2002-01-01
An autoadaptive neuro-fuzzy segmentation and edge detection architecture is presented. The system consists of a multilayer perceptron (MLP)-like network that performs image segmentation by adaptive thresholding of the input image using labels automatically pre-selected by a fuzzy clustering technique. The proposed architecture is feedforward, but unlike the conventional MLP the learning is unsupervised. The output status of the network is
Application of Fuzzy Logic for Adaptive Interference Canceller in CDMA Systems
Yung-fa Huang; Ping-ho Ting; Tan-hsu Tan
2007-01-01
In this paper, the performance of the proposed fuzzy logic parallel interference cancellation (FLPIC) multiuser detector is\\u000a evaluated for frequency-selective fading channels in wireless CDMA communication systems. A modified fuzzy logic system (FLS)\\u000a with an adequate scaling factor (SF) is proposed to infer adequate partial factors (PFs) for the PIC scheme. Simulation results\\u000a show that the proposed FLS can adapt
Zhu Feng; Song Yuqing; Chen Jianmei
2010-01-01
The application of fuzzy c-means algorithm to image segmentation is not taking into account spatial information apart from intensity values , which will lead a misclassification on the boundaries and inhomogeneous regions with noises.In order to solve this problem, a new image segmentation method is proposed using adaptive spatially median neighborhood information and fuzzy c-means algorithm in this paper. First,
Comparison and application of three integral-improved methods on conventional fuzzy control strategy
Y. B. He; G. H. Lim; P. S. K. Chua
2003-01-01
This paper presents three integral methods of a fuzzy knowledge-based control system, to diminish the steady-state output errors. The three controllers, viz the three-input fuzzy controller, fuzzy plus integral controller and are fuzzy-PI controller, have been described and are applied to the two systems. The first is a zero-type-number first-order system whose input is step, and the other is a
Application of a fuzzy learning algorithm to nuclear steam generator level control
Gee Yong Park; Poong Hyun Seong
1995-01-01
In order to reduce the load of tuning works by trial-and-error for obtaining the best control performance of conventional fuzzy control algorithm, a fuzzy control algorithm with learning function is investigated in this work. This fuzzy control algorithm can make its rule base and tune the membership functions automatically by use of learning function which needs the data from the
Performance comparison of alternative fuzzy control modalities
House, Corey Dean
1998-01-01
set functions used, differing only in their normalization. Probability functions are amplitude normalized to unity. Probability density functions are volume normalized to unity. Fuzzy Logic performs mathematical operations similar... to that of Probabilistic Logic and for the same purposes. In Fuzzy Logic, the probability functions are replaced by so-called "membership functions. " The pointwise mathematical manipulations of the set functions use the "multiply" and "minimum" operators, alternatively...
Fuzzy Control of Flexible-Link Manipulators: A Review
NASA Technical Reports Server (NTRS)
Akbarzadeh-T, M.-R.; Quintana, S.; Jamshidi, M.
1998-01-01
Several recent research efforts are reviewed here which have applied fuzzy logic in control of flexible-link manipulators. A flexible robot is a distributed parameter system represented by complex nonlinear dynamics, its actuator and the control parameters are non-colocated, and lastly, unstructured/unknown parameters play a significant role in model dynamics of a flexible robot operating in the real world. As a result, control of flexible robots is considered a promising area for application of intelligent control methodologies such as fuzzy logic, genetic algorithms, and neural networks.
Composite fuzzy sliding mode control of nonlinear singularly perturbed systems.
Nagarale, Ravindrakumar M; Patre, B M
2014-05-01
This paper deals with the robust asymptotic stabilization for a class of nonlinear singularly perturbed systems using the fuzzy sliding mode control technique. In the proposed approach the original system is decomposed into two subsystems as slow and fast models by the singularly perturbed method. The composite fuzzy sliding mode controller is designed for stabilizing the full order system by combining separately designed slow and fast fuzzy sliding mode controllers. The two-time scale design approach minimizes the effect of boundary layer system on the full order system. A stability analysis allows us to provide sufficient conditions for the asymptotic stability of the full order closed-loop system. The simulation results show improved system performance of the proposed controller as compared to existing methods. The experimentation results validate the effectiveness of the proposed controller. PMID:24636524
Autonomous vehicle motion control, approximate maps, and fuzzy logic
NASA Technical Reports Server (NTRS)
Ruspini, Enrique H.
1993-01-01
Progress on research on the control of actions of autonomous mobile agents using fuzzy logic is presented. The innovations described encompass theoretical and applied developments. At the theoretical level, results of research leading to the combined utilization of conventional artificial planning techniques with fuzzy logic approaches for the control of local motion and perception actions are presented. Also formulations of dynamic programming approaches to optimal control in the context of the analysis of approximate models of the real world are examined. Also a new approach to goal conflict resolution that does not require specification of numerical values representing relative goal importance is reviewed. Applied developments include the introduction of the notion of approximate map. A fuzzy relational database structure for the representation of vague and imprecise information about the robot's environment is proposed. Also the central notions of control point and control structure are discussed.
An architecture for designing fuzzy logic controllers using neural networks
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.
1991-01-01
Described here is an architecture for designing fuzzy controllers through a hierarchical process of control rule acquisition and by using special classes of neural network learning techniques. A new method for learning to refine a fuzzy logic controller is introduced. A reinforcement learning technique is used in conjunction with a multi-layer neural network model of a fuzzy controller. The model learns by updating its prediction of the plant's behavior and is related to the Sutton's Temporal Difference (TD) method. The method proposed here has the advantage of using the control knowledge of an experienced operator and fine-tuning it through the process of learning. The approach is applied to a cart-pole balancing system.
Application of genetic algorithms to tuning fuzzy control systems
NASA Technical Reports Server (NTRS)
Espy, Todd; Vombrack, Endre; Aldridge, Jack
1993-01-01
Real number genetic algorithms (GA) were applied for tuning fuzzy membership functions of three controller applications. The first application is our 'Fuzzy Pong' demonstration, a controller that controls a very responsive system. The performance of the automatically tuned membership functions exceeded that of manually tuned membership functions both when the algorithm started with randomly generated functions and with the best manually-tuned functions. The second GA tunes input membership functions to achieve a specified control surface. The third application is a practical one, a motor controller for a printed circuit manufacturing system. The GA alters the positions and overlaps of the membership functions to accomplish the tuning. The applications, the real number GA approach, the fitness function and population parameters, and the performance improvements achieved are discussed. Directions for further research in tuning input and output membership functions and in tuning fuzzy rules are described.
Genetic optimization of fuzzy fractional PD+I controllers.
Jesus, Isabel S; Barbosa, Ramiro S
2015-07-01
Fractional order calculus is a powerful emerging mathematical tool in science and engineering. There is currently an increasing interest in generalizing classical control theories and developing novel control strategies. The genetic algorithms (GA) are a stochastic search and optimization methods based on the reproduction processes found in biological systems, used for solving engineering problems. In the context of process control, the fuzzy logic usually means variables that are described by imprecise terms, and represented by quantities that are qualitative and vague. In this article we consider the development of an optimal fuzzy fractional PD+I controller in which the parameters are tuned by a GA. The performance of the proposed fuzzy fractional control is illustrated through some application examples. PMID:25661162
Analysis and design for a class of complex control systems part II: Fuzzy controller design
S. G. Cao; N. W. Rees; G. Feng
1997-01-01
In this second paper on the analysis and design of complex control systems, we present a controller design method for a class of complex control systems. This class of systems can be represented by a discrete-time dynamical fuzzy model as discussed in Part I, the companion paper. A necessary and sufficient condition for stabilization of this kind of discrete-time fuzzy
Application of fuzzy sliding mode control to a command interceptor
Y. Z. Elhalwagy; M. Tarbouchi
2002-01-01
This paper is concerned with the application of robust sliding mode control to highly nonlinear dynamical system. Extensive research efforts have been made to design or improve flight guidance and control systems. With the phenomenal growth in soft computing techniques, there is a growing interest to apply these techniques to missile flight guidance and control systems. A fuzzy sliding mode
Fuzzy Controller for Automatic Drug Infusion in Cardiac Patients
M. Logesh Kumar; R. Harikumar; A. Keerthi Vasan; V. K. Sudhaman
2009-01-01
Control of mean arterial blood pressure and cardiac output is highly desirable in certain operative procedures and in post cardiac operation. This paper emphasizes on a fuzzy controller to control these two variables within the present limits by administering three drugs dopamine, Sodium Nitro Prusside and Phenylephrine which perform the function of increasing heartbeat rate, decreases, increases blood pressure respectively.
Fuzzy controller of drum water level for Industrial boile
Zang Haihe; Wang Li; Yu Xinjun
2010-01-01
Drum water level control of Industrial boiler is one of main technical specifications for boiler control system. Because of the phenomenon of False Water Level (FWL), according to the human thinking processes in manual adjustment, three measurement signals, i.e. drum water level, water supply flow and steam flow are introduced, and a fuzzy control algorithm is used to calculate the
An Approach to Supervisory Control of an Energy Management Control System Using Fuzzy Logic
Langari, R.
1997-01-01
AN APPROACH TO SUPERVISORY CONTROL OF AN ENERGY MANAGEMENT CONTROL SYSTEM USING FUZZY LOGIC Reza Langari Center for Fuzzy Logic, Robotics and Intelligent Systems Research and Department of Mechanical Engineering Texas A&M University... use of fuzzy logic as its underlying mechanism for representation and processing of information. The rationale here is that the apparently large number of conflicting perfor mance objectives often associated with complex multi-stage processes can...
Adaptive neural-based fuzzy modeling for biological systems.
Wu, Shinq-Jen; Wu, Cheng-Tao; Chang, Jyh-Yeong
2013-04-01
The inverse problem of identifying dynamic biological networks from their time-course response data set is a cornerstone of systems biology. Hill and Michaelis-Menten model, which is a forward approach, provides local kinetic information. However, repeated modifications and a large amount of experimental data are necessary for the parameter identification. S-system model, which is composed of highly nonlinear differential equations, provides the direct identification of an interactive network. However, the identification of skeletal-network structure is challenging. Moreover, biological systems are always subject to uncertainty and noise. Are there suitable candidates with the potential to deal with noise-contaminated data sets? Fuzzy set theory is developed for handing uncertainty, imprecision and complexity in the real world; for example, we say "driving speed is high" wherein speed is a fuzzy variable and high is a fuzzy set, which uses the membership function to indicate the degree of a element belonging to the set (words in Italics to denote fuzzy variables or fuzzy sets). Neural network possesses good robustness and learning capability. In this study we hybrid these two together into a neural-fuzzy modeling technique. A biological system is formulated to a multi-input-multi-output (MIMO) Takagi-Sugeno (T-S) fuzzy system, which is composed of rule-based linear subsystems. Two kinds of smooth membership functions (MFs), Gaussian and Bell-shaped MFs, are used. The performance of the proposed method is tested with three biological systems. PMID:23376801
Adaptive control for accelerators
Eaton, Lawrie E. (Los Alamos, NM); Jachim, Stephen P. (Los Alamos, NM); Natter, Eckard F. (Santa Fe, NM)
1991-01-01
An adaptive feedforward control loop is provided to stabilize accelerator beam loading of the radio frequency field in an accelerator cavity during successive pulses of the beam into the cavity. A digital signal processor enables an adaptive algorithm to generate a feedforward error correcting signal functionally determined by the feedback error obtained by a beam pulse loading the cavity after the previous correcting signal was applied to the cavity. Each cavity feedforward correcting signal is successively stored in the digital processor and modified by the feedback error resulting from its application to generate the next feedforward error correcting signal. A feedforward error correcting signal is generated by the digital processor in advance of the beam pulse to enable a composite correcting signal and the beam pulse to arrive concurrently at the cavity.
Manansala, E.C.
1989-01-01
This work presents a centralized control scheme applied to a power system. The scheme has adaptive characteristics which allow the controller to keep track of the changing power system operating point and to control nonlinear functions of state variables. Feedback to the controller is obtained from phasor measurements at chosen power system buses, generator field voltage measurements, and state estimators. Control effort is aimed at minimizing the oscillations and influencing the power system state trajectory through the control of linear and nonlinear functions of state variables during a power system disturbance. The main contributions of this dissertation are the simultaneous introduction and utilization of measurement based terms in the state and output equations in the derivation and implementation of the control law, the study of limits on controller performance as the state residual vector becomes very large, and the simulation of the performance of local state estimators to prove the need for faster phasor measurement systems. The test system is a hypothetical 39-Bus AC power system consisting of typical components which have been sufficiently modelled for the simulation of power system performance in a dynamic stability study.
Adaptive nonlinear flight control
NASA Astrophysics Data System (ADS)
Rysdyk, Rolf Theoduor
1998-08-01
Research under supervision of Dr. Calise and Dr. Prasad at the Georgia Institute of Technology, School of Aerospace Engineering. has demonstrated the applicability of an adaptive controller architecture. The architecture successfully combines model inversion control with adaptive neural network (NN) compensation to cancel the inversion error. The tiltrotor aircraft provides a specifically interesting control design challenge. The tiltrotor aircraft is capable of converting from stable responsive fixed wing flight to unstable sluggish hover in helicopter configuration. It is desirable to provide the pilot with consistency in handling qualities through a conversion from fixed wing flight to hover. The linear model inversion architecture was adapted by providing frequency separation in the command filter and the error-dynamics, while not exiting the actuator modes. This design of the architecture provides for a model following setup with guaranteed performance. This in turn allowed for convenient implementation of guaranteed handling qualities. A rigorous proof of boundedness is presented making use of compact sets and the LaSalle-Yoshizawa theorem. The analysis allows for the addition of the e-modification which guarantees boundedness of the NN weights in the absence of persistent excitation. The controller is demonstrated on the Generic Tiltrotor Simulator of Bell-Textron and NASA Ames R.C. The model inversion implementation is robustified with respect to unmodeled input dynamics, by adding dynamic nonlinear damping. A proof of boundedness of signals in the system is included. The effectiveness of the robustification is also demonstrated on the XV-15 tiltrotor. The SHL Perceptron NN provides a more powerful application, based on the universal approximation property of this type of NN. The SHL NN based architecture is also robustified with the dynamic nonlinear damping. A proof of boundedness extends the SHL NN augmentation with robustness to unmodeled actuator dynamics. The architecture is demonstrated on the XV-15 tiltrotor.
Fuzzy logic control system to provide autonomous collision avoidance for Mars rover vehicle
NASA Technical Reports Server (NTRS)
Murphy, Michael G.
1990-01-01
NASA is currently involved with planning unmanned missions to Mars to investigate the terrain and process soil samples in advance of a manned mission. A key issue involved in unmanned surface exploration on Mars is that of supporting autonomous maneuvering since radio communication involves lengthy delays. It is anticipated that specific target locations will be designated for sample gathering. In maneuvering autonomously from a starting position to a target position, the rover will need to avoid a variety of obstacles such as boulders or troughs that may block the shortest path to the target. The physical integrity of the rover needs to be maintained while minimizing the time and distance required to attain the target position. Fuzzy logic lends itself well to building reliable control systems that function in the presence of uncertainty or ambiguity. The following major issues are discussed: (1) the nature of fuzzy logic control systems and software tools to implement them; (2) collision avoidance in the presence of fuzzy parameters; and (3) techniques for adaptation in fuzzy logic control systems.
Full design of fuzzy controllers using genetic algorithms
NASA Technical Reports Server (NTRS)
Homaifar, Abdollah; Mccormick, ED
1992-01-01
This paper examines the applicability of genetic algorithms (GA) in the complete design of fuzzy logic controllers. While GA has been used before in the development of rule sets or high performance membership functions, the interdependence between these two components dictates that they should be designed together simultaneously. GA is fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. We show the application of this new method to the development of a cart controller.
Full design of fuzzy controllers using genetic algorithms
NASA Technical Reports Server (NTRS)
Homaifar, Abdollah; Mccormick, ED
1992-01-01
This paper examines the applicability of genetic algorithms in the complete design of fuzzy logic controllers. While GA has been used before in the development of rule sets or high performance membership functions, the interdependence between these two components dictates that they should be designed together simultaneously. GA is fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. We show the application of this new method to the development of a cart controller.
Reduced order adaptive controller studies
NASA Technical Reports Server (NTRS)
Johnson, C. R., Jr.; Balas, M. J.
1980-01-01
The use of a reduced-order adaptive controller, can arise from a desire to reduce control complexity with a commensurate reduction in controller sensitivity or from necessity in an attempt to use a finite dimensional controller on an infinite dimensional system. An interest in developing adaptive controllers for flexible structures by application of existing lumped-parameter system (LPS) adaptive controller strategies to truncated expansion descriptions of the distributed parameter system (DPS) behavior of flexible structures has led to two qualitative descriptions of the misbehavior of reduced-order adaptive controllers. A summary is provided of these interpretations of the additional difficulties facing reduced-order adaptive controllers, which are bypassed by exact-order adaptive controllers. A test problem, which initializes attempts to quantify the qualitative insights, is also formulated.
Call Admission Control in Mobile Cellular CDMA Systems using Fuzzy Associative Memory
Sarkar, Dilip
S, Fuzzy Associative Memory, Call Admission Control, CDMA. I. INTRODUCTION WIRELESS cellular networksCall Admission Control in Mobile Cellular CDMA Systems using Fuzzy Associative Memory Rupenaguntla. Therefore, Call Admission Control (CAC) for maintaining ¡¤£ at § ¡¤£ is a very challenging task. A fuzzy
Singh, Pritpal
FUZZY LOGIC-BASED SOLAR CHARGE CONTROLLER FOR MICROBATTERIES Pritpal Singh and Jagadeesan of a micro- charge/discharge controller has not. In this paper we present a novel, fuzzy logic-based solar is adjusted by modulating the duty cycle of the buck converter's switching MOSFET using a fuzzy logic control
Fuzzy Logic Decoupled Longitudinal Control for General Aviation Airplanes
NASA Technical Reports Server (NTRS)
Duerksen, Noel
1996-01-01
It has been hypothesized that a human pilot uses the same set of generic skills to control a wide variety of aircraft. If this is true, then it should be possible to construct an electronic controller which embodies this generic skill set such that it can successfully control difference airplanes without being matched to a specific airplane. In an attempt to create such a system, a fuzzy logic controller was devised to control throttle position and another to control elevator position. These two controllers were used to control flight path angle and airspeed for both a piston powered single engine airplane simulation and a business jet simulation. Overspeed protection and stall protection were incorporated in the form of expert systems supervisors. It was found that by using the artificial intelligence techniques of fuzzy logic and expert systems, a generic longitudinal controller could be successfully used on two general aviation aircraft types that have very difference characteristics. These controllers worked for both airplanes over their entire flight envelopes including configuration changes. The controllers for both airplanes were identical except for airplane specific limits (maximum allowable airspeed, throttle lever travel, etc.). The controllers also handled configuration changes without mode switching or knowledge of the current configuration. This research validated the fact that the same fuzzy logic based controller can control two very different general aviation airplanes. It also developed the basic controller architecture and specific control parameters required for such a general controller.
Advanced Control Technology Development of Sulfuric Acid-Connecting System Based on Fuzzy Control
Yan Dong; Qin Bin
2010-01-01
The theme of the paper is treatment of discharges of sulfur dioxide from process of petrochemical, aiming at the problems of the long time, none linearity and the precise mathematic that is hard to build, we consider combining the advanced fuzzy control with the traditional automation technology to achieve low cost automatization. According to the principles of fuzzy control and
NASA Technical Reports Server (NTRS)
Ying, Hao
1993-01-01
The fuzzy controllers studied in this paper are the ones that employ N trapezoidal-shaped members for input fuzzy sets, Zadeh fuzzy logic and a centroid defuzzification algorithm for output fuzzy set. The author analytically proves that the structure of the fuzzy controllers is the sum of a global nonlinear controller and a local nonlinear proportional-integral-like controller. If N approaches infinity, the global controller becomes a nonlinear controller while the local controller disappears. If linear control rules are used, the global controller becomes a global two-dimensional multilevel relay which approaches a global linear proportional-integral (PI) controller as N approaches infinity.
Fuzzy logic control of a switched reluctance motor
M. G. Rodrigues; W. I. Suemitsu; P. Branco; J. A. Dente; L. G. B. Rolim
1997-01-01
This paper presents the use of fuzzy logic control (FLC) for switched reluctance motor (SRM) speed. The PLC performs a PI-like control strategy, giving the current reference variation based on speed error and its change. The performance of the drive system was evaluated through digital simulations through the toolbox Simulink of the Matlab program
Wind energy conversion systems using fuzzy sliding mode control
Qi Chen; LiangHai Chen; LinGao Wang
2011-01-01
The paper describes a manner in which the energy-reliability optimization of wind energy conversion system's operation can be achieved by means of the fuzzy sliding mode control. An appropriate sliding surface has been found in the speed-power plane, which allows the operation more or less close to the optimal regimes characteristic. What is more, by torque controlling the generator, an
A Framework for Fuzzy Logic based UAV Navigation and Control
Lefteris Doitsidis; Kimon P. Valavanis; Nikos Tsourveloudis; Michael Kontitsis
2004-01-01
A two module fuzzy logic controller that also includes a separate error calculating box is derived for autonomous navigation and control of small manned - unmanned aerial vehicles demonstrating ability to fly through specified waypoints in a 3-D environment repeatedly, perform trajectory tracking, and, duplicate \\/ follow another vehicle's trajectory. A MATLAB standard configuration environment and the Aerosim Aeronautical Simulation
Tuning a fuzzy controller using quadratic response surfaces
NASA Technical Reports Server (NTRS)
Schott, Brian; Whalen, Thomas
1992-01-01
Response surface methodology, an alternative method to traditional tuning of a fuzzy controller, is described. An example based on a simulated inverted pendulum 'plant' shows that with (only) 15 trial runs, the controller can be calibrated using a quadratic form to approximate the response surface.
Passivity and fuzzy control of singularly perturbed systems
G. Calcevt; R. GorezS; V. Wertz
1999-01-01
It is shown that the passivity of a singularly perturbed system is equivalent to the passivity of both the boundary layer system and the reduced (slow) system. This result is used to formulate a stability condition in terms of LMIs for a nonlinear singularly perturbed system controlled by a Takagi-Sugeno fuzzy controller
A fuzzy satisficing method for multiobjective linear optimal control problems
Masatoshi Sakawa; Masahiro Inuiguchi; Kosuke Kato; Tomohiro Ikeda
1996-01-01
In this paper, we propose a fuzzy satisficing method for the solution of multiobjective linear continuous optimal control problems. To solve these multiobjective linear continuous optimal control problems, we first discretize the time and replace the system of differential equations by difference equations. By introducing suitable auxiliary variables, approximate linear multiobjective programming problems are formulated. Then by considering the vague
Fuzzy control of blood pressure during anesthesia with isoflurane
R. Meier; J. Nieuwland; S. Hacisalihzade; D. Steck; A. Zbinden
1992-01-01
A fuzzy controller which controls the depth of anesthesia during surgery with isoflurane was designed and implemented on a personal computer. The mean arterial pressure (MAP) was taken as a measure for the depth of anesthesia. The design process was iterative and the reference points of the membership functions as well as the linguistic rules were determined by trial and
Fuzzy predictive control for nitrogen removal in biological wastewater treatment
Fuzzy predictive control for nitrogen removal in biological wastewater treatment S. Marsili wastewater is too low, full denitrification is difficult to obtain and an additional source of organic carbon predictive control; wastewater treatment plant Introduction The problem of improving the nitrogen removal
Fuzzy Role-Based Access Control Carles Martinez-Garciaa,
Navarro-Arribas, Guillermo
, Consejo Superior de Investigaciones CientÂ´ificas, Campus UAB s/n, 08193 Bellaterra, Spain Abstract RBAC of classical identity- based access control. However, despite the benefits of RBAC, there are environments in which RBAC can hardly be applied. We present FRBAC (Fuzzy Role-Based Access Control), a generalization
Fuzzy-immune PID control for AMB systems
Su Yixin; Li Xuan; Zhou Zude; Chen Youping; Zhang Danhong
2006-01-01
In order to improve the dynamic performance of active magnetic bearing systems with highly nonlinear and naturally unstable\\u000a dynamics, a new nonlinear fuzzy-immune proportional-integral-derivative (PID) controller is proposed by combining the immune\\u000a feedback law with linear PID control. This controller consists of a PID controller and a basic immune proportional controller\\u000a in cascaded connection, the nonlinear function of the immune
NASA Astrophysics Data System (ADS)
Abdurrahim, Mahabuba; Abdullah Khan, M.; Edriss, Ali Ahmed
2012-01-01
This paper presents a design procedure for a Robust and Adaptive Fuzzy Logic based Power System Stabilizer (RAFLPSS) to improve the small signal stability of Power System. The parameters of RAFLPSS are tuned by adaptive neural network. This RAFLPSS uses ANFIS network (Adaptive Network based Fuzzy Inference System) which provides a natural framework of multi-layered feed forward adaptive network using fuzzy logic inference system. In this approach, the hybrid-learning algorithm tunes the fuzzy rules and the membership functions of the RAFLPSS. The dynamic performance of SMIB system with the proposed RAFLPSS under different operating conditions and change in system parameters has been investigated. The simulation results obtained from the conventional PSS (CPSS) and Fuzzy logic based PSS (FPSS) are compared with the proposed RAFLPSS. The simulation results demonstrate that the proposed RAFLPSS performs well in damping and quicker response when compared with the other two PSSs.
NASA Astrophysics Data System (ADS)
Abdurrahim, Mahabuba; Abdullah Khan, M.; Edriss, Ali Ahmed
2011-12-01
This paper presents a design procedure for a Robust and Adaptive Fuzzy Logic based Power System Stabilizer (RAFLPSS) to improve the small signal stability of Power System. The parameters of RAFLPSS are tuned by adaptive neural network. This RAFLPSS uses ANFIS network (Adaptive Network based Fuzzy Inference System) which provides a natural framework of multi-layered feed forward adaptive network using fuzzy logic inference system. In this approach, the hybrid-learning algorithm tunes the fuzzy rules and the membership functions of the RAFLPSS. The dynamic performance of SMIB system with the proposed RAFLPSS under different operating conditions and change in system parameters has been investigated. The simulation results obtained from the conventional PSS (CPSS) and Fuzzy logic based PSS (FPSS) are compared with the proposed RAFLPSS. The simulation results demonstrate that the proposed RAFLPSS performs well in damping and quicker response when compared with the other two PSSs.
Research on fuzzy PID control to electronic speed regulator
NASA Astrophysics Data System (ADS)
Xu, Xiao-gang; Chen, Xue-hui; Zheng, Sheng-guo
2007-12-01
As an important part of diesel engine, the speed regulator plays an important role in stabilizing speed and improving engine's performance. Because there are so many model parameters of diesel-engine considered in traditional PID control and these parameters present non-linear characteristic.The method to adjust engine speed using traditional PID is not considered as a best way. Especially for the diesel-engine generator set. In this paper, the Fuzzy PID control strategy is proposed. Some problems about its utilization in electronic speed regulator are discussed. A mathematical model of electric control system for diesel-engine generator set is established and the way of the PID parameters in the model to affect the function of system is analyzed. And then it is proposed the differential coefficient must be applied in control design for reducing dynamic deviation of system and adjusting time. Based on the control theory, a study combined control with PID calculation together for turning fuzzy PID parameter is implemented. And also a simulation experiment about electronic speed regulator system was conducted using Matlab/Simulink and the Fuzzy-Toolbox. Compared with the traditional PID Algorithm, the simulated results presented obvious improvements in the instantaneous speed governing rate and steady state speed governing rate of diesel-engine generator set when the fuzzy logic control strategy used.
Novel Hybrid Adaptive Controller for Manipulation in Complex Perturbation Environments
Smith, Alex M. C.; Yang, Chenguang; Ma, Hongbin; Culverhouse, Phil; Cangelosi, Angelo; Burdet, Etienne
2015-01-01
In this paper we present a hybrid control scheme, combining the advantages of task-space and joint-space control. The controller is based on a human-like adaptive design, which minimises both control effort and tracking error. Our novel hybrid adaptive controller has been tested in extensive simulations, in a scenario where a Baxter robot manipulator is affected by external disturbances in the form of interaction with the environment and tool-like end-effector perturbations. The results demonstrated improved performance in the hybrid controller over both of its component parts. In addition, we introduce a novel method for online adaptation of learning parameters, using the fuzzy control formalism to utilise expert knowledge from the experimenter. This mechanism of meta-learning induces further improvement in performance and avoids the need for tuning through trial testing. PMID:26029916
Novel hybrid adaptive controller for manipulation in complex perturbation environments.
Smith, Alex M C; Yang, Chenguang; Ma, Hongbin; Culverhouse, Phil; Cangelosi, Angelo; Burdet, Etienne
2015-01-01
In this paper we present a hybrid control scheme, combining the advantages of task-space and joint-space control. The controller is based on a human-like adaptive design, which minimises both control effort and tracking error. Our novel hybrid adaptive controller has been tested in extensive simulations, in a scenario where a Baxter robot manipulator is affected by external disturbances in the form of interaction with the environment and tool-like end-effector perturbations. The results demonstrated improved performance in the hybrid controller over both of its component parts. In addition, we introduce a novel method for online adaptation of learning parameters, using the fuzzy control formalism to utilise expert knowledge from the experimenter. This mechanism of meta-learning induces further improvement in performance and avoids the need for tuning through trial testing. PMID:26029916
NASA Technical Reports Server (NTRS)
Abihana, Osama A.; Gonzalez, Oscar R.
1993-01-01
The main objectives of our research are to present a self-contained overview of fuzzy sets and fuzzy logic, develop a methodology for control system design using fuzzy logic controllers, and to design and implement a fuzzy logic controller for a real system. We first present the fundamental concepts of fuzzy sets and fuzzy logic. Fuzzy sets and basic fuzzy operations are defined. In addition, for control systems, it is important to understand the concepts of linguistic values, term sets, fuzzy rule base, inference methods, and defuzzification methods. Second, we introduce a four-step fuzzy logic control system design procedure. The design procedure is illustrated via four examples, showing the capabilities and robustness of fuzzy logic control systems. This is followed by a tuning procedure that we developed from our design experience. Third, we present two Lyapunov based techniques for stability analysis. Finally, we present our design and implementation of a fuzzy logic controller for a linear actuator to be used to control the direction of the Free Flight Rotorcraft Research Vehicle at LaRC.
CINTIA: A Neuro-Fuzzy Real Time Controller for Low Power Embedded Systems
Reyneri, Leonardo
CINTIA: A Neuro-Fuzzy Real Time Controller for Low Power Embedded Systems L.M. Reyneri , M@iet.unipi.it e.mail: chiaberge@polito.it Abstract 1 This paper describes CINTIA, a Neuro-Fuzzy real embedded systems. The pro- posed system mixes two di erent approaches, namely Neuro-Fuzzy Controllers
Neuro-Fuzzy Hardware and DSPs: a Promising Marriage for Control of Complex Systems
Reyneri, Leonardo
Neuro-Fuzzy Hardware and DSPs: a Promising Marriage for Control of Complex Systems B. Bona, S intelligent con- trol paradigms mixing Neuro-Fuzzy algorithms with nite state automata and or digital con of control problems. Neuro-Fuzzy systems, specially when com- bined with DSPs can solve e ciently both
New approaches to relaxed quadratic stability condition of fuzzy control systems
Euntai Kim; Heejin Lee
2000-01-01
This paper deals with the quadratic stability conditions of fuzzy control systems that relax the existing conditions reported in the previous literatures. Two new conditions are proposed and shown to be useful in analyzing and designing fuzzy control systems. The first one employs the S-procedure to utilize information regarding the premise parts of the fuzzy systems. The next one enlarges
Fuzzy observer-based controller design for singularly perturbed nonlinear systems: an LMI approach
Wudhichai Assawinchaichote; Sing Kiong Nguang
2002-01-01
This paper considers the problem of designing a fuzzy observer-based controller for a class of nonlinear singularly perturbed systems described by Takagi-Sugeno-Kang (TSK) fuzzy model. Fast and slow decomposition approach is utilized to derive a fuzzy observer-based controller which stabilises this class of singularly perturbed nonlinear systems.
Robust fuzzy H[infin] control for uncertain nonlinear systems via state feedback: an LMI approach
Kap Rai Lee; Eun Tae Jeung; Hong Bae Park
2001-01-01
This paper presents a method for designing robust fuzzy H? controllers which stabilize nonlinear systems and guarantee an induced L2 norm bound constraint on disturbance attenuation for all admissible uncertainties. Takagi and Sugeno fuzzy models with uncertainties are used as the model for the uncertain nonlinear systems. Fuzzy control systems utilize the concept of the so-called parallel distributed compensation (PDC).
Fuzzy hypercubes: Linguistic learning\\/reasoning systems for intelligent control and identification
Hoon Kang; George Vachtsevanos
1993-01-01
This paper introduces a new tool for intelligent control and identification. A robust and reliable learning and reasoning mechanism is addressed based upon fuzzy set theory and fuzzy associative memories. The mechanism storesa priori an initial knowledge base via approximate learning and utilizes this information for identification and control via fuzzy inferencing. This architecture parallels a well-known scheme in which
Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients
?nan Güler; Elif Derya Übeyli
2005-01-01
This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of EEG signals were used as input patterns of
Estimation of pile group scour using adaptive neuro-fuzzy approach
S. M. Bateni; D.-S. Jeng
2007-01-01
An accurate estimation of scour depth around piles is important for coastal and ocean engineers involved in the design of marine structures. Owing to the complexity of the problem, most conventional approaches are often unable to provide sufficiently accurate results. In this paper, an alternative attempt is made herein to develop adaptive neuro-fuzzy inference system (ANFIS) models for predicting scour
A new approach to estimate anthropometric measurements by adaptive neuro-fuzzy inference system
M. Dursun Kaya; A. Samet Hasiloglu; Mahmut Bayramoglu; Hakki Yesilyurt; A. Fahri Ozok
2003-01-01
Eighteen anthropometric measurements were taken in standing and sitting positions, from 387 subjects between 15 and 17 years old. “Adaptive Neuro-Fuzzy Inference System (ANFIS)” was used to estimate anthropometric measurements as an alternative to stepwise regression analysis. Six outputs (shoulder width, hip width, knee height, buttock-popliteal height, popliteal height, and height) were selected for estimation purpose. The results showed that
Sam Darvishi; Ahmed Al-Ani
2007-01-01
The purpose of this paper is to analyze the electroencephalogram (EEG) signals of imaginary left and right hand movements, an application of brain-computer interface (BCI). We propose here to use an adaptive neuron- fuzzy inference system (ANFIS) as the classification algorithm. ANFIS has an advantage over many classification algorithms in that it provides a set of parameters and linguistic rules
Research on adaptive Kalman filter algorithm based on fuzzy neural network
Zhen Shi; Peng Yue; Xiuzhi Wang
2010-01-01
When the plant of an integrated SINS\\/GPS navigation system dynamics or noise processes are not exactly known, or the noise processes are not zero mean white noise, divergence problems will occur. In this paper, a based on intelligent information fusion technology -fuzzy neural network adaptive system is used to adjust the exponential weighting of a weighted Kalman filtering and prevent
Koksal Erenturk
2009-01-01
This paper presents application of adaptive network based fuzzy inference system (ANFIS) to estimate critical flashover voltage on polluted insulators. Diameter, height, creepage distance, form factor and equivalent salt deposit density were used as input variables for ANFIS, and critical flashover voltage was estimated. In order to train the network and to test its performance, the data sets are derived
Fuzzy adaptive agent for supply chain Yain-Whar Si a,*
Si, Yain Whar "Lawrence"
Fuzzy adaptive agent for supply chain management Yain-Whar Si a,* and Sio-Fan Lou b a,b Faculty for supply chain management. These strategies are essential in guiding various activities within a supply in fast changing market environments. In this paper, we describe the strategies of a supply chain
Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling Breast Cancer Survival
Aickelin, Uwe
Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling Breast Cancer Survival Hazlina Hamdan for breast cancer. I. INTRODUCTION Breast cancer is one of the most common cancers to afflict the female population. It is estimated that one in nine women in the UK will develop breast cancer at some point
Modeling and Simulation of An Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning
ERIC Educational Resources Information Center
Al-Hmouz, A.; Shen, Jun; Al-Hmouz, R.; Yan, Jun
2012-01-01
With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy…
A fuzzy behaviorist approach to sensor-based robot control
Pin, F.G.
1996-05-01
Sensor-based operation of autonomous robots in unstructured and/or outdoor environments has revealed to be an extremely challenging problem, mainly because of the difficulties encountered when attempting to represent the many uncertainties which are always present in the real world. These uncertainties are primarily due to sensor imprecisions and unpredictability of the environment, i.e., lack of full knowledge of the environment characteristics and dynamics. An approach. which we have named the {open_quotes}Fuzzy Behaviorist Approach{close_quotes} (FBA) is proposed in an attempt to remedy some of these difficulties. This approach is based on the representation of the system`s uncertainties using Fuzzy Set Theory-based approximations and on the representation of the reasoning and control schemes as sets of elemental behaviors. Using the FBA, a formalism for rule base development and an automated generator of fuzzy rules have been developed. This automated system can automatically construct the set of membership functions corresponding to fuzzy behaviors. Once these have been expressed in qualitative terms by the user. The system also checks for completeness of the rule base and for non-redundancy of the rules (which has traditionally been a major hurdle in rule base development). Two major conceptual features, the suppression and inhibition mechanisms which allow to express a dominance between behaviors are discussed in detail. Some experimental results obtained with the automated fuzzy, rule generator applied to the domain of sensor-based navigation in aprion unknown environments. using one of our autonomous test-bed robots as well as a real car in outdoor environments, are then reviewed and discussed to illustrate the feasibility of large-scale automatic fuzzy rule generation using the {open_quotes}Fuzzy Behaviorist{close_quotes} concepts.
Neuro-fuzzy control of vertical vibrations in railcars using magnetorheological dampers
Atray, Vipul Sunil
2002-01-01
. Fuzzy Logic in Vibration Control of Automobiles. . . . . Neuro-Fuzzy Systems. Other Neuro-Fuzzy Techniques. Summary and Conclusion. 5 9 15 17 19 25 25 3 DESIGN AND FABRICATION OF MR DAMPERS . 27 3. 1 3. 2 3. 3 3. 4 3. 5 3. 6 3. 7... schemes for railcars. Next the review treats one of the most prominent damping devices that has been recently developed ? the magnetorheological damper. Another subsection reviews recent efforts to control vibrations of vehicles using fuzzy logic...
Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions
B. McBratney; Inakwu O. A. Odeh
Fuzzy systems, including fuzzy set theory and fuzzy logic, provide a rich and meaningful improvement, or extension of conventional logic. The mathematics generated by this theory is consistent, and fuzzy set theory may be seen as a generalisation of classic set theory. Applications in soil science, which may be generated from, or adapted to fuzzy set theory and fuzzy logic,
Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions
Alex. B. McBratney; Inakwu O. A. Odeh
1997-01-01
Fuzzy systems, including fuzzy set theory and fuzzy logic, provide a rich and meaningful improvement, or extension of conventional logic. The mathematics generated by this theory is consistent, and fuzzy set theory may be seen as a generalisation of classic set theory. Applications in soil science, which may be generated from, or adapted to fuzzy set theory and fuzzy logic,
Fuzzy Adaptive Interacting Multiple Model Nonlinear Filter for Integrated Navigation Sensor Fusion
Tseng, Chien-Hao; Chang, Chih-Wen; Jwo, Dah-Jing
2011-01-01
In this paper, the application of the fuzzy interacting multiple model unscented Kalman filter (FUZZY-IMMUKF) approach to integrated navigation processing for the maneuvering vehicle is presented. The unscented Kalman filter (UKF) employs a set of sigma points through deterministic sampling, such that a linearization process is not necessary, and therefore the errors caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. The nonlinear filters naturally suffer, to some extent, the same problem as the EKF for which the uncertainty of the process noise and measurement noise will degrade the performance. As a structural adaptation (model switching) mechanism, the interacting multiple model (IMM), which describes a set of switching models, can be utilized for determining the adequate value of process noise covariance. The fuzzy logic adaptive system (FLAS) is employed to determine the lower and upper bounds of the system noise through the fuzzy inference system (FIS). The resulting sensor fusion strategy can efficiently deal with the nonlinear problem for the vehicle navigation. The proposed FUZZY-IMMUKF algorithm shows remarkable improvement in the navigation estimation accuracy as compared to the relatively conventional approaches such as the UKF and IMMUKF. PMID:22319400
A comparison of idle speed control schemes
M. Thornhill; S. Thompson; H. Sindano
2000-01-01
This paper examines the idle speed regulation control problem in multi-point spark ignited petrol engines. Several possible solutions are presented, including proportional plus integral control, fuzzy logic control, adaptive fuzzy logic control, adaptive fuzzy logic control in conjunction with Smith prediction and dynamic matrix control. All of the controllers are compared in simulation and, where possible, on a production vehicle.
Adaptive PID control of a stepper motor driving a flexible rotor
Nehal M. Elsodany; Sohair F. Rezeka; Noman A. Maharem
2011-01-01
Stepping motors are widely used in robotics and in the numerical control of machine tools to perform high precision positioning operations. The classical closed-loop control of the stepper motor can not respond properly to the system variations unless adaptive technique is used. In this paper, the feasibility of fuzzy gain scheduling control for stepping motor driving flexible rotor has been
Adaptive control with hysteresis estimation and compensation using RFNN for piezo-actuator
Faa-Jeng Lin; Hsin-Jang Shieh; Po-Kai Huang; Li-Tao Teng
2006-01-01
Because the control performance of a piezoactuator is always severely deteriorated due to hysteresis effect, an adaptive control with hysteresis estimation and compensation using recurrent fuzzy neural network (RFNN) is proposed in this study to improve the control performance of the piezo-actuator. A new hysteresis model by modifying and parameterizing the hysteresis friction model is proposed. Then, the overall dynamics
NASA Technical Reports Server (NTRS)
Yen, John; Wang, Haojin; Daugherity, Walter C.
1992-01-01
Fuzzy logic controllers have some often-cited advantages over conventional techniques such as PID control, including easier implementation, accommodation to natural language, and the ability to cover a wider range of operating conditions. One major obstacle that hinders the broader application of fuzzy logic controllers is the lack of a systematic way to develop and modify their rules; as a result the creation and modification of fuzzy rules often depends on trial and error or pure experimentation. One of the proposed approaches to address this issue is a self-learning fuzzy logic controller (SFLC) that uses reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of its fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of a self-learning fuzzy controller is highly contingent on its design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for application to a petrochemical process are discussed, and its performance is compared with that of a PID and a self-tuning fuzzy logic controller.
Implementation Of Fuzzy Automated Brake Controller Using TSK Algorithm
NASA Astrophysics Data System (ADS)
Mittal, Ruchi; Kaur, Magandeep
2010-11-01
In this paper an application of Fuzzy Logic for Automatic Braking system is proposed. Anti-blocking system (ABS) brake controllers pose unique challenges to the designer: a) For optimal performance, the controller must operate at an unstable equilibrium point, b) Depending on road conditions, the maximum braking torque may vary over a wide range, c) The tire slippage measurement signal, crucial for controller performance, is both highly uncertain and noisy. A digital controller design was chosen which combines a fuzzy logic element and a decision logic network. The controller identifies the current road condition and generates a command braking pressure signal Depending upon the speed and distance of train. This paper describes design criteria, and the decision and rule structure of the control system. The simulation results present the system's performance depending upon the varying speed and distance of the train.
Robust and fast learning for fuzzy cerebellar model articulation controllers
Shun-feng Su; Zne-jung Lee; Yan-ping Wang
2006-01-01
In this paper, the online learning capability and the robust property for the learning algorithms of cerebellar model articulation controllers (CMAC) are discussed. Both the traditional CMAC and fuzzy CMAC are considered. In the study, we find a way of embedding the idea of M-estimators into the CMAC learning algorithms to provide the robust property against outliers existing in training
Optimal neuro-fuzzy control of parallel hybrid electric vehicles
M. Mohebbi; M. Charkhgard; M. Farrokhi
2005-01-01
In this paper an optimal method based on neuro-fuzzy for controlling parallel hybrid electric vehicles is presented. In parallel hybrid electric vehicles the required torque for driving and operating the onboard accessories is generated by a combination of internal combustion engine and an electric motor. The power sharing between the internal combustion engine and the electric motor is the key
Control of permanent magnet AC servo motors via fuzzy reasoning
Dong-Il Kim; Jin-Won Lee; Sung Kwun Kim
1992-01-01
A control method which drives the permanent magnet AC servomotor without the detection of the rotor position by an absolute position transducer such as an absolute encoder or a resolver is described. An incremental encoder is only coupled to the motor shaft in order to obtain the information for electrical commutation, motor speed, and motor position. A fuzzy algorithm based
Fuzzy self-tuning immune feedback controller for tissue hyperthermia
Yongsheng Ding; Lihong Ren
2000-01-01
A new universal controller structure suitable for various low or high order plants is presented. The controller includes a basic fuzzy self-tuning P-type immune controller inspired from the immune feedback mechanism of biological immune systems and an incremental block with the purpose to treat with the order of the plants. The P-type immune feedback law is automatically tuned by a
Backstepping fuzzy-neural-network control design for hybrid maglev transportation system.
Wai, Rong-Jong; Yao, Jing-Xiang; Lee, Jeng-Dao
2015-02-01
This paper focuses on the design of a backstepping fuzzy-neural-network control (BFNNC) for the online levitated balancing and propulsive positioning of a hybrid magnetic levitation (maglev) transportation system. The dynamic model of the hybrid maglev transportation system including levitated hybrid electromagnets to reduce the suspension power loss and the friction force during linear movement and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics is first constructed. The ultimate goal is to design an online fuzzy neural network (FNN) control methodology to cope with the problem of the complicated control transformation and the chattering control effort in backstepping control (BSC) design, and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. In the proposed BFNNC scheme, an FNN control is utilized to be the major control role by imitating the BSC strategy, and adaptation laws for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. The effectiveness of the proposed control strategy for the hybrid maglev transportation system is verified by experimental results, and the superiority of the BFNNC scheme is indicated in comparison with the BSC strategy and the backstepping particle-swarm-optimization control system in previous research. PMID:25608292
On structuring the rules of a fuzzy controller
NASA Technical Reports Server (NTRS)
Zhou, Jun; Raju, G. V. S.
1993-01-01
Since the pioneering work of Zadeh and Mamdani and Assilian, fuzzy logic control has emerged as one of the most active and fruitful research areas. The applications of fuzzy logic control can be found in many fields such as control of stream generators, automatic train operation systems, elevator control, nuclear reactor control, automobile transmission control, etc. In this paper, two new structures of hierarchical fuzzy rule-based controller are proposed to reduce the number of rules in a complete rule set of a controller. In one approach, the overall system is split into sub-systems which are treated independently in parallel. A coordinator is then used to take into account the interactions. This is done via an iterating information exchange between the lower level and the coordinator level. From the point of view of information used, this structure is very similar to central structure in that the coordinator can have at least in principle, all the information that the local controllers have.
Hamdy, M; Hamdan, I
2015-07-01
In this paper, a robust H? fuzzy output feedback controller is designed for a class of affine nonlinear systems with disturbance via Takagi-Sugeno (T-S) fuzzy bilinear model. The parallel distributed compensation (PDC) technique is utilized to design a fuzzy controller. The stability conditions of the overall closed loop T-S fuzzy bilinear model are formulated in terms of Lyapunov function via linear matrix inequality (LMI). The control law is robustified by H? sense to attenuate external disturbance. Moreover, the desired controller gains can be obtained by solving a set of LMI. A continuous stirred tank reactor (CSTR), which is a benchmark problem in nonlinear process control, is discussed in detail to verify the effectiveness of the proposed approach with a comparative study. PMID:25765955
A reinforcement learning-based architecture for fuzzy logic control
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.
1992-01-01
This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.
Semiautonomous adaptive cruise control systems
R. Rajamani; C. Zhu
2002-01-01
The concept of a semi-autonomous adaptive cruise control (SAACC) system is developed, which enjoys significant advantages over present day adaptive cruise control (ACC) systems in terms of highway safety and traffic flow capacity. The semi-autonomous systems combine the deployment advantages of autonomous vehicles with the performance advantages of fully automated highway systems (AHSs) in which vehicles operate cooperatively as a
Adaptive excitation control in power systems
Chiu, Pei-Chen
2006-08-16
This thesis presents an adaptive excitation controller of power systems. The control law is derived by using model reference adaptive control (MRAC) or adaptive pole placement control (APPC) and an equilibrium tracking ...
Research on cam grinding process used on-line variable velocity based on fuzzy control theory
Peng Baoying; Han Qiushi
2010-01-01
To get to constant force control is very important in non-circular parts manufacting. By gathering the singal from force transducer, adopt grinding force and its rate of change and C axis velocity to developing a fuzzy models. The fuzzy program codes is wrote in several PLC programs. By the on-line fuzzy caculating, the final C Axis velocity is got and
Implementation of parallel fuzzy logic controller in FPGA circuit for guiding electric wheelchair
M. Poplawski; Michal Bialko
2008-01-01
This paper describes an implementation of a fuzzy logic control system for guiding a wheelchair, using an architecture based on a FPGA circuit. For this purpose, a dedicated architecture was elaborated, which was simulated in FPGA circuit. Input and output linguistic variables and corresponding fuzzy sets were defined and based on those a fuzzy rule base was formed. The proposed
Trends and Issues in Fuzzy Control and Neuro-Fuzzy Modeling
NASA Technical Reports Server (NTRS)
Chiu, Stephen
1996-01-01
Everyday experience in building and repairing things around the home have taught us the importance of using the right tool for the right job. Although we tend to think of a 'job' in broad terms, such as 'build a bookcase,' we understand well that the 'right job' associated with each 'right tool' is typically a narrowly bounded subtask, such as 'tighten the screws.' Unfortunately, we often lose sight of this principle when solving engineering problems; we treat a broadly defined problem, such as controlling or modeling a system, as a narrow one that has a single 'right tool' (e.g., linear analysis, fuzzy logic, neural network). We need to recognize that a typical real-world problem contains a number of different sub-problems, and that a truly optimal solution (the best combination of cost, performance and feature) is obtained by applying the right tool to the right sub-problem. Here I share some of my perspectives on what constitutes the 'right job' for fuzzy control and describe recent advances in neuro-fuzzy modeling to illustrate and to motivate the synergistic use of different tools.
Ramesh, Tejavathu; Kumar Panda, Anup; Shiva Kumar, S
2015-07-01
In this research study, a model reference adaptive system (MRAS) speed estimator for speed sensorless direct torque and flux control (DTFC) of an induction motor drive (IMD) using two adaptation mechanism schemes are proposed to replace the conventional proportional integral controller (PIC). The first adaptation mechanism scheme is based on Type-1 fuzzy logic controller (T1FLC), which is used to achieve high performance sensorless drive in both transient as well as steady state conditions. However, the Type-1 fuzzy sets are certain and unable to work effectively when higher degree of uncertainties presents in the system which can be caused by sudden change in speed or different load disturbances, process noise etc. Therefore, a new Type-2 fuzzy logic controller (T2FLC) based adaptation mechanism scheme is proposed to better handle the higher degree of uncertainties and improves the performance and also robust to various load torque and sudden change in speed conditions, respectively. The detailed performances of various adaptation mechanism schemes are carried out in a MATLAB/Simulink environment with a speed sensor and speed sensorless modes of operation when an IMD is operating under different operating conditions, such as, no-load, load and sudden change in speed, respectively. To validate the different control approaches, the system also implemented on real-time system and adequate results are reported for its validation. PMID:25887841
A fuzzy logic based spacecraft controller for six degree of freedom control and performance results
NASA Technical Reports Server (NTRS)
Lea, Robert N.; Hoblit, Jeffrey; Jani, Yashvant
1991-01-01
The development philosophy of the fuzzy logic controller is explained, details of the rules and membership functions used are given, and the early results of testing of the control system for a representative range of scenarios are reported. The fuzzy attitude controller was found capable of performing all rotational maneuvers, including rate hold and rate maneuvers. It handles all orbital perturbations very efficiently and is very responsive in correcting errors.
How to control if even experts are not sure: Robust fuzzy control
NASA Technical Reports Server (NTRS)
Nguyen, Hung T.; Kreinovich, Vladik YA.; Lea, Robert; Tolbert, Dana
1992-01-01
In real life, the degrees of certainty that correspond to one of the same expert can differ drastically, and fuzzy control algorithms translate these different degrees of uncertainty into different control strategies. In such situations, it is reasonable to choose a fuzzy control methodology that is the least vulnerable to this kind of uncertainty. It is shown that this 'robustness' demand leads to min and max for &- and V-operations, to 1-x for negation, and to centroid as a defuzzification procedure.
Motion Control of the Soccer Robot Based on Fuzzy Logic
NASA Astrophysics Data System (ADS)
Coman, Daniela; Ionescu, Adela
2009-08-01
Robot soccer is a challenging platform for multi-agent research, involving topics such as real-time image processing and control, robot path planning, obstacle avoidance and machine learning. The conventional robot control consists of methods for path generation and path following. When a robot moves away the estimated path, it must return immediately, and while doing so, the obstacle avoidance behavior and the effectiveness of such a path are not guaranteed. So, motion control is a difficult task, especially in real time and high speed control. This paper describes the use of fuzzy logic control for the low level motion of a soccer robot. Firstly, the modelling of the soccer robot is presented. The soccer robot based on MiroSoT Small Size league is a differential-drive mobile robot with non-slipping and pure-rolling. Then, the design of fuzzy controller is describes. Finally, the computer simulations in MATLAB Simulink show that proposed fuzzy logic controller works well.
Optimal Fuzzy Switching Grey Prediction with RGA for TRMS Control
Jih-Gau Juang; Kai-Ti Tu; Wen-Kai Liu
2006-01-01
This paper presents a fuzzy switching grey prediction PID controller to an experimental propeller setup which is called the twin rotor multi-input multi-output system (TRMS). The goal of this study is to stabilize the TRMS in significant cross coupling conditions and to experiment with set-point control and trajectory tracking. The proposed scheme enhances the grey prediction method of difference equations,
K. Guney; N. Sarikaya
2009-01-01
This paper presents a method based on adaptive-network-based fuzzy inference system (ANFIS) to compute the resonant frequency\\u000a of a circular microstrip antenna (MSA). The ANFIS is a class of adaptive networks which are functionally equivalent to fuzzy\\u000a inference systems (FISs). Seven optimization algorithms, least-squares, nelder-mead, differential evolution, genetic, hybrid\\u000a learning, particle swarm, and simulated annealing, are used to determine optimally
Adaptive Call Admission Control for Multi-Class Services in Wireless Networks
Hongwei Liao; Xinbing Wang; Hsiao-hwa Chen
2008-01-01
This paper presents an adaptive call admission control scheme for multi-class services in wireless networks. We propose a closed-loop feedback control mechanism, which is composed of three model blocks: priority-based queueing model, adaptive fuzzy service degradation control model, and optimal service degradation allocation model. Based on the established service differentiation scheme, we present a priority-based queueing model, which provides a
The PLC System of Egg Powder Treatment Based on Fuzzy Control Algorithm
Yanmin Song; Zhongwei Bi; Kun Liu
2007-01-01
In this paper, we take the electric control system of egg powder treatment as an example. By means of fuzzy control technology and transducer technology, the system overcomes the instability of the system, the difficulty in parameter tuning and the problem of grading speed regulation in the traditional control field. Fuzzy controller based on PLC (programmable logic controller) direct lookup
Chia-Feng Juang; Chia-Hung Hsu
2009-01-01
This paper proposes a new reinforcement-learning method using online rule generation and Q-value-aided ant colony optimization (ORGQACO) for fuzzy controller design. The fuzzy controller is based on an interval type-2 fuzzy system (IT2FS). The antecedent part in the designed IT2FS uses interval type-2 fuzzy sets to improve controller robustness to noise. There are initially no fuzzy rules in the IT2FS.
Fuzzy logic for control of roll and moment for a flexible wing aircraft
Stephen Chiu; Sujeet Chand; Doug Moore; Ashwani Chaudhary
1991-01-01
A fuzzy-logic based multi-input\\/multi-output roll controller designed for the Advanced Technology Wing (ATW) aircraft model is presented. The ATW integrates active controls with a flexible wing structure to provide optimal wing shapes to meet particular flight performance criteria. The use of a fuzzy controller for roll rate and load alleviation control was investigated. Fuzzy rules were developed to determine the
The Design of an Adaptive Multiple Agent ConstraintBased Controller for a Complex Hydraulic System
Bahler, Dennis R.
The Design of an Adaptive Multiple Agent ConstraintBased Controller for a Complex Hydraulic Systemorder predicate calculus (FOPC) language applied in a complex hydraulic system are presented. The concept of ``multi ple agent'' and ``fuzzy constraint subnetwork'' in a complex control system is introduced
The integration and application of fuzzy and grey modeling methods
Yo-Ping Huang; Chi-Chang Huang
1996-01-01
An integrated fuzzy and grey model and its applications to the prediction control problems are presented. The basic grey model GM(1,1) is accompanied with the adaptive fuzzy method to improve its prediction capability. The gradient descent scheme is applied to the fuzzy rules to determine whether the predicted results from the grey model should be adjusted. The quantity of adjustment
Hybrid Adaptive Flight Control with Model Inversion Adaptation
NASA Technical Reports Server (NTRS)
Nguyen, Nhan
2011-01-01
This study investigates a hybrid adaptive flight control method as a design possibility for a flight control system that can enable an effective adaptation strategy to deal with off-nominal flight conditions. The hybrid adaptive control blends both direct and indirect adaptive control in a model inversion flight control architecture. The blending of both direct and indirect adaptive control provides a much more flexible and effective adaptive flight control architecture than that with either direct or indirect adaptive control alone. The indirect adaptive control is used to update the model inversion controller by an on-line parameter estimation of uncertain plant dynamics based on two methods. The first parameter estimation method is an indirect adaptive law based on the Lyapunov theory, and the second method is a recursive least-squares indirect adaptive law. The model inversion controller is therefore made to adapt to changes in the plant dynamics due to uncertainty. As a result, the modeling error is reduced that directly leads to a decrease in the tracking error. In conjunction with the indirect adaptive control that updates the model inversion controller, a direct adaptive control is implemented as an augmented command to further reduce any residual tracking error that is not entirely eliminated by the indirect adaptive control.
Fuzzy graphic rule network and its application on water bath temperature control system
C. Treesatayapun; S. Uatrongjit; K. Kantapanit
2002-01-01
In this paper, a novel fuzzy neural network called fuzzy graphic rule network (FGRN) is presented. FGRN has a simple structure and the initial value of its parameters can be easily chosen based on human experience. These parameters are then adjusted during system operation using steepest descent technique. The step length or learning rate is adaptively selected to ensure system
Multiple model adaptive control of automatic quasi-synchronization for 500 kW hydropower generator
Haiqing Yang; Li Yu; Chi Xu
2004-01-01
A multiple model adaptive control algorithm is proposed in this paper to solve whole-process control problem for 500 kW hydropower generators more accurately and reliably. It provides a new architecture of hybrid control. In the new architecture, the control scheme includes four parts: fast servo-control at the stage of generator start-up, fuzzy control of frequency and voltage on the period
Hardware implementation of fuzzy Petri net as a controller.
Gniewek, Les?aw; Kluska, Jacek
2004-06-01
The paper presents a new approach to fuzzy Petri net (FPN) and its hardware implementation. The authors' motivation is as follows. Complex industrial processes can be often decomposed into many parallelly working subprocesses, which can, in turn, be modeled using Petri nets. If all the process variables (or events) are assumed to be two-valued signals, then it is possible to obtain a hardware or software control device, which works according to the algorithm described by conventional Petri net. However, the values of real signals are contained in some bounded interval and can be interpreted as events which are not only true or false, but rather true in some degree from the interval [0, 1]. Such a natural interpretation from multivalued logic (fuzzy logic) point of view, concerns sensor outputs, control signals, time expiration, etc. It leads to the idea of FPN as a controller, which one can rather simply obtain, and which would be able to process both analog, and binary signals. In the paper both graphical, and algebraic representations of the proposed FPN are given. The conditions under which transitions can be fired are described. The algebraic description of the net and a theorem which enables computation of new marking in the net, based on current marking, are formulated. Hardware implementation of the FPN, which uses fuzzy JK flip-flops and fuzzy gates, are proposed. An example illustrating usefulness of the proposed FPN for control algorithm description and its synthesis as a controller device for the concrete production process are presented. PMID:15484905
Adaptive neuro-fuzzy inference systems for automatic detection of breast cancer.
Ubeyli, Elif Derya
2009-10-01
This paper intends to an integrated view of implementing adaptive neuro-fuzzy inference system (ANFIS) for breast cancer detection. The Wisconsin breast cancer database contained records of patients with known diagnosis. The ANFIS classifiers learned how to differentiate a new case in the domain by given a training set of such records. The ANFIS classifier was used to detect the breast cancer when nine features defining breast cancer indications were used as inputs. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of breast cancer were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performances and classification accuracies and the results confirmed that the proposed ANFIS model has potential in detecting the breast cancer. PMID:19827261
Design of a New Practical Expert Fuzzy Controller in Central Air Conditioning Control System
Mingfang Du; Tongshun Fan; Wei Su; Hongxing Li
2008-01-01
The improved control algorithm in central air conditioning control system is discussed in this paper. Based on a number of practical projects, a primary theory model of expert fuzzy logic controller is established. The expert control ideal is applied in the framework design process of the air conditioning control system. The boundaries of membership function are decided by expert experience,
A fuzzy sliding-mode controller design for a synchronous reluctance motor drive
Tian-Hua Liu; Ming-Tsan Lin
1996-01-01
A new method for controlling a synchronous reluctance drive system using a sliding mode with fuzzy controller design is presented. The fuzzy controller is used to adjust the sliding line of the sliding-mode controller. Using this method, the system has a fast response and a good disturbance rejection capability. In addition, the chattering of the speed is reduced. In this
Fuzzy logic control of steam generator water level in pressurized water reactors
C. C. Kuan; C. Lin; C. C. Hsu
1992-01-01
In this paper a fuzzy logic controller is applied to control the steam generator water level in a pressurized water reactor. The method does not require a detailed mathematical mode of the object to be controlled. The design is based on a set of linguistic rules that were adopted from the human operator's experience. After off-line fuzzy computation, the controller
Comparison of fuzzy and PI controllers for a benchmark drum-boiler model
Kai-Pong Cheung; Li-Xin Wang
1998-01-01
We test both fuzzy- and PI-controllers for a benchmark drum-boiler model and compare their performance. We consider two cases: set-point tracking and disturbance rejection. For set-point tracking, the fuzzy controller shows better performance than the PI controller; and for disturbance rejection, the two controllers give comparable performance
Dejan J. Sobajic
1997-01-01
This paper presents a design technique for a new hydropower plant controller using fuzzy set theory and artificial neural networks. The controller is suitable for real time operation, with the aim of improving the generating unit transients by acting through the exciter input, the guide vane and the runner blade positions. The developed fuzzy logic based controller (FLC) whose control
Damir Sumina; Tomislav Idzotic; Igor Erceg
2006-01-01
This article is focused on the implementation of fuzzy logic excitation control of a synchronous generator. A simple fuzzy logic control scheme for voltage control and generator stabilization is tested on the real laboratory model that includes digital system for excitation control (based on four DSPs) and synchronous generator connected to an AC system through transformer and two parallel transmission
Karami, Ali; Keiter, Steffen; Hollert, Henner; Courtenay, Simon C
2013-03-01
This study represents a first attempt at applying a fuzzy inference system (FIS) and an adaptive neuro-fuzzy inference system (ANFIS) to the field of aquatic biomonitoring for classification of the dosage and time of benzo[a]pyrene (BaP) injection through selected biomarkers in African catfish (Clarias gariepinus). Fish were injected either intramuscularly (i.m.) or intraperitoneally (i.p.) with BaP. Hepatic glutathione S-transferase (GST) activities, relative visceral fat weights (LSI), and four biliary fluorescent aromatic compounds (FACs) concentrations were used as the inputs in the modeling study. Contradictory rules in FIS and ANFIS models appeared after conversion of bioassay results into human language (rule-based system). A "data trimming" approach was proposed to eliminate the conflicts prior to fuzzification. However, the model produced was relevant only to relatively low exposures to BaP, especially through the i.m. route of exposure. Furthermore, sensitivity analysis was unable to raise the classification rate to an acceptable level. In conclusion, FIS and ANFIS models have limited applications in the field of fish biomarker studies. PMID:22752811
Toward a fuzzy logic control of the infant incubator.
Reddy, Narender P; Mathur, Garima; Hariharan, S I
2009-10-01
Premature birth is a world wide problem. Thermo regulation is a major problem in premature infants. Premature infants are often kept in infant incubators providing convective heating. Currently either the incubator air temperature is sensed and used to control the heat flow, or infant's skin temperature is sensed and used in the close loop control. Skin control often leads to large fluctuations in the incubator air temperature. Air control also leads to skin temperature fluctuations. The question remains if both the infant's skin temperature and the incubator air temperature can be simultaneously used in the control. The purpose of the present study was to address this question by developing a fuzzy logic control which incorporates both incubator air temperature and infant's skin temperature to control the heating. The control was evaluated using a lumped parameter mathematical model of infant-incubator system (Simon, B. N., N. P. Reddy, and A. Kantak, J. Biomech. Eng. 116:263-266, 1994). Simulation results confirmed previous experimental results that the on-off skin control could lead to fluctuations in the incubator air temperature, and the air control could lead to too slow rise time in the core temperature. The fuzzy logic provides a smooth control with the desired rise time. PMID:19609677
State-feedback control of fuzzy discrete-event systems.
Lin, Feng; Ying, Hao
2010-06-01
In a 2002 paper, we combined fuzzy logic with discrete-event systems (DESs) and established an automaton model of fuzzy DESs (FDESs). The model can effectively represent deterministic uncertainties and vagueness, as well as human subjective observation and judgment inherent to many real-world problems, particularly those in biomedicine. We also investigated optimal control of FDESs and applied the results to optimize HIV/AIDS treatments for individual patients. Since then, other researchers have investigated supervisory control problems in FDESs, and several results have been obtained. These results are mostly derived by extending the traditional supervisory control of (crisp) DESs, which are string based. In this paper, we develop state-feedback control of FDESs that is different from the supervisory control extensions. We use state space to describe the system behaviors and use state feedback in control. Both disablement and enforcement are allowed. Furthermore, we study controllability based on the state space and prove that a controller exists if and only if the controlled system behavior is (state-based) controllable. We discuss various properties of the state-based controllability. Aside from novelty, the proposed new framework has the advantages of being able to address a wide range of practical problems that cannot be effectively dealt with by existing approaches. We use the diabetes treatment as an example to illustrate some key aspects of our theoretical results. PMID:19884087
Design of self-learning fuzzy sliding mode controllers based on genetic algorithms
Sinn-Cheng Lin; Yung-Yaw Chen
1997-01-01
In this paper, genetic algorithms were applied to search a sub-optimal fuzzy rule-base for a fuzzy sliding mode controller. Two types of fuzzy sliding mode controllers based on genetic algorithms were proposed. The fitness functions were defined so that the controllers which can drive and keep the state on the user-defined sliding surface would be assigned a higher fitness value.
Effective digital implementation of fuzzy control systems based on approximate discrete-time models
Do-wan Kim; Jin Bae Park; Young Hoon Joo
2007-01-01
This paper addresses an effective digital implementation of fuzzy control systems via an intelligent digital redesign (IDR) approach. The purpose of IDR is to effectively convert an existing continuous-time fuzzy controller to an equivalent sampled-data fuzzy controller in the sense of the state-matching. The authors show that, under reasonable assumptions, the IDR based on the exact discrete-time models can be
Time-Delayed and Quantized Fuzzy Systems: Stability Analysis and Controller Design
Chang-Woo Park; Hyung-Jin Kang; Jung-Hwan Kim; Mignon Park
2000-01-01
In this paper, the design methodology of digital fuzzy controller(DFC) for the systems with time-delay is presented and the qualitative effects of the quantizers in the digital implementation of a fuzzy controllers are investigated. We propose the fuzzy feed- back controller whose output is delayed with unit sampling period and predicted. The analysis and the design problem considering time-delay become
Boiler water-level fuzzy control system design and parameters optimization based on rough set theory
Ju Lincang; Wu Jinlong; Li Lei
2009-01-01
Rough set theory is used to acquire the fuzzy rules from the history data of a boiler water-level control system and a water-level fuzzy control system for the boiler is designed according to it. Then this paper applied rough set theory to design a quantum factor and proportional factor self-tuning fuzzy controller. The simulation results show that the method presented
Clustering of noisy image data using an adaptive neuro-fuzzy system
NASA Technical Reports Server (NTRS)
Pemmaraju, Surya; Mitra, Sunanda
1992-01-01
Identification of outliers or noise in a real data set is often quite difficult. A recently developed adaptive fuzzy leader clustering (AFLC) algorithm has been modified to separate the outliers from real data sets while finding the clusters within the data sets. The capability of this modified AFLC algorithm to identify the outliers in a number of real data sets indicates the potential strength of this algorithm in correct classification of noisy real data.
Adaptive Neuro-Fuzzy Inference Systems for Automatic Detection of Breast Cancer
Elif Derya Übeyli
2009-01-01
This paper intends to an integrated view of implementing adaptive neuro-fuzzy inference system (ANFIS) for breast cancer detection.\\u000a The Wisconsin breast cancer database contained records of patients with known diagnosis. The ANFIS classifiers learned how\\u000a to differentiate a new case in the domain by given a training set of such records. The ANFIS classifier was used to detect\\u000a the breast
Lithology Recognition During Oil Well Drilling Based on Fuzzy-adaptive Hamming Network
Tiehong Gao; Junyi Cao; Minglu Zhang; Jiangbo Qi
2006-01-01
In order to satisfy the urgent demand for real-time lithology recognition of bit position during oil well drilling, a method of lithology feature extraction had been presented using the relation between curve variation trend of the real-time estimated drillable value and material of core lithology. Based on this method and fuzzy-adaptive Hamming network, a novel method had been proposed to
Semiautonomous adaptive cruise control systems
Rajesh Rajamani; Chunyu Zhu
1999-01-01
The concept of a semi-autonomous adaptive cruise control system is developed that enjoys significant advantages over present-day adaptive cruise control systems in terms of highway safety and traffic flow. The semi-autonomous systems combine the advantages of autonomous vehicles with the advantages of fully automated highway systems in which vehicles operate cooperatively as a platoon. Unlike platoon systems, the semi-autonomous systems
Adaptive control of compound manipulators
P. Pittman; R. Colbaugh
1993-01-01
This paper presents the motion control of a kinematically redundant manipulator. The controller is a direct adaptive control strategy that is stable and is applied through computer simulations with a nine degree-of-freedom compound manipulator consisting of a small, fast six DOF manipulator mounted on a large three DOF positioning device
Intelligent call admission control using fuzzy logic in wireless networks
Yufeng Ma; Xiulin Hu; Yunyu Zhang; Yimei Shi
2005-01-01
Scarcity of the spectrum resource and mobility of users make quality-of-service (QoS) provision a critical issue in wireless networks. This paper presents a fuzzy call admission control scheme to meet the requirement of the QoS. It searches automatically the optimal number of the guard channels in a base station to make an effective use of resource and guarantee the QoS
Adaptive neuro-fuzzy fusion of sensor data
NASA Astrophysics Data System (ADS)
Petkovi?, Dalibor
2014-11-01
A framework is proposed, which consolidates the benefits of a fuzzy rationale and a neural system. The framework joins together Kalman separating and delicate processing guideline i.e. ANFIS to structure an effective information combination strategy for the target following framework. A novel versatile calculation focused around ANFIS is proposed to adjust logical progressions and to weaken the questionable aggravation of estimation information from multisensory. Fuzzy versatile combination calculation is a compelling device to make the genuine quality of the leftover covariance steady with its hypothetical worth. ANFIS indicates great taking in and forecast proficiencies, which makes it a productive device to manage experienced vulnerabilities in any framework. A neural system is presented, which can concentrate the measurable properties of the samples throughout the preparation sessions. Reproduction results demonstrate that the calculation can successfully alter the framework to adjust context oriented progressions and has solid combination capacity in opposing questionable data. This sagacious estimator is actualized utilizing Matlab/Simulink and the exhibitions are explored.
Tzuu-Hseng S. Li; Shih-Jie Chang; Yi-Xiang Chen
2003-01-01
In this paper, the concepts of car maneuvers, fuzzy logic control (FLC), and sensor-based behaviors are merged to implement the human-like driving skills by an autonomous car-like mobile robot (CLMR). Four kinds of FLCs, fuzzy wall-following control, fuzzy corner control, fuzzy garage-parking control, and fuzzy parallel-parking control, are synthesized to accomplish the autonomous fuzzy behavior control (AFBC). Computer simulation results
Computer-aided mass detection on digitized mammograms using adaptive thresholding and fuzzy entropy.
Younesi, F; Alam, N; Zoroofi, R A; Ahmadian, A; Guiti, M
2007-01-01
In this paper, a segmentation method for detection of masses in digitized mammograms has been developed using two parallel approaches: adaptive thresholding method and fuzzy entropy feature as a CAD scheme. The algorithm consists of the following steps: a) Preprocessing of the digitized mammograms including identification of region of interest (ROI) as candidate for massive lesion through breast region extraction, b) Image enhancement using linear transformation and subtracting enhanced from the original image, c) Characterization of the ROI by extracting the fuzzy entropy feature, d) Local adaptive thresholding for segmentation of mass areas, e) Combine expert of the last two parallel approaches for mass detection. The proposed method was tested on 78 mammograms (30 normal & 48 cancerous) from the BIRADS and local databases. The detected regions validated by comparing them with the radiologists' hand-sketched boundaries of real masses. The current algorithm can achieve a sensitivity of 90.73% and specificity of 89.17%. This approach showed that the behavior of local adaptive thresholding and fuzzy entropy technique could be a useful method for mass detection on digitized mammograms. Our results suggest that the proposed method could help radiologists as a second reader in mammographic screening of masses. PMID:18003291
NASA Astrophysics Data System (ADS)
Liu, Fang
2011-06-01
Image segmentation remains one of the major challenges in image analysis and computer vision. Fuzzy clustering, as a soft segmentation method, has been widely studied and successfully applied in mage clustering and segmentation. The fuzzy c-means (FCM) algorithm is the most popular method used in mage segmentation. However, most clustering algorithms such as the k-means and the FCM clustering algorithms search for the final clusters values based on the predetermined initial centers. The FCM clustering algorithms does not consider the space information of pixels and is sensitive to noise. In the paper, presents a new fuzzy c-means (FCM) algorithm with adaptive evolutionary programming that provides image clustering. The features of this algorithm are: 1) firstly, it need not predetermined initial centers. Evolutionary programming will help FCM search for better center and escape bad centers at local minima. Secondly, the spatial distance and the Euclidean distance is also considered in the FCM clustering. So this algorithm is more robust to the noises. Thirdly, the adaptive evolutionary programming is proposed. The mutation rule is adaptively changed with learning the useful knowledge in the evolving process. Experiment results shows that the new image segmentation algorithm is effective. It is providing robustness to noisy images.
ON AN ADAPTIVE CONTROL ALGORITHM FOR ADAPTIVE OPTICS APPLICATIONS
ON AN ADAPTIVE CONTROL ALGORITHM FOR ADAPTIVE OPTICS APPLICATIONS MOODY T. CHU \\Lambda Abstract imaging system. Adaptive optics refers to the process of removing unwanted wave front distortions with the use of a phase corrector before the image is formed. The basic idea in adaptive optics is to control
ON AN ADAPTIVE CONTROL ALGORITHM FOR ADAPTIVE OPTICS APPLICATIONS
ON AN ADAPTIVE CONTROL ALGORITHM FOR ADAPTIVE OPTICS APPLICATIONS MOODY T. CHU Abstract imaging system. Adaptive optics refers to the process of removing unwanted wave front distortions with the use of a phase corrector before the image is formed. The basic idea in adaptive optics is to control
Chia-Feng Juang; Yu-Cheng Chang
2011-01-01
This paper proposes an evolutionary-group-based particle-swarm-optimization (EGPSO) algorithm for fuzzy- controller (FC) design. The EGPSO uses a group-based framework to incorporate crossover and mutation operations into particle- swarm optimization. The EGPSO dynamically forms different groups to select parents in crossover operations, particle up- dates, and replacements. An adaptive velocity-mutated operation (AVMO) is incorporated to improve search ability. The EGPSO is
Noppadol Khaehintung; P. Sirisuk
2007-01-01
This paper presents the development of maximum power point tracking (MPPT) using an adjustable self-organizing fuzzy logic controller (SOFLC) for a solar-powered traffic light equipment (SPTLE) with an integrated maximum power point tracking (MPPT) system on a low-cost microcontroller. The proposed system is integrated with a boost converter for realizing of high performance SPTLE, whose adaptability properties are very attractive
Aircraft adaptive learning control
NASA Technical Reports Server (NTRS)
Lee, P. S. T.; Vanlandingham, H. F.
1979-01-01
The optimal control theory of stochastic linear systems is discussed in terms of the advantages of distributed-control systems, and the control of randomly-sampled systems. An optimal solution to longitudinal control is derived and applied to the F-8 DFBW aircraft. A randomly-sampled linear process model with additive process and noise is developed.
Rong-jong Wai; Hsin-hai Lin; Faa-jeng Lin
2000-01-01
An induction servo motor drive with a hybrid controller, which combines the advantages of the integral-proportional (IP) position controller and the fuzzy neural network controller (FNNC), is introduced in this study. First, the IP position controller is designed according to the estimated plant model to match the time-domain command tracking specifications. Then, a compensated signal generated from FNNC is augmented
Rollover prevention for sport utility vehicle using fuzzy logic controller
NASA Astrophysics Data System (ADS)
Lee, Yong-hwi; Yi, Seung-Jong
2005-12-01
The purpose of this study is to develop the fuzzy logic RSC(Roll Stability Control) system to prevent the rollover for the SUV(sport utility vehicle). The SUV model used in this study is the 8-DOF model considering the longitudinal, lateral, yaw and roll motions. The longitudinal and transversal weight transfers are considered in the computation of the vertical forces acting on a wheel. The engine torque is obtained from the throttle position and the r.p.m. of the engine map. The fuzzy logic controller input consists of the roll angle error and its derivative. The output is the brake torque and the throttle angle. The engine torque controller controls the throttle valve angle. The brake controller independently controls both right and left wheels. When the roll angle is +/-4.5° defined as the critical roll angle, the front inner tire experiences the 1/100 ~ 1/50 of the total vertical forces, and the rollover starts. To prevent the rollover in advance, the target angle +/-4.5° is adopted to control the vehicle stability. The RSC system begins operating at +/-4.5° and stops at 0°. The simulations are conducted to evaluate the controller performance at right turns for the excessive steering angle. When the roll angle error and its derivative exceed the limited point, the RSC system makes the longitudinal velocity of the SUV decrease the brake torque and adjusts the throttle angle. The roll motion of the SUV is then stabilized.
Neuro-fuzzy controller for gas turbine in biomass-based electric power plant
Francisco Jurado; Manuel Ortega; Antonio Cano; José Carpio
2002-01-01
Biomass gasification is a technology that transforms solid biomass into syngas. The gas turbine controller regulates both the gas turbine and the gas turbine generator. Two fuzzy logic controllers have been developed using speed and mechanical power deviations, and a neural network has been designed to tune the gains of the fuzzy logic controllers based on the operating conditions of
Neuro-fuzzy controller in biomass-based electric power plant
Francisco Jurado; A. Lopez; J. R. Saenz
2001-01-01
Biomass gasification is a technology that transforms solid biomass into gas. The gas motor controller regulates both the gas motor and the gas motor generator. In this paper two fuzzy logic controllers have been developed using speed and mechanical power deviations, and a neural network has been designed to tune the gains of the fuzzy logic controllers based on the
Sensitivity-based self-learning fuzzy logic controller as a PLC super block
S. Bogdan; Z. Kovacic; D. Krapinec
2007-01-01
In this article, implementation of a self-learning fuzzy logic controller (SLFLC) in a form of PLC super block is described. The SLFLC contains a learning algorithm that utilizes a second-order reference model and a sensitivity model related to the fuzzy controller parameters. The effectiveness of the PLC super block has been tested by experiment in the position control loop of
Design of fuzzy logic speed controller for brushless DC motor drives
V. Donescu; D. O. Neacsu; G. Griva
1996-01-01
This paper presents a fuzzy logic speed controller (FLSC) for variable speed drives using current-controlled brushless DC motors. The fuzzy logic (FL) approach applied to speed control leads to an improved dynamic behaviour of the motor drive system and an improved immunity to load perturbations and parameter variations. The FLSC is designed using a simple algorithm based on a supposed
A weighted algorithm of fuzzy logic strategy on water level control of steam generator
Xiangjie Liu; Tianyou Chai
1997-01-01
This paper presents the newly developed water level control system for drum boiler using the fuzzy control strategy. The major dynamics of boiler water level include nonlinearities, nonminimum phase behavior, instabilities, time delays, and load disturbances. A weighted algorithm of fuzzy logic strategy is applied to control the steam boiler using the GPE (Gaussian partition with evenly spaced midpoints) system.
A Modified PI-Like Fuzzy Logic Controller for Switched Reluctance Motor Drives
Shun-Chung Wang; Yi-Hwa Liu
2011-01-01
Based on the redevelopment of control rule base, two modified PI-like fuzzy logic controllers with output scaling factor (SF) self-tuning mechanism are proposed and verified in this paper for application in the switched reluctance motor (SRM) drive system. The motivation of this paper is to simplify the program complexity of the controller by reducing the number of fuzzy sets of
A Fuzzy Logic Controlled Braking Resistor Scheme for Damping Shaft Torsional Oscillations
Mohd. Hasan Ali; Takumi Mikami; Toshiaki Murata; Junji Tamura
2004-01-01
This paper presents a fuzzy logic switching for the thyristor controlled braking resistor to damp turbine-generator shaft torsional oscillations. Following a major disturbance in electric power system, variable rotor speed of the generator is measured, and then the current through the braking resistor is controlled by the firing-angle of the thyristor switch which is controlled by the fuzzy logic. Thus
A fuzzy logic based coagulant real time control scheme for water purification system
Bai Hua
2009-01-01
Water purification process involves many complex physical and chemical phenomena, and thus it is difficult to realize optimum coagulant dosing control with traditional methods. In this paper, the feasibility of applying a fuzzy logic inference to determine optimum chemical dosage and to control the performance of coagulation process was discussed. Prior to field test, a fuzzy logic based control scheme,
Real-Time Fuzzy Control of Sensorless PM Drive Systems Dr. Kasim M. Al-Aubidy
Real-Time Fuzzy Control of Sensorless PM Drive Systems Dr. Kasim M. Al-Aubidy Philadelphia drive systems. In this paper, a fuzzy logic controller is proposed for the real-time control by a single-chip microcontroller for real-time applications. . Keywords: Sensorless drive systems, Rotor
A self-adjusting fuzzy control for the drum water level
Yinong Zhang; Hongxing Li
2010-01-01
Based on the analysis of dynamic characteristics of the drum water level, a control strategy of self-adjusting PID fuzzy control of the drum water level in a CFB boiler is presented in this paper. Compared the effectiveness of self-adjusting PID fuzzy control with the general PID on the model of the drum water level in a CFB boiler and the