WorldWideScience

Sample records for intelligent fuzzy controller

  1. Fuzzy Logic Controller Design for Intelligent Robots

    Directory of Open Access Journals (Sweden)

    Ching-Han Chen

    2017-01-01

    Full Text Available This paper presents a fuzzy logic controller by which a robot can imitate biological behaviors such as avoiding obstacles or following walls. The proposed structure is implemented by integrating multiple ultrasonic sensors into a robot to collect data from a real-world environment. The decisions that govern the robot’s behavior and autopilot navigation are driven by a field programmable gate array- (FPGA- based fuzzy logic controller. The validity of the proposed controller was demonstrated by simulating three real-world scenarios to test the bionic behavior of a custom-built robot. The results revealed satisfactorily intelligent performance of the proposed fuzzy logic controller. The controller enabled the robot to demonstrate intelligent behaviors in complex environments. Furthermore, the robot’s bionic functions satisfied its design objectives.

  2. Intelligent control-III: fuzzy control system

    International Nuclear Information System (INIS)

    Nagrial, M.H.

    2004-01-01

    During the last decade or so, fuzzy logic control (FLC) has emerged as one of the most active and fruitful areas of research and development. The applications include industrial process control to medical diagnostic and financial markets. Many consumer products using this technology are available in the market place. FLC is best suited to complex ill-defined processes that can be controlled by a skilled human operator without much knowledge of their underlying dynamics. This lecture will cover the basic architecture and the design methodology of fuzzy logic controllers. FLC will be strongly based on the concepts of fuzzy set theory, introduced in first lecture. Some practical applications will also be discussed and presented. (author)

  3. Adaptive Fuzzy-Lyapunov Controller Using Biologically Inspired Swarm Intelligence

    Directory of Open Access Journals (Sweden)

    Alejandro Carrasco Elizalde

    2008-01-01

    Full Text Available The collective behaviour of swarms produces smarter actions than those achieved by a single individual. Colonies of ants, flocks of birds and fish schools are examples of swarms interacting with their environment to achieve a common goal. This cooperative biological intelligence is the inspiration for an adaptive fuzzy controller developed in this paper. Swarm intelligence is used to adjust the parameters of the membership functions used in the adaptive fuzzy controller. The rules of the controller are designed using a computing-with-words approach called Fuzzy-Lyapunov synthesis to improve the stability and robustness of an adaptive fuzzy controller. Computing-with-words provides a powerful tool to manipulate numbers and symbols, like words in a natural language.

  4. Fuzzy gain scheduling of velocity PI controller with intelligent learning algorithm for reactor control

    International Nuclear Information System (INIS)

    Dong Yun Kim; Poong Hyun Seong; .

    1997-01-01

    In this research, we propose a fuzzy gain scheduler (FGS) with an intelligent learning algorithm for a reactor control. In the proposed algorithm, the gradient descent method is used in order to generate the rule bases of a fuzzy algorithm by learning. These rule bases are obtained by minimizing an objective function, which is called a performance cost function. The objective of the FGS with an intelligent learning algorithm is to generate gains, which minimize the error of system. The proposed algorithm can reduce the time and effort required for obtaining the fuzzy rules through the intelligent learning function. It is applied to reactor control of nuclear power plant (NPP), and the results are compared with those of a conventional PI controller with fixed gains. As a result, it is shown that the proposed algorithm is superior to the conventional PI controller. (author)

  5. Intelligent neural network and fuzzy logic control of industrial and power systems

    Science.gov (United States)

    Kuljaca, Ognjen

    The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for the several algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed. An experimental method for determining describing function of SISO fuzzy controller is given. The adaptive neural network tracking controller for an autonomous underwater vehicle is analyzed. A novel stability proof is provided. The implementation of the backstepping neural network controller for the coupled motor drives is described. Analysis and synthesis of adaptive critic neural network control is also provided in the work. Novel tuning laws for the system with action generating neural network and adaptive fuzzy critic are given. Stability proofs are derived for all those control methods. It is shown how these control algorithms and approaches can be used in practical engineering control. Stability proofs are given. Adaptive fuzzy logic control is analyzed. Simulation study is conducted to analyze the behavior of the adaptive fuzzy system on the different environment changes. A novel stability proof for adaptive fuzzy logic systems is given. Also, adaptive elastic fuzzy logic control architecture is described and analyzed. A novel membership function is used for elastic fuzzy logic system. The stability proof is proffered. Adaptive elastic fuzzy logic control is compared with the adaptive nonelastic fuzzy logic control. The work described in this dissertation serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of

  6. Intelligent control aspects of fuzzy logic and neural nets

    CERN Document Server

    Harris, C J; Brown, M

    1993-01-01

    With increasing demands for high precision autonomous control over wide operating envelopes, conventional control engineering approaches are unable to adequately deal with system complexity, nonlinearities, spatial and temporal parameter variations, and with uncertainty. Intelligent Control or self-organising/learning control is a new emerging discipline that is designed to deal with problems. Rather than being model based, it is experiential based. Intelligent Control is the amalgam of the disciplines of Artificial Intelligence, Systems Theory and Operations Research. It uses most recent expe

  7. Wireless Intelligent Monitoring and Control System of Greenhouse Temperature Based on Fuzzy-PID

    Directory of Open Access Journals (Sweden)

    Mei ZHAN

    2014-03-01

    Full Text Available Control effect is not ideal for traditional control method and wired control system, since greenhouse temperature has such characteristics as nonlinear and longtime lag. Therefore, Fuzzy- PID control method was introduced and radio frequency chip CC1110 was applied to design greenhouse wireless intelligent monitoring and control system. The design of the system, the component of nodes and the developed intelligent management software system were explained in this paper. Then describe the design of the control algorithm Fuzzy-PID. By simulating the new method in Matlab software, the results showed that Fuzzy-PID method small overshoot and better dynamic performance compared with general PID control. It has shorter settling time and no steady-state error compared with fuzzy control. It can meet requirements in greenhouse production.

  8. Intelligent control-I: review of fuzzy logic and fuzzy set theory

    International Nuclear Information System (INIS)

    Nagrial, M.H.

    2004-01-01

    In the past decade or so, fuzzy systems have supplanted conventional technologies in many engineering systems, in particular in control systems and pattern recognition. Fuzzy logic has found applications in a variety of consumer products e.g. washing machines, camcorders, digital cameras, air conditioners, subway trains, cement kilns and many others. The fuzzy technology is also being applied in information technology, where it provides decision-support and expert systems with powerful reasoning capabilities. Fuzzy sets, introduced by Zadeh in 1965 as a mathematical way to represent vagueness in linguistics, can be considered a generalisation of classical set theory. Fuzziness is often confused with probability. This lecture will introduce the principal concepts and mathematical notions of fuzzy set theory. (author)

  9. Intelligent control of PV system on the basis of the fuzzy recurrent neuronet*

    Science.gov (United States)

    Engel, E. A.; Kovalev, I. V.; Engel, N. E.

    2016-04-01

    This paper presents the fuzzy recurrent neuronet for PV system’s control. Based on the PV system’s state, the fuzzy recurrent neural net tracks the maximum power point under random perturbations. The validity and advantages of the proposed intelligent control of PV system are demonstrated by numerical simulations. The simulation results show that the proposed intelligent control of PV system achieves real-time control speed and competitive performance, as compared to a classical control scheme on the basis of the perturbation & observation algorithm.

  10. Fuzzy gain scheduling of velocity PI controller with intelligent learning algorithm for reactor control

    International Nuclear Information System (INIS)

    Kim, Dong Yun

    1997-02-01

    In this research, we propose a fuzzy gain scheduler (FGS) with an intelligent learning algorithm for a reactor control. In the proposed algorithm, the gradient descent method is used in order to generate the rule bases of a fuzzy algorithm by learning. These rule bases are obtained by minimizing an objective function, which is called a performance cost function. The objective of the FGS with an intelligent learning algorithm is to generate adequate gains, which minimize the error of system. The proposed algorithm can reduce the time and efforts required for obtaining the fuzzy rules through the intelligent learning function. The evolutionary programming algorithm is modified and adopted as the method in order to find the optimal gains which are used as the initial gains of FGS with learning function. It is applied to reactor control of nuclear power plant (NPP), and the results are compared with those of a conventional PI controller with fixed gains. As a result, it is shown that the proposed algorithm is superior to the conventional PI controller

  11. Intelligent control a hybrid approach based on fuzzy logic, neural networks and genetic algorithms

    CERN Document Server

    Siddique, Nazmul

    2014-01-01

    Intelligent Control considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller.  The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of t...

  12. Intelligent Mechatronics Systems for Transport Climate Parameters Optimization Using Fuzzy Logic Control

    OpenAIRE

    Beinarts, I; Ļevčenkovs, A; Kuņicina, N

    2007-01-01

    In article interest is concentrated on the climate parameters optimization in passengers’ salon of public electric transportation vehicles. The article presents mathematical problem for using intelligent agents in mechatronics problems for climate parameters optimal control. Idea is to use fuzzy logic and intelligent algorithms to create coordination mechanism for climate parameters control to save electrical energy, and it increases the level of comfort for passengers. A special interest for...

  13. Fuzzy gain scheduling of velocity PI controller with intelligent learning algorithm for reactor control

    International Nuclear Information System (INIS)

    Kim, Dong Yun; Seong, Poong Hyun

    1996-01-01

    In this study, we proposed a fuzzy gain scheduler with intelligent learning algorithm for a reactor control. In the proposed algorithm, we used the gradient descent method to learn the rule bases of a fuzzy algorithm. These rule bases are learned toward minimizing an objective function, which is called a performance cost function. The objective of fuzzy gain scheduler with intelligent learning algorithm is the generation of adequate gains, which minimize the error of system. The condition of every plant is generally changed as time gose. That is, the initial gains obtained through the analysis of system are no longer suitable for the changed plant. And we need to set new gains, which minimize the error stemmed from changing the condition of a plant. In this paper, we applied this strategy for reactor control of nuclear power plant (NPP), and the results were compared with those of a simple PI controller, which has fixed gains. As a result, it was shown that the proposed algorithm was superior to the simple PI controller

  14. A Lateral Control Method of Intelligent Vehicle Based on Fuzzy Neural Network

    Directory of Open Access Journals (Sweden)

    Linhui Li

    2015-01-01

    Full Text Available A lateral control method is proposed for intelligent vehicle to track the desired trajectory. Firstly, a lateral control model is established based on the visual preview and dynamic characteristics of intelligent vehicle. Then, the lateral error and orientation error are melded into an integrated error. Considering the system parameter perturbation and the external interference, a sliding model control is introduced in this paper. In order to design a sliding surface, the integrated error is chosen as the parameter of the sliding mode switching function. The sliding mode switching function and its derivative are selected as two inputs of the controller, and the front wheel angle is selected as the output. Next, a fuzzy neural network is established, and the self-learning functions of neural network is utilized to construct the fuzzy rules. Finally, the simulation results demonstrate the effectiveness and robustness of the proposed method.

  15. Neural-Network-Based Fuzzy Logic Navigation Control for Intelligent Vehicles

    Directory of Open Access Journals (Sweden)

    Ahcene Farah

    2002-06-01

    Full Text Available This paper proposes a Neural-Network-Based Fuzzy logic system for navigation control of intelligent vehicles. First, the use of Neural Networks and Fuzzy Logic to provide intelligent vehicles  with more autonomy and intelligence is discussed. Second, the system  for the obstacle avoidance behavior is developed. Fuzzy Logic improves Neural Networks (NN obstacle avoidance approach by handling imprecision and rule-based approximate reasoning. This system must make the vehicle able, after supervised learning, to achieve two tasks: 1- to make one’s way towards its target by a NN, and 2- to avoid static or dynamic obstacles by a Fuzzy NN capturing the behavior of a human expert. Afterwards, two association phases between each task and the appropriate actions are carried out by Trial and Error learning and their coordination allows to decide the appropriate action. Finally, the simulation results display the generalization and adaptation abilities of the system by testing it in new unexplored environments.

  16. Comparison of intelligent fuzzy based AGC coordinated PID controlled and PSS controlled AVR system

    Energy Technology Data Exchange (ETDEWEB)

    Mukherjee, V. [Department of Electrical Engineering, Asansol Engineering College, Asansol, West Bengal (India); Ghoshal, S.P. [Department of Electrical Engineering, National Institute of Technology, Durgapur, West Bengal (India)

    2007-11-15

    This paper attempts to investigate the performance of intelligent fuzzy based coordinated control of the Automatic Generation Control (AGC) loop and the excitation loop equipped with Proportional Integral Derivative (PID) controlled Automatic Voltage Regulator (AVR) system and Power System Stabilizer (PSS) controlled AVR system. The work establishes that PSS controlled AVR system is much more robust in dynamic performance of the system over a wide range of system operating configurations. Thus, it is revealed that PSS equipped AVR is much more superior than PID equipped AVR in damping the oscillation resulting in improved transient response. The paper utilizes a novel class of Particle Swarm Optimization (PSO) termed as Craziness based Particle Swarm Optimization (CRPSO) as optimizing tool to get optimal tuning of PSS parameters as well as the gains of PID controllers. For on-line, off-nominal operating conditions Takagi Sugeno Fuzzy Logic (TSFL) has been applied to obtain the off-nominal optimal gains of PID controllers and parameters of PSS. Implementation of TSFL helps to achieve very fast dynamic response. Fourth order model of generator with AVR and high gain thyristor excitation system is considered for PSS controlled system while normal gain exciter is considered for PID controlled system. Simulation study also reveals that with high gain exciter, PID control is not at all effective. Transient responses are achieved by using modal analysis. (author)

  17. Voltage Stability Control of Electrical Network Using Intelligent Load Shedding Strategy Based on Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    Houda Jouini

    2010-01-01

    Full Text Available As a perspective to ensure the power system stability and to avoid the vulnerability leading to the blackouts, several preventive and curative means are adopted. In order to avoid the voltage collapse, load shedding schemes represent a suitable action to maintain the power system service quality and to control its vulnerability. In this paper, we try to propose an intelligent load shedding strategy as a new approach based on fuzzy controllers. This strategy was founded on the calculation of generated power sensitivity degree related to those injected at different network buses. During the fault phase, fuzzy controller algorithms generate monitor vectors ensuring a precalculated load shedding ratio in the purpose to reestablish the power balance and conduct the network to a new steady state.

  18. Design of Power Cable UAV Intelligent Patrol System Based on Adaptive Kalman Filter Fuzzy PID Control

    Directory of Open Access Journals (Sweden)

    Chen Siyu

    2017-01-01

    Full Text Available Patrol UAV has poor aerial posture stability and is largely affected by anthropic factors, which lead to some shortages such as low power cable tracking precision, captured image loss and inconvenient temperature measurement, etc. In order to solve these disadvantages, this article puts forward a power cable intelligent patrol system. The core innovation of the system is a 360° platform. This collects the position information of power cables by using far infrared sensors and carries out real-time all-direction adjustment of UAV lifting platform through the adaptive Kalman filter fuzzy PID control algorithm, so that the precise tracking of power cables is achieved. An intelligent patrol system is established to detect the faults more accurately, so that a high intelligence degree of power cable patrol system is realized.

  19. Research on the Intelligent Control and Simulation of Automobile Cruise System Based on Fuzzy System

    Directory of Open Access Journals (Sweden)

    Xue-wen Chen

    2016-01-01

    Full Text Available In order to improve the active safety driving vehicle and alleviate the intension of driving fatigue, an intelligent control strategy of automobile cruise is put forward based on the throttle or braking pedal combined control adopting the fuzzy control theory. A fuzzy logic controller is presented, which consists of the two input variables, the deviation of the theoretical safe distance and relative distance and the relative velocity between the preceding vehicle and the cruise vehicle, and the single output variable, that is, the throttle opening or the braking pedal travel. Taking the test data of 1.6 L vehicle with auto-transmission as an example, the function on the intelligent cruise control system is simulated adopting MATLAB/Simulink aiming at different working conditions on the city road. The simulation results show that the control strategy possesses integrated capability of automated Stop & Go control, actively following the preceding vehicle on the conditions of keeping the safety distance and the constant velocity cruise. The research results can offer the theory and technology reference for setting dSPACE type and developing the integrated control product of automobile cruise system.

  20. Development of Intelligent Fuzzy Controller for a Two-Axis Solar Tracking System

    Directory of Open Access Journals (Sweden)

    Cong-Hui Huang

    2016-05-01

    Full Text Available This paper proposes the development of a two-axis sun tracking solar energy system using fuzzy logic as intelligent quality policy. To achieve maximum efficiency for solar panels, it is necessary to follow the sun’s path in the sky. Therefore, the architecture for the two-axis sun tracking solar energy system uses software to control the hardware. The hardware comprises (i solar cells; (ii lead-acid batteries; (iii a gear box; (iv a stepping motor; and (v a light detection circuit, while the software comprises (i a detection system; (ii a fuzzy tracking controller; and (iii a database system. A fuzzy logic controller is designed as the software architecture of the system to decide the timing for tracking the sun. The nearest position that results in receiving direct sunlight is obtained from the database. Our system is fully automatic in a changing environment and takes into account meteorological changes and the effects of the external environment arising from a malfunction. This approach reduces the number of starting motors and results in smaller energy loss in cloudy, cloud mask, or unstable weather conditions.

  1. Perancangan Autonomous Landing pada Quadcopter Menggunakan Behavior-Based Intelligent Fuzzy Control

    Directory of Open Access Journals (Sweden)

    Chalidia Nurin Hamdani

    2013-09-01

    Full Text Available Quadcopter adalah salah satu platform unmanned aerial vehicle (UAV yang saat ini banyak diriset karena kemampuannya melakukan take-off dan landing secara vertikal. Karena menggunakan 4 motor brushless sebagai penggerak utama, quadcopter memiliki kompleksitas yang cukup tinggi baik dalam pemodelan maupun pengendalian. Landing merupakan salah satu mekanisme pada quadcopter yang membutuhkan kecepatan yang akurat dan aman dengan tetap mempertahankan keseimbangan. Pada penelitian ini, penulis menggunakan Behavior-Based Intelligent Fuzzy Control (BBIFC sebagai dasar kontrol untuk penerapan autonomous landing pada quadcopter. BBIFC adalah salah satu skema high-level control di mana desain kontrol terdiri dari beberapa layer. Ada 2 layer yang digunakan pada penelitian ini yaitu layer untuk pengendalian sudut pitch, roll, yaw dan layer untuk pengendalian ketinggian. Setiap layer memiliki mekanisme kontrol yang berbeda yang didesain menggunakan Intelligent Fuzzy Controller dan kontroler PID. Dengan metode ini dihasilkan algoritma untuk mekanisme safe autonomous landing dengan mengikuti sinyal eksponensial di mana quadcopter mencapai titik 0 (nol meter dalam waktu 15 detik dan Kontroler PID dapat mengendalikan keseimbangan quadcopter dalam waktu 7.97 detik untuk roll dan pitch serta 1.25 detik untuk yaw sejak gangguan sudut diberikan.

  2. Genetic Fuzzy Trees for Intelligent Control of Unmanned Combat Aerial Vehicles

    Science.gov (United States)

    Ernest, Nicholas D.

    Fuzzy Logic Control is a powerful tool that has found great success in a variety of applications. This technique relies less on complex mathematics and more "expert knowledge" of a system to bring about high-performance, resilient, and efficient control through linguistic classification of inputs and outputs and if-then rules. Genetic Fuzzy Systems (GFSs) remove the need of this expert knowledge and instead rely on a Genetic Algorithm (GA) and have similarly found great success. However, the combination of these methods suffer severely from scalability; the number of rules required to control the system increases exponentially with the number of states the inputs and outputs can take. Therefor GFSs have thus far not been applicable to complex, artificial intelligence type problems. The novel Genetic Fuzzy Tree (GFT) method breaks down complex problems hierarchically, makes sub-decisions when possible, and thus greatly reduces the burden on the GA. This development significantly changes the field of possible applications for GFSs. Within this study, this is demonstrated through applying this technique to a difficult air combat problem. Looking forward to an autonomous Unmanned Combat Aerial Vehicle (UCAV) in the 2030 time-frame, it becomes apparent that the mission, flight, and ground controls will utilize the emerging paradigm of Intelligent Systems (IS); namely, the ability to learn, adapt, exhibit robustness in uncertain situations, make sense of the data collected in real-time and extrapolate when faced with scenarios significantly different from those used in training. LETHA (Learning Enhanced Tactical Handling Algorithm) was created to develop intelligent controllers for these advanced unmanned craft as the first GFT. A simulation space referred to as HADES (Hoplological Autonomous Defend and Engage Simulation) was created in which LETHA can train the UCAVs. Equipped with advanced sensors, a limited supply of Self-Defense Missiles (SDMs), and a recharging

  3. Why fuzzy controllers should be fuzzy

    International Nuclear Information System (INIS)

    Nowe, A.

    1996-01-01

    Fuzzy controllers are usually looked at as crisp valued mappings especially when artificial intelligence learning techniques are used to build up the controller. By doing so the semantics of a fuzzy conclusion being a fuzzy restriction on the viable control actions is non-existing. In this paper the authors criticise from an approximation point of view using a fuzzy controller to express a crisp mapping does not seem the right way to go. Secondly it is illustrated that interesting information is contained in a fuzzy conclusion when indeed this conclusion is considered as a fuzzy restriction. This information turns out to be very valuable when viability problems are concerned, i.e. problems where the objective is to keep a system within predefined boundaries

  4. Fuzzy Sliding Mode Lateral Control of Intelligent Vehicle Based on Vision

    Directory of Open Access Journals (Sweden)

    Linhui Li

    2013-01-01

    Full Text Available The lateral control of intelligent vehicle is studied in this paper, with the intelligent vehicle DLUIV-1 based on visual navigation as the object of research. Firstly, the lateral control model based on visual preview is established. The kinematics model based on visual preview, including speed and other factors, is used to calculate the lateral error and direction error. Secondly, according to the characteristics of lateral control, an efficient strategy of intelligent vehicle lateral mode is proposed. The integration of the vehicle current lateral error and direction error is chosen as the parameter of the sliding mode switching function to design the sliding surface. The control variables are adjusted according to the fuzzy control rules to ensure that they meet the existence and reaching condition. The sliding mode switching function is regarded as the control objective, to ensure the stability of the steering wheel rotation. Simulation results show that the lateral controller can guarantee high path-tracking accuracy and strong robustness for the change of model parameters.

  5. Intelligent PV Power Smoothing Control Using Probabilistic Fuzzy Neural Network with Asymmetric Membership Function

    Directory of Open Access Journals (Sweden)

    Faa-Jeng Lin

    2017-01-01

    Full Text Available An intelligent PV power smoothing control using probabilistic fuzzy neural network with asymmetric membership function (PFNN-AMF is proposed in this study. First, a photovoltaic (PV power plant with a battery energy storage system (BESS is introduced. The BESS consisted of a bidirectional DC/AC 3-phase inverter and LiFePO4 batteries. Then, the difference of the actual PV power and smoothed power is supplied by the BESS. Moreover, the network structure of the PFNN-AMF and its online learning algorithms are described in detail. Furthermore, the three-phase output currents of the PV power plant are converted to the dq-axis current components. The resulted q-axis current is the input of the PFNN-AMF power smoothing control, and the output is a smoothing PV power curve to achieve the effect of PV power smoothing. Comparing to the other smoothing methods, a minimum energy capacity of the BESS with a small fluctuation of the grid power can be achieved by the PV power smoothing control using PFNN-AMF. In addition, a personal computer- (PC- based PV power plant emulator and BESS are built for the experimentation. From the experimental results of various irradiance variation conditions, the effectiveness of the proposed intelligent PV power smoothing control can be verified.

  6. Fuzzy Logic Based Controller for Maintaining Human Comfort within Intelligent Building System

    Directory of Open Access Journals (Sweden)

    Nasrodin .T. Mustapha, Momoh J. E. Salami, Nazim and M. Nasiri

    2012-10-01

    Full Text Available This paper presents an intelligent control approach for air handling unit (AHU which is an integral part of heat, ventilation, and air conditioning (HVAC system. In the past years various control design for HVAC have been proposed as this system remarkably consumes very high energy. But most of the proposed designs were focused on the control flow of heat-transfer medium such as chilled or heated water while the importance of the efficient mixture of outdoor and indoor enthalpies is sometimes ignored. These enthalpies invariably determine the best strategy to overcome thermal load in a controlled environment to satisfy human comfort, hence a control design strategy must be able to efficiently regulate the flow and mixture of outdoor and indoor enthalpies by a proper control of AHU dampers and fans. This approach requires sensors to measure temperature and relative humidity of both outdoor and indoor environments. However, unpredictable level of disturbances coming from many sources including heat generated by occupants, electrical items and air leaking and the continuous changes of outdoor enthalpy makes it difficult to model the process. Consequently, conventional controllers are not suitable, hence the use of fuzzy logic controller (FLC is proposed in this paper. This proposed controller operates in a master and slave control loop so as to control the AHU dampers and fans with adjustable output membership function whilst at the same time a scaling-factor method is used to drive the master operation. To implement the proposed system, a small scale prototype has been designed and fabricated. This prototype is an AHU model which consists of ductwork, temperature and humidity sensors, dampers, air cooling and heating systems. A small box is used as a conditioning space in which a room temperature is measured. The control algorithm is programmed using National Instrument (NI LabVIEW and executed using NI FieldPoint. Experimental results reveal that

  7. The Design of Artificial Intelligence Robot Based on Fuzzy Logic Controller Algorithm

    Science.gov (United States)

    Zuhrie, M. S.; Munoto; Hariadi, E.; Muslim, S.

    2018-04-01

    Artificial Intelligence Robot is a wheeled robot driven by a DC motor that moves along the wall using an ultrasonic sensor as a detector of obstacles. This study uses ultrasonic sensors HC-SR04 to measure the distance between the robot with the wall based ultrasonic wave. This robot uses Fuzzy Logic Controller to adjust the speed of DC motor. When the ultrasonic sensor detects a certain distance, sensor data is processed on ATmega8 then the data goes to ATmega16. From ATmega16, sensor data is calculated based on Fuzzy rules to drive DC motor speed. The program used to adjust the speed of a DC motor is CVAVR program (Code Vision AVR). The readable distance of ultrasonic sensor is 3 cm to 250 cm with response time 0.5 s. Testing of robots on walls with a setpoint value of 9 cm to 10 cm produce an average error value of -12% on the wall of L, -8% on T walls, -8% on U wall, and -1% in square wall.

  8. Intelligent PID controller based on ant system algorithm and fuzzy inference and its application to bionic artificial leg

    Institute of Scientific and Technical Information of China (English)

    谭冠政; 曾庆冬; 李文斌

    2004-01-01

    A designing method of intelligent proportional-integral-derivative(PID) controllers was proposed based on the ant system algorithm and fuzzy inference. This kind of controller is called Fuzzy-ant system PID controller. It consists of an off-line part and an on-line part. In the off-line part, for a given control system with a PID controller,by taking the overshoot, setting time and steady-state error of the system unit step response as the performance indexes and by using the ant system algorithm, a group of optimal PID parameters K*p , Ti* and T*d can be obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-line part, based on Kp* , Ti*and Td* and according to the current system error e and its time derivative, a specific program is written, which is used to optimize and adjust the PID parameters on-line through a fuzzy inference mechanism to ensure that the system response has optimal transient and steady-state performance. This kind of intelligent PID controller can be used to control the motor of the intelligent bionic artificial leg designed by the authors. The result of computer simulation experiment shows that the controller has less overshoot and shorter setting time.

  9. Design, Modelling, and Implementation of a Fuzzy Controller for an Intelligent Road Signaling System

    Directory of Open Access Journals (Sweden)

    José Manuel Lozano Domínguez

    2018-01-01

    Full Text Available Crossing points are not always 100% visible for drivers due to different factors (e.g., poor road maintenance, occlusion of vertical signs, and adverse weather conditions. USA estimated in 2015 the number of traffic accidents involving pedestrians and vehicles in 70,000 of whom 5,376 resulted in deceased people. To contribute in this field, this paper presents the design, implementation, and testing of a smart prototype system applied to pedestrian crossings—not regulated by semaphores—which try to reduce the accident rate on roads. The hardware and software system consists of a set of autonomous, intelligent, and wireless low-cost devices that generate a visual warning barrier perceived by drivers from a suitable distance when pedestrians traverse a crosswalk. In this way, drivers can reduce the speed of their vehicles and stop safely. The system’s intelligence is carried out by a fuzzy controller that performs sensory fusion at both low level and high level with various types of sensors from local and neighboring devices. The tests conducted have determined an average success of 94.64% and a precision of 100%, thus corresponding with a very good test according to a ROC analysis. As a result, the system proposed has been patented and extended to international PCT.

  10. Fuzzy Control Teaching Models

    Directory of Open Access Journals (Sweden)

    Klaus-Dietrich Kramer

    2016-05-01

    Full Text Available Many degree courses at technical universities include the subject of control systems engineering. As an addition to conventional approaches Fuzzy Control can be used to easily find control solutions for systems, even if they include nonlinearities. To support further educational training, models which represent a technical system to be controlled are required. These models have to represent the system in a transparent and easy cognizable manner. Furthermore, a programming tool is required that supports an easy Fuzzy Control development process, including the option to verify the results and tune the system behavior. In order to support the development process a graphical user interface is needed to display the fuzzy terms under real time conditions, especially with a debug system and trace functionality. The experiences with such a programming tool, the Fuzzy Control Design Tool (FHFCE Tool, and four fuzzy teaching models will be presented in this paper. The methodical and didactical objective in the utilization of these teaching models is to develop solution strategies using Computational Intelligence (CI applications for Fuzzy Controllers in order to analyze different algorithms of inference or defuzzyfication and to verify and tune those systems efficiently.

  11. Fuzzy logic applied to the control of the energy consumption in intelligent buildings; Logica fuzzy aplicada ao controle do consumo de energia eletrica em edificios inteligentes

    Energy Technology Data Exchange (ETDEWEB)

    Costa, Herbert R. do N.

    1998-02-01

    This work shows a study on the using of fuzzy control algorithms for the energy optimization of a standard building. The simulation of this type of control was performed using a central conditioned air model and the fuzzy control architecture already used in various control projects. This situation allowed a comparative study among the the control algorithms normally used in conditioned air installations, and the control performed through the building automation system, using an algorithm based on fuzzy logic.

  12. A Review of Fuzzy Logic and Neural Network Based Intelligent Control Design for Discrete-Time Systems

    Directory of Open Access Journals (Sweden)

    Yiming Jiang

    2016-01-01

    Full Text Available Over the last few decades, the intelligent control methods such as fuzzy logic control (FLC and neural network (NN control have been successfully used in various applications. The rapid development of digital computer based control systems requires control signals to be calculated in a digital or discrete-time form. In this background, the intelligent control methods developed for discrete-time systems have drawn great attentions. This survey aims to present a summary of the state of the art of the design of FLC and NN-based intelligent control for discrete-time systems. For discrete-time FLC systems, numerous remarkable design approaches are introduced and a series of efficient methods to deal with the robustness, stability, and time delay of FLC discrete-time systems are recommended. Techniques for NN-based intelligent control for discrete-time systems, such as adaptive methods and adaptive dynamic programming approaches, are also reviewed. Overall, this paper is devoted to make a brief summary for recent progresses in FLC and NN-based intelligent control design for discrete-time systems as well as to present our thoughts and considerations of recent trends and potential research directions in this area.

  13. AN INTELLIGENT NEURO-FUZZY TERMINAL SLIDING MODE CONTROL METHOD WITH APPLICATION TO ATOMIC FORCE MICROSCOPE

    Directory of Open Access Journals (Sweden)

    Seied Yasser Nikoo

    2016-11-01

    Full Text Available In this paper, a neuro-fuzzy fast terminal sliding mode control method is proposed for controlling a class of nonlinear systems with bounded uncertainties and disturbances. In this method, a nonlinear terminal sliding surface is firstly designed. Then, this sliding surface is considered as input for an adaptive neuro-fuzzy inference system which is the main controller. A proportinal-integral-derivative controller is also used to asist the neuro-fuzzy controller in order to improve the performance of the system at the begining stage of control operation. In addition, bee algorithm is used in this paper to update the weights of neuro-fuzzy system as well as the parameters of the proportinal-integral-derivative controller. The proposed control scheme is simulated for vibration control in a model of atomic force microscope system and the results are compared with conventional sliding mode controllers. The simulation results show that the chattering effect in the proposed controller is decreased in comparison with the sliding mode and the terminal sliding mode controllers. Also, the method provides the advantages of fast convergence and low model dependency compared to the conventional methods.

  14. FUZZY LOGIC BASED INTELLIGENT CONTROL OF A VARIABLE SPEED CAGE MACHINE WIND GENERATION SYSTEM

    Science.gov (United States)

    The paper describes a variable-speed wind generation system where fuzzy logic principles are used to optimize efficiency and enhance performance control. A squirrel cage induction generator feeds the power to a double-sided pulse width modulated converter system which either pump...

  15. Fuzzy logic and intelligent technologies in nuclear science

    International Nuclear Information System (INIS)

    Ruan, D.

    1998-01-01

    The research project on Fuzzy Logic and Intelligent technologies (FLINS) aims to bridge the gap between novel technologies and the nuclear industry. It aims to initiate research and development programs for solving intricate problems pertaining to the nuclear environment by using modern technologies as additional tool. The major achievements for 1997 include the application of the fuzzy-logic to the BR-1 reactor, the elaboration of a Fuzzy-control model as well as contributions to several workshops and publications

  16. Expert systems with fuzzy logic for intelligent diagnosis and control of nuclear power plants

    International Nuclear Information System (INIS)

    Abdelhai, M.I.; Upadhyaya, B.R.

    1990-01-01

    A model-based production-rule analysis system was developed for the tracking and diagnosis of the condition of a reactor coolant system (RCS) using a fuzzy logic algorithm. Since nuclear power plants are large and complex systems, it is natural that vagueness, uncertainty, and imprecision are introduced to such systems. Even in fully automated power plants, the critical diagnostic and control changes must be made by operators who usually express their diagnostic and control strategies linguistically as sets of heuristic decision rules. Therefore, additional imprecisions are introduced into the systems because of the imprecise nature of such qualitative strategies when they are converted into quantitative rules. Such problems, in which the source of imprecision is the absence of sharply defined criteria of class membership, could be dealt with using fuzzy set theory. Hence, a fuzzy logic algorithm could be initiated to implement a known heuristic whenever the given information is vague and qualitative, and it will allow operators to introduce certain linguistic assertions and commands to diagnose and control the system

  17. Intelligent control-II: review of fuzzy systems and theory of approximate reasoning

    International Nuclear Information System (INIS)

    Nagrial, M.H.

    2004-01-01

    Fuzzy systems are knowledge-based or rule-based systems. The heart of a fuzzy systems knowledge base consisting of the so-called fuzzy IF -THEN rules. This paper reviews various aspects of fuzzy IF-THEN rules. The theory of approximate reasoning, which provides a powerful framework for reasoning the imprecise and uncertain information, , is also reviewed. Additional properties of fuzzy systems are also discussed. (author)

  18. Modeling, control, and simulation of grid connected intelligent hybrid battery/photovoltaic system using new hybrid fuzzy-neural method.

    Science.gov (United States)

    Rezvani, Alireza; Khalili, Abbas; Mazareie, Alireza; Gandomkar, Majid

    2016-07-01

    Nowadays, photovoltaic (PV) generation is growing increasingly fast as a renewable energy source. Nevertheless, the drawback of the PV system is its dependence on weather conditions. Therefore, battery energy storage (BES) can be considered to assist for a stable and reliable output from PV generation system for loads and improve the dynamic performance of the whole generation system in grid connected mode. In this paper, a novel topology of intelligent hybrid generation systems with PV and BES in a DC-coupled structure is presented. Each photovoltaic cell has a specific point named maximum power point on its operational curve (i.e. current-voltage or power-voltage curve) in which it can generate maximum power. Irradiance and temperature changes affect these operational curves. Therefore, the nonlinear characteristic of maximum power point to environment has caused to development of different maximum power point tracking techniques. In order to capture the maximum power point (MPP), a hybrid fuzzy-neural maximum power point tracking (MPPT) method is applied in the PV system. Obtained results represent the effectiveness and superiority of the proposed method, and the average tracking efficiency of the hybrid fuzzy-neural is incremented by approximately two percentage points in comparison to the conventional methods. It has the advantages of robustness, fast response and good performance. A detailed mathematical model and a control approach of a three-phase grid-connected intelligent hybrid system have been proposed using Matlab/Simulink. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  19. Fuzzy Logic and Intelligent Technologies in Nuclear Science (FLINS)

    International Nuclear Information System (INIS)

    Da Ruan

    2000-01-01

    FLINS is the acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science. In 1994, SCK-CEN launched a programme on FLINS. The first FLINS project dealt with the specific prototyping of fuzzy logic control (FLC) of the BR-1 research reactor. This project focussed on controlling the power level of the BR1 reactor added value of FLC for both safety and economic aspects for a nuclear reactor control operation. Main achievements in 1999 are reported

  20. Designing PID-Fuzzy Controller for Pendubot System

    Directory of Open Access Journals (Sweden)

    Ho Trong Nguyen

    2017-12-01

    Full Text Available In the paper, authors analize dynamic equation of a pendubot system. Familiar kinds of controller – PID, fuzzy controllers – are concerned. Then, a structure of PID-FUZZY is presented. The comparison of three kinds of controllers – PID, fuzzy and PID-FUZZY shows the better response of system under PID-FUZZY controller. Then, the experiments on the real model also prove the better stabilization of the hybrid controller which is combined between linear and intelligent controller.

  1. Foundations Of Fuzzy Control

    DEFF Research Database (Denmark)

    Jantzen, Jan

    The objective of this textbook is to acquire an understanding of the behaviour of fuzzy logic controllers. Under certain conditions a fuzzy controller is equivalent to a proportional-integral-derivative (PID) controller. Using that equivalence as a link, the book applies analysis methods from...... linear and nonlinear control theory. In the linear domain, PID tuning methods and stability analyses are transferred to linear fuzzy controllers. The Nyquist plot shows the robustness of different settings of the fuzzy gain parameters. As a result, a fuzzy controller is guaranteed to perform as well...... as any PID controller. In the nonlinear domain, the stability of four standard control surfaces is analysed by means of describing functions and Nyquist plots. The self-organizing controller (SOC) is shown to be a model reference adaptive controller. There is a possibility that a nonlinear fuzzy PID...

  2. Fuzzy Logic and Intelligent Technologies in Nuclear Science

    International Nuclear Information System (INIS)

    Da Ruan

    1998-01-01

    FLINS is the acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science. The main task for FLINS is to solve intricate problems pertaining to the nuclear environment by using modern technologies as additional tools and to bridge the gap between novel technologies and the industrial nuclear world. In 1997, major efforts went to the specific prototyping of Fuzzy Logic Control of SCK-CEN's BR1 research Reactor. Progress and achievements are reported

  3. Fuzzy Control Tutorial

    DEFF Research Database (Denmark)

    Dotoli, M.; Jantzen, Jan

    1999-01-01

    The tutorial concerns automatic control of an inverted pendulum, especially rule based control by means of fuzzy logic. A ball balancer, implemented in a software simulator in Matlab, is used as a practical case study. The objectives of the tutorial are to teach the basics of fuzzy control......, and to show how to apply fuzzy logic in automatic control. The tutorial is distance learning, where students interact one-to-one with the teacher using e-mail....

  4. Fuzzy control and identification

    CERN Document Server

    Lilly, John H

    2010-01-01

    This book gives an introduction to basic fuzzy logic and Mamdani and Takagi-Sugeno fuzzy systems. The text shows how these can be used to control complex nonlinear engineering systems, while also also suggesting several approaches to modeling of complex engineering systems with unknown models. Finally, fuzzy modeling and control methods are combined in the book, to create adaptive fuzzy controllers, ending with an example of an obstacle-avoidance controller for an autonomous vehicle using modus ponendo tollens logic.

  5. Fuzzy logic control for camera tracking system

    Science.gov (United States)

    Lea, Robert N.; Fritz, R. H.; Giarratano, J.; Jani, Yashvant

    1992-01-01

    A concept utilizing fuzzy theory has been developed for a camera tracking system to provide support for proximity operations and traffic management around the Space Station Freedom. Fuzzy sets and fuzzy logic based reasoning are used in a control system which utilizes images from a camera and generates required pan and tilt commands to track and maintain a moving target in the camera's field of view. This control system can be implemented on a fuzzy chip to provide an intelligent sensor for autonomous operations. Capabilities of the control system can be expanded to include approach, handover to other sensors, caution and warning messages.

  6. Smart Spectrometer for Distributed Fuzzy Control

    OpenAIRE

    Benoit, Eric; Foulloy, Laurent

    2009-01-01

    Document rédigé sous FrameMaker (pas sous Latex); International audience; If the main use of colour measurement is the metrology, it is now possible to find industrial control applications which uses this information. Using colour in process control leads to specific problems where human perception has to be replaced by colour sensors. This paper relies on the fuzzy representation of colours that can be taken into account by fuzzy controllers. If smart sensors already include intelligent func...

  7. Some uses and limitations of Fuzzy Logic in artificial intelligence reasoning for reactor control

    International Nuclear Information System (INIS)

    Guth, M.A.S.

    1989-01-01

    This paper describes some potential uses for Fuzzy Logic as well as its limitations based on experience designing a small prototype expert system that can be used in a computer laboratory to study a government research reactor. The expert system designed in this study diagnoses problems in the interface between the heat exchanger and the core. Engineers who had first-hand experience with the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory suggested logical relations incorporated in the knowledge base. The expert system has a production rule backward-chaining-based architecture, and the knowledge base incorporates four kinds of information. First, the structural relationship between causes and consequences are given by nuclear engineering experts. Second, numerical values for the initiating events can be taken from observed performance of the HFIR during normal conditions. Third, the causes of particular events are ordinally ranked by their expected chance of occuring based on a combination of knowledge about the reactor design and actual experiences with the reactor in operation. Fourth, Bellman-Zadeh Fuzzy Logic is introduced to maintain truth values for expert system parameter values that can be true with some degree of certainty. (orig.)

  8. Model predictive control using fuzzy decision functions

    NARCIS (Netherlands)

    Kaymak, U.; Costa Sousa, da J.M.

    2001-01-01

    Fuzzy predictive control integrates conventional model predictive control with techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the

  9. A novel optimized hybrid fuzzy logic intelligent PID controller for an interconnected multi-area power system with physical constraints and boiler dynamics.

    Science.gov (United States)

    Gomaa Haroun, A H; Li, Yin-Ya

    2017-11-01

    In the fast developing world nowadays, load frequency control (LFC) is considered to be a most significant role for providing the power supply with good quality in the power system. To deliver a reliable power, LFC system requires highly competent and intelligent control technique. Hence, in this article, a novel hybrid fuzzy logic intelligent proportional-integral-derivative (FLiPID) controller has been proposed for LFC of interconnected multi-area power systems. A four-area interconnected thermal power system incorporated with physical constraints and boiler dynamics is considered and the adjustable parameters of the FLiPID controller are optimized using particle swarm optimization (PSO) scheme employing an integral square error (ISE) criterion. The proposed method has been established to enhance the power system performances as well as to reduce the oscillations of uncertainties due to variations in the system parameters and load perturbations. The supremacy of the suggested method is demonstrated by comparing the simulation results with some recently reported heuristic methods such as fuzzy logic proportional-integral (FLPI) and intelligent proportional-integral-derivative (PID) controllers for the same electrical power system. the investigations showed that the FLiPID controller provides a better dynamic performance and outperform compared to the other approaches in terms of the settling time, and minimum undershoots of the frequency as well as tie-line power flow deviations following a perturbation, in addition to perform appropriate settlement of integral absolute error (IAE). Finally, the sensitivity analysis of the plant is inspected by varying the system parameters and operating load conditions from their nominal values. It is observed that the suggested controller based optimization algorithm is robust and perform satisfactorily with the variations in operating load condition, system parameters and load pattern. Copyright © 2017 ISA. Published by

  10. An intelligent nuclear reactor core controller for load following operations, using recurrent neural networks and fuzzy systems

    International Nuclear Information System (INIS)

    Boroushaki, M.; Ghofrani, M.B.; Lucas, C.; Yazdanpanah, M.J.

    2003-01-01

    In the last decade, the intelligent control community has paid great attention to the topic of intelligent control systems for nuclear plants (core, steam generator...). Papers mostly used approximate and simple mathematical SISO (single-input-single-output) model of nuclear plants for testing and/or tuning of the control systems. They also tried to generalize theses models to a real MIMO (multi-input-multi-output) plant, while nuclear plants are typically of complex nonlinear and multivariable nature with high interactions between their state variables and therefore, many of these proposed intelligent control systems are not appropriate for real cases. In this paper, we designed an on-line intelligent core controller for load following operations, based on a heuristic control algorithm, using a valid and updatable recurrent neural network (RNN). We have used an accurate 3-dimensional core calculation code to represent the real plant and to train the RNN. The results of simulation show that this intelligent controller can control the reactor core during load following operations, using optimum control rod groups manoeuvre and variable overlapping strategy. This methodology represents a simple and reliable procedure for controlling other complex nonlinear MIMO plants, and may improve the responses, comparing to other control systems

  11. Developed adaptive neuro-fuzzy algorithm to control air conditioning ...

    African Journals Online (AJOL)

    The paper developed artificial intelligence technique adaptive neuro-fuzzy controller for air conditioning systems at different pressures. The first order Sugeno fuzzy inference system was implemented and utilized for modeling and controller design. In addition, the estimation of the heat transfer rate and water mass flow rate ...

  12. The foundations of fuzzy control

    CERN Document Server

    Lewis, Harold W

    1997-01-01

    Harold Lewis applied a cross-disciplinary approach in his highly accessible discussion of fuzzy control concepts. With the aid of fifty-seven illustrations, he thoroughly presents a unique mathematical formalism to explain the workings of the fuzzy inference engine and a novel test plant used in the research. Additionally, the text posits a new viewpoint on why fuzzy control is more popular in some countries than in others. A direct and original view of Japanese thinking on fuzzy control methods, based on the author's personal knowledge of - and association with - Japanese fuzzy research, is also included.

  13. A Novel Supercapacitor/Lithium-Ion Hybrid Energy System with a Fuzzy Logic-Controlled Fast Charging and Intelligent Energy Management System

    Directory of Open Access Journals (Sweden)

    Muhammad Adil Khan

    2018-05-01

    Full Text Available The electric powered wheelchair (EPW is an essential assistive tool for people with serious injuries or disability. This manuscript describes the validation of applied research for reducing the charging time of an electric wheelchair using a hybrid electric system (HES composed of a supercapacitor (SC bank and a lithium-ion battery with a fuzzy logic controller (FLC-based fast charging system for Li-ion batteries and a fuzzy logic-based intelligent energy management system (FLIEMS for controlling the power flow within the HES. The fast charging FLC was designed to drive the voltage difference (Vd among the different cells of a multi-cell battery and the cell voltage (Vc of an individual cell. These parameters (voltage difference and cell voltage were used as input voltages to reduce the charge time and activate a bypass equalization (BPE scheme. BPE was introduced in this paper so that the battery operates within the safe voltage range. For SC/Li-ion HES, the FLIEMS presented in this paper controls the bi-directional power flow to smooth the power extracted from Li-ion batteries. Moreover, a dual active bridge isolated bidirectional DC converter (DAB-IBDC was used for power conversion. The DAB-IBDC presented in this paper has the characteristics of galvanic isolation, and high power conversion efficiency compared to the conventional converter circuits due to the reduced reverse power flow and current stresses.

  14. Fuzzy control. Fundamentals, stability and design of fuzzy controllers

    Energy Technology Data Exchange (ETDEWEB)

    Michels, K. [Fichtner GmbH und Co. KG, Stuttgart (Germany); Klawonn, F. [Fachhochschule Braunschweig/Wolfenbuettel (Germany). Fachbereich Informatik; Kruse, R. [Magdeburg Univ. (Germany). Fakultaet Informatik, Abt. Wiss.- und Sprachverarbeitung; Nuernberger, A. (eds.) [California Univ., Berkeley, CA (United States). Computer Science Division

    2006-07-01

    The book provides a critical discussion of fuzzy controllers from the perspective of classical control theory. Special emphases are placed on topics that are of importance for industrial applications, like (self-) tuning of fuzzy controllers, optimisation and stability analysis. The book is written as a textbook for graduate students as well as a comprehensive reference book about fuzzy control for researchers and application engineers. Starting with a detailed introduction to fuzzy systems and control theory the reader is guided to up-to-date research results. (orig.)

  15. Developed adaptive neuro-fuzzy algorithm to control air conditioning ...

    African Journals Online (AJOL)

    user

    The paper developed artificial intelligence technique adaptive neuro-fuzzy ... system is highly appreciated and essential in most of our daily life. ... It can construct an input-output mapping based on human knowledge and specific input-output data ... fuzzy controllers to produce desirable internal temperature and air quality, ...

  16. Ontology-based intelligent fuzzy agent for diabetes application

    NARCIS (Netherlands)

    Acampora, G.; Lee, C.-S.; Wang, M.-H.; Hsu, C.-Y.; Loia, V.

    2009-01-01

    It is widely pointed out that classical ontologies are not sufficient to deal with imprecise and vague knowledge for some real world applications, but the fuzzy ontology can effectively solve data and knowledge with uncertainty. In this paper, an ontology-based intelligent fuzzy agent (OIFA),

  17. Classification of Children Intelligence with Fuzzy Logic Method

    Science.gov (United States)

    Syahminan; ika Hidayati, Permata

    2018-04-01

    Intelligence of children s An Important Thing To Know The Parents Early on. Typing Can be done With a Child’s intelligence Grouping Dominant Characteristics Of each Type of Intelligence. To Make it easier for Parents in Determining The type of Children’s intelligence And How to Overcome them, for It Created A Classification System Intelligence Grouping Children By Using Fuzzy logic method For determination Of a Child’s degree of intelligence type. From the analysis We concluded that The presence of Intelligence Classification systems Pendulum Children With Fuzzy Logic Method Of determining The type of The Child’s intelligence Can be Done in a way That is easier And The results More accurate Conclusions Than Manual tests.

  18. Method of dynamic fuzzy symptom vector in intelligent diagnosis

    International Nuclear Information System (INIS)

    Sun Hongyan; Jiang Xuefeng

    2010-01-01

    Aiming at the requirement of diagnostic symptom real-time updating brought from diagnostic knowledge accumulation and great gap in unit and value of diagnostic symptom in multi parameters intelligent diagnosis, the method of dynamic fuzzy symptom vector is proposed. The concept of dynamic fuzzy symptom vector is defined. Ontology is used to specify the vector elements, and the vector transmission method based on ontology is built. The changing law of symptom value is analyzed and fuzzy normalization method based on fuzzy membership functions is built. An instance proved method of dynamic fussy symptom vector is efficient to solve the problems of symptom updating and unify of symptom value and unit. (authors)

  19. Fuzzy control in environmental engineering

    CERN Document Server

    Chmielowski, Wojciech Z

    2016-01-01

    This book is intended for engineers, technicians and people who plan to use fuzzy control in more or less developed and advanced control systems for manufacturing processes, or directly for executive equipment. Assuming that the reader possesses elementary knowledge regarding fuzzy sets and fuzzy control, by way of a reminder, the first parts of the book contain a reminder of the theoretical foundations as well as a description of the tools to be found in the Matlab/Simulink environment in the form of a toolbox. The major part of the book presents applications for fuzzy controllers in control systems for various manufacturing and engineering processes. It presents seven processes and problems which have been programmed using fuzzy controllers. The issues discussed concern the field of Environmental Engineering. Examples are the control of a flood wave passing through a hypothetical, and then the real Dobczyce reservoir in the Raba River, which is located in the upper Vistula River basin in Southern Poland, th...

  20. Fuzzy Behaviors for Control of Mobile Robots

    Directory of Open Access Journals (Sweden)

    Saleh Zein-Sabatto

    2003-02-01

    Full Text Available In this research work, an RWI B-14 robot has been used as the development platform to embody some basic behaviors that can be combined to build more complex robotics behaviors. Emergency, avoid-obstacle, left wall- following, right wall-following, and move-to-point behaviors have been designed and embodied as basic robot behaviors. The basic behaviors developed in this research are designed based on fuzzy control technique and are integrated and coordinated to from complex robotics system. More behaviors can be added into the system as needed. A robot task can be defined by the user and executed by the intelligent robot control system. Testing results showed that fuzzy behaviors made the robot move intelligently and adapt to changes in its environment.

  1. Intelligent micro blood typing system using a fuzzy algorithm

    International Nuclear Information System (INIS)

    Kang, Taeyun; Cho, Dong-Woo; Lee, Seung-Jae; Kim, Yonggoo; Lee, Gyoo-Whung

    2010-01-01

    ABO typing is the first analysis performed on blood when it is tested for transfusion purposes. The automated machines used in hospitals for this purpose are typically very large and the process is complicated. In this paper, we present a new micro blood typing system that is an improved version of our previous system (Kang et al 2004 Trans. ASME, J. Manuf. Sci. Eng. 126 766, Lee et al 2005 Sensors Mater. 17 113). This system, fabricated using microstereolithography, has a passive valve for controlling the flow of blood and antibodies. The intelligent micro blood typing system has two parts: a single-line micro blood typing device and a fuzzy expert system for grading the strength of agglutination. The passive valve in the single-line micro blood typing device makes the blood stop at the entrance of a micro mixer and lets it flow again after the blood encounters antibodies. Blood and antibodies are mixed in the micro mixer and agglutination occurs in the chamber. The fuzzy expert system then determines the degree of agglutination from images of the agglutinated blood. Blood typing experiments using this device were successful, and the fuzzy expert system produces a grading decision comparable to that produced by an expert conducting a manual analysis

  2. A neural fuzzy controller learning by fuzzy error propagation

    Science.gov (United States)

    Nauck, Detlef; Kruse, Rudolf

    1992-01-01

    In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

  3. Safety critical application of fuzzy control

    International Nuclear Information System (INIS)

    Schildt, G.H.

    1995-01-01

    After an introduction into safety terms a short description of fuzzy logic will be given. Especially, for safety critical applications of fuzzy controllers a possible controller structure will be described. The following items will be discussed: Configuration of fuzzy controllers, design aspects like fuzzfiication, inference strategies, defuzzification and types of membership functions. As an example a typical fuzzy rule set will be presented. Especially, real-time behaviour a fuzzy controllers is mentioned. An example of fuzzy controlling for temperature control purpose within a nuclear reactor together with membership functions and inference strategy of such a fuzzy controller will be presented. (author). 4 refs, 17 figs

  4. A Neuro-Control Design Based on Fuzzy Reinforcement Learning

    DEFF Research Database (Denmark)

    Katebi, S.D.; Blanke, M.

    This paper describes a neuro-control fuzzy critic design procedure based on reinforcement learning. An important component of the proposed intelligent control configuration is the fuzzy credit assignment unit which acts as a critic, and through fuzzy implications provides adjustment mechanisms....... The fuzzy credit assignment unit comprises a fuzzy system with the appropriate fuzzification, knowledge base and defuzzification components. When an external reinforcement signal (a failure signal) is received, sequences of control actions are evaluated and modified by the action applier unit. The desirable...... ones instruct the neuro-control unit to adjust its weights and are simultaneously stored in the memory unit during the training phase. In response to the internal reinforcement signal (set point threshold deviation), the stored information is retrieved by the action applier unit and utilized for re...

  5. Type-2 fuzzy logic uncertain systems’ modeling and control

    CERN Document Server

    Antão, Rómulo

    2017-01-01

    This book focuses on a particular domain of Type-2 Fuzzy Logic, related to process modeling and control applications. It deepens readers’understanding of Type-2 Fuzzy Logic with regard to the following three topics: using simpler methods to train a Type-2 Takagi-Sugeno Fuzzy Model; using the principles of Type-2 Fuzzy Logic to reduce the influence of modeling uncertainties on a locally linear n-step ahead predictor; and developing model-based control algorithms according to the Generalized Predictive Control principles using Type-2 Fuzzy Sets. Throughout the book, theory is always complemented with practical applications and readers are invited to take their learning process one step farther and implement their own applications using the algorithms’ source codes (provided). As such, the book offers avaluable referenceguide for allengineers and researchers in the field ofcomputer science who are interested in intelligent systems, rule-based systems and modeling uncertainty.

  6. Computational intelligence synergies of fuzzy logic, neural networks and evolutionary computing

    CERN Document Server

    Siddique, Nazmul

    2013-01-01

    Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspect

  7. Hierarchical type-2 fuzzy aggregation of fuzzy controllers

    CERN Document Server

    Cervantes, Leticia

    2016-01-01

    This book focuses on the fields of fuzzy logic, granular computing and also considering the control area. These areas can work together to solve various control problems, the idea is that this combination of areas would enable even more complex problem solving and better results. In this book we test the proposed method using two benchmark problems: the total flight control and the problem of water level control for a 3 tank system. When fuzzy logic is used it make it easy to performed the simulations, these fuzzy systems help to model the behavior of a real systems, using the fuzzy systems fuzzy rules are generated and with this can generate the behavior of any variable depending on the inputs and linguistic value. For this reason this work considers the proposed architecture using fuzzy systems and with this improve the behavior of the complex control problems.

  8. On the application of bezier surfaces for GA-Fuzzy controller design for use in automatic generation control

    CSIR Research Space (South Africa)

    Boesack, CD

    2012-03-01

    Full Text Available Automatic Generation Control (AGC) of large interconnected power systems are typically controlled by a PI or PID type control law. Recently intelligent control techniques such as GA-Fuzzy controllers have been widely applied within the power...

  9. Fuzzy control of pressurizer dynamic process

    International Nuclear Information System (INIS)

    Ming Zhedong; Zhao Fuyu

    2006-01-01

    Considering the characteristics of pressurizer dynamic process, the fuzzy control system that takes the advantages of both fuzzy controller and PID controller is designed for the dynamic process in pressurizer. The simulation results illustrate this type of composite control system is with better qualities than those of single fuzzy controller and single PID controller. (authors)

  10. A fuzzy controller for NPPs

    International Nuclear Information System (INIS)

    Schildt, G.H.

    1997-01-01

    A fuzzy controller for safety related process control is presented for applications in the field of NPPs. The size of necessary rules is relatively small. Thus, there exists a real chance for verification and validation of software due to the fact that the whole software can be structured into standard fuzzy software (like fuzzyfication, inference algorithms, and defuzzyfication), real-time operating system software, and the contents of the rule base. Furthermore, there is an excellent advantage fuel to real-time behaviour, because program execution time is much more predictable than for conventional PID-controller software. Additionally, up to now special know-how does exist to prove stability of fuzzy controller. Hardware design has been done due to fundamental principles of safety technique like watch dog function, dynamization principles, and quiescent current principle. (author). 3 refs, 5 figs

  11. A fuzzy controller for NPPs

    International Nuclear Information System (INIS)

    Schildt, G.H.

    1996-01-01

    After an introduction into safety terms a fuzzy controller for safety related process control will be presented, especially for applications in the field of NPPs. One can show that the size of necessary rules is relatively small. Thus, there exists a real chance for verification and validation of software due to the fact that the whole software can be structured into standard fuzzy software (like fuzzyfication, inference algorithms, and defuzzyfication), real-time operating system software, and the contents of the rule base. Furthermore, there is an excellent advantage due to real-time behaviour, because program execution time can be much more planned than for conventional PID-controller software. Additionally, up to now special know-how does exist to prove stability of fuzzy controller. Hardware design has been done due to fundamental principles of safety technique like watch dog function, dynamization principle, and quiescent current principle

  12. A fuzzy controller for NPPs

    Energy Technology Data Exchange (ETDEWEB)

    Schildt, G H [Technische Univ., Vienna (Austria)

    1997-07-01

    A fuzzy controller for safety related process control is presented for applications in the field of NPPs. The size of necessary rules is relatively small. Thus, there exists a real chance for verification and validation of software due to the fact that the whole software can be structured into standard fuzzy software (like fuzzyfication, inference algorithms, and defuzzyfication), real-time operating system software, and the contents of the rule base. Furthermore, there is an excellent advantage fuel to real-time behaviour, because program execution time is much more predictable than for conventional PID-controller software. Additionally, up to now special know-how does exist to prove stability of fuzzy controller. Hardware design has been done due to fundamental principles of safety technique like watch dog function, dynamization principles, and quiescent current principle. (author). 3 refs, 5 figs.

  13. Neuro-fuzzy Control of Integrating Processes

    Directory of Open Access Journals (Sweden)

    Anna Vasičkaninová

    2011-11-01

    Full Text Available Fuzzy technology is adaptive and easily applicable in different areas.Fuzzy logic provides powerful tools to capture the perceptionof natural phenomena. The paper deals with tuning of neuro-fuzzy controllers for integrating plant and for integrating plantswith time delay. The designed approach is verified on three examples by simulations and compared plants with classical PID control.Designed fuzzy controllers lead to better closed-loop control responses then classical PID controllers.

  14. Intelligent Mission Controller Node

    National Research Council Canada - National Science Library

    Perme, David

    2002-01-01

    The goal of the Intelligent Mission Controller Node (IMCN) project was to improve the process of translating mission taskings between real-world Command, Control, Communications, Computers, and Intelligence (C41...

  15. Fuzzy attitude control for a nanosatellite in leo orbit

    Science.gov (United States)

    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

  16. Solar-Based Fuzzy Intelligent Water Sprinkle System

    Directory of Open Access Journals (Sweden)

    Riza Muhida

    2012-03-01

    Full Text Available A solar-based intelligent water sprinkler system project that has been developed to ensure the effectiveness in watering the plant is improved by making the system automated. The control system consists of an electrical capacitance soil moisture sensor installed into the ground which is interfaced to a controller unit of Motorola 68HC11 Handy board microcontroller. The microcontroller was programmed based on the decision rules made using fuzzy logic approach on when to water the lawn. The whole system is powered up by the solar energy which is then interfaced to a particular type of irrigation timer for plant fertilizing schedule and rain detector through a simple design of rain dual-collector tipping bucket. The controller unit automatically disrupted voltage signals sent to the control valves whenever irrigation was not needed. Using this system we combined the logic implementation in the area of irrigation and weather sensing equipment, and more efficient water delivery can be made possible. 

  17. A SELF-ORGANISING FUZZY LOGIC CONTROLLER

    African Journals Online (AJOL)

    ES Obe

    One major drawback of fuzzy logic controllers is the difficulty encountered in the construction of a rule- base ... The greatest limitation of fuzzy logic control is the lack ..... c(kT)= e(kT)-e((k-1)T). (16) .... with the aid of fuzzy models”, It in Industrial.

  18. Fuzzy process control and knowledge engineering in petrochemical and robotic manufacturing

    Energy Technology Data Exchange (ETDEWEB)

    Aliev, R. (Azerbaijan Industrial Univ., Dept. of Automatic Control Systems, Baku (Russia)); Aliev, F. (Azerbaijan Polytechnique Institute, Dept. of Automation and Computer Science, Baku (Russia)); Babaev, M. (Azerbaijan Industrial Univ., Laboratory of Intelligent Control Systems, Baku (Russia))

    1991-01-01

    This book presents the methodology, the functionality and the pragmatics of implementing and applying AI (Artificial Intelligence) techniques enhanced by the new mathematical discipline of fuzzy sets. Emphasis is put on the design and modelling of fuzzy controllers and intelligent control equipment for the oil processing and chemical industries, as well as on robotics and CAM (Computer-Aided Manufacturing), including the development of appropriate algorithms and computer programs. The content is strongly application-oriented in order to explain the main features of the theory of fuzzy systems using different real examples from concrete engineering projects. It excels over the present literature available on this subject by its descriptions new classes of industrial systems to be controlled with fuzzy logic, as well as by its descriptive introduction to intelligent control systems and fuzzy controllers developed and successfully implemented by the authors in working industrial plants. (orig.).

  19. Fuzzy regulator design for wind turbine yaw control.

    Science.gov (United States)

    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.

  20. Fuzzy Regulator Design for Wind Turbine Yaw Control

    Directory of Open Access Journals (Sweden)

    Stefanos Theodoropoulos

    2014-01-01

    Full Text Available 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.

  1. Fuzzy predictive filtering in nonlinear economic model predictive control for demand response

    DEFF Research Database (Denmark)

    Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.

    2016-01-01

    problem. Moreover, to reduce the computation time and improve the controller's performance, a fuzzy predictive filter is introduced. With the purpose of testing the developed EMPC, a simulation controlling the temperature levels of an intelligent office building (PowerFlexHouse), with and without fuzzy...

  2. Comparing fuzzy AHP and fuzzy TOPSIS for evaluation of business intelligence vendors

    Directory of Open Access Journals (Sweden)

    Alireza Soloukdar

    2015-04-01

    Full Text Available The main objective of this study is to identify the most important criteria and indicators in selection of business intelligence vendors, and ranking the vendors of such tools using Fuzzy Analytical hierarchy Process (FAHP and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS, to compare results of these two methods and to provide appropriate solutions for the sample company, namely National Iranian Oil Company (NIOC. Spearman's rank correlation test was used for comparing the methods and determining their correlation. A strong positive correlation was observed between the ranks of business intelligence tools at the significance level of 0.05 in both methods. The results of the ranking by means of FAHP method show that IBM Company was the best one, followed by Oracle, SAS, QlikTech, SAP and Microsoft. However, based on the FTOPSIS method, Oracle was the leading company, followed by IBM, SAS and SAP and finally Microsoft.

  3. Application and Simulation of Fuzzy Neural Network PID Controller in the Aircraft Cabin Temperature

    Directory of Open Access Journals (Sweden)

    Ding Fang

    2013-06-01

    Full Text Available Considering complex factors of affecting ambient temperature in Aircraft cabin, and some shortages of traditional PID control like the parameters difficult to be tuned and control ineffective, this paper puts forward the intelligent PID algorithm that makes fuzzy logic method and neural network together, scheming out the fuzzy neural net PID controller. After the correction of the fuzzy inference and dynamic learning of neural network, PID parameters of the controller get the optimal parameters. MATLAB simulation results of the cabin temperature control model show that the performance of the fuzzy neural network PID controller has been greatly improved, with faster response, smaller overshoot and better adaptability.

  4. Fuzzy Control of Robotic Arm

    Science.gov (United States)

    Lin, Kyaw Kyaw; Soe, Aung Kyaw; Thu, Theint Theint

    2008-10-01

    This research work investigates a Self-Tuning Proportional Derivative (PD) type Fuzzy Logic Controller (STPDFLC) for a two link robot system. The proposed scheme adjusts on-line the output Scaling Factor (SF) by fuzzy rules according to the current trend of the robot. The rule base for tuning the output scaling factor is defined on the error (e) and change in error (de). The scheme is also based on the fact that the controller always tries to manipulate the process input. The rules are in the familiar if-then format. All membership functions for controller inputs (e and de) and controller output (UN) are defined on the common interval [-1,1]; whereas the membership functions for the gain updating factor (α) is defined on [0,1]. There are various methods to calculate the crisp output of the system. Center of Gravity (COG) method is used in this application due to better results it gives. Performances of the proposed STPDFLC are compared with those of their corresponding PD-type conventional Fuzzy Logic Controller (PDFLC). The proposed scheme shows a remarkably improved performance over its conventional counterpart especially under parameters variation (payload). The two-link results of analysis are simulated. These simulation results are illustrated by using MATLAB® programming.

  5. Principles of expert fuzzy controller design: AI mobile wall climbing robots for decontamination of nuclear power-station

    International Nuclear Information System (INIS)

    Gradetsky, V.G.; Ul'yanov, S.; Slesarev, Y.V.; Pospelov, D.A.

    1994-01-01

    The arrangement principles for a complex control framework of artificial intelligence control systems are introduced. The notions of intelligence levels with the top boundary (intelligence in large) and the bottom boundary (intelligence in small) are defined. A special methodology for the design of an artificial intelligence control system design for the decontamination of a nuclear power plant using a wall climbing robot with different intelligence levels is presented. The application of WARP (Weight Associative Rule Processor) to the design of an automatic fuzzy controller for the fuzzy correction of the motion of the manipulator and WCR is examined

  6. Fuzzy logic controller using different inference methods

    International Nuclear Information System (INIS)

    Liu, Z.; De Keyser, R.

    1994-01-01

    In this paper the design of fuzzy controllers by using different inference methods is introduced. Configuration of the fuzzy controllers includes a general rule-base which is a collection of fuzzy PI or PD rules, the triangular fuzzy data model and a centre of gravity defuzzification algorithm. The generalized modus ponens (GMP) is used with the minimum operator of the triangular norm. Under the sup-min inference rule, six fuzzy implication operators are employed to calculate the fuzzy look-up tables for each rule base. The performance is tested in simulated systems with MATLAB/SIMULINK. Results show the effects of using the fuzzy controllers with different inference methods and applied to different test processes

  7. Fuzzy logic control of nuclear power plant

    International Nuclear Information System (INIS)

    Yao Liangzhong; Guo Renjun; Ma Changwen

    1996-01-01

    The main advantage of the fuzzy logic control is that the method does not require a detailed mathematical model of the object to be controlled. In this paper, the shortcomings and limitations of the model-based method in nuclear power plant control were presented, the theory of the fuzzy logic control was briefly introduced, and the applications of the fuzzy logic control technology in nuclear power plant controls were surveyed. Finally, the problems to be solved by using the fuzzy logic control in nuclear power plants were discussed

  8. Application of fuzzy logic control in industry

    International Nuclear Information System (INIS)

    Van der Wal, A.J.

    1994-01-01

    An overview is given of the various ways fuzzy logic can be used to improve industrial control. The application of fuzzy logic in control is illustrated by two case studies. The first example shows how fuzzy logic, incorporated in the hardware of an industrial controller, helps to finetune a PID controller, without the operator having any a priori knowledge of the system to be controlled. The second example is from process industry. Here, fuzzy logic supervisory control is implemented in software and enhances the operation of a sintering oven through a subtle combination of priority management and deviation-controlled timing

  9. Fuzzy logic control and optimization system

    Science.gov (United States)

    Lou, Xinsheng [West Hartford, CT

    2012-04-17

    A control system (300) for optimizing a power plant includes a chemical loop having an input for receiving an input signal (369) and an output for outputting an output signal (367), and a hierarchical fuzzy control system (400) operably connected to the chemical loop. The hierarchical fuzzy control system (400) includes a plurality of fuzzy controllers (330). The hierarchical fuzzy control system (400) receives the output signal (367), optimizes the input signal (369) based on the received output signal (367), and outputs an optimized input signal (369) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.

  10. Granular, soft and fuzzy approaches for intelligent systems dedicated to professor Ronald R. Yager

    CERN Document Server

    Filev, Dimitar; Beliakov, Gleb

    2017-01-01

    This book offers a comprehensive report on the state-of-the art in the broadly-intended field of “intelligent systems”. After introducing key theoretical issues, it describes a number of promising models for data and system analysis, decision making, and control. It discusses important theories, including possibility theory, the Dempster-Shafer theory, the theory of approximate reasoning, as well as computing with words, together with novel applications in various areas, such as information aggregation and fusion, linguistic data summarization, participatory learning, systems modeling, and many others. By presenting the methods in their application contexts, the book shows how granular computing, soft computing and fuzzy logic techniques can provide novel, efficient solutions to real-world problems. It is dedicated to Professor Ronald R. Yager for his great scientific and scholarly achievements, and for his long-lasting service to the fuzzy logic, and the artificial and computational intelligence communit...

  11. Advances in chaos theory and intelligent control

    CERN Document Server

    Vaidyanathan, Sundarapandian

    2016-01-01

    The book reports on the latest advances in and applications of chaos theory and intelligent control. Written by eminent scientists and active researchers and using a clear, matter-of-fact style, it covers advanced theories, methods, and applications in a variety of research areas, and explains key concepts in modeling, analysis, and control of chaotic and hyperchaotic systems. Topics include fractional chaotic systems, chaos control, chaos synchronization, memristors, jerk circuits, chaotic systems with hidden attractors, mechanical and biological chaos, and circuit realization of chaotic systems. The book further covers fuzzy logic controllers, evolutionary algorithms, swarm intelligence, and petri nets among other topics. Not only does it provide the readers with chaos fundamentals and intelligent control-based algorithms; it also discusses key applications of chaos as well as multidisciplinary solutions developed via intelligent control. The book is a timely and comprehensive reference guide for graduate s...

  12. Fuzzy systems for process identification and control

    International Nuclear Information System (INIS)

    Gorrini, V.; Bersini, H.

    1994-01-01

    Various issues related to the automatic construction and on-line adaptation of fuzzy controllers are addressed. A Direct Adaptive Fuzzy Control (this is an adaptive control methodology requiring a minimal knowledge of the processes to be coupled with) derived in a way reminiscent of neurocontrol methods, is presented. A classical fuzzy controller and a fuzzy realization of a PID controller is discussed. These systems implement a highly non-linear control law, and provide to be quite robust, even in the case of noisy inputs. In order to identify dynamic processes of order superior to one, we introduce a more complex architecture, called Recurrent Fuzzy System, that use some fuzzy internal variables to perform an inferential chaining.I

  13. Adaptive fuzzy controller based MPPT for photovoltaic systems

    International Nuclear Information System (INIS)

    Guenounou, Ouahib; Dahhou, Boutaib; Chabour, Ferhat

    2014-01-01

    Highlights: • We propose a fuzzy controller with adaptive output scaling factor as a maximum power point tracker of photovoltaic system. • The proposed controller integrates two different rule bases defined on error and change of error. • Our controller can track the maximum power point with better performances when compared to its conventional counterpart. - Abstract: This paper presents an intelligent approach to optimize the performances of photovoltaic systems. The system consists of a PV panel, a DC–DC boost converter, a maximum power point tracker controller and a resistive load. The key idea of the proposed approach is the use of a fuzzy controller with an adaptive gain as a maximum power point tracker. The proposed controller integrates two different rule bases. The first is used to adjust the duty cycle of the boost converter as in the case of a conventional fuzzy controller while the second rule base is designed for an online adjusting of the controller’s gain. The performances of the adaptive fuzzy controller are compared with those obtained using a conventional fuzzy controllers with different gains and in each case, the proposed controller outperforms its conventional counterpart

  14. Application of artificial intelligence in process control

    CERN Document Server

    Krijgsman, A

    1993-01-01

    This book is the result of a united effort of six European universities to create an overall course on the appplication of artificial intelligence (AI) in process control. The book includes an introduction to key areas including; knowledge representation, expert, logic, fuzzy logic, neural network, and object oriented-based approaches in AI. Part two covers the application to control engineering, part three: Real-Time Issues, part four: CAD Systems and Expert Systems, part five: Intelligent Control and part six: Supervisory Control, Monitoring and Optimization.

  15. Hybrid Intelligent Control for Submarine Stabilization

    Directory of Open Access Journals (Sweden)

    Minghui Wang

    2013-05-01

    Full Text Available Abstract While sailing near the sea surface, submarines will often undergo rolling motion caused by wave disturbance. Fierce rolling motion seriously affects their normal operation and even threatens their security. We propose a new control method for roll stabilization. This paper studies hybrid intelligent control combining a fuzzy control, a neural network and extension control technology. Every control strategy can achieve the ideal control effect within the scope of its effective control. The neuro-fuzzy control strategy is used to improve the robustness of the controller. The speed control strategy and the course control strategy are conducted to extend the control range. The paper also proposes the design of the controller and carries out the simulation experiment in different sea conditions. The simulation results show that the control method proposed can indeed effectively improve the control performance of submarine stabilization.

  16. Fuzzy control of small servo motors

    Science.gov (United States)

    Maor, Ron; Jani, Yashvant

    1993-01-01

    To explore the benefits of fuzzy logic and understand the differences between the classical control methods and fuzzy control methods, the Togai InfraLogic applications engineering staff developed and implemented a motor control system for small servo motors. The motor assembly for testing the fuzzy and conventional controllers consist of servo motor RA13M and an encoder with a range of 4096 counts. An interface card was designed and fabricated to interface the motor assembly and encoder to an IBM PC. The fuzzy logic based motor controller was developed using the TILShell and Fuzzy C Development System on an IBM PC. A Proportional-Derivative (PD) type conventional controller was also developed and implemented in the IBM PC to compare the performance with the fuzzy controller. Test cases were defined to include step inputs of 90 and 180 degrees rotation, sine and square wave profiles in 5 to 20 hertz frequency range, as well as ramp inputs. In this paper we describe our approach to develop a fuzzy as well as PH controller, provide details of hardware set-up and test cases, and discuss the performance results. In comparison, the fuzzy logic based controller handles the non-linearities of the motor assembly very well and provides excellent control over a broad range of parameters. Fuzzy technology, as indicated by our results, possesses inherent adaptive features.

  17. Quality control of intelligence research

    International Nuclear Information System (INIS)

    Lu Yan; Xin Pingping; Wu Jian

    2014-01-01

    Quality control of intelligence research is the core issue of intelligence management, is a problem in study of information science This paper focuses on the performance of intelligence to explain the significance of intelligence research quality control. In summing up the results of the study on the basis of the analysis, discusses quality control methods in intelligence research, introduces the experience of foreign intelligence research quality control, proposes some recommendations to improve quality control in intelligence research. (authors)

  18. Adaptive fuzzy control for a simulation of hydraulic analogy of a nuclear reactor

    International Nuclear Information System (INIS)

    Ruan, D.; Li, X.; Eynde, G. van den

    2000-01-01

    In the framework of the on-going R and D project on fuzzy control applications to the Belgian Reactor 1 (BR1) at the Belgian Nuclear Research Centre (SCK-CEN), we have constructed a real fuzzy-logic-control demo model. The demo model is suitable for us to test and compare some new algorithms of fuzzy control and intelligent systems, which is advantageous because it is always difficult and time consuming, due to safety aspects, to do all experiments in a real nuclear environment. In this chapter, we first report briefly on the construction of the demo model, and then introduce the results of a fuzzy control, a proportional-integral-derivative (PID) control and an advanced fuzzy control, in which the advanced fuzzy control is a fuzzy control with an adaptive function that can self-regulate the fuzzy control rules. Afterwards, we present a comparative study of those three methods. The results have shown that fuzzy control has more advantages in terms of flexibility, robustness, and easily updated facilities with respect to the PID control of the demo model, but that PID control has much higher regulation resolution due to its integration terms. The adaptive fuzzy control can dynamically adjust the rule base, therefore it is more robust and suitable to those very uncertain occasions. (orig.)

  19. Analysis of inventory difference using fuzzy controllers

    International Nuclear Information System (INIS)

    Zardecki, A.

    1994-01-01

    The principal objectives of an accounting system for safeguarding nuclear materials are as follows: (a) to provide assurance that all material quantities are present in the correct amount; (b) to provide timely detection of material loss; and (c) to estimate the amount of any loss and its location. In fuzzy control, expert knowledge is encoded in the form of fuzzy rules, which describe recommended actions for different classes of situations represented by fuzzy sets. The concept of a fuzzy controller is applied to the forecasting problem in a time series, specifically, to forecasting and detecting anomalies in inventory differences. This paper reviews the basic notion underlying the fuzzy control systems and provides examples of application. The well-known material-unaccounted-for diffusion plant data of Jaech are analyzed using both feedforward neural networks and fuzzy controllers. By forming a deference between the forecasted and observed signals, an efficient method to detect small signals in background noise is implemented

  20. A computationally efficient fuzzy control s

    Directory of Open Access Journals (Sweden)

    Abdel Badie Sharkawy

    2013-12-01

    Full Text Available This paper develops a decentralized fuzzy control scheme for MIMO nonlinear second order systems with application to robot manipulators via a combination of genetic algorithms (GAs and fuzzy systems. The controller for each degree of freedom (DOF consists of a feedforward fuzzy torque computing system and a feedback fuzzy PD system. The feedforward fuzzy system is trained and optimized off-line using GAs, whereas not only the parameters but also the structure of the fuzzy system is optimized. The feedback fuzzy PD system, on the other hand, is used to keep the closed-loop stable. The rule base consists of only four rules per each DOF. Furthermore, the fuzzy feedback system is decentralized and simplified leading to a computationally efficient control scheme. The proposed control scheme has the following advantages: (1 it needs no exact dynamics of the system and the computation is time-saving because of the simple structure of the fuzzy systems and (2 the controller is robust against various parameters and payload uncertainties. The computational complexity of the proposed control scheme has been analyzed and compared with previous works. Computer simulations show that this controller is effective in achieving the control goals.

  1. THE FUZZY OVERLAY STUDENT MODEL IN AN INTELLIGENT TUTORING SYSTEM

    Directory of Open Access Journals (Sweden)

    D. I. Popov

    2015-01-01

    Full Text Available The article is devoted to the development of the student model for use in an intelligent tutoring system (ITS designed for the evaluation of students’ competencies in different Higher Education Facilities. There are classification and examples of the various student models, the most suitable for the evaluation of competencies is selected and finalized. The dynamic overlay fuzzy student model builded on the domain model based on the concept of didactic units is described in this work. The formulas, chart and diagrams are provided.

  2. Fuzzy Logic Decoupled Lateral Control for General Aviation Airplanes

    Science.gov (United States)

    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.

  3. Fuzzy Markup language : a new solution for transparent intelligent agents

    NARCIS (Netherlands)

    Acampora, G.; Loia, V.

    2011-01-01

    From an industrial and technological point of view, fuzzy control theory deals with the development of a particular system controller on a specific hardware by means of an open or legacy programming language that is useful to address, in a high-level fashion, the hardware constraints. Independently

  4. Fundamentals of computational intelligence neural networks, fuzzy systems, and evolutionary computation

    CERN Document Server

    Keller, James M; Fogel, David B

    2016-01-01

    This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basi function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzz...

  5. Robust Fuzzy Controllers Using FPGAs

    Science.gov (United States)

    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.

  6. Fuzzy logic-based battery charge controller

    International Nuclear Information System (INIS)

    Daoud, A.; Midoun, A.

    2006-01-01

    Photovoltaic power system are generally classified according to their functional and operational requirements, their component configurations, and how the equipment is connected to other power sources and electrical loads, photovoltaic systems can be designed to provide DC and/or AC power service, can operate interconnected with or independent of the utility grid, and can be connected with other energy sources and energy storage systems. Batteries are often used in PV systems for the purpose of storing energy produced by the PV array during the day, and to supply it to electrical loads as needed (during the night and periods of cloudy weather). The lead acid battery, although know for more than one hundred years, has currently offered the best response in terms of price, energetic efficiency and lifetime. The main function of controller or regulator in PV system is too fully charge the battery without permitting overcharge while preventing reverse current flow at night. If a no-self-regulating solar array is connected to lead acid batteries with no overcharge protection, battery life will be compromised. Simple controllers contain a transistor that disconnects or reconnects the PV in the charging circuit once a pre-set voltage is reached. More sophisticated controllers utilize pulse with modulation (PWM) to assure the battery is being fully charged. The first 70% to 80% of battery capacity is easily replaced, but the last 20% to 30% requires more attention and therefore more complexity. This complexity is avoided by using a skilled operators experience in the form of the rules. Thus a fuzzy control system seeks to control the battery that cannot be controlled well by a conventional control such as PID, PD, PI etc., due to the unavailability of an accurate mathematical model of the battery. In this paper design of an intelligent battery charger, in which the control algorithm is implemented with fuzzy logic is discussed. The digital architecture is implemented with

  7. ASSESSMENT OF A WIND TURBINE INTELLIGENT CONTROLLER FOR ENHANCED ENERGY PRODUCTION AND POLLUTION REDUCTION

    Science.gov (United States)

    This study assessed the enhanced energy production which is possible when variable-speed wind turbines are electronically controlled by an intelligent controller for efficiency optimization and performance improvement. The control system consists of three fuzzy- logic controllers...

  8. Fuzzy logic for structural system control

    Directory of Open Access Journals (Sweden)

    Herbert Martins Gomes

    Full Text Available This paper provides some information and numerical tests that aims to investigate the use of a Fuzzy Controller applied to control systems. Some advantages are reported regarding the use of this controller, such as the characteristic ease of implementation due to its semantic feature in the statement of the control rules. On the other hand, it is also hypothesized that these systems have a lower performance loss when the system to be controlled is nonlinear or has time varying parameters. Numerical tests are performed using modal LQR optimal control and Fuzzy control of non-collocated systems with full state feedback in a two-dimensional structure. The paper proposes a way of designing a controller that may be a supervisory Fuzzy controller for a traditional controller or even a fuzzy controller independent from the traditional control, consisting on individual mode controllers. Some comments are drawn regarding the performance of these proposals in a number of arrangements.

  9. Implementation of a fuzzy logic/neural network multivariable controller

    International Nuclear Information System (INIS)

    Cordes, G.A.; Clark, D.E.; Johnson, J.A.; Smartt, H.B.; Wickham, K.L.; Larson, T.K.

    1992-01-01

    This paper describes a multivariable controller developed at the Idaho National Engineering Laboratory (INEL) that incorporates both fuzzy logic rules and a neural network. The controller was implemented in a laboratory demonstration and was robust, producing smooth temperature and water level response curves with short time constants. In the future, intelligent control systems will be a necessity for optimal operation of autonomous reactor systems located on earth or in space. Even today, there is a need for control systems that adapt to the changing environment and process. Hybrid intelligent control systems promise to provide this adaptive capability. Fuzzy logic implements our imprecise, qualitative human reasoning. The values of system variables (controller inputs) and control variables (controller outputs) are described in linguistic terms and subdivided into fully overlapping value ranges. The fuzzy rule base describes how combinations of input parameter ranges determine the output control values. Neural networks implement our human learning. In this controller, neural networks were embedded in the software to explore their potential for adding adaptability

  10. Vector control of wind turbine on the basis of the fuzzy selective neural net*

    Science.gov (United States)

    Engel, E. A.; Kovalev, I. V.; Engel, N. E.

    2016-04-01

    An article describes vector control of wind turbine based on fuzzy selective neural net. Based on the wind turbine system’s state, the fuzzy selective neural net tracks an maximum power point under random perturbations. Numerical simulations are accomplished to clarify the applicability and advantages of the proposed vector wind turbine’s control on the basis of the fuzzy selective neuronet. The simulation results show that the proposed intelligent control of wind turbine achieves real-time control speed and competitive performance, as compared to a classical control model with PID controllers based on traditional maximum torque control strategy.

  11. Active structural control with stable fuzzy PID techniques

    CERN Document Server

    Yu, Wen

    2016-01-01

    This book presents a detailed discussion of intelligent techniques to measure the displacement of buildings when they are subjected to vibration. It shows how these techniques are used to control active devices that can reduce vibration 60–80% more effectively than widely used passive anti-seismic systems. After introducing various structural control devices and building-modeling and active structural control methods, the authors propose offset cancellation and high-pass filtering techniques to solve some common problems of building-displacement measurement using accelerometers. The most popular control algorithms in industrial settings, PD/PID controllers, are then analyzed and then combined with fuzzy compensation. The stability of this combination is proven with standard weight-training algorithms. These conditions provide explicit methods for selecting PD/PID controllers. Finally, fuzzy-logic and sliding-mode control are applied to the control of wind-induced vibration. The methods described are support...

  12. Fuzzy modeling and control theory and applications

    CERN Document Server

    Matía, Fernando; Jiménez, Emilio

    2014-01-01

    Much work on fuzzy control, covering research, development and applications, has been developed in Europe since the 90's. Nevertheless, the existing books in the field are compilations of articles without interconnection or logical structure or they express the personal point of view of the author. This book compiles the developments of researchers with demonstrated experience in the field of fuzzy control following a logic structure and a unified the style. The first chapters of the book are dedicated to the introduction of the main fuzzy logic techniques, where the following chapters focus on concrete applications. This book is supported by the EUSFLAT and CEA-IFAC societies, which include a large number of researchers in the field of fuzzy logic and control. The central topic of the book, Fuzzy Control, is one of the main research and development lines covered by these associations.

  13. reactor power control using fuzzy logic

    International Nuclear Information System (INIS)

    Ahmed, A.E.E.

    2001-01-01

    power stabilization is a critical issue in nuclear reactors. convention pd- controller is currently used in egypt second testing research reactor (ETRR-2). two fuzzy controllers are proposed to control the reactor power of ETRR-2 reactor. the design of the first one is based on a set of linguistic rules that were adopted from the human operators experience. after off-line fuzzy computations, the controller is a lookup table, and thus, real time controller is achieved. comparing this f lc response with the pd-controller response, which already exists in the system, through studying the expected transients during the normal operation of ETRR-2 reactor, the simulation results show that, fl s has the better response, the second controller is adaptive fuzzy controller, which is proposed to deal with system non-linearity . The simulation results show that the proposed adaptive fuzzy controller gives a better integral square error (i se) index than the existing conventional od controller

  14. Adaptive fuzzy trajectory control for biaxial motion stage system

    Directory of Open Access Journals (Sweden)

    Wei-Lung Mao

    2016-04-01

    Full Text Available Motion control is an essential part of industrial machinery and manufacturing systems. In this article, the adaptive fuzzy controller is proposed for precision trajectory tracking control in biaxial X-Y motion stage system. The theoretical analyses of direct fuzzy control which is insensitive to parameter uncertainties and external load disturbances are derived to demonstrate the feasibility to track the reference trajectories. The Lyapunov stability theorem has been used to testify the asymptotic stability of the whole system, and all the signals are bounded in the closed-loop system. The intelligent position controller combines the merits of the adaptive fuzzy control with robust characteristics and learning ability for periodic command tracking of a servo drive mechanism. The simulation and experimental results on square, triangle, star, and circle reference contours are presented to show that the proposed controller indeed accomplishes the better tracking performances with regard to model uncertainties. It is observed that the convergence of parameters and tracking errors can be faster and smaller compared with the conventional adaptive fuzzy control in terms of average tracking error and tracking error standard deviation.

  15. Application of artificial intelligence in load frequency control of ...

    African Journals Online (AJOL)

    This paper presents the use of artificial intelligence to study the load frequency control of interconnected power system. In the proposed scheme, a control methodology is developed using Artificial Neural Network (ANN) and Fuzzy Logic controller (FLC) for interconnected hydro-thermal power system. The control strategies ...

  16. A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns

    Science.gov (United States)

    Li, Xiang; Zhang, Yang; Wong, Hau-San; Qin, Zhongfeng

    2009-11-01

    Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean-variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.

  17. A neuro-fuzzy controlling algorithm for wind turbine

    Energy Technology Data Exchange (ETDEWEB)

    Lin, Li [Tampere Univ. of Technology (Finland); Eriksson, J T [Tampere Univ. of Technology (Finland)

    1996-12-31

    The wind turbine control system is stochastic and nonlinear, offering a demanding field for different control methods. An improved and efficient controller will have great impact on the cost-effectiveness of the technology. In this article, a design method for a self-organizing fuzzy controller is discussed, which combines two popular computational intelligence techniques, neural networks and fuzzy logic. Based on acquisited dynamic parameters of the wind, it can effectively predict wind changes in speed and direction. Maximum power can always be extracted from the kinetic energy of the wind. Based on the stimulating experiments applying nonlinear dynamics to a `Variable Speed Fixed Angle` wind turbine, it is demonstrated that the proposed control model 3rd learning algorithm provide a predictable, stable and accurate performance. The robustness of the controller to system parameter variations and measurement disturbances is also discussed. (author)

  18. A neuro-fuzzy controlling algorithm for wind turbine

    Energy Technology Data Exchange (ETDEWEB)

    Li Lin [Tampere Univ. of Technology (Finland); Eriksson, J.T. [Tampere Univ. of Technology (Finland)

    1995-12-31

    The wind turbine control system is stochastic and nonlinear, offering a demanding field for different control methods. An improved and efficient controller will have great impact on the cost-effectiveness of the technology. In this article, a design method for a self-organizing fuzzy controller is discussed, which combines two popular computational intelligence techniques, neural networks and fuzzy logic. Based on acquisited dynamic parameters of the wind, it can effectively predict wind changes in speed and direction. Maximum power can always be extracted from the kinetic energy of the wind. Based on the stimulating experiments applying nonlinear dynamics to a `Variable Speed Fixed Angle` wind turbine, it is demonstrated that the proposed control model 3rd learning algorithm provide a predictable, stable and accurate performance. The robustness of the controller to system parameter variations and measurement disturbances is also discussed. (author)

  19. Intelligent control systems 1990

    International Nuclear Information System (INIS)

    Shoureshi, R.

    1991-01-01

    The field of artificial intelligence (Al) has generated many useful ideas and techniques that can be integrated into the design of control systems. It is believed and, for special cases, has been demonstrated, that integration of Al into control systems would provide the necessary tools for solving many of the complex problems that present control techniques and Al algorithms are unable to do, individually. However, this integration requires the development of basic understanding and new fundamentals to provide scientific bases for achievement of its potential. This book presents an overview of some of the latest research studies in the area of intelligent control systems. These papers present techniques for formulation of intelligent control, and development of the rule-based control systems. Papers present applications of control systems in nuclear power plants and HVAC systems

  20. Intelligent Lighting Control System

    OpenAIRE

    García, Elena; Rodríguez González, Sara; de Paz Santana, Juan F.; Bajo Pérez, Javier

    2014-01-01

    This paper presents an adaptive architecture that allows centralized control of public lighting and intelligent management, in order to economise on lighting and maintain maximum comfort status of the illuminated areas. To carry out this management, architecture merges various techniques of artificial intelligence (AI) and statistics such as artificial neural networks (ANN), multi-agent systems (MAS), EM algorithm, methods based on ANOVA and a Service Oriented Aproach (SOA). It performs optim...

  1. Efficiency of particle swarm optimization applied on fuzzy logic DC motor speed control

    Directory of Open Access Journals (Sweden)

    Allaoua Boumediene

    2008-01-01

    Full Text Available This paper presents the application of Fuzzy Logic for DC motor speed control using Particle Swarm Optimization (PSO. Firstly, the controller designed according to Fuzzy Logic rules is such that the systems are fundamentally robust. Secondly, the Fuzzy Logic controller (FLC used earlier was optimized with PSO so as to obtain optimal adjustment of the membership functions only. Finally, the FLC is completely optimized by Swarm Intelligence Algorithms. Digital simulation results demonstrate that in comparison with the FLC the designed FLC-PSO speed controller obtains better dynamic behavior and superior performance of the DC motor, as well as perfect speed tracking with no overshoot.

  2. Fuzzy Control of Yaw and Roll Angles of a Simulated Helicopter Model Includes Articulated Manipulators

    Directory of Open Access Journals (Sweden)

    Hossein Sadegh Lafmejani

    2015-09-01

    Full Text Available Fuzzy logic controller (FLC is a heuristic method by If-Then Rules which resembles human intelligence and it is a good method for designing Non-linear control systems. In this paper, an arbitrary helicopter model includes articulated manipulators has been simulated with Matlab SimMechanics toolbox. Due to the difficulties of modeling this complex system, a fuzzy controller with simple fuzzy rules has been designed for its yaw and roll angles in order to stabilize the helicopter while it is in the presence of disturbances or its manipulators are moving for a task. Results reveal that a simple FLC can appropriately control this system.

  3. Neuro-Fuzzy DC Motor Speed Control Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Boumediene ALLAOUA

    2009-12-01

    Full Text Available This paper presents an application of Adaptive Neuro-Fuzzy Inference System (ANFIS control for DC motor speed optimized with swarm collective intelligence. First, the controller is designed according to Fuzzy rules such that the systems are fundamentally robust. Secondly, an adaptive Neuro-Fuzzy controller of the DC motor speed is then designed and simulated; the ANFIS has the advantage of expert knowledge of the Fuzzy inference system and the learning capability of neural networks. Finally, the ANFIS is optimized by Swarm Intelligence. Digital simulation results demonstrate that the deigned ANFIS-Swarm speed controller realize a good dynamic behavior of the DC motor, a perfect speed tracking with no overshoot, give better performance and high robustness than those obtained by the ANFIS alone.

  4. A Novel Strain-Based Method to Estimate Tire Conditions Using Fuzzy Logic for Intelligent Tires

    Directory of Open Access Journals (Sweden)

    Daniel Garcia-Pozuelo

    2017-02-01

    Full Text Available The so-called intelligent tires are one of the most promising research fields for automotive engineers. These tires are equipped with sensors which provide information about vehicle dynamics. Up to now, the commercial intelligent tires only provide information about inflation pressure and their contribution to stability control systems is currently very limited. Nowadays one of the major problems for intelligent tire development is how to embed feasible and low cost sensors to obtain reliable information such as inflation pressure, vertical load or rolling speed. These parameters provide key information for vehicle dynamics characterization. In this paper, we propose a novel algorithm based on fuzzy logic to estimate the mentioned parameters by means of a single strain-based system. Experimental tests have been carried out in order to prove the suitability and durability of the proposed on-board strain sensor system, as well as its low cost advantages, and the accuracy of the obtained estimations by means of fuzzy logic.

  5. Design and implementation of fuzzy-PD controller based on relation models: A cross-entropy optimization approach

    Science.gov (United States)

    Anisimov, D. N.; Dang, Thai Son; Banerjee, Santo; Mai, The Anh

    2017-07-01

    In this paper, an intelligent system use fuzzy-PD controller based on relation models is developed for a two-wheeled self-balancing robot. Scaling factors of the fuzzy-PD controller are optimized by a Cross-Entropy optimization method. A linear Quadratic Regulator is designed to bring a comparison with the fuzzy-PD controller by control quality parameters. The controllers are ported and run on STM32F4 Discovery Kit based on the real-time operating system. The experimental results indicate that the proposed fuzzy-PD controller runs exactly on embedded system and has desired performance in term of fast response, good balance and stabilize.

  6. Fuzzy controllers in nuclear material accounting

    International Nuclear Information System (INIS)

    Zardecki, A.

    1994-01-01

    Fuzzy controllers are applied to predicting and modeling a time series, with particular emphasis on anomaly detection in nuclear material inventory differences. As compared to neural networks, the fuzzy controllers can operate in real time; their learning process does not require many iterations to converge. For this reason fuzzy controllers are potentially useful in time series forecasting, where the authors want to detect and identify trends in real time. They describe an object-oriented implementation of the algorithm advanced by Wang and Mendel. Numerical results are presented both for inventory data and time series corresponding to chaotic situations, such as encountered in the context of strange attractors. In the latter case, the effects of noise on the predictive power of the fuzzy controller are explored

  7. Fuzzy Control in the Process Industry

    DEFF Research Database (Denmark)

    Jantzen, Jan; Verbruggen, Henk; Østergaard, Jens-Jørgen

    1999-01-01

    Control problems in the process industry are dominated by non-linear and time-varying behaviour, many inner loops, and much interaction between the control loops. Fuzzy controllers have in some cases nevertheless mimicked the control actions of a human operator. Simple fuzzy controllers can...... be designed starting from PID controllers, and in more complex cases these can be used in connection with model-based predictive control. For high level control and supervisory control several simple controllers can be combined in a priority hierarchy such as the one developed in the cement industry...

  8. A study of fuzzy control in nuclear scale system

    International Nuclear Information System (INIS)

    Wang Yu; Zhang Yongming; Wu Ruisheng; Du Xianbin; Liu Shixing

    2001-01-01

    The new development of the nuclear scale system which uses fuzzy control strategy is presented. Good results have been obtained in using fuzzy control to solve the problems, such as un-linearities, instabilities, time delays, which are difficultly described by formula, etc. The fuzzy variance, membership function and fuzzy rules are given, and the noise disturbances of fuzzy control and PID control are also given

  9. Application of adaptive fuzzy control technology to pressure control of a pressurizer

    Institute of Scientific and Technical Information of China (English)

    YANG Ben-kun; BIAN Xin-qian; GUO Wei-lai

    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 coolant within allowed range because the sharp change of coolant pressure affects the security of reactor,therefor,the study of pressurizer's pressure control methods is very important. In this paper, an adaptive fuzzy controller is presented for pressure control of a pressurizer in a nuclear power plant. The controller can on-line tune fuzzy control rules and parameters by self-learning in the actual control process, which possesses the way of thinking like human to make a decision. The simulation results for a pressurized water reactor plant show that the adaptive fuzzy controller has optimum and intelligent characteristics, which prove the controller is effective.

  10. Intelligent control with implementation on the wind energy conversion system

    International Nuclear Information System (INIS)

    Basma, Mohamad Khalil

    1997-05-01

    In this thesis our main job is to compare intelligent control and conventional control algorithms, by applying each scheme to the same control problem. Based on simulation, we analyze and compare the results of applying fuzzy logic and neural networks controllers on a popular control problem: variable speed wind energy conversion system. The reason behind our choice is the challenging nature of the problem where the plant should be controlled to maximize the power generated, while respecting its hardware constraints under varying operating conditions and disturbances. We have shown the effectiveness of fuzzy logic exciter controller for the adopted wind energy generator when compared to a conventional PI exciter. It showed better performance in the whole operating range. However, in the high wind speeds region, both controllers were unable to deliver the rpm requirements. We proposed the use of neural network intelligent techniques to supply us the optimal pitch. Our aim was to develop a simple and reliable controller that can deliver this optimal output, while remaining adaptive to system uncertainties and disturbances. The proposed fuzzy controller with a neural pitch controller showed best dynamic and robust performance as compared to the adaptive pitch controller together with the PI exciter. This study has shown that artificial neural networks and fuzzy logic control algorithms can be implemented for real time control implementations. the neuro-fuzzy control approach is robust and its performance is superior to that of traditional control methods. (author)

  11. Fuzzy logic applications to control engineering

    Science.gov (United States)

    Langari, Reza

    1993-12-01

    This paper presents the results of a project presently under way at Texas A&M which focuses on the use of fuzzy logic in integrated control of manufacturing systems. The specific problems investigated here include diagnosis of critical tool wear in machining of metals via a neuro-fuzzy algorithm, as well as compensation of friction in mechanical positioning systems via an adaptive fuzzy logic algorithm. The results indicate that fuzzy logic in conjunction with conventional algorithmic based approaches or neural nets can prove useful in dealing with the intricacies of control/monitoring of manufacturing systems and can potentially play an active role in multi-modal integrated control systems of the future.

  12. Control of a mechanical gripper with a fuzzy controller

    International Nuclear Information System (INIS)

    Alberdi, J.; Barcala, J.M.; Gamero, E.; Navarrete, J.J.

    1995-01-01

    A fuzzy logic system is used to control a mechanical gripper. System is based in a NLX230 fuzzy micro controller. Control rules are programmed by a 68020 microprocessor in the micro controller memory. Stress and its derived are used as feedback signals in the control. This system can adapt its effort to the mechanical resistance of the object between the fingers. (Author)

  13. Decentralized fuzzy control of multiple nonholonomic vehicles

    Energy Technology Data Exchange (ETDEWEB)

    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.

  14. Neuro-fuzzy control of structures using acceleration feedback

    Science.gov (United States)

    Schurter, Kyle C.; Roschke, Paul N.

    2001-08-01

    This paper described a new approach for the reduction of environmentally induced vibration in constructed facilities by way of a neuro-fuzzy technique. The new control technique is presented and tested in a numerical study that involves two types of building models. The energy of each building is dissipated through magnetorheological (MR) dampers whose damping properties are continuously updated by a fuzzy controller. This semi-active control scheme relies on the development of a correlation between the accelerations of the building (controller input) and the voltage applied to the MR damper (controller output). This correlation forms the basis for the development of an intelligent neuro-fuzzy control strategy. To establish a context for assessing the effectiveness of the semi-active control scheme, responses to earthquake excitation are compared with passive strategies that have similar authority for control. According to numerical simulation, MR dampers are less effective control mechanisms than passive dampers with respect to a single degree of freedom (DOF) building model. On the other hand, MR dampers are predicted to be superior when used with multiple DOF structures for reduction of lateral acceleration.

  15. Fuel Saving Strategy in Spark Ignition Engine Using Fuzzy Logic Engine Torque Control

    OpenAIRE

    Aris Triwiyatno; Sumardi

    2012-01-01

    In the case of injection gasoline engine, or better known as spark ignition engines, an effort to improve engine performance as well as to reduce fuel consumption is a fairly complex problem. Generally, engine performance improvement efforts will lead to increase in fuel consumption. However, this problem can be solved by implementing engine torque control based on intelligent regulation such as the fuzzy logic inference system. In this study, fuzzy logic engine torque regulation is used to c...

  16. Intellectual technologies in the problems of thermal power engineering control: formalization of fuzzy information processing results using the artificial intelligence methodology

    Science.gov (United States)

    Krokhin, G.; Pestunov, A.

    2017-11-01

    Exploitation conditions of power stations in variable modes and related changes of their technical state actualized problems of creating models for decision-making and state recognition basing on diagnostics using the fuzzy logic for identification their state and managing recovering processes. There is no unified methodological approach for obtaining the relevant information is a case of fuzziness and inhomogeneity of the raw information about the equipment state. The existing methods for extracting knowledge are usually unable to provide the correspondence between of the aggregates model parameters and the actual object state. The switchover of the power engineering from the preventive repair to the one, which is implemented according to the actual technical state, increased the responsibility of those who estimate the volume and the duration of the work. It may lead to inadequacy of the diagnostics and the decision-making models if corresponding methodological preparations do not take fuzziness into account, because the nature of the state information is of this kind. In this paper, we introduce a new model which formalizes the equipment state using not only exact information, but fuzzy as well. This model is more adequate to the actual state, than traditional analogs, and may be used in order to increase the efficiency and the service period of the power installations.

  17. Intelligent Flow Control Valve

    Science.gov (United States)

    Kelley, Anthony R (Inventor)

    2015-01-01

    The present invention is an intelligent flow control valve which may be inserted into the flow coming out of a pipe and activated to provide a method to stop, measure, and meter flow coming from the open or possibly broken pipe. The intelligent flow control valve may be used to stop the flow while repairs are made. Once repairs have been made, the valve may be removed or used as a control valve to meter the amount of flow from inside the pipe. With the addition of instrumentation, the valve may also be used as a variable area flow meter and flow controller programmed based upon flowing conditions. With robotic additions, the valve may be configured to crawl into a desired pipe location, anchor itself, and activate flow control or metering remotely.

  18. Intelligent Power Control of DC Microgrid

    DEFF Research Database (Denmark)

    Hajizadeh, Amin; N. Soltani, Mohsen; Norum, Lars

    2017-01-01

    In this paper, an intelligent power management strategy is proposed for hybrid DC microgrid, including wind turbine, fuel cell and battery energy storage. The considered wind turbine has a permanent magnet synchronous generator (PMSG). In the considered structure, wind turbine operates as the main...... condition and fuel cell will not generate excessive power. The proposed control scheme is based on the fuzzy algorithm. All simulations in variant operational modes are performed by MATLAB/Simulink and results show the effectiveness of the proposed control strategy....

  19. On fuzzy control of water desalination plants

    Energy Technology Data Exchange (ETDEWEB)

    Titli, A. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Jamshidi, M. [New Mexico Univ., Albuquerque, NM (United States); Olafsson, F. [Institute of Technology, Norway (Norway)

    1995-12-31

    In this report we have chosen a sub-system of an MSF water desalination plant, the brine heater, for analysis, synthesis, and simulation. This system has been modelled and implemented on computer. A fuzzy logic controller (FLC) for the top brine temperature control loop has been designed and implemented on the computer. The performance of the proposed FLC is compared with three other conventional control strategies: PID, cascade and disturbance rejection control. One major concern on FLC`s has been the lack of stability criteria. An up to-date survey of stability of fuzzy control systems is given. We have shown stability of the proposed FLC using the Sinusoidal Input Describing Functions (SIDF) method. The potential applications of fuzzy controllers for complex and large-scale systems through hierarchy of rule sets and hybridization with conventional approaches are also investigated. (authors)

  20. On fuzzy control of water desalination plants

    Energy Technology Data Exchange (ETDEWEB)

    Titli, A [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Jamshidi, M [New Mexico Univ., Albuquerque, NM (United States); Olafsson, F [Institute of Technology, Norway (Norway)

    1996-12-31

    In this report we have chosen a sub-system of an MSF water desalination plant, the brine heater, for analysis, synthesis, and simulation. This system has been modelled and implemented on computer. A fuzzy logic controller (FLC) for the top brine temperature control loop has been designed and implemented on the computer. The performance of the proposed FLC is compared with three other conventional control strategies: PID, cascade and disturbance rejection control. One major concern on FLC`s has been the lack of stability criteria. An up to-date survey of stability of fuzzy control systems is given. We have shown stability of the proposed FLC using the Sinusoidal Input Describing Functions (SIDF) method. The potential applications of fuzzy controllers for complex and large-scale systems through hierarchy of rule sets and hybridization with conventional approaches are also investigated. (authors)

  1. A new hybrid optimization method inspired from swarm intelligence: Fuzzy adaptive swallow swarm optimization algorithm (FASSO

    Directory of Open Access Journals (Sweden)

    Mehdi Neshat

    2015-11-01

    Full Text Available In this article, the objective was to present effective and optimal strategies aimed at improving the Swallow Swarm Optimization (SSO method. The SSO is one of the best optimization methods based on swarm intelligence which is inspired by the intelligent behaviors of swallows. It has been able to offer a relatively strong method for solving optimization problems. However, despite its many advantages, the SSO suffers from two shortcomings. Firstly, particles movement speed is not controlled satisfactorily during the search due to the lack of an inertia weight. Secondly, the variables of the acceleration coefficient are not able to strike a balance between the local and the global searches because they are not sufficiently flexible in complex environments. Therefore, the SSO algorithm does not provide adequate results when it searches in functions such as the Step or Quadric function. Hence, the fuzzy adaptive Swallow Swarm Optimization (FASSO method was introduced to deal with these problems. Meanwhile, results enjoy high accuracy which are obtained by using an adaptive inertia weight and through combining two fuzzy logic systems to accurately calculate the acceleration coefficients. High speed of convergence, avoidance from falling into local extremum, and high level of error tolerance are the advantages of proposed method. The FASSO was compared with eleven of the best PSO methods and SSO in 18 benchmark functions. Finally, significant results were obtained.

  2. Adaptive fuzzy PID control of hydraulic servo control system for large axial flow compressor

    Science.gov (United States)

    Wang, Yannian; Wu, Peizhi; Liu, Chengtao

    2017-09-01

    To improve the stability of the large axial compressor, an efficient and special intelligent hydraulic servo control system is designed and implemented. The adaptive fuzzy PID control algorithm is used to control the position of the hydraulic servo cylinder steadily, which overcomes the drawback that the PID parameters should be adjusted based on the different applications. The simulation and the test results show that the system has a better dynamic property and a stable state performance.

  3. An intelligent CPIB controller

    International Nuclear Information System (INIS)

    Wikne, J.C.

    1987-12-01

    An intelligent GPIB (General Purpose Interface Bus) controller is described. It employs an autonomous slave CPU together with a dedicated controller/talker/listener chip to handle the GPIB bus protocol, thus freeing the host computer from this time-consuming task. Distribution of a large part of the necessary software to the slave side, assures that the system can be implemented on virtually any computer with a minimum of effort

  4. Automatic intelligent cruise control

    OpenAIRE

    Stanton, NA; Young, MS

    2006-01-01

    This paper reports a study on the evaluation of automatic intelligent cruise control (AICC) from a psychological perspective. It was anticipated that AICC would have an effect upon the psychology of driving—namely, make the driver feel like they have less control, reduce the level of trust in the vehicle, make drivers less situationally aware, but might reduce the workload and make driving might less stressful. Drivers were asked to drive in a driving simulator under manual and automatic inte...

  5. Optimization of Inventories for Multiple Companies by Fuzzy Control Method

    OpenAIRE

    Kawase, Koichi; Konishi, Masami; Imai, Jun

    2008-01-01

    In this research, Fuzzy control theory is applied to the inventory control of the supply chain between multiple companies. The proposed control method deals with the amountof inventories expressing supply chain between multiple companies. Referring past demand and tardiness, inventory amounts of raw materials are determined by Fuzzy inference. The method that an appropriate inventory control becomes possible optimizing fuzzy control gain by using SA method for Fuzzy control. The variation of ...

  6. Robust position control of induction motor using fuzzy logic control

    International Nuclear Information System (INIS)

    Kim, Sei Chan; Kim, Duk Hun; Yang, Seung Ho; Won, Chung Yuen

    1993-01-01

    In recent years, fuzzy logic or fuzzy set theory has reveived attention of a number of researchers in the area of power electronics and motion control. The paper describes a vector-controlled induction motor position servo drive where fuzzy control is used to get robustness against parameter variation and load torque disturbance effects. Both coarse and fine control with the help of look-up rule tables are used to improve transient response and system settling time. The performance characteristics are then compared with those of proportional-integral(PI) control. The simulation results clearly indicate the superiority of fuzzy control with larger number of rules. The fuzzy controller was implemented with a 16-bit microprocessor and tested in laboratory on a 3-hp IGBT inverter induction motor drive system. The test results verify the simulation performance. (Author)

  7. Review of Recent Type-2 Fuzzy Controller Applications

    Directory of Open Access Journals (Sweden)

    Kevin Tai

    2016-06-01

    Full Text Available Type-2 fuzzy logic controllers (T2 FLC can be viewed as an emerging class of intelligent controllers because of their abilities in handling uncertainties; in many cases, they have been shown to outperform their Type-1 counterparts. This paper presents a literature review on recent applications of T2 FLCs. To follow the developments in this field, we first review general T2 FLCs and the most well-known interval T2 FLS algorithms that have been used for control design. Certain applications of these controllers include robotic control, bandwidth control, industrial systems control, electrical control and aircraft control. The most promising applications are found in the robotics and automotive areas, where T2 FLCs have been demonstrated and proven to perform better than traditional controllers. With the development of enhanced algorithms, along with the advancement in both hardware and software, we shall witness increasing applications of these frontier controllers.

  8. Stable Hovering Flight for a Small Unmanned Helicopter Using Fuzzy Control

    Directory of Open Access Journals (Sweden)

    Arbab Nighat Khizer

    2014-01-01

    Full Text Available Stable hover flight control for small unmanned helicopter under light air turbulent environment is presented. Intelligent fuzzy logic is chosen because it is a nonlinear control technique based on expert knowledge and is capable of handling sensor created noise and contradictory inputs commonly encountered in flight control. The fuzzy nonlinear control utilizes these distinct qualities for attitude, height, and position control. These multiple controls are developed using two-loop control structure by first designing an inner-loop controller for attitude angles and height and then by establishing outer-loop controller for helicopter position. The nonlinear small unmanned helicopter model used comes from X-Plane simulator. A simulation platform consisting of MATLAB/Simulink and X-Plane© flight simulator was introduced to implement the proposed controls. The main objective of this research is to design computationally intelligent control laws for hovering and to test and analyze this autopilot for small unmanned helicopter model on X-Plane under ideal and mild turbulent condition. Proposed fuzzy flight controls are validated using an X-Plane helicopter model before being embedded on actual helicopter. To show the effectiveness of the proposed fuzzy control method and its ability to cope with the external uncertainties, results are compared with a classical PD controller. Simulated results show that two-loop fuzzy controllers have a good ability to establish stable hovering for a class of unmanned rotorcraft in the presence of light turbulent environment.

  9. Study on Design of Control Module and Fuzzy Control System

    International Nuclear Information System (INIS)

    Lee, Chang Kyu; Sohn, Chang Ho; Kim, Jung Seon; Kim, Min Kyu

    2005-01-01

    Performance of control unit is improved by introduction of fuzzy control theory and compensation for input of control unit as FLC(Fuzzy Logic Controller). Here, FLC drives thermal control system by linguistic rule-base. Hence, In case of using compensative PID control unit, it doesn't need to revise or compensate for PID control unit. Consequently, this study shows proof that control system which implements H/W module and then uses fuzzy algorism in this system is stable and has reliable performance

  10. Implementation of fuzzy logic control algorithm in embedded ...

    African Journals Online (AJOL)

    Fuzzy logic control algorithm solves problems that are difficult to address with traditional control techniques. This paper describes an implementation of fuzzy logic control algorithm using inexpensive hardware as well as how to use fuzzy logic to tackle a specific control problem without any special software tools. As a case ...

  11. Nuclear reactor control with fuzzy logic approaches - strengths, weakness, opportunities, and threats

    International Nuclear Information System (INIS)

    Ruan, Da

    2004-01-01

    As part of the special track on 'Lessons learned from computational intelligence in nuclear applications' at the forthcoming FLINS 2004 conference on Applied Computational Intelligence (Blankenberge, Belgium, September 1-3, 2004), research experiences on fuzzy logic techniques in applications of nuclear reactor control operation are critically reviewed in this presentation. Assessment of four real fuzzy control applications at the MIT research reactor in the US, the FUGEN heavy water reactor in Japan, the BR1 research reactor in Belgium, and a TRIGA Mark III reactor in Mexico will be examined thought a SWOT analysis (strengths, weakness, opportunities, and threats). Special attention will be paid to the current cooperation between the Belgian Nuclear Research Centre (SCK-CEN) and the Mexican Nuclear Centre (ININ) on the fuzzy logic control for nuclear reactor control project under the partial support of the National Council for Science and Technology of Mexico (CONACYT). (Author)

  12. control of a dc motor using fuzzy logic control algorithm

    African Journals Online (AJOL)

    user

    controller in the control performance of an industrial type DC motor using MATLAB. The fuzzy logic .... controlled separately excited permanent magnet DC motor (PMDC). ... When the field current is constant, the flux induced by the field ...

  13. Different control applications on a vehicle using fuzzy logic control

    Indian Academy of Sciences (India)

    Vehicle vibrations; active suspensions; fuzzy logic control; vehicle model. 1. .... The general expression of the mathematical model is shown below: .... Figure 5a presents the time history of the control force when the controller exists only under.

  14. Applications of Fuzzy adaptive PID control in the thermal power plant denitration liquid ammonia evaporation

    Directory of Open Access Journals (Sweden)

    Li Jing

    2016-01-01

    Full Text Available For the control of the liquid level of liquid ammonia in thermal power plant’s ammonia vaporization room, traditional PID controller parameter tuning is difficult to adapt to complex control systems, the setting of the traditional PID controller parameters is difficult to adapt to the complex control system. For the disadvantage of bad parameter setting, poor performance and so on the fuzzy adaptive PID control is proposed. Fuzzy adaptive PID control combines the advantages of traditional PID technology and fuzzy control. By using the fuzzy controller to intelligent control the object, the performance of the PID controller is further improved, and the control precision of the system is improved[1]. The simulation results show that the fuzzy adaptive PID controller not only has the advantages of high accuracy of PID controller, but also has the characteristics of fast and strong adaptability of fuzzy controller. It realizes the optimization of PID parameters which are in the optimal state, and the maximum increase production efficiency, so that are more suitable for nonlinear dynamic system.

  15. FUZZY SLIDING MODE CONTROLLER FOR DOUBLY FED ...

    African Journals Online (AJOL)

    2010-12-31

    Dec 31, 2010 ... against internal and external perturbations, but the FSMC is superior to ... controller, doubly fed induction motor, fuzzy logic control. 1. ... capabilities in accounting for modeling imprecision and bounded disturbances. It ..... To show the effect of the parameters uncertainties, we have simulated the system with.

  16. Fuzzy Logic Based Autonomous Traffic Control System

    Directory of Open Access Journals (Sweden)

    Muhammad ABBAS

    2012-01-01

    Full Text Available The aim of this paper is to design and implement fuzzy logic based traffic light Control system to solve the traffic congestion issues. In this system four input parameters: Arrival, Queue, Pedestrian and Emergency Vehicle and two output parameters: Extension in Green and Pedestrian Signals are used. Using Fuzzy Rule Base, the system extends or terminates the Green Signal according to the Traffic situation at the junction. On the presence of emergency vehicle, the system decides which signal(s should be red and how much an extension should be given to Green Signal for Emergency Vehicle. The system also monitors the density of people and makes decisions accordingly. In order to verify the proposed design algorithm MATLAB simulation is adopted and results obtained show concurrency to the calculated values according to the Mamdani Model of the Fuzzy Control System.

  17. Fuzzy mobile-robot positioning in intelligent spaces using wireless sensor networks.

    Science.gov (United States)

    Herrero, David; Martínez, Humberto

    2011-01-01

    This work presents the development and experimental evaluation of a method based on fuzzy logic to locate mobile robots in an Intelligent Space using wireless sensor networks (WSNs). The problem consists of locating a mobile node using only inter-node range measurements, which are estimated by radio frequency signal strength attenuation. The sensor model of these measurements is very noisy and unreliable. The proposed method makes use of fuzzy logic for modeling and dealing with such uncertain information. Besides, the proposed approach is compared with a probabilistic technique showing that the fuzzy approach is able to handle highly uncertain situations that are difficult to manage by well-known localization methods.

  18. Efficient fuzzy logic controller for magnetic levitation systems | Shu ...

    African Journals Online (AJOL)

    In this paper magnetic levitation controller using fuzzy logic is proposed. The proposed Fuzzy logic controller (FLC) is designed, and developed using triangular membership function with 7×7 rules. The system model was implemented in MATLAB/SIMULINK and the system responses to Fuzzy controller with different input ...

  19. Design and Implementation of an Intelligent Windowsill System Using Smart Handheld Device and Fuzzy Microcontroller.

    Science.gov (United States)

    Wang, Jing-Min; Yang, Ming-Ta; Chen, Po-Lin

    2017-04-11

    With the advance of science and technology, people have a desire for convenient and comfortable living. Creating comfortable and healthy indoor environments is a major consideration for designing smart homes. As handheld devices become increasingly powerful and ubiquitous, this paper proposes an innovative use of smart handheld devices (SHD), using MIT App Inventor and fuzzy control, to perform the real-time monitoring and smart control of the designed intelligent windowsill system (IWS) in a smart home. A compact weather station that consists of environment sensors was constructed in the IWS for measuring of indoor illuminance, temperature-humidity, carbon dioxide (CO₂) concentration and outdoor rain and wind direction. According to the measured environment information, the proposed system can automatically send a command to a fuzzy microcontroller performed by Arduino UNO to fully or partly open the electric curtain and electric window for adapting to climate changes in the indoor and outdoor environment. Moreover, the IWS can automatically close windows for rain splashing on the window. The presented novel control method for the windowsill not only expands the SHD applications, but greatly enhances convenience to users. To validate the feasibility and effectiveness of the IWS, a laboratory prototype was built and confirmed experimentally.

  20. Fuzzy model-based control of a nuclear reactor

    International Nuclear Information System (INIS)

    Van Den Durpel, L.; Ruan, D.

    1994-01-01

    The fuzzy model-based control of a nuclear power reactor is an emerging research topic world-wide. SCK-CEN is dealing with this research in a preliminary stage, including two aspects, namely fuzzy control and fuzzy modelling. The aim is to combine both methodologies in contrast to conventional model-based PID control techniques, and to state advantages of including fuzzy parameters as safety and operator feedback. This paper summarizes the general scheme of this new research project

  1. Introduction to type-2 fuzzy logic control theory and applications

    CERN Document Server

    Mendel, Jerry M; Tan, Woei-Wan; Melek, William W; Ying, Hao

    2014-01-01

    Written by world-class leaders in type-2 fuzzy logic control, this book offers a self-contained reference for both researchers and students. The coverage provides both background and an extensive literature survey on fuzzy logic and related type-2 fuzzy control. It also includes research questions, experiment and simulation results, and downloadable computer programs on an associated website. This key resource will prove useful to students and engineers wanting to learn type-2 fuzzy control theory and its applications.

  2. Improved fuzzy PID controller design using predictive functional control structure.

    Science.gov (United States)

    Wang, Yuzhong; Jin, Qibing; Zhang, Ridong

    2017-11-01

    In conventional PID scheme, the ensemble control performance may be unsatisfactory due to limited degrees of freedom under various kinds of uncertainty. To overcome this disadvantage, a novel PID control method that inherits the advantages of fuzzy PID control and the predictive functional control (PFC) is presented and further verified on the temperature model of a coke furnace. Based on the framework of PFC, the prediction of the future process behavior is first obtained using the current process input signal. Then, the fuzzy PID control based on the multi-step prediction is introduced to acquire the optimal control law. Finally, the case study on a temperature model of a coke furnace shows the effectiveness of the fuzzy PID control scheme when compared with conventional PID control and fuzzy self-adaptive PID control. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  3. A utilization of fuzzy control for design automation of nuclear structures

    International Nuclear Information System (INIS)

    Yoshimura, Shinobu; Yagawa, Genki; Mochizuki, Yoshihiko

    1991-01-01

    This paper describes an automated design of nuclear structures by means of some artificial intelligence techniques. The 'generate and test' strategy is adopted as a basic strategy of design. An empirical approach with the fuzzy control is introduced for efficient design modification. This system is applied to the design of some 2D models of the fusion first wall. (author)

  4. Fuzzy batch controller for granular materials

    OpenAIRE

    Zamyatin Nikolaj; Smirnov Gennadij; Fedorchuk Yuri; Rusina Olga

    2018-01-01

    The paper focuses on batch control of granular materials in production of building materials from fluorine anhydrite. Batching equipment is intended for smooth operation and timely feeding of supply hoppers at a required level. Level sensors and a controller of an asynchronous screw drive motor are used to control filling of the hopper with industrial anhydrite binders. The controller generates a required frequency and ensures required productivity of a feed conveyor. Mamdani-type fuzzy infer...

  5. Intelligent tuning of vibration mitigation process for single link manipulator using fuzzy logic

    Directory of Open Access Journals (Sweden)

    Ahmed A. Ali

    2017-08-01

    Full Text Available In this work, active vibration mitigation for smart single link manipulator is presented. Two piezoelectric transducers were utilized to act as actuator and sensor respectively. Classical Proportional (P controller was tested numerically and experimentally. The comparison between measured results showed good agreement. The proposed work includes the introducing of fuzzy logic for tuning controller's gain within finite element method. Classical Proportional-Integral (PI, Fuzzy-P and Fuzzy-PI controllers were totally integrated as a series of [IF-Then] states and solved numerically by using Finite Element (FE solver (ANSYS. Proposed method will pave the way on solving the tuning process totally within single FE solver with high efficiency. Proposed method satisfied mitigation in the overall free response with about 52% and 74% of the manipulator settling time when Fuzzy-P and Fuzzy-PI controllers were activated respectively. This contribution can be utilized for many other applications related to fuzzy topics.

  6. Neuro-Fuzzy Computational Technique to Control Load Frequency in Hydro-Thermal Interconnected Power System

    Science.gov (United States)

    Prakash, S.; Sinha, S. K.

    2015-09-01

    In this research work, two areas hydro-thermal power system connected through tie-lines is considered. The perturbation of frequencies at the areas and resulting tie line power flows arise due to unpredictable load variations that cause mismatch between the generated and demanded powers. Due to rising and falling power demand, the real and reactive power balance is harmed; hence frequency and voltage get deviated from nominal value. This necessitates designing of an accurate and fast controller to maintain the system parameters at nominal value. The main purpose of system generation control is to balance the system generation against the load and losses so that the desired frequency and power interchange between neighboring systems are maintained. The intelligent controllers like fuzzy logic, artificial neural network (ANN) and hybrid fuzzy neural network approaches are used for automatic generation control for the two area interconnected power systems. Area 1 consists of thermal reheat power plant whereas area 2 consists of hydro power plant with electric governor. Performance evaluation is carried out by using intelligent (ANFIS, ANN and fuzzy) control and conventional PI and PID control approaches. To enhance the performance of controller sliding surface i.e. variable structure control is included. The model of interconnected power system has been developed with all five types of said controllers and simulated using MATLAB/SIMULINK package. The performance of the intelligent controllers has been compared with the conventional PI and PID controllers for the interconnected power system. A comparison of ANFIS, ANN, Fuzzy and PI, PID based approaches shows the superiority of proposed ANFIS over ANN, fuzzy and PI, PID. Thus the hybrid fuzzy neural network controller has better dynamic response i.e., quick in operation, reduced error magnitude and minimized frequency transients.

  7. Contributions to fuzzy polynomial techniques for stability analysis and control

    OpenAIRE

    Pitarch Pérez, José Luis

    2014-01-01

    The present thesis employs fuzzy-polynomial control techniques in order to improve the stability analysis and control of nonlinear systems. Initially, it reviews the more extended techniques in the field of Takagi-Sugeno fuzzy systems, such as the more relevant results about polynomial and fuzzy polynomial systems. The basic framework uses fuzzy polynomial models by Taylor series and sum-of-squares techniques (semidefinite programming) in order to obtain stability guarantees...

  8. Fuzzy batch controller for granular materials

    Directory of Open Access Journals (Sweden)

    Zamyatin Nikolaj

    2018-01-01

    Full Text Available The paper focuses on batch control of granular materials in production of building materials from fluorine anhydrite. Batching equipment is intended for smooth operation and timely feeding of supply hoppers at a required level. Level sensors and a controller of an asynchronous screw drive motor are used to control filling of the hopper with industrial anhydrite binders. The controller generates a required frequency and ensures required productivity of a feed conveyor. Mamdani-type fuzzy inference is proposed for controlling the speed of the screw that feeds mixture components. As related to production of building materials based on fluoride anhydrite, this method is used for the first time. A fuzzy controller is proven to be effective in controlling the filling level of the supply hopper. In addition, the authors determined optimal parameters of the batching process to ensure smooth operation and production of fluorine anhydrite materials of specified properties that can compete with gypsum-based products.

  9. Neural-fuzzy control of adept one SCARA

    International Nuclear Information System (INIS)

    Er, M.J.; Toh, B.H.; Toh, B.Y.

    1998-01-01

    This paper presents an Intelligent Control Strategy for the Adept One SCARA (Selective Compliance Assembly Robot Arm). It covers the design and simulation study of a Neural-Fuzzy Controller (NFC) for the SCARA with a view of tracking a predetermined trajectory of motion in the joint space. The SCARA was simulated as a three-axis manipulator with the dynamics of the tool (fourth link) neglected and the mass of the load incorporated into the mass of the third link. The overall performance of the control system under different conditions, namely variation in playload, variations in coefficients of static, dynamic and viscous friction and different trajectories were studied and comparison made with an existing Neural Network Controller and two Computed Torque Controllers. The NFC was shown to be robust and is able to overcome the drawback of the existing Neural Network Controller

  10. Fuzzy multivariable control of domestic heat pumps

    International Nuclear Information System (INIS)

    Underwood, C.P.

    2015-01-01

    Poor control has been identified as one of the reasons why recent field trials of domestic heat pumps in the UK have produced disappointing results. Most of the technology in use today uses a thermostatically-controlled fixed speed compressor with a mechanical expansion device. This article investigates improved control of these heat pumps through the design and evaluation of a new multivariable fuzzy logic control system utilising a variable speed compressor drive with capacity control linked through to evaporator superheat control. A new dynamic thermal model of a domestic heat pump validated using experimental data forms the basis of the work. The proposed control system is evaluated using median and extreme daily heating demand profiles for a typical UK house compared with a basic thermostatically-controlled alternative. Results show good tracking of the heating temperature and superheat control variables, reduced cycling and an improvement in performance averaging 20%. - Highlights: • A new dynamic model of a domestic heat pump is developed and validated. • A new multivariable fuzzy logic heat pump control system is developed/reported. • The fuzzy controller regulates both plant capacity and evaporator superheat degree. • Thermal buffer storage is also considered as well as compressor cycling. • The new controller shows good variable tracking and a reduction in energy of 20%.

  11. Design Sliding Mode Controller of with Parallel Fuzzy Inference System Compensator to Control of Robot Manipulator

    Directory of Open Access Journals (Sweden)

    Farzin Piltan

    2013-06-01

    Full Text Available Sliding mode controller (SMC is a significant nonlinear controller under condition of partly uncertain dynamic parameters of system. This controller is used to control of highly nonlinear systems especially for robot manipulators, because this controller is a robust and stable. Conversely, pure sliding mode controller is used in many applications; it has two important drawbacks namely; chattering phenomenon, and nonlinear equivalent dynamic formulation in uncertain dynamic parameter. The nonlinear equivalent dynamic formulation problem and chattering phenomenon in uncertain system can be solved by using artificial intelligence theorem. However fuzzy logic controller is used to control complicated nonlinear dynamic systems, but it cannot guarantee stability and robustness.  In this research parallel fuzzy logic theory is used to compensate the system dynamic uncertainty.

  12. A framework for the intelligent control of nuclear rockets

    International Nuclear Information System (INIS)

    Parlos, A.G.; Metzger, J.D.

    1993-01-01

    An intelligent control system architecture is proposed for nuclear rockets, and its various components are briefly described. The objective of the intelligent controller is the satisfaction of performance, robustness, fault-tolerance and reliability design specifications. The proposed hierarchical architecture consists of three levels: hardware, signal processing, and knowledge processing. The functionality of the intelligent controller is implemented utilizing advanced information processing technologies such as artificial neutral networks and fuzzy expert systems. The feasibility of a number of the controller architecture components have been independently validated using computer simulations. Preliminary results are presented demonstrating some of the signal processing capabilities of the intelligent nuclear rocket controller. Further work, currently in progress, is attempting to implement a number of the knowledge processing capabilities of the controller and their interface with the lower levels of the proposed architecture

  13. ARTIFICIAL NEURAL NETWORK AND FUZZY LOGIC CONTROLLER FOR GTAW MODELING AND CONTROL

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    An artificial neural network(ANN) and a self-adjusting fuzzy logic controller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented. The discussion is mainly focused on the modeling and control of the weld pool depth with ANN and the intelligent control for weld seam tracking with FLC. The proposed neural network can produce highly complex nonlinear multi-variable model of the GTAW process that offers the accurate prediction of welding penetration depth. A self-adjusting fuzzy controller used for seam tracking adjusts the control parameters on-line automatically according to the tracking errors so that the torch position can be controlled accurately.

  14. Fuzzy controller for an uncertain dynamical system

    DEFF Research Database (Denmark)

    Kulczycki, P.; Wisniewski, Rafal

    2002-01-01

    The present paper deals with the time-optimal control for mechanical systems with uncertain load. A fuzzy approach is used in the design of suboptimal feedback controllers, robust with respect to the load. Statistical kernel estimators are used for the specification of crucial parameters. The met......The present paper deals with the time-optimal control for mechanical systems with uncertain load. A fuzzy approach is used in the design of suboptimal feedback controllers, robust with respect to the load. Statistical kernel estimators are used for the specification of crucial parameters....... The methodology proposed in this work may be easily adopted to other modeling uncertainties of mechanical systems, e.g. motion resistance....

  15. Improved Fuzzy Logic based DTC of Induction machine for wide range of speed control using AI based controllers

    Directory of Open Access Journals (Sweden)

    H. Sudheer

    2016-06-01

    Full Text Available This paper presents improvements in Direct Torque control of induction motor using Fuzzy logic switching controller (FDTC. The conventional DTC (CDTC and FDTC drive performance is compared using Conventional PI, Fuzzy controller and Neural Network controllers. The major disadvantages of CDTC are high torque and flux ripples in steady state operation of the drive, inferior performance at low speed operation and variable switching frequency. The presence of hysteresis bands is the major reason for high torque and flux ripples in CDTC. In FDTC the hysteresis band and switching table are replaced by Fuzzy logic switching controller. Using fuzzy logic torque, stator flux space are divided into smaller subsections which results in precise and optimal selection of switching state to meet load torque. In high performance drives accurate tuning of PI speed controller is required. The conventional PI controller cannot adapt to the variation in model parameters. Artificial intelligence based fuzzy controller and neural network controller are compared with PI controller for both CDTC and FDTC of Induction machine. The proposed schemes are developed in Matlab/Simulink environment. Simulation results shows reduction in torque and flux ripples in FDTC and dynamic performance of the drive at low speeds and sudden change in load torque can be improved using Fuzzy logic controller compared to PI and neural network controller.

  16. Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system

    Energy Technology Data Exchange (ETDEWEB)

    Larbes, C.; Ait Cheikh, S.M.; Obeidi, T.; Zerguerras, A. [Laboratoire des Dispositifs de Communication et de Conversion Photovoltaique, Departement d' Electronique, Ecole Nationale Polytechnique, 10, Avenue Hassen Badi, El Harrach, Alger 16200 (Algeria)

    2009-10-15

    This paper presents an intelligent control method for the maximum power point tracking (MPPT) of a photovoltaic system under variable temperature and irradiance conditions. First, for the purpose of comparison and because of its proven and good performances, the perturbation and observation (P and O) technique is briefly introduced. A fuzzy logic controller based MPPT (FLC) is then proposed which has shown better performances compared to the P and O MPPT based approach. The proposed FLC has been also improved using genetic algorithms (GA) for optimisation. Different development stages are presented and the optimized fuzzy logic MPPT controller (OFLC) is then simulated and evaluated, which has shown better performances. (author)

  17. Artificial Intelligent Control for a Novel Advanced Microwave Biodiesel Reactor

    Energy Technology Data Exchange (ETDEWEB)

    Wali, W A; Hassan, K H; Cullen, J D; Al-Shamma' a, A I; Shaw, A; Wylie, S R, E-mail: w.wali@2009.ljmu.ac.uk [Built Environment and Sustainable Technologies Institute (BEST), School of the Built Environment, Faculty of Technology and Environment Liverpool John Moores University, Byrom Street, Liverpool L3 3AF (United Kingdom)

    2011-08-17

    Biodiesel, an alternative diesel fuel made from a renewable source, is produced by the transesterification of vegetable oil or fat with methanol or ethanol. In order to control and monitor the progress of this chemical reaction with complex and highly nonlinear dynamics, the controller must be able to overcome the challenges due to the difficulty in obtaining a mathematical model, as there are many uncertain factors and disturbances during the actual operation of biodiesel reactors. Classical controllers show significant difficulties when trying to control the system automatically. In this paper we propose a comparison of artificial intelligent controllers, Fuzzy logic and Adaptive Neuro-Fuzzy Inference System(ANFIS) for real time control of a novel advanced biodiesel microwave reactor for biodiesel production from waste cooking oil. Fuzzy logic can incorporate expert human judgment to define the system variables and their relationships which cannot be defined by mathematical relationships. The Neuro-fuzzy system consists of components of a fuzzy system except that computations at each stage are performed by a layer of hidden neurons and the neural network's learning capability is provided to enhance the system knowledge. The controllers are used to automatically and continuously adjust the applied power supplied to the microwave reactor under different perturbations. A Labview based software tool will be presented that is used for measurement and control of the full system, with real time monitoring.

  18. Artificial Intelligent Control for a Novel Advanced Microwave Biodiesel Reactor

    International Nuclear Information System (INIS)

    Wali, W A; Hassan, K H; Cullen, J D; Al-Shamma'a, A I; Shaw, A; Wylie, S R

    2011-01-01

    Biodiesel, an alternative diesel fuel made from a renewable source, is produced by the transesterification of vegetable oil or fat with methanol or ethanol. In order to control and monitor the progress of this chemical reaction with complex and highly nonlinear dynamics, the controller must be able to overcome the challenges due to the difficulty in obtaining a mathematical model, as there are many uncertain factors and disturbances during the actual operation of biodiesel reactors. Classical controllers show significant difficulties when trying to control the system automatically. In this paper we propose a comparison of artificial intelligent controllers, Fuzzy logic and Adaptive Neuro-Fuzzy Inference System(ANFIS) for real time control of a novel advanced biodiesel microwave reactor for biodiesel production from waste cooking oil. Fuzzy logic can incorporate expert human judgment to define the system variables and their relationships which cannot be defined by mathematical relationships. The Neuro-fuzzy system consists of components of a fuzzy system except that computations at each stage are performed by a layer of hidden neurons and the neural network's learning capability is provided to enhance the system knowledge. The controllers are used to automatically and continuously adjust the applied power supplied to the microwave reactor under different perturbations. A Labview based software tool will be presented that is used for measurement and control of the full system, with real time monitoring.

  19. A proposal of ubiquitous fuzzy computing for ambient Intelligence

    NARCIS (Netherlands)

    Acampora, G.; Loia, V.

    2008-01-01

    Ambient Intelligence is considered as the composition of three emergent technologies: Ubiquitous Computing, Ubiquitous Communication and Intelligent User Interfaces. The aim of integration of aforesaid technologies is to make wider the interaction between human beings and information technology

  20. Ubiquitous fuzzy computing in open ambient intelligence environments

    NARCIS (Netherlands)

    Acampora, G.; Loia, V.

    2006-01-01

    Ambient intelligence (AmI) is considered as the composition of three emergent technologies: ubiquitous computing, ubiquitous communication and intelligent user interfaces. The aim of integration of aforesaid technologies is to make wider the interaction between human beings and information

  1. Prototyping qualitative controllers for fuzzy-logic controller design

    International Nuclear Information System (INIS)

    Bakhtiari, S.; Jabedar-Maralani, P.

    1999-05-01

    Qualitative controls can be designed for linear and nonlinear models with the same computational complexity. At the same time they show the general form of the proper control. These properties can help ease the design process for quantitative controls. In this paper qualitative controls are used as prototypes for the design of linear or nonlinear, and in particular Sugeno-type fuzzy, controls. The LMS identification method is used to approximate the qualitative control with the nearest fuzzy control. The method is applied to the problem of position control in a permanent magnet synchronous motor; moreover, the performance and the robustness of the two controllers are compared

  2. Optimizing pipeline transportation using a fuzzy controller

    Energy Technology Data Exchange (ETDEWEB)

    Aramaki, Thiago L.; Correa, Joao L. L.; Montalvoa, Antonio F. F. [National Control and Operation Center Tranpetro, Rio de Janeiro, (Brazil)

    2010-07-01

    The optimization of pipeline transportation is a big concern for the transporter companies. This paper is the third of a series of three articles which investigated the application of a system to simulate the human ability to operate a pipeline in an optimized way. The present paper presents the development of a proportional integral (PI) fuzzy controller, in order to optimize pipeline transportation capacity. The fuzzy adaptive PI controller system was developed and tested with a hydraulic simulator. On-field data were used from the OSBRA pipeline. The preliminary tests showed that the performance of the software simulation was satisfactory. It varied the set-point of the conventional controller within the limits of flow meters. The transport capacity of the pipe was maximize without compromising the integrity of the commodities transported. The system developed proved that it can be easily deployed as a specialist optimizing system to be added to SCADA systems.

  3. Self tuning fuzzy PID type load and frequency controller

    International Nuclear Information System (INIS)

    Yesil, E.; Guezelkaya, M.; Eksin, I.

    2004-01-01

    In this paper, a self tuning fuzzy PID type controller is proposed for solving the load frequency control (LFC) problem. The fuzzy PID type controller is constructed as a set of control rules, and the control signal is directly deduced from the knowledge base and the fuzzy inference. Moreover, there exists a self tuning mechanism that adjusts the input scaling factor corresponding to the derivative coefficient and the output scaling factor corresponding to the integral coefficient of the PID type fuzzy logic controller in an on-line manner. The self tuning mechanism depends on the peak observer idea, and this idea is modified and adapted to the LFC problem. A two area interconnected system is assumed for demonstrations. The proposed self tuning fuzzy PID type controller has been compared with the fuzzy PID type controller without a self tuning mechanism and the conventional integral controller through some performance indices

  4. Implementation of Fuzzy Logic Based Temperature-Controlled Heat ...

    African Journals Online (AJOL)

    This research then compares the control performance of PID (Proportional Integral and Derivative) and Fuzzy logic controllers. Conclusions are made based on these control performances. The results show that the control performance for a Fuzzy controller is quite similar to PID controller but comparatively gives a better ...

  5. Integrated circuit implementation of fuzzy controllers

    OpenAIRE

    Huertas Díaz, José Luis; Sánchez Solano, Santiago; Baturone Castillo, María Iluminada; Barriga Barros, Ángel

    1996-01-01

    This paper presents mixed-signal current-mode CMOS circuits to implement programmable fuzzy controllers that perform the singleton or zero-order Sugeno’s method. Design equations to characterize these circuits are provided to explain the precision and speed that they offer. This analysis is illustrated with the experimental results of prototypes integrated in standard CMOS technologies. These tests show that an equivalent precision of 6 bits is achieved. The connection of these...

  6. Sensorless speed control of switched reluctance motor using brain emotional learning based intelligent controller

    International Nuclear Information System (INIS)

    Dehkordi, Behzad Mirzaeian; Parsapoor, Amir; Moallem, Mehdi; Lucas, Caro

    2011-01-01

    In this paper, a brain emotional learning based intelligent controller (BELBIC) is developed to control the switched reluctance motor (SRM) speed. Like other intelligent controllers, BELBIC is model free and is suitable to control nonlinear systems. Motor parameter changes, operating point changes, measurement noise, open circuit fault in one phase and asymmetric phases in SRM are also simulated to show the robustness and superior performance of BELBIC. To compare the BELBIC performance with other intelligent controllers, Fuzzy Logic Controller (FLC) is developed. System responses with BELBIC and FLC are compared. Furthermore, by eliminating the position sensor, a method is introduced to estimate the rotor position. This method is based on Adaptive Neuro Fuzzy Inference System (ANFIS). The estimator inputs are four phase flux linkages. Suggested rotor position estimator is simulated in different conditions. Simulation results confirm the accurate rotor position estimation in different loads and speeds.

  7. Sensorless speed control of switched reluctance motor using brain emotional learning based intelligent controller

    Energy Technology Data Exchange (ETDEWEB)

    Dehkordi, Behzad Mirzaeian, E-mail: mirzaeian@eng.ui.ac.i [Department of Electrical Engineering, Faculty of Engineering, University of Isfahan, Hezar-Jerib St., Postal code 8174673441, Isfahan (Iran, Islamic Republic of); Parsapoor, Amir, E-mail: amirparsapoor@yahoo.co [Department of Electrical Engineering, Faculty of Engineering, University of Isfahan, Hezar-Jerib St., Postal code 8174673441, Isfahan (Iran, Islamic Republic of); Moallem, Mehdi, E-mail: moallem@cc.iut.ac.i [Department of Electrical Engineering, Isfahan University of Technology, Isfahan (Iran, Islamic Republic of); Lucas, Caro, E-mail: lucas@ut.ac.i [Centre of Excellence for Control and Intelligent Processing, Electrical and Computer Engineering Faculty, College of Engineering, University of Tehran, Tehran (Iran, Islamic Republic of)

    2011-01-15

    In this paper, a brain emotional learning based intelligent controller (BELBIC) is developed to control the switched reluctance motor (SRM) speed. Like other intelligent controllers, BELBIC is model free and is suitable to control nonlinear systems. Motor parameter changes, operating point changes, measurement noise, open circuit fault in one phase and asymmetric phases in SRM are also simulated to show the robustness and superior performance of BELBIC. To compare the BELBIC performance with other intelligent controllers, Fuzzy Logic Controller (FLC) is developed. System responses with BELBIC and FLC are compared. Furthermore, by eliminating the position sensor, a method is introduced to estimate the rotor position. This method is based on Adaptive Neuro Fuzzy Inference System (ANFIS). The estimator inputs are four phase flux linkages. Suggested rotor position estimator is simulated in different conditions. Simulation results confirm the accurate rotor position estimation in different loads and speeds.

  8. Control of beam halo-chaos using fuzzy logic controller

    International Nuclear Information System (INIS)

    Gao Yuan; Yuan Haiying; Tan Guangxing; Luo Wenguang

    2012-01-01

    Considering the ion beam with initial K-V distribution in the periodic focusing magnetic filed channels (PFCs) as a typical sample, a fuzzy control method for control- ling beam halo-chaos was studied. A fuzzy proportional controller, using output of fuzzy inference as a control factor, was presented for adjusting exterior focusing magnetic field. The stability of controlled system was proved by fuzzy phase plane analysis. The simulation results demonstrate that the chaotic radius of envelope can be controlled to the matched radius via controlling magnetic field. This method was also applied to the multi-particle model. Under the control condition, the beam halos and its regeneration can be eliminated effectively, and that both the compactness and the uniformity of ion beam are improved evidently. Since the exterior magnetic field can be rather easily adjusted by proportional control and the fuzzy logic controller is independent to the mathematical model, this method has adaptive ability and is easily realized in experiment. The research offers a valuable reference for the design of the PFCs in the high- current linear ion accelerators. (authors)

  9. Fuzzy logic control to be conventional method

    Energy Technology Data Exchange (ETDEWEB)

    Eker, Ilyas [University of Gaziantep, Gaziantep (Turkey). Department of Electrical and Electronic Engineering; Torun, Yunis [University of Gaziantep, Gaziantep (Turkey). Technical Vocational School of Higher Education

    2006-03-01

    Increasing demands for flexibility and fast reactions in modern process operation and production methods result in nonlinear system behaviour of partly unknown systems, and this necessitates application of alternative control methods to meet the demands. Fuzzy logic (FL) control can play an important role because knowledge based design rules can easily be implemented in systems with unknown structure, and it is going to be a conventional control method since the control design strategy is simple and practical and is based on linguistic information. Computational complexity is not a limitation any more because the computing power of computers has been significantly improved even for high speed industrial applications. This makes FL control an important alternative method to the conventional PID control method for use in nonlinear industrial systems. This paper presents a practical implementation of the FL control to an electrical drive system. Such drive systems used in industry are composed of masses moving under the action of position and velocity dependent forces. These forces exhibit nonlinear behaviour. For a multi-mass drive system, the nonlinearities, like Coulomb friction and dead zone, significantly influence the operation of the systems. The proposed FL control configuration is based on speed error and change of speed error. The feasibility and effectiveness of the control method are experimentally demonstrated. The results obtained from conventional FL control, fuzzy PID and adaptive FL control are compared with traditional PID control for the dynamic responses of the closed loop drive system. (author)

  10. Fuzzy logic control to be conventional method

    International Nuclear Information System (INIS)

    Eker, Ilyas; Torun, Yunis

    2006-01-01

    Increasing demands for flexibility and fast reactions in modern process operation and production methods result in nonlinear system behaviour of partly unknown systems, and this necessitates application of alternative control methods to meet the demands. Fuzzy logic (FL) control can play an important role because knowledge based design rules can easily be implemented in systems with unknown structure, and it is going to be a conventional control method since the control design strategy is simple and practical and is based on linguistic information. Computational complexity is not a limitation any more because the computing power of computers has been significantly improved even for high speed industrial applications. This makes FL control an important alternative method to the conventional PID control method for use in nonlinear industrial systems. This paper presents a practical implementation of the FL control to an electrical drive system. Such drive systems used in industry are composed of masses moving under the action of position and velocity dependent forces. These forces exhibit nonlinear behaviour. For a multi-mass drive system, the nonlinearities, like Coulomb friction and dead zone, significantly influence the operation of the systems. The proposed FL control configuration is based on speed error and change of speed error. The feasibility and effectiveness of the control method are experimentally demonstrated. The results obtained from conventional FL control, fuzzy PID and adaptive FL control are compared with traditional PID control for the dynamic responses of the closed loop drive system

  11. New fuzzy EWMA control charts for monitoring phase II fuzzy profiles

    Directory of Open Access Journals (Sweden)

    Ghazale Moghadam

    2016-01-01

    Full Text Available In many quality control applications, the quality of a process or product is explained by the relationship between response variable and one or more explanatory variables, called a profile. In this paper, a new fuzzy EWMA control chart for phase II fuzzy profile monitoring is proposed. To this end, we extend EWMA control charts to its equivalent Fuzzy type and then implement fuzzy ranking methods to determine whether the process fuzzy profile is under or out of control. The proposed method is capable of identifying small changes in process under condition of process profile explaining parameters vagueness, roughness and uncertainty. Determining the source of changes, this method provides us with the possibility of recognizing the causes of process transition from stable mode, removing these causes and restoring the process stable mode.

  12. Design of a stable fuzzy controller for an articulated vehicle.

    Science.gov (United States)

    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.

  13. A fuzzy controlled three-phase centrifuge for waste separation

    International Nuclear Information System (INIS)

    Parkinson, W.J.; Smith, R.E.; Miller, N.

    1998-02-01

    The three-phase centrifuge technology discussed in this paper was developed by Neal Miller, president of Centech, Inc. The three-phase centrifuge is an excellent device for cleaning up oil field and refinery wastes which are typically composed of hydrocarbons, water, and solids. The technology is unique. It turns the waste into salable oil, reusable water, and landfill-able solids. No secondary waste is produced. The problem is that only the inventor can set up and run the equipment well enough to provide an optimal cleanup. Demand for this device has far exceeded a one man operation. There is now a need for several centrifuges to be operated at different locations at the same time. This has produced a demand for an intelligent control system, one that could replace a highly skilled operator, or at least supplement the skills of a less experienced operator. The control problem is ideally suited to fuzzy logic, since the centrifuge is a highly complicated machine operated entirely by the skill and experience of the operator. A fuzzy control system was designed for and used with the centrifuge

  14. Intelligent Technique for Signal Processing to Identify the Brain Disorder for Epilepsy Captures Using Fuzzy Systems

    Directory of Open Access Journals (Sweden)

    Gurumurthy Sasikumar

    2016-01-01

    Full Text Available The new direction of understand the signal that is created from the brain organization is one of the main chores in the brain signal processing. Amid all the neurological disorders the human brain epilepsy is measured as one of the extreme prevalent and then programmed artificial intelligence detection technique is an essential due to the crooked and unpredictable nature of happening of epileptic seizures. We proposed an Improved Fuzzy firefly algorithm, which would enhance the classification of the brain signal efficiently with minimum iteration. An important bunching technique created on fuzzy logic is the Fuzzy C means. Together in the feature domain with the spatial domain the features gained after multichannel EEG signals remained combined by means of fuzzy algorithms. And for better precision segmentation process the firefly algorithm is applied to optimize the Fuzzy C-means membership function. Simultaneously for the efficient clustering method the convergence criteria are set. On the whole the proposed technique yields more accurate results and that gives an edge over other techniques. This proposed algorithm result compared with other algorithms like fuzzy c means algorithm and PSO algorithm.

  15. Experimental Study on Intelligent Control Scheme for Fan Coil Air-Conditioning System

    Directory of Open Access Journals (Sweden)

    Yanfeng Li

    2013-01-01

    Full Text Available An intelligent control scheme for fan coil air-conditioning systems has been put forward in order to overcome the shortcomings of the traditional proportion-integral-derivative (PID control scheme. These shortcomings include the inability of anti-interference and large inertia. An intelligent control test rig of fan coil air-conditioning system has been built, and MATLAB/Simulink dynamics simulation software has been adopted to implement the intelligent control scheme. A software for data exchange has been developed to combine the intelligence control system and the building automation (BA system. Experimental tests have been conducted to investigate the effectiveness of different control schemes including the traditional PID control, fuzzy control, and fuzzy-PID control for fan coil air-conditioning system. The effects of control schemes have been compared and analyzed in robustness, static and dynamic character, and economy. The results have shown that the developed data exchange interface software can induce the intelligent control scheme of the BA system more effectively. Among the proposed control strategies, fuzzy-PID control scheme which has the advantages of both traditional PID and fuzzy schemes is the optimal control scheme for the fan coil air-conditioning system.

  16. An intelligent temporal pattern classification system using fuzzy ...

    Indian Academy of Sciences (India)

    In this paper, we propose a new pattern classification system by combining Temporal features with Fuzzy Min–Max (TFMM) neural network based classifier for effective decision support in medical diagnosis. Moreover, a Particle Swarm Optimization (PSO) algorithm based rule extractor is also proposed in this work for ...

  17. FUZZY LOGIC CONTROLLER IMPLEMENTATION FOR PHOTOVOLTAIC STATION

    Directory of Open Access Journals (Sweden)

    Imad Zein

    2014-01-01

    Full Text Available Solar panels have a nonlinear voltage-current characteristic, with a distinct maximum power point (MPP, which depends on the environmental factors, such as temperature and irradiation. In order to continuously harvest maximum power from the solar panels, they have to operate at their MPP despite the inevitable changes in the environment. This is why the controllers of all solar power electronic converters employ some method for maximum power point tracking (MPPT . Over the past years many MPPT techniques have been published and based on that the main paper’s objective is to analyze one of the most promising MPPT control algorithms: fuzzy logic controller

  18. Using FML and fuzzy technology in adaptive ambient intelligent environments

    NARCIS (Netherlands)

    Acampora, G.; Loia, V.

    2005-01-01

    Ambient Intelligence (AmI, shortly) gathers best re-sults from three key technologies, Ubiquitous Computing, Ubiq-uitous Communication, and Intelligent User Friendly Inter-faces. The functional and spatial distribution of tasks is a natu-ral thrust to employ multi-agent paradigm to design and

  19. Intelligent control of HVAC systems. Part II: perceptron performance analysis

    Directory of Open Access Journals (Sweden)

    Ioan URSU

    2013-09-01

    Full Text Available This is the second part of a paper on intelligent type control of Heating, Ventilating, and Air-Conditioning (HVAC systems. The whole study proposes a unified approach in the design of intelligent control for such systems, to ensure high energy efficiency and air quality improving. In the first part of the study it is considered as benchmark system a single thermal space HVAC system, for which it is assigned a mathematical model of the controlled system and a mathematical model(algorithm of intelligent control synthesis. The conception of the intelligent control is of switching type, between a simple neural network, a perceptron, which aims to decrease (optimize a cost index,and a fuzzy logic component, having supervisory antisaturating role for neuro-control. Based on numerical simulations, this Part II focuses on the analysis of system operation in the presence only ofthe neural control component. Working of the entire neuro-fuzzy system will be reported in a third part of the study.

  20. System identification and adaptive control theory and applications of the neurofuzzy and fuzzy cognitive network models

    CERN Document Server

    Boutalis, Yiannis; Kottas, Theodore; Christodoulou, Manolis A

    2014-01-01

    Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented.  Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN model  stems  from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering s...

  1. fuzzy control technique fuzzy control technique applied to modified

    African Journals Online (AJOL)

    eobe

    epidemiological parameters) to the malaria model simulated by 9 fully ... The Mamdani controllers use a standard max-min inference process and a fast centre of min inference process and a ... Numerical results obtained using Matlab 2008a software software .... simulation environment using the 9 ODE Simulators. The test ...

  2. Fuzzy Mobile-Robot Positioning in Intelligent Spaces Using Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    David Herrero

    2011-11-01

    Full Text Available This work presents the development and experimental evaluation of a method based on fuzzy logic to locate mobile robots in an Intelligent Space using Wireless Sensor Networks (WSNs. The problem consists of locating a mobile node using only inter-node range measurements, which are estimated by radio frequency signal strength attenuation. The sensor model of these measurements is very noisy and unreliable. The proposed method makes use of fuzzy logic for modeling and dealing with such uncertain information. Besides, the proposed approach is compared with a probabilistic technique showing that the fuzzy approach is able to handle highly uncertain situations that are difficult to manage by well-known localization methods.

  3. Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System

    Directory of Open Access Journals (Sweden)

    Stephan Birle

    2016-01-01

    Full Text Available In food industry, bioprocesses like fermentation often are a crucial part of the manufacturing process and decisive for the final product quality. In general, they are characterized by highly nonlinear dynamics and uncertainties that make it difficult to control these processes by the use of traditional control techniques. In this context, fuzzy logic controllers offer quite a straightforward way to control processes that are affected by nonlinear behavior and uncertain process knowledge. However, in order to maintain process safety and product quality it is necessary to specify the controller performance and to tune the controller parameters. In this work, an approach is presented to establish an intelligent control system for oxidoreductive yeast propagation as a representative process biased by the aforementioned uncertainties. The presented approach is based on statistical process control and fuzzy logic feedback control. As the cognitive uncertainty among different experts about the limits that define the control performance as still acceptable may differ a lot, a data-driven design method is performed. Based upon a historic data pool statistical process corridors are derived for the controller inputs control error and change in control error. This approach follows the hypothesis that if the control performance criteria stay within predefined statistical boundaries, the final process state meets the required quality definition. In order to keep the process on its optimal growth trajectory (model based reference trajectory a fuzzy logic controller is used that alternates the process temperature. Additionally, in order to stay within the process corridors, a genetic algorithm was applied to tune the input and output fuzzy sets of a preliminarily parameterized fuzzy controller. The presented experimental results show that the genetic tuned fuzzy controller is able to keep the process within its allowed limits. The average absolute error to the

  4. Fuzzy logic and neural networks in artificial intelligence and pattern recognition

    Science.gov (United States)

    Sanchez, Elie

    1991-10-01

    With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.

  5. A Fuzzy Control Course on the TED Server

    DEFF Research Database (Denmark)

    Dotoli, Mariagrazia; Jantzen, Jan

    1999-01-01

    , an educational server that serves as a learning central for students and professionals working with fuzzy logic. Through the server, TED offers an online course on fuzzy control. The course concerns automatic control of an inverted pendulum, with a focus on rule based control by means of fuzzy logic. A ball......The Training and Education Committee (TED) is a committee under ERUDIT, a Network of Excellence for fuzzy technology and uncertainty in Europe. The main objective of TED is to improve the training and educational possibilities for the nodes of ERUDIT. Since early 1999, TED has set up the TED server...

  6. Soft computing in intelligent control

    CERN Document Server

    Jung, Jin-Woo; Kubota, Naoyuki

    2014-01-01

    Nowadays, people have tendency to be fond of smarter machines that are able to collect data, make learning, recognize things, infer meanings, communicate with human and perform behaviors. Thus, we have built advanced intelligent control affecting all around societies; automotive, rail, aerospace, defense, energy, healthcare, telecoms and consumer electronics, finance, urbanization. Consequently, users and consumers can take new experiences through the intelligent control systems. We can reshape the technology world and provide new opportunities for industry and business, by offering cost-effective, sustainable and innovative business models. We will have to know how to create our own digital life. The intelligent control systems enable people to make complex applications, to implement system integration and to meet society’s demand for safety and security. This book aims at presenting the research results and solutions of applications in relevance with intelligent control systems. We propose to researchers ...

  7. Fuzzy Cognitive Map for Software Testing Using Artificial Intelligence Techniques

    OpenAIRE

    Larkman , Deane; Mohammadian , Masoud; Balachandran , Bala; Jentzsch , Ric

    2010-01-01

    International audience; This paper discusses a framework to assist test managers to evaluate the use of AI techniques as a potential tool in software testing. Fuzzy Cognitive Maps (FCMs) are employed to evaluate the framework and make decision analysis easier. A what-if analysis is presented that explores the general application of the framework. Simulations are performed to show the effectiveness of the proposed method. The framework proposed is innovative and it assists managers in making e...

  8. Implementation of a Fuzzy Logic Speed Controller for a Permanent ...

    African Journals Online (AJOL)

    In this paper DC motor control models were mathematically extracted and implemented using fuzzy logic speed controller. All control systems suffer from problems related to undesirable overshoot, longer settling times and vibrations while going from one state to another. To overcome the maximum overshoot, fuzzy logic ...

  9. A fuzzy logic intelligent diagnostic system for spacecraft integrated vehicle health management

    Science.gov (United States)

    Wu, G. Gordon

    1995-01-01

    Due to the complexity of future space missions and the large amount of data involved, greater autonomy in data processing is demanded for mission operations, training, and vehicle health management. In this paper, we develop a fuzzy logic intelligent diagnostic system to perform data reduction, data analysis, and fault diagnosis for spacecraft vehicle health management applications. The diagnostic system contains a data filter and an inference engine. The data filter is designed to intelligently select only the necessary data for analysis, while the inference engine is designed for failure detection, warning, and decision on corrective actions using fuzzy logic synthesis. Due to its adaptive nature and on-line learning ability, the diagnostic system is capable of dealing with environmental noise, uncertainties, conflict information, and sensor faults.

  10. What procedure to choose while designing a fuzzy control? Towards mathematical foundations of fuzzy control

    Science.gov (United States)

    Kreinovich, Vladik YA.; Quintana, Chris; Lea, Robert

    1991-01-01

    Fuzzy control has been successfully applied in industrial systems. However, there is some caution in using it. The reason is that it is based on quite reasonable ideas, but each of these ideas can be implemented in several different ways, and depending on which of the implementations chosen different results are achieved. Some implementations lead to a high quality control, some of them not. And since there are no theoretical methods for choosing the implementation, the basic way to choose it now is experimental. But if one chooses a method that is good for several examples, there is no guarantee that it will work fine in all of them. Hence the caution. A theoretical basis for choosing the fuzzy control procedures is provided. In order to choose a procedure that transforms a fuzzy knowledge into a control, one needs, first, to choose a membership function for each of the fuzzy terms that the experts use, second, to choose operations of uncertainty values that corresponds to 'and' and 'or', and third, when a membership function for control is obtained, one must defuzzy it, that is, somehow generate a value of the control u that will be actually used. A general approach that will help to make all these choices is described: namely, it is proved that under reasonable assumptions membership functions should be linear or fractionally linear, defuzzification must be described by a centroid rule and describe all possible 'and' and 'or' operations. Thus, a theoretical explanation of the existing semi-heuristic choices is given and the basis for the further research on optimal fuzzy control is formulated.

  11. Intelligent energy management control of vehicle air conditioning system coupled with engine

    International Nuclear Information System (INIS)

    Khayyam, Hamid; Abawajy, Jemal; Jazar, Reza N.

    2012-01-01

    Vehicle Air Conditioning (AC) systems consist of an engine powered compressor activated by an electrical clutch. The AC system imposes an extra load to the vehicle's engine increasing the vehicle fuel consumption and emissions. Energy management control of the vehicle air conditioning is a nonlinear dynamic system, influenced by uncertain disturbances. In addition, the vehicle energy management control system interacts with different complex systems, such as engine, air conditioning system, environment, and driver, to deliver fuel consumption improvements. In this paper, we describe the energy management control of vehicle AC system coupled with vehicle engine through an intelligent control design. The Intelligent Energy Management Control (IEMC) system presented in this paper includes an intelligent algorithm which uses five exterior units and three integrated fuzzy controllers to produce desirable internal temperature and air quality, improved fuel consumption, low emission, and smooth driving. The three fuzzy controllers include: (i) a fuzzy cruise controller to adapt vehicle cruise speed via prediction of the road ahead using a Look-Ahead system, (ii) a fuzzy air conditioning controller to produce desirable temperature and air quality inside vehicle cabin room via a road information system, and (iii) a fuzzy engine controller to generate the required engine torque to move the vehicle smoothly on the road. We optimised the integrated operation of the air conditioning and the engine under various driving patterns and performed three simulations. Results show that the proposed IEMC system developed based on Fuzzy Air Conditioning Controller with Look-Ahead (FAC-LA) method is a more efficient controller for vehicle air conditioning system than the previously developed Coordinated Energy Management Systems (CEMS). - Highlights: ► AC interacts: vehicle, environment, driver components, and the interrelationships between them. ► Intelligent AC algorithm which uses

  12. 视觉移动机器人的模糊智能路径规划%Intelligent Path Planning of Vision- Based Mobile Robot with Fuzzy Approach

    Institute of Scientific and Technical Information of China (English)

    张一巍; 黄源清

    2002-01-01

    The path planning problem for intelligent mobile robots inwbves two main problems: the represent of task emionment including obstacles and the development of a strategy to determine a collision - free route. In this paper, new approaches have been developed to solve these problems .The first problem was solve using the fuzzy system approach, which represent obstacles with a circle. The other problem was overcome throughthe use of a strategy selector, which chooses the best stategy between velocity control strategy and direction control strategy.

  13. Expert system driven fuzzy control application to power reactors

    International Nuclear Information System (INIS)

    Tsoukalas, L.H.; Berkan, R.C.; Upadhyaya, B.R.; Uhrig, R.E.

    1990-01-01

    For the purpose of nonlinear control and uncertainty/imprecision handling, fuzzy controllers have recently reached acclaim and increasing commercial application. The fuzzy control algorithms often require a ''supervisory'' routine that provides necessary heuristics for interface, adaptation, mode selection and other implementation issues. Performance characteristics of an on-line fuzzy controller depend strictly on the ability of such supervisory routines to manipulate the fuzzy control algorithm and enhance its control capabilities. This paper describes an expert system driven fuzzy control design application to nuclear reactor control, for the automated start-up control of the Experimental Breeder Reactor-II. The methodology is verified through computer simulations using a valid nonlinear model. The necessary heuristic decisions are identified that are vitally important for the implemention of fuzzy control in the actual plant. An expert system structure incorporating the necessary supervisory routines is discussed. The discussion also includes the possibility of synthesizing the fuzzy, exact and combined reasoning to include both inexact concepts, uncertainty and fuzziness, within the same environment

  14. Expectation-based intelligent control

    International Nuclear Information System (INIS)

    Zak, Michail

    2006-01-01

    New dynamics paradigms-negative diffusion and terminal attractors-are introduced to control noise and chaos. The applied control forces are composed of expectations governed by the associated Fokker-Planck and Liouville equations. The approach is expanded to a general concept of intelligent control via expectations. Relevance to control in livings is emphasized and illustrated by neural nets with mirror neurons

  15. Rule-bases construction through self-learning for a table-based Sugeno-Takagi fuzzy logic control system

    Directory of Open Access Journals (Sweden)

    C. Boldisor

    2009-12-01

    Full Text Available A self-learning based methodology for building the rule-base of a fuzzy logic controller (FLC is presented and verified, aiming to engage intelligent characteristics to a fuzzy logic control systems. The methodology is a simplified version of those presented in today literature. Some aspects are intentionally ignored since it rarely appears in control system engineering and a SISO process is considered here. The fuzzy inference system obtained is a table-based Sugeno-Takagi type. System’s desired performance is defined by a reference model and rules are extracted from recorded data, after the correct control actions are learned. The presented algorithm is tested in constructing the rule-base of a fuzzy controller for a DC drive application. System’s performances and method’s viability are analyzed.

  16. Parameter optimization via cuckoo optimization algorithm of fuzzy controller for energy management of a hybrid power system

    International Nuclear Information System (INIS)

    Berrazouane, S.; Mohammedi, K.

    2014-01-01

    Highlights: • Optimized fuzzy logic controller (FLC) for operating a standalone hybrid power system based on cuckoo search algorithm. • Comparison between optimized fuzzy logic controller based on cuckoo search and swarm intelligent. • Loss of power supply probability and levelized energy cost are introduced. - Abstract: This paper presents the development of an optimized fuzzy logic controller (FLC) for operating a standalone hybrid power system based on cuckoo search algorithm. The FLC inputs are batteries state of charge (SOC) and net power flow, FLC outputs are the power rate of batteries, photovoltaic and diesel generator. Data for weekly solar irradiation, ambient temperature and load profile are used to tune the proposed controller by using cuckoo search algorithm. The optimized FLC is able to minimize loss of power supply probability (LPSP), excess energy (EE) and levelized energy cost (LEC). Moreover, the results of CS optimization are better than of particle swarm optimization PSO for fuzzy system controller

  17. Coordinated signal control for arterial intersections using fuzzy logic

    Science.gov (United States)

    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.

  18. CONVERGENCE OF POWERS OF CONTROLLABLE INTUITIONISTIC FUZZY MATRICES

    OpenAIRE

    Riyaz Ahmad Padder; P. Murugadas

    2016-01-01

    Convergences of powers of controllable intuitionistic fuzzy matrices have been stud¬ied. It is shown that they oscillate with period equal to 2, in general. Some equalities and sequences of inequalities about powers of controllable intuitionistic fuzzy matrices have been obtained.

  19. Determination Of Adaptive Control Parameter Using Fuzzy Logic Controller

    Directory of Open Access Journals (Sweden)

    Omur Can Ozguney

    2017-08-01

    Full Text Available The robot industry has developed along with the increasing the use of robots in industry. This has led to increase the studies on robots. The most important part of these studies is that the robots must be work with minimum tracking trajectory error. But it is not easy for robots to track the desired trajectory because of the external disturbances and parametric uncertainty. Therefore adaptive and robust controllers are used to decrease tracking error. The aim of this study is to increase the tracking performance of the robot and minimize the trajectory tracking error. For this purpose adaptive control law for robot manipulator is identified and fuzzy logic controller is applied to find the accurate values for adaptive control parameter. Based on the Lyapunov theory stability of the uncertain system is guaranteed. In this study robot parameters are assumed to be unknown. This controller is applied to a robot model and the results of simulations are given. Controller with fuzzy logic and without fuzzy logic are compared with each other. Simulation results show that the fuzzy logic controller has improved the results.

  20. Intelligent control and automation technology for nuclear applications

    International Nuclear Information System (INIS)

    Kim, Jae Hui; Huh, Young Hwan; Lee, Jang Soo; Kim, Ko Ryeo; Cha, Kyoung Ho; Lee, Jae Cheol; Dong, In Sook

    1993-01-01

    This project intends to establish the basic technology of intelligent control and automation to be applied to the next generation nuclear plant. For that, the research status of those technologies is surveyed for various application areas at first. The characteristics and availability of those techniques such as neural network, fuzzy rule based control and reasoning, multimedia, real-time software and qualitative modelling are studied through a series of simulations and experiments. By integrating each technologies studied above, we developed a hierarchical, intelligent control system for an autonomous mobile robot as a test bed. The system is composed of several modules of software and hardware subsystems, which are implemented by use of the intelligent techniques. Through the analysis of the results and experiences, we investigated the feasibility of application of the basic technology to the next generation plant. (Author)

  1. Advances in soft computing, intelligent robotics and control

    CERN Document Server

    Fullér, Robert

    2014-01-01

    Soft computing, intelligent robotics and control are in the core interest of contemporary engineering. Essential characteristics of soft computing methods are the ability to handle vague information, to apply human-like reasoning, their learning capability, and ease of application. Soft computing techniques are widely applied in the control of dynamic systems, including mobile robots. The present volume is a collection of 20 chapters written by respectable experts of the fields, addressing various theoretical and practical aspects in soft computing, intelligent robotics and control. The first part of the book concerns with issues of intelligent robotics, including robust xed point transformation design, experimental verification of the input-output feedback linearization of differentially driven mobile robot and applying kinematic synthesis to micro electro-mechanical systems design. The second part of the book is devoted to fundamental aspects of soft computing. This includes practical aspects of fuzzy rule ...

  2. A fuzzy intelligent system for land consolidation - a case study in Shunde, China

    Science.gov (United States)

    Wang, J.; Ge, A.; Hu, Y.; Li, C.; Wang, L.

    2015-04-01

    Traditionally, potential evaluation methods for farmland consolidation have depended mainly on the experts' experiences, statistical computations or subjective adjustments. Some biases usually exist in the results. Thus, computer-aided technology has become essential. In this study, an intelligent evaluation system based on a fuzzy decision tree was established, and this system can deal with numerical data, discrete data and symbolic data. When the original land data are input, the level of potential of the agricultural land for development will be output by this new model. The provision of objective proof for decision making by authorities in rural management is helpful. Agricultural land data characteristically comprise large volumes, complex varieties and more indexes. In land consolidation, it is very important to construct an effective index system. We needed to select a group of indexes useful for land consolidation according to the concrete demand. In this paper, a fuzzy measure, which can describe the importance of a single feature or a group of features, is adopted to accomplish the selection of specific features. A fuzzy integral that is based on a fuzzy measure is a type of fusion tool. We obtained the optimal solution for a fuzzy measure by solving a fuzzy integral. The fuzzy integrals can be transformed to a set of linear equations. We applied the L1-norm regularization method to solve the linear equations, and we found a solution with the fewest nonzero elements for the fuzzy measure; this solution shows the contribution of corresponding features or the combinations of decisions. This algorithm provides a quick and optimal way to identify the land index system when preparing to conduct the research, such as we describe herein, on land consolidation. Shunde's "Three Old" consolidation project provides the data for this work. Our estimation system was compared with a conventional evaluation system that is still accepted by the public. Our results prove

  3. Application of fuzzy logic operation and control to BWRs

    International Nuclear Information System (INIS)

    Junichi Tanji; Mitsuo Kinoshita; Takaharu Fukuzaki; Yasuhiro Kobayashi

    1993-01-01

    Fuzzy logic control schemes employing linguistic decision rules for flexible operator control strategies have undergone application tests in dynamic systems. The advantages claimed for fuzzy logic control are its abilities: (a) to facilitate direct use of skillful operator know-how for automatic operation and control of the systems and (b) to provide robust multivariable control for complex plants. The authors have also studied applications of fuzzy logic control to automatic startup operations and load-following control in boiling water reactors, pursuing these same advantages

  4. Fuzzy model predictive control algorithm applied in nuclear power plant

    International Nuclear Information System (INIS)

    Zuheir, Ahmad

    2006-01-01

    The aim of this paper is to design a predictive controller based on a fuzzy model. The Takagi-Sugeno fuzzy model with an Adaptive B-splines neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is applied to the control of a U-tube steam generation unit (UTSG) used for electricity generation. (author)

  5. Self-learning fuzzy controllers based on temporal back propagation

    Science.gov (United States)

    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.

  6. 3Es System Optimization under Uncertainty Using Hybrid Intelligent Algorithm: A Fuzzy Chance-Constrained Programming Model

    Directory of Open Access Journals (Sweden)

    Jiekun Song

    2016-01-01

    Full Text Available Harmonious development of 3Es (economy-energy-environment system is the key to realize regional sustainable development. The structure and components of 3Es system are analyzed. Based on the analysis of causality diagram, GDP and industrial structure are selected as the target parameters of economy subsystem, energy consumption intensity is selected as the target parameter of energy subsystem, and the emissions of COD, ammonia nitrogen, SO2, and NOX and CO2 emission intensity are selected as the target parameters of environment system. Fixed assets investment of three industries, total energy consumption, and investment in environmental pollution control are selected as the decision variables. By regarding the parameters of 3Es system optimization as fuzzy numbers, a fuzzy chance-constrained goal programming (FCCGP model is constructed, and a hybrid intelligent algorithm including fuzzy simulation and genetic algorithm is proposed for solving it. The results of empirical analysis on Shandong province of China show that the FCCGP model can reflect the inherent relationship and evolution law of 3Es system and provide the effective decision-making support for 3Es system optimization.

  7. VAR control in distribution systems by using artificial intelligence techniques

    Energy Technology Data Exchange (ETDEWEB)

    Golkar, M.A. [Curtin Univ. of Technology, Sarawak (Malaysia). School of Engineering and Science

    2005-07-01

    This paper reviewed artificial intelligence techniques used in VAR control systems. Reactive power controls in distribution systems were also reviewed. While artificial intelligence methods are widely used in power control systems, the techniques require extensive human knowledge bases and experiences in order to operate correctly. Expert systems use knowledge and interface procedures to solve problems that often require human expertise. Expert systems often cause knowledge bottlenecks as they are unable to learn or adopt to new situations. While neural networks possess learning ability, they are computationally expensive. However, test results in recent neural network studies have demonstrated that they work well in a variety of loading conditions. Fuzzy logic techniques are used to accurately represent the operational constraints of power systems. Fuzzy logic has an advantage over other artificial intelligence techniques as it is able to remedy uncertainties in data. Evolutionary computing algorithms use probabilistic transition rules which can search complicated data to determine optimal constraints and parameters. Over 95 per cent of all papers published on power systems use genetic algorithms. It was concluded that hybrid systems using various artificial intelligence techniques are now being used by researchers. 69 refs.

  8. Fuzzy logic control of vehicle suspensions with dry friction nonlinearity

    Indian Academy of Sciences (India)

    Fuzzy logic control; active vehicle suspension; suspension space. 1. ... surface unevenness, stability and directional control during handling ..... Burton A W, Truscott A J, Wellstead P E 1995 Analysis, modeling and control of an advanced.

  9. Autonomous composition of fuzzy granules in ambient intelligence scenarios

    NARCIS (Netherlands)

    Acampora, G.; Loia, V.; Vasilakos, A.V.; Pedrycz, W.; Bargiela, A.

    2009-01-01

    Pervasive and human-centric computing is beginning to be fact: with cell phones, laptops and handhelds, human beings can work pretty much anywhere. Ambient Intelligence (AmI) is a novel human-centric computer discipline based on three emergent technologies: Ubiquitous Computing, Ubiquitous

  10. Multi-Model Adaptive Fuzzy Controller for a CSTR Process

    Directory of Open Access Journals (Sweden)

    Shubham Gogoria

    2015-09-01

    Full Text Available Continuous Stirred Tank Reactors are intensively used to control exothermic reactions in chemical industries. It is a very complex multi-variable system with non-linear characteristics. This paper deals with linearization of the mathematical model of a CSTR Process. Multi model adaptive fuzzy controller has been designed to control the reactor concentration and temperature of CSTR process. This method combines the output of multiple Fuzzy controllers, which are operated at various operating points. The proposed solution is a straightforward implementation of Fuzzy controller with gain scheduler to control the linearly inseparable parameters of a highly non-linear process.

  11. Hierarchical fuzzy control of low-energy building systems

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Zhen; Dexter, Arthur [Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ (United Kingdom)

    2010-04-15

    A hierarchical fuzzy supervisory controller is described that is capable of optimizing the operation of a low-energy building, which uses solar energy to heat and cool its interior spaces. The highest level fuzzy rules choose the most appropriate set of lower level rules according to the weather and occupancy information; the second level fuzzy rules determine an optimal energy profile and the overall modes of operation of the heating, ventilating and air-conditioning system (HVAC); the third level fuzzy rules select the mode of operation of specific equipment, and assign schedules to the local controllers so that the optimal energy profile can be achieved in the most efficient way. Computer simulation is used to compare the hierarchical fuzzy control scheme with a supervisory control scheme based on expert rules. The performance is evaluated by comparing the energy consumption and thermal comfort. (author)

  12. Conventional control and fuzzy control of a dc-dc converter for machine drive

    Energy Technology Data Exchange (ETDEWEB)

    Radoi, C.; Florescu, A. [Department of Power Electronics `Politecnica` University Bucharest (Romania)

    1997-12-31

    Fuzzy logic or fuzzy set theory is recently getting increasing emphasis in process control applications. The paper describes an application of fuzzy logic in speed control system that uses a dc-dc converter. The fuzzy control is used to linearize the family of external characteristics of the converter in discontinuous-conduction mode occurring at light load and/or high speed. In order to compare the conventional control with the fuzzy logic control, algorithms have been developed in detail and verified by Microsoft Excel simulation. The simulation study indicates that fuzzy control is a good alternative for conventional control methods, being used particularly in non-linear complex systems ill defined or totally unknown. Where the mathematical model exists, it is useful. The applications of fuzzy set theory in power electronics are almost entirely new; fuzzy logic seems to have a lot of premises in the large industrial control field. (orig.) 2 refs.

  13. DC motor speed control using fuzzy logic controller

    Science.gov (United States)

    Ismail, N. L.; Zakaria, K. A.; Nazar, N. S. Moh; Syaripuddin, M.; Mokhtar, A. S. N.; Thanakodi, S.

    2018-02-01

    The automatic control has played a vital role in the advance of engineering and science. Nowadays in industries, the control of direct current (DC) motor is a common practice thus the implementation of DC motor controller speed is important. The main purpose of motor speed control is to keep the rotation of the motor at the present speed and to drive a system at the demand speed. The main purpose of this project is to control speed of DC Series Wound Motor using Fuzzy Logic Controller (FLC). The expectation of this project is the Fuzzy Logic Controller will get the best performance compared to dc motor without controller in terms of settling time (Ts), rise time (Tr), peak time (Tp) and percent overshoot (%OS).

  14. Simulation Study of IMC and Fuzzy Controller for HVAC System

    Directory of Open Access Journals (Sweden)

    Umamaheshwari

    2009-06-01

    Full Text Available This paper presents how the fuzzy logic controller is used to solve the control problems of complex and non linear process and show that it is more robust and their performance are less sensitive to parametric variations than conventional controllers. These systems will yield a linear response when compared to ordinary controllers. The main advantage of Fuzzy control over conventional controllers is regulation can be done without over shoot.

  15. Welding Penetration Control of Fixed Pipe in TIG Welding Using Fuzzy Inference System

    Science.gov (United States)

    Baskoro, Ario Sunar; Kabutomori, Masashi; Suga, Yasuo

    This paper presents a study on welding penetration control of fixed pipe in Tungsten Inert Gas (TIG) welding using fuzzy inference system. The welding penetration control is essential to the production quality welds with a specified geometry. For pipe welding using constant arc current and welding speed, the bead width becomes wider as the circumferential welding of small diameter pipes progresses. Having welded pipe in fixed position, obviously, the excessive arc current yields burn through of metals; in contrary, insufficient arc current produces imperfect welding. In order to avoid these errors and to obtain the uniform weld bead over the entire circumference of the pipe, the welding conditions should be controlled as the welding proceeds. This research studies the intelligent welding process of aluminum alloy pipe 6063S-T5 in fixed position using the AC welding machine. The monitoring system used a charge-coupled device (CCD) camera to monitor backside image of molten pool. The captured image was processed to recognize the edge of molten pool by image processing algorithm. Simulation of welding control using fuzzy inference system was constructed to simulate the welding control process. The simulation result shows that fuzzy controller was suitable for controlling the welding speed and appropriate to be implemented into the welding system. A series of experiments was conducted to evaluate the performance of the fuzzy controller. The experimental results show the effectiveness of the control system that is confirmed by sound welds.

  16. Autonomous Control of a Quadrotor UAV Using Fuzzy Logic

    Science.gov (United States)

    Sureshkumar, Vijaykumar

    UAVs are being increasingly used today than ever before in both military and civil applications. They are heavily preferred in "dull, dirty or dangerous" mission scenarios. Increasingly, UAVs of all kinds are being used in policing, fire-fighting, inspection of structures, pipelines etc. Recently, the FAA gave its permission for UAVs to be used on film sets for motion capture and high definition video recording. The rapid development in MEMS and actuator technology has made possible a plethora of UAVs that are suited for commercial applications in an increasingly cost effective manner. An emerging popular rotary wing UAV platform is the Quadrotor A Quadrotor is a helicopter with four rotors, that make it more stable; but more complex to model and control. Characteristics that provide a clear advantage over other fixed wing UAVs are VTOL and hovering capabilities as well as a greater maneuverability. It is also simple in construction and design compared to a scaled single rotorcraft. Flying such UAVs using a traditional radio Transmitter-Receiver setup can be a daunting task especially in high stress situations. In order to make such platforms widely applicable, a certain level of autonomy is imperative to the future of such UAVs. This thesis paper presents a methodology for the autonomous control of a Quadrotor UAV using Fuzzy Logic. Fuzzy logic control has been chosen over conventional control methods as it can deal effectively with highly nonlinear systems, allows for imprecise data and is extremely modular. Modularity and adaptability are the key cornerstones of FLC. The objective of this thesis is to present the steps of designing, building and simulating an intelligent flight control module for a Quadrotor UAV. In the course of this research effort, a Quadrotor UAV is indigenously developed utilizing the resources of an online open source project called Aeroquad. System design is comprehensively dealt with. A math model for the Quadrotor is developed and a

  17. Fuzzy Backstepping Sliding Mode Control for Mismatched Uncertain System

    Directory of Open Access Journals (Sweden)

    H. Q. Hou

    2014-06-01

    Full Text Available Sliding mode controllers have succeeded in many control problems that the conventional control theories have difficulties to deal with; however it is practically impossible to achieve high-speed switching control. Therefore, in this paper an adaptive fuzzy backstepping sliding mode control scheme is derived for mismatched uncertain systems. Firstly fuzzy sliding mode controller is designed using backstepping method based on the Lyapunov function approach, which is capable of handling mismatched problem. Then fuzzy sliding mode controller is designed using T-S fuzzy model method, it can improve the performance of the control systems and their robustness. Finally this method of control is applied to nonlinear system as a case study; simulation results are also provided the performance of the proposed controller.

  18. A Fuzzy Rule-based Controller For Automotive Vehicle Guidance

    OpenAIRE

    Hessburg, Thomas; Tomizuka, Masayoshi

    1991-01-01

    A fuzzy rule-based controller is applied to lateral guidance of a vehicle for an automated highway system. The fuzzy rules, based on human drivers' experiences, are developed to track the center of a lane in the presence of external disturbances and over a range of vehicle operating conditions.

  19. System control fuzzy neural sewage pumping stations using genetic algorithms

    Directory of Open Access Journals (Sweden)

    Владлен Николаевич Кузнецов

    2015-06-01

    Full Text Available It is considered the system of management of sewage pumping station with regulators based on a neuron network with fuzzy logic. Linguistic rules for the controller based on fuzzy logic, maintaining the level of effluent in the receiving tank within the prescribed limits are developed. The use of genetic algorithms for neuron network training is shown.

  20. Robust adaptive fuzzy neural tracking control for a class of unknown ...

    Indian Academy of Sciences (India)

    In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is proposed. The proposed AFNC is comprised of a fuzzy neural controller and a robust controller. The fuzzy neural controller including a fuzzy neural network identifier (FNNI) is the principal controller. The FNNI is used for ...

  1. Wide-range nuclear reactor temperature control using automatically tuned fuzzy logic controller

    International Nuclear Information System (INIS)

    Ramaswamy, P.; Edwards, R.M.; Lee, K.Y.

    1992-01-01

    In this paper, a fuzzy logic controller design for optimal reactor temperature control is presented. Since fuzzy logic controllers rely on an expert's knowledge of the process, they are hard to optimize. An optimal controller is used in this paper as a reference model, and a Kalman filter is used to automatically determine the rules for the fuzzy logic controller. To demonstrate the robustness of this design, a nonlinear six-delayed-neutron-group plant is controlled using a fuzzy logic controller that utilizes estimated reactor temperatures from a one-delayed-neutron-group observer. The fuzzy logic controller displayed good stability and performance robustness characteristics for a wide range of operation

  2. Distributed traffic signal control using fuzzy logic

    Science.gov (United States)

    Chiu, Stephen

    1992-01-01

    We present a distributed approach to traffic signal control, where the signal timing parameters at a given intersection are adjusted as functions of the local traffic condition and of the signal timing parameters at adjacent intersections. Thus, the signal timing parameters evolve dynamically using only local information to improve traffic flow. This distributed approach provides for a fault-tolerant, highly responsive traffic management system. The signal timing at an intersection is defined by three parameters: cycle time, phase split, and offset. We use fuzzy decision rules to adjust these three parameters based only on local information. The amount of change in the timing parameters during each cycle is limited to a small fraction of the current parameters to ensure smooth transition. We show the effectiveness of this method through simulation of the traffic flow in a network of controlled intersections.

  3. A fuzzy TOPSIS model to evaluate the Business Intelligence competencies of Port Community Systems

    Directory of Open Access Journals (Sweden)

    Ghazanfari Mehdi

    2014-04-01

    Full Text Available Evaluation of the Business Intelligence (BI competencies of port community systems before they are bought and deployed is a vital importance for establishment of a decision-support environment for managers. This study proposes a new model which provides a simple approach to the assessment of the BI competencies of port community systems in organization. This approach helps decision-makers to select an enterprise system with appropriate intelligence requirements to support the managers’ decision-making tasks. Thirtyfour criteria for BI specifications are determined from a thorough review of the literature. The proposed model uses the fuzzy TOPSIS technique, which employs fuzzy weights of the criteria and fuzzy judgments of port community systems to compute the evaluation scores and rankings. The application of the model is realized in the evaluation, ranking and selecting of the needed port community systems in a port and maritime organization, in order to validate the proposed model with a real application. With utilizing the proposed model organizations can assess, select, and purchase port community systems which will provide a better decision-support environment for their business systems.

  4. An Intelligent Information Retrieval Approach Based on Two Degrees of Uncertainty Fuzzy Ontology

    Directory of Open Access Journals (Sweden)

    Maryam Hourali

    2011-01-01

    Full Text Available In spite of the voluminous studies in the field of intelligent retrieval systems, effective retrieving of information has been remained an important unsolved problem. Implementations of different conceptual knowledge in the information retrieval process such as ontology have been considered as a solution to enhance the quality of results. Furthermore, the conceptual formalism supported by typical ontology may not be sufficient to represent uncertainty information due to the lack of clear-cut boundaries between concepts of the domains. To tackle this type of problems, one possible solution is to insert fuzzy logic into ontology construction process. In this article, a novel approach for fuzzy ontology generation with two uncertainty degrees is proposed. Hence, by implementing linguistic variables, uncertainty level in domain's concepts (Software Maintenance Engineering (SME domain has been modeled, and ontology relations have been modeled by fuzzy theory consequently. Then, we combined these uncertain models and proposed a new ontology with two degrees of uncertainty both in concept expression and relation expression. The generated fuzzy ontology was implemented for expansion of initial user's queries in SME domain. Experimental results showed that the proposed model has better overall retrieval performance comparing to keyword-based or crisp ontology-based retrieval systems.

  5. Intelligent control of non-linear dynamical system based on the adaptive neurocontroller

    Science.gov (United States)

    Engel, E.; Kovalev, I. V.; Kobezhicov, V.

    2015-10-01

    This paper presents an adaptive neuro-controller for intelligent control of non-linear dynamical system. The formed as the fuzzy selective neural net the adaptive neuro-controller on the base of system's state, creates the effective control signal under random perturbations. The validity and advantages of the proposed adaptive neuro-controller are demonstrated by numerical simulations. The simulation results show that the proposed controller scheme achieves real-time control speed and the competitive performance, as compared to PID, fuzzy logic controllers.

  6. Tracking control of DC motors via mimo nonlinear fuzzy control

    International Nuclear Information System (INIS)

    Harb, Ahmad M.; Smadi, Issam A.

    2009-01-01

    This paper proposed a nonlinear controller for speed tracking of separately excited DC motors (SEDCM's) using the multi-input multi-output (MIMO) fuzzy logic controller (FLC's). Based on a nonlinear mathematical model of SEDCM, a FLC is designed to achieve high performance speed tracking through rejection load disturbance. Computer simulations are presented to show speed tracking performance and the effectiveness of the proposed controller.

  7. Hybrid fuzzy logic control of laser surface heat treatments

    International Nuclear Information System (INIS)

    Perez, Jose Antonio; Ocana, Jose Luis; Molpeceres, Carlos

    2007-01-01

    This paper presents an advanced hybrid fuzzy logic control system for laser surface heat treatments, which allows to increase significantly the uniformity and final quality of the obtained product, reducing the rejection rate and increasing the productivity and efficiency of the treatment. Basically, the proposed hybrid control structure combines a fuzzy logic controller, with a pure integral action, both fully decoupled, improving the performances of the process with a reasonable design cost, since the system nonlinearities are fully compensated by the fuzzy component of the controller, while the integral action contributes to eliminate the steady-state error

  8. Efficient Fuzzy Logic Controller for Magnetic Levitation Systems

    African Journals Online (AJOL)

    Akorede

    ABSTRACT: Magnetic levitation is a system of suspending a body or a complete system against gravity. Suspending a system ... disturbance signal was applied to the input of the control system. Fuzzy ..... Automatic Control System, fifth edition.

  9. Fuzzy power control algorithm for a pressurized water reactor

    International Nuclear Information System (INIS)

    Hah, Y.J.; Lee, B.W.

    1994-01-01

    A fuzzy power control algorithm is presented for automatic reactor power control in a pressurized water reactor (PWR). Automatic power shape control is complicated by the use of control rods with a conventional proportional-integral-differential controller because it is highly coupled with reactivity compensation. Thus, manual shape controls are usually employed even for the limited capability needed for load-following operations including frequency control. In an attempt to achieve automatic power shape control without any design modifications to the core, a fuzzy power control algorithm is proposed. For the fuzzy control, the rule base is formulated based on a multiple-input multiple-output system. The minimum operation rule and the center of area method are implemented for the development of the fuzzy algorithm. The fuzzy power control algorithm has been applied to Yonggwang Nuclear Unit 3. The simulation results show that the fuzzy control can be adapted as a practical control strategy for automatic reactor power control of PWRs during the load-following operations

  10. Fuzzy Control of a Lead Acid Battery Charger

    Directory of Open Access Journals (Sweden)

    A. DAOUD

    2005-03-01

    Full Text Available In this paper, an alternative battery charging control technique based on fuzzy logic for photovoltaic (PV applications is presented. A PV module is connected to a buck type DC/DC power converter and a microcontroller based unit is used to control the lead acid battery charging voltage. The fuzzy control is used due to the simplicity of implementation, robustness and independence from the complex mathematical representation of the battery. The usefulness of this control method is confirmed by experiments.

  11. Adaptive learning fuzzy control of a mobile robot

    International Nuclear Information System (INIS)

    Tsukada, Akira; Suzuki, Katsuo; Fujii, Yoshio; Shinohara, Yoshikuni

    1989-11-01

    In this report a problem is studied to construct a fuzzy controller for a mobile robot to move autonomously along a given reference direction curve, for which control rules are generated and acquired through an adaptive learning process. An adaptive learning fuzzy controller has been developed for a mobile robot. Good properties of the controller are shown through the travelling experiments of the mobile robot. (author)

  12. Application of fuzzy control in cooling systems save energy design

    Energy Technology Data Exchange (ETDEWEB)

    Chen, M.L.; Liang, H.Y. [Chienkuo Technology Univ., Changhua, Taiwan (China). Dept. of Electrical Engineering

    2005-07-01

    A fuzzy logic programmable logic controller (PLC) was used to control the cooling systems of frigorific equipment. Frigorific equipment is used to move unwanted heat outside of building in order to control indoor temperatures. The aim of the fuzzy logic PLC was to improve the energy efficiency of the cooling system. Control of the cooling pump and cooling tower in the system was based on the water temperature of the condenser during frigorific system operation. A human computer design for the cooling system control was used to set speeds and to automate and adjust the motor according to the fuzzy logic controller. It was concluded that if fuzzy logic controllers are used with all components of frigorific equipment, energy efficiency will be significantly increased. 5 refs., 3 tabs., 9 figs.

  13. Fuzzy Controllers for a Gantry Crane System with Experimental Verifications

    Directory of Open Access Journals (Sweden)

    Naif B. Almutairi

    2016-01-01

    Full Text Available The control problem of gantry cranes has attracted the attention of many researchers because of the various applications of these cranes in the industry. In this paper we propose two fuzzy controllers to control the position of the cart of a gantry crane while suppressing the swing angle of the payload. Firstly, we propose a dual PD fuzzy controller where the parameters of each PD controller change as the cart moves toward its desired position, while maintaining a small swing angle of the payload. This controller uses two fuzzy subsystems. Then, we propose a fuzzy controller which is based on heuristics. The rules of this controller are obtained taking into account the knowledge of an experienced crane operator. This controller is unique in that it uses only one fuzzy system to achieve the control objective. The validity of the designed controllers is tested through extensive MATLAB simulations as well as experimental results on a laboratory gantry crane apparatus. The simulation results as well as the experimental results indicate that the proposed fuzzy controllers work well. Moreover, the simulation and the experimental results demonstrate the robustness of the proposed control schemes against output disturbances as well as against uncertainty in some of the parameters of the crane.

  14. Chemical optimization algorithm for fuzzy controller design

    CERN Document Server

    Astudillo, Leslie; Castillo, Oscar

    2014-01-01

    In this book, a novel optimization method inspired by a paradigm from nature is introduced. The chemical reactions are used as a paradigm to propose an optimization method that simulates these natural processes. The proposed algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm. Simulation results show that the proposed optimization algorithm can outperform other methods in a set of benchmark functions. This chemical reaction optimization paradigm is also applied to solve the tracking problem for the dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on fuzzy logic theory. Computer simulations are presented confirming that this optimization paradigm is able to outperform other optimization techniques applied to this particular robot application

  15. Artificial Intelligence for Controlling Robotic Aircraft

    Science.gov (United States)

    Krishnakumar, Kalmanje

    2005-01-01

    A document consisting mostly of lecture slides presents overviews of artificial-intelligence-based control methods now under development for application to robotic aircraft [called Unmanned Aerial Vehicles (UAVs) in the paper] and spacecraft and to the next generation of flight controllers for piloted aircraft. Following brief introductory remarks, the paper presents background information on intelligent control, including basic characteristics defining intelligent systems and intelligent control and the concept of levels of intelligent control. Next, the paper addresses several concepts in intelligent flight control. The document ends with some concluding remarks, including statements to the effect that (1) intelligent control architectures can guarantee stability of inner control loops and (2) for UAVs, intelligent control provides a robust way to accommodate an outer-loop control architecture for planning and/or related purposes.

  16. The comparison of manual and LabVIEW-based fuzzy control on mechanical ventilation.

    Science.gov (United States)

    Guler, Hasan; Ata, Fikret

    2014-09-01

    The aim of this article is to develop a knowledge-based therapy for management of rats with respiratory distress. A mechanical ventilator was designed to achieve this aim. The designed ventilator is called an intelligent mechanical ventilator since fuzzy logic was used to control the pneumatic equipment according to the rat's status. LabVIEW software was used to control all equipments in the ventilator prototype and to monitor respiratory variables in the experiment. The designed ventilator can be controlled both manually and by fuzzy logic. Eight female Wistar-Albino rats were used to test the designed ventilator and to show the effectiveness of fuzzy control over manual control on pressure control ventilation mode. The anesthetized rats were first ventilated for 20 min manually. After that time, they were ventilated for 20 min by fuzzy logic. Student's t-test for p < 0.05 was applied to the measured minimum, maximum and mean peak inspiration pressures to analyze the obtained results. The results show that there is no statistical difference in the rat's lung parameters before and after the experiments. It can be said that the designed ventilator and developed knowledge-based therapy support artificial respiration of living things successfully. © IMechE 2014.

  17. Fuzzy adaptive speed control of a permanent magnet synchronous motor

    Science.gov (United States)

    Choi, Han Ho; Jung, Jin-Woo; Kim, Rae-Young

    2012-05-01

    A fuzzy adaptive speed controller is proposed for a permanent magnet synchronous motor (PMSM). The proposed fuzzy adaptive speed regulator is insensitive to model parameter and load torque variations because it does not need any accurate knowledge about the motor parameter and load torque values. The stability of the proposed control system is also proven. The proposed adaptive speed regulator system is implemented by using a TMS320F28335 floating point DSP. Simulation and experimental results are presented to verify the effectiveness of the proposed fuzzy adaptive speed controller under uncertainties such as motor parameter and load torque variations using a prototype PMSM drive system.

  18. Soft Computing Optimizer For Intelligent Control Systems Design: The Structure And Applications

    Directory of Open Access Journals (Sweden)

    Sergey A. Panfilov

    2003-10-01

    Full Text Available Soft Computing Optimizer (SCO as a new software tool for design of robust intelligent control systems is described. It is based on the hybrid methodology of soft computing and stochastic simulation. It uses as an input the measured or simulated data about the modeled system. SCO is used to design an optimal fuzzy inference system, which approximates a random behavior of control object with the certain accuracy. The task of the fuzzy inference system construction is reduced to the subtasks such as forming of the linguistic variables for each input and output variable, creation of rule data base, optimization of rule data base and refinement of the parameters of the membership functions. Each task by the corresponding genetic algorithm (with an appropriate fitness function is solved. The result of SCO application is the design of Knowledge Base of a Fuzzy Controller, which contains the value information about developed fuzzy inference system. Such value information can be downloaded into the actual fuzzy controller to perform online fuzzy control. Simulations results of robust fuzzy control of nonlinear dynamic systems and experimental results of application on automotive semi-active suspension control are demonstrated.

  19. Enhancing transparent fuzzy controllers through temporal concepts : an application to computer games

    NARCIS (Netherlands)

    Acampora, G.; Loia, V.; Vitiello, A.

    2010-01-01

    In the last years, FML (Fuzzy Markup Language) is emerging as one of the most efficient and useful language to define a fuzzy control thanks to its capability of modeling Fuzzy Logic Controllers in a human-readable and hardware independent way, i.e. the so-called Transparent Fuzzy Controllers

  20. Multi-stage fuzzy load frequency control using PSO

    International Nuclear Information System (INIS)

    Shayeghi, H.; Jalili, A.; Shayanfar, H.A.

    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 has two rule base sets. In the proposed method, for achieving the desired level of robust performance, exact tuning of membership functions is very important. Thus, to reduce the design effort and find a better fuzzy system control, membership functions are designed automatically by PSO algorithm, that has a strong ability to find the most optimistic results. The motivation for using the PSO technique is to reduce fuzzy system effort and take large parametric uncertainties into account. This newly developed control strategy combines the advantage of PSO and fuzzy system control techniques and leads to a flexible controller with simple stricture that is easy to implement. The proposed PSO based MSF (PSOMSF) controller is tested on a three-area restructured power system under different operating conditions and contract variations. The results of the proposed PSOMSF controller are compared with genetic algorithm based multi-stage fuzzy (GAMSF) control through some performance indices to illustrate its robust performance for a wide range of system parameters and load changes

  1. Multi-stage fuzzy load frequency control using PSO

    Energy Technology Data Exchange (ETDEWEB)

    Shayeghi, H. [Technical Engineering Department, University of Mohaghegh Ardabili, Ardabil (Iran); Jalili, A. [Islamic Azad University, Ardabil Branch, Ardabil (Iran); Shayanfar, H.A. [Center of Excellence for Power Automation and Operation, Electrical Engineering Department, Iran University of Science and Technology, Tehran (Iran)

    2008-10-15

    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 has two rule base sets. In the proposed method, for achieving the desired level of robust performance, exact tuning of membership functions is very important. Thus, to reduce the design effort and find a better fuzzy system control, membership functions are designed automatically by PSO algorithm, that has a strong ability to find the most optimistic results. The motivation for using the PSO technique is to reduce fuzzy system effort and take large parametric uncertainties into account. This newly developed control strategy combines the advantage of PSO and fuzzy system control techniques and leads to a flexible controller with simple stricture that is easy to implement. The proposed PSO based MSF (PSOMSF) controller is tested on a three-area restructured power system under different operating conditions and contract variations. The results of the proposed PSOMSF controller are compared with genetic algorithm based multi-stage fuzzy (GAMSF) control through some performance indices to illustrate its robust performance for a wide range of system parameters and load changes. (author)

  2. Cascade fuzzy control for gas engine driven heat pump

    International Nuclear Information System (INIS)

    Li Shuze; Zhang Wugao; Zhang Rongrong; Lv Dexu; Huang Zhen

    2005-01-01

    In addition to absorption chillers, today's gas cooling technology includes gas engine driven heat pump systems (GEHP) in a range of capacities and temperature capacities suitable for most commercial air conditioning and refrigeration applications. Much is expected from GEHPs as a product that would help satisfy the air conditioning system demand from medium and small sized buildings, restrict electric power demand peaks in summer and save energy in general. This article describes a kind of control strategy for a GEHP, a cascade fuzzy control. GEHPs have large and varying time constants and their dynamic modeling cannot be easily achieved. A cascade control strategy is effective for systems that have large time constants and disturbances, and a fuzzy control strategy is fit for a system that lacks an accurate model. This cascade fuzzy control structure brings together the best merits of fuzzy control and cascade control structures. The performance of the cascade fuzzy control is compared to that of a cascade PI (proportional and integral) control strategy, and it is shown by example that the cascade fuzzy control strategy gives a better performance, reduced reaction time and smaller overshoot temperature

  3. Adaptive neuro-fuzzy controller of switched reluctance motor

    Directory of Open Access Journals (Sweden)

    Tahour Ahmed

    2007-01-01

    Full Text Available This paper presents an application of adaptive neuro-fuzzy (ANFIS control for switched reluctance motor (SRM speed. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. An adaptive neuro-fuzzy controller of the motor speed is then designed and simulated. Digital simulation results show that the designed ANFIS speed controller realizes a good dynamic behaviour of the motor, a perfect speed tracking with no overshoot and a good rejection of impact loads disturbance. The results of applying the adaptive neuro-fuzzy controller to a SRM give better performance and high robustness than those obtained by the application of a conventional controller (PI.

  4. Adaptive Artificial intelligence based fuzzy logic MPPTcontrol for stande-alone photovoltaic system under different atmospheric conditions

    Directory of Open Access Journals (Sweden)

    Zaghba Layachi

    2015-08-01

    Full Text Available there is an increased need for analysing the effect of atmospheric variables on photovoltaic (PV production and performance. The outputs from the different PV cells in different atmospheric conditions, such as irradiation and temperature , differ from each other evidencing knowledge deficiency in PV systems [14]. Maximum power point tracking (MPPT methods are used to maximize the PV array output power by tracking continuously the maximum power point (MPP. Among all MPPT methods existing in the literature, perturb and observe (P&O is the most commonly used for its simplicity and ease of implementation; however, it presents drawbacks such as slow response speed, oscillation around the MPP in steady state, and even tracking in wrong way under rapidly changing atmospheric conditions. In order to allow a functioning around the optimal point Mopt, we have inserted a DC-DC converter (Buck–Boost for a better matching between the PV and the load. This paper, we study the Maximum power point tracking using adaptive Intelligent fuzzy logic and conventional (P&O control for stande-alone photovoltaic Array system .In particular, the performances of the controllers are analyzed under variation weather conditions with are constant temperature and variable irradiation. The proposed system is simulated by using MATLAB-SIMULINK. According to the results, fuzzy logic controller has shown better performance during the optimization.

  5. A fuzzy logic pitch angle controller for power system stabilization

    Energy Technology Data Exchange (ETDEWEB)

    Jauch, Clemens; Cronin, Tom; Sorensen, Poul [Wind Energy Department, Riso National Laboratory, PO Box 49, DK-4000 Roskilde, (Denmark); Jensen, Birgitte Bak [Institute of Energy Technology, Aalborg University, Pontoppidanstraede 101, DK-9220 Aalborg East, (Denmark)

    2006-07-12

    In this article the design of a fuzzy logic pitch angle controller for a fixed speed, active-stall wind turbine, which is used for power system stabilization, is presented. The system to be controlled, which is the wind turbine and the power system to which the turbine is connected, is described. The advantages of fuzzy logic control when applied to large-signal control of active-stall wind turbines are outlined. The general steps of the design process for a fuzzy logic controller, including definition of the controller inputs, set-up of the fuzzy rules and the method of defuzzification, are described. The performance of the controller is assessed by simulation, where the wind turbine's task is to dampen power system oscillations. In the scenario simulated for this work, the wind turbine has to ride through a transient short-circuit fault and subsequently contribute to the damping of the grid frequency oscillations that are caused by the transient fault. It is concluded that the fuzzy logic controller enables the wind turbine to dampen power system oscillations. It is also concluded that, owing to the inherent non-linearities in a wind turbine and the unpredictability of the whole system, the fuzzy logic controller is very suitable for this application. (Author).

  6. Fuzzy attitude control of solar sail via linear matrix inequalities

    Science.gov (United States)

    Baculi, Joshua; Ayoubi, Mohammad A.

    2017-09-01

    This study presents a fuzzy tracking controller based on the Takagi-Sugeno (T-S) fuzzy model of the solar sail. First, the T-S fuzzy model is constructed by linearizing the existing nonlinear equations of motion of the solar sail. Then, the T-S fuzzy model is used to derive the state feedback controller gains for the Twin Parallel Distributed Compensation (TPDC) technique. The TPDC tracks and stabilizes the attitude of the solar sail to any desired state in the presence of parameter uncertainties and external disturbances while satisfying actuator constraints. The performance of the TPDC is compared to a PID controller that is tuned using the Ziegler-Nichols method. Numerical simulation shows the TPDC outperforms the PID controller when stabilizing the solar sail to a desired state.

  7. Simple Neuron-Fuzzy Tool for Small Control Devices

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    2008-01-01

    Small control computers, running a kind of Fuzzy controller, are more and more used in many systems from household machines to large industrial systems. The purpose of this paper is firstly to describe a tool that is easy to use for implementing self learning Fuzzy systems, that can be executed...... can be described by four different kinds of membership functions. The output fuzzyfication is based on singletons. The rule base can be written in a natural language. The result of the learning is a new version of the Fuzzy system described in the FuNNy language. A simple shower control example...... is shown.  This example shows that FuNNy is able to control the shower and that the learning is able to optimize the Fuzzy system....

  8. Robust hydraulic position controller by a fuzzy state controller

    International Nuclear Information System (INIS)

    Zhao, T.; Van der Wal, A.J.

    1994-01-01

    In nuclear industry, one of the most important design considerations of controllers is their robustness. Robustness in this context is defined as the ability of a system to be controlled in a stable way over a wide range of system parameters. Generally the systems to be controlled are linearized, and stability is subsequently proven for this idealized system. By combining classical control theory and fuzzy set theory, a new kind of state controller is proposed and successfully applied to a hydraulic position servo with excellent robustness against variation of system parameters

  9. Switching Fuzzy Guaranteed Cost Control for Nonlinear Networked Control Systems

    Directory of Open Access Journals (Sweden)

    Linqin Cai

    2014-01-01

    Full Text Available This paper deals with the problem of guaranteed cost control for a class of nonlinear networked control systems (NCSs with time-varying delay. A guaranteed cost controller design method is proposed to achieve the desired control performance based on the switched T-S fuzzy model. The switching mechanism is introduced to handle the uncertainties of NCSs. Based on Lyapunov functional approach, some sufficient conditions for the existence of state feedback robust guaranteed cost controller are presented. Simulation results show that the proposed method is effective to guarantee system’s global asymptotic stability and quality of service (QoS.

  10. Intelligent power supply controller

    International Nuclear Information System (INIS)

    Rumrill, R.S.; Reinagel, D.J.

    1991-01-01

    The authors have developed a new power supply controller which would combine 20-bit precision, simple interfacing, and versatile software control. It performs many tasks internal to the power supply and also communicates with an external host computer. Parameters can be entered and/or read over a serial link using one of the 82 command words. In addition, an optional remote control panel can be located up to thousands of feet away. This new controller will reduce the software development time normally spent by the user, while increasing the reliability of the system. The cost is less than buying the equivalent separate CAMAC system. Nonvolatile memory remembers all configuration data; one generic controller can thus be programmed to use anywhere from the smallest power supply to the largest. The controllers will be used at the Clinton P. Anderson Meson Facility at Los Alamos

  11. Intelligent networked teleoperation control

    CERN Document Server

    Li, Zhijun; Su, Chun-Yi

    2015-01-01

    This book describes a unified framework for networked teleoperation systems involving multiple research fields: networked control systems for linear and nonlinear forms, bilateral teleoperation, trilateral teleoperation, multilateral teleoperation and cooperative teleoperation. It closely examines networked control as a field at the intersection of systems & control and robotics and presents a number of experimental case studies on testbeds for robotic systems, including networked haptic devices, robotic network systems and sensor network systems. The concepts and results outlined are easy to understand, even for readers fairly new to the subject. As such, the book offers a valuable reference work for researchers and engineers in the fields of systems & control and robotics.

  12. Cylinder Position Servo Control Based on Fuzzy PID

    Directory of Open Access Journals (Sweden)

    Shibo Cai

    2013-01-01

    Full Text Available The arbitrary position control of cylinder has always been the hard challenge in pneumatic system. We try to develop a cylinder position servo control method by combining fuzzy PID with the theoretical model of the proportional valve-controlled cylinder system. The pressure differential equation of cylinder, pressure-flow equation of proportional valve, and moment equilibrium equation of cylinder are established. And the mathematical models of the cylinder driving system are linearized. Then fuzzy PID control algorithm is designed for the cylinder position control, including the detail analysis of fuzzy variables and domain, fuzzy logic rules, and defuzzification. The stability of the proposed fuzzy PID controller is theoretically proved according to the small gain theorem. Experiments for targets position of 250 mm, 300 mm, and 350 mm were done and the results showed that the absolute error of the position control is less than 0.25 mm. And comparative experiment between fuzzy PID and classical PID verified the advantage of the proposed algorithm.

  13. DYNAMIC SIMULATION AND FUZZY CONTROL OF A CONTINUOUS DISTILLATION COLUMN

    OpenAIRE

    Arbildo López, A.; Lombira Echevarría, J.; Osario López, l.

    2014-01-01

    The objective of this work is the study of the dinamic simulation and fuzzy control of a multicomponent continuous distillation column. In this work, the mathematical model of the distillation column and the computing program for the simulation are described. Also, the structure and implementation of the fuzzy controller are presentad. Finally, the results obtained using this programare compared with those reported in the scientific literature for different mixtures. El objetivo de nuestra...

  14. Fuzzy logic controller for weaning neonates from mechanical ventilation.

    OpenAIRE

    Hatzakis, G. E.; Davis, G. M.

    2002-01-01

    Weaning from mechanical ventilation is the gradual detachment from any ventilatory support till normal spontaneous breathing can be fully resumed. To date, we have developed a fuzzy logic controller for weaning COPD adults using pressure support ventilation (PS). However, adults and newborns differ in the pathophysiology of lung disease. We therefore used our fuzzy logic-based weaning platform to develop modularized components for weaning newborns with lung disease. Our controller uses the he...

  15. Study of an intelligent system for wells elevation and petroliferous processes control; Estudo de um sistema inteligente para elevacao de pocos e controle de processos petroliferos

    Energy Technology Data Exchange (ETDEWEB)

    Patricio, Antonio Rodrigues

    1996-11-01

    The petroleum production problems were studied by means of an integrated process evaluation of a rod pumping well, a gas lift well and a process until for produced fluids. Using the artificial intelligent concepts as fuzzy logic and neural systems is presented SIEP, An Intelligent for Production Lift and Process Control, aimed to do the integrated management of the petroleum production process. (author)

  16. Modelling and Intelligent Control of an Elastic Link Robot Manipulator

    Directory of Open Access Journals (Sweden)

    Malik Loudini

    2013-01-01

    Full Text Available In this paper, precise control of the end-point position of a planar single-link elastic manipulator robot is discussed. The Timoshenko beam theory (TBT has been used to characterize the structural link elasticity including important damping mechanisms. A suitable nonlinear model is derived based on the Lagrangian assumed modes method. Elastic link manipulators are classified as systems possessing highly complex dynamics. In addition, the environment in which they operate may have a lot of disturbances. These give rise to special problems that may be solved using intelligent control techniques. The application of two advanced control strategies based on fuzzy set theory is investigated. The first closed-loop control scheme to be applied is the standard Proportional-Derivative (PD type fuzzy logic controller (FLC, also known as PD-type Mamdani's FLC (MPDFLC. Then, a genetic algorithm (GA is used to optimize the MPDFLC parameters with innovative tuning procedures. Both the MPDFLC and the GA optimized FLC (GAOFLC are implemented and tested to achieve a precise control of the manipulator end-point. The performances of the adopted closed-loop intelligent control strategies are examined via simulation experiments.

  17. Cooperative Intelligence in Roundabout Intersections Using Hierarchical Fuzzy Behavior Calculation of Vehicle Speed Profile

    Directory of Open Access Journals (Sweden)

    Bosankić Ivan

    2016-01-01

    Full Text Available In this paper, a new fuzzy-behavior-based algorithm for roundabout intersection management is presented. The algorithm employs cooperative intelligence and includes intelligent vehicles and infrastructure to calculate speed profiles for different vehicles, in order to achieve more comfortable driving profiles, as well to reduce congestion and CO2 emissions. The algorithm uses adaptive spatio-temporal reservation technique and was tested in MATLAB/Simulink environment. The algorithm is designed to function in different scenarios with both cooperative and non-cooperative vehicles, as well as optional intersection infrastructure. Results have show that using the proposed algorithm different vehicle communication types can be successfully combined in order to increase traffic flow through roundabout intersections.

  18. Developing an Intelligent Automatic Appendix Extraction Method from Ultrasonography Based on Fuzzy ART and Image Processing

    Directory of Open Access Journals (Sweden)

    Kwang Baek Kim

    2015-01-01

    Full Text Available Ultrasound examination (US does a key role in the diagnosis and management of the patients with clinically suspected appendicitis which is the most common abdominal surgical emergency. Among the various sonographic findings of appendicitis, outer diameter of the appendix is most important. Therefore, clear delineation of the appendix on US images is essential. In this paper, we propose a new intelligent method to extract appendix automatically from abdominal sonographic images as a basic building block of developing such an intelligent tool for medical practitioners. Knowing that the appendix is located at the lower organ area below the bottom fascia line, we conduct a series of image processing techniques to find the fascia line correctly. And then we apply fuzzy ART learning algorithm to the organ area in order to extract appendix accurately. The experiment verifies that the proposed method is highly accurate (successful in 38 out of 40 cases in extracting appendix.

  19. Anticipatory Monitoring and Control of Complex Systems using a Fuzzy based Fusion of Support Vector Regressors

    Energy Technology Data Exchange (ETDEWEB)

    Miltiadis Alamaniotis; Vivek Agarwal

    2014-10-01

    This paper places itself in the realm of anticipatory systems and envisions monitoring and control methods being capable of making predictions over system critical parameters. Anticipatory systems allow intelligent control of complex systems by predicting their future state. In the current work, an intelligent model aimed at implementing anticipatory monitoring and control in energy industry is presented and tested. More particularly, a set of support vector regressors (SVRs) are trained using both historical and observed data. The trained SVRs are used to predict the future value of the system based on current operational system parameter. The predicted values are then inputted to a fuzzy logic based module where the values are fused to obtain a single value, i.e., final system output prediction. The methodology is tested on real turbine degradation datasets. The outcome of the approach presented in this paper highlights the superiority over single support vector regressors. In addition, it is shown that appropriate selection of fuzzy sets and fuzzy rules plays an important role in improving system performance.

  20. Neuro-fuzzy controller to navigate an unmanned vehicle.

    Science.gov (United States)

    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).

  1. Fuzzy Logic System for Intermixed Biogas and Photovoltaics Measurement and Control

    Directory of Open Access Journals (Sweden)

    Liston Matindife

    2018-01-01

    Full Text Available This study develops a new integrated measurement and control system for intermixed biogas and photovoltaic systems to achieve safe and optimal energy usage. Literature and field studies show that existing control methods on small- to medium-scale systems fall short of comprehensive system optimization and fault diagnosis, hence the need to revisit these control methods. The control strategy developed in this study is intelligent as it is wholly based on fuzzy logic algorithms. Fuzzy logic controllers due to their superior nonlinear problem solving capabilities to classical controllers considerably simplify controller design. The mathematical models that define classical controllers are difficult or impossible to realize in biogas and photovoltaic generation process. A microcontroller centered fuzzy logic measurement and control embedded system is designed and developed on the existing hybrid biogas and photovoltaic installations. The designed system is able to accurately predict digester stability, quantify biogas output, and carry out biogas fault detection and control. Optimized battery charging and photovoltaic fault detection and control are also successfully implemented. The system is able to optimize the operation and performance of biogas and photovoltaic energy generation.

  2. a Novel 3d Intelligent Fuzzy Algorithm Based on Minkowski-Clustering

    Science.gov (United States)

    Toori, S.; Esmaeily, A.

    2017-09-01

    Assessing and monitoring the state of the earth surface is a key requirement for global change research. In this paper, we propose a new consensus fuzzy clustering algorithm that is based on the Minkowski distance. This research concentrates on Tehran's vegetation mass and its changes during 29 years using remote sensing technology. The main purpose of this research is to evaluate the changes in vegetation mass using a new process by combination of intelligent NDVI fuzzy clustering and Minkowski distance operation. The dataset includes the images of Landsat8 and Landsat TM, from 1989 to 2016. For each year three images of three continuous days were used to identify vegetation impact and recovery. The result was a 3D NDVI image, with one dimension for each day NDVI. The next step was the classification procedure which is a complicated process of categorizing pixels into a finite number of separate classes, based on their data values. If a pixel satisfies a certain set of standards, the pixel is allocated to the class that corresponds to those criteria. This method is less sensitive to noise and can integrate solutions from multiple samples of data or attributes for processing data in the processing industry. The result was a fuzzy one dimensional image. This image was also computed for the next 28 years. The classification was done in both specified urban and natural park areas of Tehran. Experiments showed that our method worked better in classifying image pixels in comparison with the standard classification methods.

  3. Advanced intelligent systems

    CERN Document Server

    Ryoo, Young; Jang, Moon-soo; Bae, Young-Chul

    2014-01-01

    Intelligent systems have been initiated with the attempt to imitate the human brain. People wish to let machines perform intelligent works. Many techniques of intelligent systems are based on artificial intelligence. According to changing and novel requirements, the advanced intelligent systems cover a wide spectrum: big data processing, intelligent control, advanced robotics, artificial intelligence and machine learning. This book focuses on coordinating intelligent systems with highly integrated and foundationally functional components. The book consists of 19 contributions that features social network-based recommender systems, application of fuzzy enforcement, energy visualization, ultrasonic muscular thickness measurement, regional analysis and predictive modeling, analysis of 3D polygon data, blood pressure estimation system, fuzzy human model, fuzzy ultrasonic imaging method, ultrasonic mobile smart technology, pseudo-normal image synthesis, subspace classifier, mobile object tracking, standing-up moti...

  4. Implementation of the fuzzy theory in control of alternative energy generation system; Aplicacao da teoria fuzzy no controle de sistemas de geracao de energias alternativas

    Energy Technology Data Exchange (ETDEWEB)

    Caneppele, Fernando de Lima [Universidade Estadual Paulista (FCA/UNESP), Botucatu, SP (Brazil). Fac. de Ciencias Agronomicas. Curso de Pos-Graduacao em Energia na Agricultura], E-mail: fernando@itapeva.unesp.br; Seraphim, Odivaldo Jose [Universidade Estadual Paulista (FCA/UNESP), Botucatu, SP (Brazil). Fac. de Ciencias Agronomicas. Dept. de Engenharia Rural], E-mail: seraphim@fca.unesp.br

    2010-07-01

    This paper presents the application and use of a methodology based on fuzzy theory and simulates its use in intelligent control of a hybrid system for generating electricity, using solar energy, photovoltaic and wind. When using a fuzzy control system, it reached the point of maximum generation of energy, thus shifting all energy generated from the alternative sources-solar photovoltaic and wind, cargo and / or batteries when its use not immediately. The model uses three variables used for entry, which are: wind speed, solar radiation and loading the bank of batteries. For output variable has to choose which of the batteries of the battery bank is charged. For the simulations of this work is used MATLAB software. In this environment mathematical computational are analyzed and simulated all mathematical modeling, rules and other variables in the system described fuzzy. This model can be used in a system of control of hybrid systems for generating energy, providing the best use of energy sources, sun and wind, so we can extract the maximum energy possible these alternative sources without any prejudice to the environment. (author)

  5. Fuzzy Logic Temperature Control System For The Induction Furnace

    Directory of Open Access Journals (Sweden)

    Lei Lei Hnin

    2015-08-01

    Full Text Available This research paper describes the fuzzy logic temperature control system of the induction furnace. Temperature requirement of the heating system varies during the heating process. In the conventional control schemes the switching losses increase with the change in the load. A closed loop control is required to have a smooth control on the system. In this system pulse width modulation based power control scheme for the induction heating system is developed using the fuzzy logic controller. The induction furnace requires a good voltage regulation to have efficient response. The controller controls the temperature depending upon weight of meat water and time. This control system is implemented in hardware system using microcontroller. Here the fuzzy logic controller is designed and simulated in MATLAB to get the desire condition.

  6. Use of UPFC device controlled by fuzzy logic controllers for decoupled power flow control

    Directory of Open Access Journals (Sweden)

    Ivković Sanja

    2014-01-01

    Full Text Available This paper investigates the possibility of decoupled active and reactive power flow control in a power system using a UPFC device controlled by fuzzy logic controllers. A Brief theoretical review of the operation principles and applications of UPFC devices and design principles of the fuzzy logic controller used are given. A Matlab/Simulink model of the system with UPFC, the fuzzy controller setup, and graphs of the results are presented. Conclusions are drawn regarding the possibility of using this system for decoupled control of the power flow in power systems based on analysis of these graphs.

  7. Robot Advanced Intelligent Control developed through Versatile ...

    African Journals Online (AJOL)

    pc

    2018-03-05

    Mar 5, 2018 ... environments of human life exposed to great dangers such as support and repair in .... intelligent control interfaces, network quality of service, shared resources and ..... Artificial Intelligence series, volume 6556, p. 336-349 ...

  8. Fuzzy Traffic Control with Vehicle-to-Everything Communication.

    Science.gov (United States)

    Salman, Muntaser A; Ozdemir, Suat; Celebi, Fatih V

    2018-01-27

    Traffic signal control (TSC) with vehicle-to everything (V2X) communication can be a very efficient solution to traffic congestion problem. Ratio of vehicles equipped with V2X communication capability in the traffic to the total number of vehicles (called penetration rate PR) is still low, thus V2X based TSC systems need to be supported by some other mechanisms. PR is the major factor that affects the quality of TSC process along with the evaluation interval. Quality of the TSC in each direction is a function of overall TSC quality of an intersection. Hence, quality evaluation of each direction should follow the evaluation of the overall intersection. Computational intelligence, more specifically swarm algorithm, has been recently used in this field in a European Framework Program FP7 supported project called COLOMBO. In this paper, using COLOMBO framework, further investigations have been done and two new methodologies using simple and fuzzy logic have been proposed. To evaluate the performance of our proposed methods, a comparison with COLOMBOs approach has been realized. The results reveal that TSC problem can be solved as a logical problem rather than an optimization problem. Performance of the proposed approaches is good enough to be suggested for future work under realistic scenarios even under low PR.

  9. Control of Angra 1' PZR by a fuzzy rule base build through genetic programming

    International Nuclear Information System (INIS)

    Caldas, Gustavo Henrique Flores; Schirru, Roberto

    2002-01-01

    There is an optimum pressure for the normal operation of nuclear power plant reactors and thresholds that must be respected during transients, what make the pressurizer an important control mechanism. Inside a pressurizer there are heaters and a shower. From their actuation levels, they control the vapor pressure inside the pressurizer and, consequently, inside the primary circuit. Therefore, the control of the pressurizer consists in controlling the actuation levels of the heaters and of the shower. In the present work this function is implemented through a fuzzy controller. Besides the efficient way of exerting control, this approach presents the possibility of extracting knowledge of how this control is been made. A fuzzy controller consists basically in an inference machine and a rule base, the later been constructed with specialized knowledge. In some circumstances, however, this knowledge is not accurate, and may lead to non-efficient results. With the development of artificial intelligence techniques, there wore found methods to substitute specialists, simulating its knowledge. Genetic programming is an evolutionary algorithm particularly efficient in manipulating rule base structures. In this work genetic programming was used as a substitute for the specialist. The goal is to test if an irrational object, a computer, is capable, by it self, to find out a rule base reproducing a pre-established actuation levels profile. The result is positive, with the discovery of a fuzzy rule base presenting an insignificant error. A remarkable result that proves the efficiency of the approach. (author)

  10. DESIGN AN INTELLIGENT CONTROLLER FOR FULL VEHICLE NONLINEAR ACTIVE SUSPENSION SYSTEMS

    OpenAIRE

    Aldair, A. A.; Wang, W. J.

    2011-01-01

    The main objective of designed the controller for a vehicle suspension system is to reduce the discomfort sensed by passengers which arises from road roughness and to increase the ride handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as much control objectives, as possible. Therefore, this paper deals with an artificial intelligence Neuro-Fuzzy (NF) technique to design a robust controller to meet the control objectives....

  11. Hybridizing fuzzy control and timed automata for modeling variable structure fuzzy systems

    NARCIS (Netherlands)

    Acampora, G.; Loia, V.; Vitiello, A.

    2010-01-01

    During the past several years, fuzzy control has emerged as one of the most suitable and efficient methods for designing and developing complex systems in environments characterized by high level of uncertainty and imprecision. Nowadays, this methodology is used to model systems in several

  12. Design and optimization of fuzzy-PID controller for the nuclear reactor power control

    International Nuclear Information System (INIS)

    Liu Cheng; Peng Jinfeng; Zhao Fuyu; Li Chong

    2009-01-01

    This paper introduces a fuzzy proportional-integral-derivative (fuzzy-PID) control strategy, and applies it to the nuclear reactor power control system. At the fuzzy-PID control strategy, the fuzzy logic controller (FLC) is exploited to extend the finite sets of PID gains to the possible combinations of PID gains in stable region and the genetic algorithm to improve the 'extending' precision through quadratic optimization for the membership function (MF) of the FLC. Thus the FLC tunes the gains of PID controller to adapt the model changing with the power. The fuzzy-PID has been designed and simulated to control the reactor power. The simulation results show the favorable performance of the fuzzy-PID controller.

  13. An intelligent sales forecasting system through integration of artificial neural networks and fuzzy neural networks with fuzzy weight elimination.

    Science.gov (United States)

    Kuo, R J; Wu, P; Wang, C P

    2002-09-01

    Sales forecasting plays a very prominent role in business strategy. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average (ARMA). However, sales forecasting is very complicated owing to influence by internal and external environments. Recently, artificial neural networks (ANNs) have also been applied in sales forecasting since their promising performances in the areas of control and pattern recognition. However, further improvement is still necessary since unique circumstances, e.g. promotion, cause a sudden change in the sales pattern. Thus, this study utilizes a proposed fuzzy neural network (FNN), which is able to eliminate the unimportant weights, for the sake of learning fuzzy IF-THEN rules obtained from the marketing experts with respect to promotion. The result from FNN is further integrated with the time series data through an ANN. Both the simulated and real-world problem results show that FNN with weight elimination can have lower training error compared with the regular FNN. Besides, real-world problem results also indicate that the proposed estimation system outperforms the conventional statistical method and single ANN in accuracy.

  14. Fuzzy control for optimal operation of complex chilling systems; Betriebsoptimierung von komplexen Kaelteanlagen mit Fuzzy-Control

    Energy Technology Data Exchange (ETDEWEB)

    Talebi-Daryani, R. [Fachhochschule Koeln (Germany). Lehrgebiet und Lab. fuer Regelungs- und Gebaeudeleittechnik; Luther, C. [JCI Regelungstechnik GmbH, Koeln (Germany)

    1998-05-01

    The optimization potentials for the operation of chilling systems within the building supervisory control systems are limited to abilities of PLC functions with their binary logic. The aim of this project is to replace inefficient PLC-solutions for the operation of chilling system by a Fuzzy control system. Optimal operation means: reducing operation time and operation costs of the system, reducing cooling energy generation- and consumption costs. Analysis of the thermal behaviour of the building and the chilling system is necessary, in order to find the current efficient cooling potentials and cooling methods during the operation. Three different Fuzzy controller have been developed with a total rule number of just 70. This realized Fuzzy control system is able to forecast the maximum cooling power of the building, but also to determine the cooling potential of the out door air. This new Fuzzy control system has been successfully commissioned, and remarkable improvement of the system behaviour is reached. Comparison of the system behaviour before and after the implementation of Fuzzy control system proved the benefits of the Fuzzy logic based operation system realized here. The system described here is a joint project between the University of applied sciences Cologne, and Johnson Controls International Cologne. The Fuzzy software tool used here (SUCO soft Fuzzy TECH 4.0), was provided by Kloeckner Moeller Bonn. (orig.) [Deutsch] Die Betriebsoptimierung von Kaelteanlagen innerhalb von Gebaeudeleitsystemen ist auf die Faehigkeiten von logischen Steuerverknuepfungen der Digitaltechnik begrenzt. In diesem Zusammenhang kann nur ein geringer Anteil der Information ueber das thermische Speicherverhalten des jeweiligen Gebaeudes herangezogen werden. Ziel des vorliegenden Projektes war es, die unzureichenden logischen Steuerverknuepfungen durch ein Fuzzy-Control-System zu ersetzen, um die Arbeitsweise der Kaelteanlage zu optimieren. Die Optimierungskriterien dieses

  15. Combined Intelligent Control (CIC an Intelligent Decision Making Algorithm

    Directory of Open Access Journals (Sweden)

    Moteaal Asadi Shirzi

    2007-03-01

    Full Text Available The focus of this research is to introduce the concept of combined intelligent control (CIC as an effective architecture for decision-making and control of intelligent agents and multi-robot sets. Basically, the CIC is a combination of various architectures and methods from fields such as artificial intelligence, Distributed Artificial Intelligence (DAI, control and biological computing. Although any intelligent architecture may be very effective for some specific applications, it could be less for others. Therefore, CIC combines and arranges them in a way that the strengths of any approach cover the weaknesses of others. In this paper first, we introduce some intelligent architectures from a new aspect. Afterward, we offer the CIC by combining them. CIC has been executed in a multi-agent set. In this set, robots must cooperate to perform some various tasks in a complex and nondeterministic environment with a low sensory feedback and relationship. In order to investigate, improve, and correct the combined intelligent control method, simulation software has been designed which will be presented and considered. To show the ability of the CIC algorithm as a distributed architecture, a central algorithm is designed and compared with the CIC.

  16. Incorporation of negative rules and evolution of a fuzzy controller for yeast fermentation process.

    Science.gov (United States)

    Birle, Stephan; Hussein, Mohamed Ahmed; Becker, Thomas

    2016-08-01

    The control of bioprocesses can be very challenging due to the fact that these kinds of processes are highly affected by various sources of uncertainty like the intrinsic behavior of the used microorganisms. Due to the reason that these kinds of process uncertainties are not directly measureable in most cases, the overall control is either done manually because of the experience of the operator or intelligent expert systems are applied, e.g., on the basis of fuzzy logic theory. In the latter case, however, the control concept is mainly represented by using merely positive rules, e.g., "If A then do B". As this is not straightforward with respect to the semantics of the human decision-making process that also includes negative experience in form of constraints or prohibitions, the incorporation of negative rules for process control based on fuzzy logic is emphasized. In this work, an approach of fuzzy logic control of the yeast propagation process based on a combination of positive and negative rules is presented. The process is guided along a reference trajectory for yeast cell concentration by alternating the process temperature. The incorporation of negative rules leads to a much more stable and accurate control of the process as the root mean squared error of reference trajectory and system response could be reduced by an average of 62.8 % compared to the controller using only positive rules.

  17. Control of multi-machine using adaptive fuzzy

    Directory of Open Access Journals (Sweden)

    Bouchiba Bousmaha

    2011-01-01

    Full Text Available An indirect Adaptive fuzzy excitation control (IAFLC of power systems based on multi-input-multi-output linearization technique is developed in this paper. The power system considered in this paper consists of two generators and infinite bus connected through a network of transformers and transmission lines. The fuzzy controller is constructed from fuzzy feedback linearization controller whose parameters are adjusted indirectly from the estimates of plant parameters. The adaptation law adjusts the controller parameters on-line so that the plant output tracks the reference model output. Simulation results shown that the proposed controller IAFLC, compared with a controller based on tradition linearization technique can enhance the transient stability of the power system.

  18. A modeling of fuzzy logic controller on gamma scanning device

    International Nuclear Information System (INIS)

    Arjoni Amir

    2010-01-01

    Modeling and simulation of controller to set the high position and direction of the source of gamma radiation isotope Co-60 and Nal(TL) detector of gamma scanning device by using fuzzy logic controller FLC have been done. The high positions and in the right direction of gamma radiation and Nal (TI) detector obtained the optimal enumeration. The counting data obtained from gamma scanning device counting system is affected by the instability of high position and direction of the gamma radiation source and Nal(TI) detector or the height and direction are not equal between the gamma radiation source and Nal(TI) detector. Assumed a high position and direction of radiation sources can be fixed while the high position detector h (2, 1,0, -1, -2) can be adjusted up and down and the detector can be changed direction to the left or right angle ω (2, 1 , 0, -1, -2) when the position and direction are no longer aligned with the direction of the source of gamma radiation, the counting results obtained will not be optimal. Movement detector direction towards the left or right and the high detector arranged by the DC motor using fuzzy logic control in order to obtain the amount of output fuzzy logic control which forms the optimal output quantity count. The variation of height difference h between the source position of the gamma radiation detector and change direction with the detector angle ω becomes the input variable membership function (member function) whereas the fuzzy logic for the output variable membership function of fuzzy logic control output is selected scale fuzzy logic is directly proportional to the amount of optimal counting. From the simulation results obtained by the relationship between the amount of data output variable of fuzzy logic controller and the amount of data input variable height h and direction detector ω is depicted in graphical form surface. (author)

  19. Business Intelligence in Process Control

    Science.gov (United States)

    Kopčeková, Alena; Kopček, Michal; Tanuška, Pavol

    2013-12-01

    The Business Intelligence technology, which represents a strong tool not only for decision making support, but also has a big potential in other fields of application, is discussed in this paper. Necessary fundamental definitions are offered and explained to better understand the basic principles and the role of this technology for company management. Article is logically divided into five main parts. In the first part, there is the definition of the technology and the list of main advantages. In the second part, an overview of the system architecture with the brief description of separate building blocks is presented. Also, the hierarchical nature of the system architecture is shown. The technology life cycle consisting of four steps, which are mutually interconnected into a ring, is described in the third part. In the fourth part, analytical methods incorporated in the online analytical processing and data mining used within the business intelligence as well as the related data mining methodologies are summarised. Also, some typical applications of the above-mentioned particular methods are introduced. In the final part, a proposal of the knowledge discovery system for hierarchical process control is outlined. The focus of this paper is to provide a comprehensive view and to familiarize the reader with the Business Intelligence technology and its utilisation.

  20. Incremental Adaptive Fuzzy Control for Sensorless Stroke Control of A Halbach-type Linear Oscillatory Motor

    Science.gov (United States)

    Lei, Meizhen; Wang, Liqiang

    2018-01-01

    The halbach-type linear oscillatory motor (HT-LOM) is multi-variable, highly coupled, nonlinear and uncertain, and difficult to get a satisfied result by conventional PID control. An incremental adaptive fuzzy controller (IAFC) for stroke tracking was presented, which combined the merits of PID control, the fuzzy inference mechanism and the adaptive algorithm. The integral-operation is added to the conventional fuzzy control algorithm. The fuzzy scale factor can be online tuned according to the load force and stroke command. The simulation results indicate that the proposed control scheme can achieve satisfied stroke tracking performance and is robust with respect to parameter variations and external disturbance.

  1. Autonomous vehicle motion control, approximate maps, and fuzzy logic

    Science.gov (United States)

    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.

  2. Application of genetic algorithms to tuning fuzzy control systems

    Science.gov (United States)

    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.

  3. Adaptive fuzzy PID control for a quadrotor stabilisation

    Science.gov (United States)

    Cherrat, N.; Boubertakh, H.; Arioui, H.

    2018-02-01

    This paper deals with the design of an adaptive fuzzy PID control law for attitude and altitude stabilization of a quadrotor despite the system model uncertainties and disturbances. To this end, a PID control with adaptive gains is used in order to approximate a virtual ideal control previously designed to achieve the predefined objective. Specifically, the control gains are estimated and adjusted by mean of a fuzzy system whose parameters are adjusted online via a gradient descent-based adaptation law. The analysis of the closed-loop system is given by the Lyapunov approach. The simulation results are presented to illustrate the efficiency of the proposed approach.

  4. Fuzzy logic controller for stabilization of biped robot gait

    Directory of Open Access Journals (Sweden)

    Ryadchikov I.V.

    2018-01-01

    Full Text Available The article centers round the problem of stabilization of biped robot gait through smoothing out the jumps of first and second order derivatives of a biped robot control vector using the fuzzy logic approach. The structure of a composite Takagi-Sugeno fuzzy logic controller developed by the authors is presented. The simulation study of a robot gait with climbing an obstacle is carried out and the results provided in the article showed that the developed controller performed significantly better than the analytical formula model in terms of smoothing out the derivatives of the control vector.

  5. Expert system for fault diagnosis in process control valves using fuzzy-logic

    International Nuclear Information System (INIS)

    Carneiro, Alvaro L.G.; Porto Junior, Almir C.S.

    2013-01-01

    The models of asset maintenance of a process plant basically are classified in corrective maintenance, preventive, predictive and proactive (online). The corrective maintenance is the elementary and most obvious way of the maintenance models. The preventive maintenance consists in a fault prevention work, based on statistical studies that can lead to low efficiency or even an unexpected shutdown of the plant. Predictive maintenance aims to prevent equipment or systems failures through monitoring and tracking of parameters, allowing continuous operation as long as possible. The proactive maintenance usually includes predictive maintenance, emphasizing the root cause analysis of the failure. The maintenance predictive/proactive planning frequently uses software that integrates data from different systems, which facilitates a quick and effective decision- making. In nuclear plants this model has an important role regarding the reliability of equipment and systems. The main focus of this work is to study the development of a model of non-intrusive monitoring and diagnosis applied to process control valves using artificial intelligence by fuzzy logic technique, contributing in the development of predictive methodologies identifying faults in incipient state. The control valve analyzed belongs to a steam plant which simulates the secondary circuit of a PWR nuclear reactor - Pressurized Water Reactor. This study makes use of MATLAB language through the fuzzy logic toolbox which uses the method of inference Mamdani, acting by fuzzy conjunction, through Triangular Norms (t-norm) and Triangular Conorms (t-conorm). As input variables are used air pressure and displacement of the valve stem. Input data coming into the fuzzy system by graph of the automation system Delta V ® available in the plant, which receives a signal of electric current from an 'intelligent' positioned installed on the valve. The output variable is the 'status' of the valve. Through a rule base

  6. Expert system for fault diagnosis in process control valves using fuzzy-logic

    Energy Technology Data Exchange (ETDEWEB)

    Carneiro, Alvaro L.G., E-mail: carneiro@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil); Porto Junior, Almir C.S., E-mail: almir@ctmsp.mar.mil.br [Centro Tecnologico da Marinha em Sao Paulo (CIANA/CTMSP), Ipero, SP (Brazil). Centro de Instrucao e Adestramento Nuclear de ARAMAR

    2013-07-01

    The models of asset maintenance of a process plant basically are classified in corrective maintenance, preventive, predictive and proactive (online). The corrective maintenance is the elementary and most obvious way of the maintenance models. The preventive maintenance consists in a fault prevention work, based on statistical studies that can lead to low efficiency or even an unexpected shutdown of the plant. Predictive maintenance aims to prevent equipment or systems failures through monitoring and tracking of parameters, allowing continuous operation as long as possible. The proactive maintenance usually includes predictive maintenance, emphasizing the root cause analysis of the failure. The maintenance predictive/proactive planning frequently uses software that integrates data from different systems, which facilitates a quick and effective decision- making. In nuclear plants this model has an important role regarding the reliability of equipment and systems. The main focus of this work is to study the development of a model of non-intrusive monitoring and diagnosis applied to process control valves using artificial intelligence by fuzzy logic technique, contributing in the development of predictive methodologies identifying faults in incipient state. The control valve analyzed belongs to a steam plant which simulates the secondary circuit of a PWR nuclear reactor - Pressurized Water Reactor. This study makes use of MATLAB language through the fuzzy logic toolbox which uses the method of inference Mamdani, acting by fuzzy conjunction, through Triangular Norms (t-norm) and Triangular Conorms (t-conorm). As input variables are used air pressure and displacement of the valve stem. Input data coming into the fuzzy system by graph of the automation system Delta V ® available in the plant, which receives a signal of electric current from an 'intelligent' positioned installed on the valve. The output variable is the 'status' of the valve. Through a

  7. Application of fuzzy logic control system for reactor feed-water control

    International Nuclear Information System (INIS)

    Iijima, T.; Nakajima, Y.

    1994-01-01

    The successful actual application of a fuzzy logic control system to the a nuclear Fugen nuclear power reactor is described. Fugen is a heavy-water moderated, light-water cooled reactor. The introduction of fuzzy logic control system has enabled operators to control the steam drum water level more effectively in comparison to a conventional proportional-integral (PI) control system

  8. The intelligence of dual simplex method to solve linear fractional fuzzy transportation problem.

    Science.gov (United States)

    Narayanamoorthy, S; Kalyani, S

    2015-01-01

    An approach is presented to solve a fuzzy transportation problem with linear fractional fuzzy objective function. In this proposed approach the fractional fuzzy transportation problem is decomposed into two linear fuzzy transportation problems. The optimal solution of the two linear fuzzy transportations is solved by dual simplex method and the optimal solution of the fractional fuzzy transportation problem is obtained. The proposed method is explained in detail with an example.

  9. The Intelligence of Dual Simplex Method to Solve Linear Fractional Fuzzy Transportation Problem

    Directory of Open Access Journals (Sweden)

    S. Narayanamoorthy

    2015-01-01

    Full Text Available An approach is presented to solve a fuzzy transportation problem with linear fractional fuzzy objective function. In this proposed approach the fractional fuzzy transportation problem is decomposed into two linear fuzzy transportation problems. The optimal solution of the two linear fuzzy transportations is solved by dual simplex method and the optimal solution of the fractional fuzzy transportation problem is obtained. The proposed method is explained in detail with an example.

  10. Control of a classical microtron and application of fuzzy logic

    International Nuclear Information System (INIS)

    Krist, Pavel; Bila, Jiri

    2011-01-01

    Control problems of the classical microtron with a Kapitza type accelerating cavity were addressed. A fuzzy controller was used, which enabled the system to be controlled even though the accelerating voltage, whose setting is vital for maintaining the accelerator in the stable state, cannot not be measured

  11. Developing a multipurpose sun tracking system using fuzzy control

    Energy Technology Data Exchange (ETDEWEB)

    Alata, Mohanad [Department of Mechanical Engineering, Jordan University of Science and Technology (JUST), PO Box 3030, Irbid 22110 (Jordan)]. E-mail: alata@just.edu.jo; Al-Nimr, M.A. [Department of Mechanical Engineering, Jordan University of Science and Technology (JUST), PO Box 3030, Irbid 22110 (Jordan); Qaroush, Yousef [Department of Mechanical Engineering, Jordan University of Science and Technology (JUST), PO Box 3030, Irbid 22110 (Jordan)

    2005-05-01

    The present work demonstrates the design and simulation of time controlled step sun tracking systems that include: one axis sun tracking with the tilted aperture equal to the latitude angle, equatorial two axis sun tracking and azimuth/elevation sun tracking. The first order Sugeno fuzzy inference system is utilized for modeling and controller design. In addition, an estimation of the insolation incident on a two axis sun tracking system is determined by fuzzy IF-THEN rules. The approach starts by generating the input/output data. Then, the subtractive clustering algorithm, along with least square estimation (LSE), generates the fuzzy rules that describe the relationship between the input/output data of solar angles that change with time. The fuzzy rules are tuned by an adaptive neuro-fuzzy inference system (ANFIS). Finally, an open loop control system is designed for each of the previous types of sun tracking systems. The results are shown using simulation and virtual reality. The site of application is chosen at Amman, Jordan (32 deg. North, 36 deg. East), and the period of controlling and simulating each type of tracking system is the year 2003.

  12. Fuzzy combination of fuzzy and switching state-feedback controllers for nonlinear systems subject to parameter uncertainties.

    Science.gov (United States)

    Lam, H K; Leung, Frank H F

    2005-04-01

    This paper presents a fuzzy controller, which involves a fuzzy combination of local fuzzy and global switching state-feedback controllers, for nonlinear systems subject to parameter uncertainties with known bounds. The nonlinear system is represented by a fuzzy combined Takagi-Sugeno-Kang model, which is a fuzzy combination of the global and local fuzzy plant models. By combining the local fuzzy and global switching state-feedback controllers using fuzzy logic techniques, the advantages of both controllers can be retained and the undesirable chattering effect introduced by the global switching state-feedback controller can be eliminated. The steady-state error introduced by the global switching state-feedback controller when a saturation function is used can also be removed. Stability conditions, which are related to the system matrices of the local and global closed-loop systems, are derived to guarantee the closed-loop system stability. An application example will be given to demonstrate the merits of the proposed approach.

  13. Fuzzy Universal Model Approximator for Distributed Solar Collector Field Control

    KAUST Repository

    Elmetennani, Shahrazed

    2014-07-01

    This paper deals with the control of concentrating parabolic solar collectors by forcing the outlet oil temperature to track a set reference. A fuzzy universal approximate model is introduced in order to accurately reproduce the behavior of the system dynamics. The proposed model is a low order state space representation derived from the partial differential equation describing the oil temperature evolution using fuzzy transform theory. The resulting set of ordinary differential equations simplifies the system analysis and the control law design and is suitable for real time control implementation. Simulation results show good performance of the proposed model.

  14. Speed control for a two-mass drive system using integrated fuzzy estimator and hybrid fuzzy PD/PI controller

    International Nuclear Information System (INIS)

    Pai, N-S; Kuo, Y-P

    2008-01-01

    This paper presents a novel speed control scheme for a 2- mass motor drive system. The speed controller is based on the estimated state feedback compensation. The integrated fuzzy observer can give a fast and accuracy estimation of the unmeasured states. Two kinds of hybrid fuzzy proportional-derivative and proportional-integral (HF PD/PI) are proposed to cope with this speed control problem. The first is the static HF PD/PI controller and the second is the dynamic one. Simulation results show that the developed integrated fuzzy observer provide the better estimation performance than that of the Kalman filter and the proposed control schemes can effectively track the desired speed in the presence of load disturbance

  15. Intelligent Controller Design for a Chemical Process

    OpenAIRE

    Mr. Glan Devadhas G; Dr.Pushpakumar S.

    2010-01-01

    Chemical process control is a challenging problem due to the strong on*line non*linearity and extreme sensitivity to disturbances of the process. Ziegler – Nichols tuned PI and PID controllers are found to provide poor performances for higher*order and non–linear systems. This paper presents an application of one*step*ahead fuzzy as well as ANFIS (adaptive*network*based fuzzy inference system) tuning scheme for an Continuous Stirred Tank Reactor CSTR process. The controller is designed based ...

  16. Theoretical analysis and real time implementation of a classical controller with intelligent properties

    Directory of Open Access Journals (Sweden)

    Essam Hendawi

    2018-05-01

    Full Text Available This paper presents theoretical analysis and experimental implementation of a classical controller with intelligent properties. The controller has constant parameters, but it performs as an intelligent controller. The controller design mimics the fuzzy logic controller in a classical form and combines the advantages of classical controllers and properties of intelligent controllers. The designed controller parameters force the controlled variable to behave such as a first order system with a desired time constant. DC motor practical system is used to demonstrate the effectiveness of the presented controller. Root locus and frequency response using Bode diagram are used to help the design of the controller parameters. Simulation and experimental results verify the high performance of the presented controller. Keywords: Classical controller, DC motor, Root locus, Frequency response, Arduino microcontroller

  17. Neuro fuzzy control of the FES assisted freely swinging leg of paraplegic subjects

    NARCIS (Netherlands)

    van der Spek, J.H.; Velthuis, W.J.R.; Veltink, Petrus H.; de Vries, Theodorus J.A.

    1996-01-01

    The authors designed a neuro fuzzy control strategy for control of cyclical leg movements of paraplegic subjects. The cyclical leg movements were specified by three `swing phase objectives', characteristic of natural human gait. The neuro fuzzy controller is a combination of a fuzzy logic controller

  18. Adaptive Interval Type-2 Fuzzy Logic Control for PMSM Drives with a Modified Reference Frame

    KAUST Repository

    Chaoui, Hicham; Khayamy, Mehdy; Aljarboua, Abdullah Abdulaziz

    2017-01-01

    In this paper, an adaptive interval type-2 fuzzy logic control scheme is proposed for high-performance permanent magnet synchronous machine drives. This strategy combines the power of type-2 fuzzy logic systems with the adaptive control theory

  19. An Intelligent Complex Event Processing with D Numbers under Fuzzy Environment

    Directory of Open Access Journals (Sweden)

    Fuyuan Xiao

    2016-01-01

    Full Text Available Efficient matching of incoming mass events to persistent queries is fundamental to complex event processing systems. Event matching based on pattern rule is an important feature of complex event processing engine. However, the intrinsic uncertainty in pattern rules which are predecided by experts increases the difficulties of effective complex event processing. It inevitably involves various types of the intrinsic uncertainty, such as imprecision, fuzziness, and incompleteness, due to the inability of human beings subjective judgment. Nevertheless, D numbers is a new mathematic tool to model uncertainty, since it ignores the condition that elements on the frame must be mutually exclusive. To address the above issues, an intelligent complex event processing method with D numbers under fuzzy environment is proposed based on the Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS method. The novel method can fully support decision making in complex event processing systems. Finally, a numerical example is provided to evaluate the efficiency of the proposed method.

  20. Computational Intelligence Method for Early Diagnosis Dengue Haemorrhagic Fever Using Fuzzy on Mobile Device

    Directory of Open Access Journals (Sweden)

    Salman Afan

    2014-03-01

    Full Text Available Mortality from Dengue Haemorrhagic Fever (DHF is still increasing in Indonesia particularly in Jakarta. Diagnosis of the dengue shall be made as early as possible so that first aid can be given in expectation of decreasing death risk. The Study will be conducted by developing expert system based on Computational Intelligence Method. On the first year, study will use the Fuzzy Inference System (FIS Method to diagnose Dengue Haemorrhagic Fever particularly in Mobile Device consist of smart phone. Expert system application which particularly using fuzzy system can be applied in mobile device and it is useful to make early diagnosis of Dengue Haemorrhagic Fever that produce outcome faster than laboratory test. The evaluation of this application is conducted by performing accuracy test before and after validation using data of patient who has the Dengue Haemorrhagic Fever. This expert system application is easy, convenient, and practical to use, also capable of making the early diagnosis of Dengue Haemorraghic to avoid mortality in the first stage.

  1. Embedded system in Arduino platform with Fuzzy control to support the grain aeration decision

    Directory of Open Access Journals (Sweden)

    Albino Szesz Junior

    Full Text Available ABSTRACT: Aeration is currently the most commonly used technique to improve the drying and storage of grain, depending on temperature and water content of the grain, as of the temperature and relative humidity of the outside air. In order to monitor temperature and humidity of the grain mass, it is possible to have a network of sensors in the cells of both internal and external storage. Use of artificial intelligence through Fuzzy theory, has been used since the 60s and enables their application on various forms. Thus, it is observed that the aeration of grain in function of representing a system of controlled environment can be studied in relation to the application of this theory. Therefore, the aim of this paper is to present an embedded Fuzzy control system based on the mathematical model of CRUZ et al. (2002 and applied to the Arduino platform, for decision support in aeration of grain. For this, an embedded Arduino system was developed, which received the environmental values of temperature and humidity to then be processed in a Fuzzy controller and return the output as a recommendation to control the aeration process rationally. Comparing the results obtained from the graph presented by LASSERAN (1981 it was observed that the system is effective.

  2. Intelligent Flight Control Simulation Research Program

    National Research Council Canada - National Science Library

    Stolarik, Brian

    2007-01-01

    ...). Under the program, entitled "Intelligent Flight Control Simulation Research Laboratory," a variety of technologies were investigated or developed during the course of the research for AFRL/VAC...

  3. Synchronization of generalized Henon map by using adaptive fuzzy controller

    Energy Technology Data Exchange (ETDEWEB)

    Xue Yueju E-mail: xueyj@mail.tsinghua.edu.cn; Yang Shiyuan E-mail: ysy-dau@tsinghua.edu.cn

    2003-08-01

    In this paper, an adaptive fuzzy control method is presented to synchronize model-unknown discrete-time generalized Henon map. The proposed method is robust to approximate errors and disturbances, because it integrates the merits of adaptive fuzzy and the variable structure control. Moreover, it can realize the synchronizations of non-identical chaotic systems. The simulation results of synchronization of generalized Henon map show that it not only can synchronize model-unknown generalized Henon map but also is robust against the noise of the systems. These merits are advantageous for engineering realization.

  4. Synchronization of generalized Henon map by using adaptive fuzzy controller

    International Nuclear Information System (INIS)

    Xue Yueju; Yang Shiyuan

    2003-01-01

    In this paper, an adaptive fuzzy control method is presented to synchronize model-unknown discrete-time generalized Henon map. The proposed method is robust to approximate errors and disturbances, because it integrates the merits of adaptive fuzzy and the variable structure control. Moreover, it can realize the synchronizations of non-identical chaotic systems. The simulation results of synchronization of generalized Henon map show that it not only can synchronize model-unknown generalized Henon map but also is robust against the noise of the systems. These merits are advantageous for engineering realization

  5. Self-tuning fuzzy logic nuclear reactor controller

    International Nuclear Information System (INIS)

    Sharif Heger, A.; Alang-Rashid, N.K.

    1996-01-01

    We present a method for self-tuning of fuzzy logic controllers based on the estimation of the optimum value of the centroids of its output fuzzy set. The method can be implemented on-line and does not require modification of membership functions and control rules. The main features of this method are: the rules are left intact to retain the operator's expertise in the FLC rule base, and the parameters that require any adjustment are identifiable in advance and their number is kept at a minimum. Therefore, the use of this method preserves the control statements in the original form. Results of simulation and actual tests show that this tuning method improves the performance of fuzzy logic controllers in following the desired reactor power level trajectories. In addition, this method demonstrates a similar improvement for power up and power down experiments, based on both simulation and actual case studies. For these experiments, the control rules for the fuzzy logic controller were derived from control statements that expressed the relationships between error, rate of error change, and duration of direction of control rod movements

  6. Pneumatic motor speed control by trajectory tracking fuzzy logic

    Indian Academy of Sciences (India)

    In this study, trajectory tracking fuzzy logic controller (TTFLC) is proposed for the speed control of a pneumatic motor (PM). A third order trajectory is defined to determine the trajectory function that has to be tracked by the PM speed. Genetic algorithm (GA) is used to find the TTFLC boundary values of membership functions ...

  7. Self-learning fuzzy logic controllers based on reinforcement

    International Nuclear Information System (INIS)

    Wang, Z.; Shao, S.; Ding, J.

    1996-01-01

    This paper proposes a new method for learning and tuning Fuzzy Logic Controllers. The self-learning scheme in this paper is composed of Bucket-Brigade and Genetic Algorithm. The proposed method is tested on the cart-pole system. Simulation results show that our approach has good learning and control performance

  8. Fuzzy self-learning control for magnetic servo system

    Science.gov (United States)

    Tarn, J. H.; Kuo, L. T.; Juang, K. Y.; Lin, C. E.

    1994-01-01

    It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.

  9. Fuzzy logic controller to improve powerline communication

    Science.gov (United States)

    Tirrito, Salvatore

    2015-12-01

    The Power Line Communications (PLC) technology allows the use of the power grid in order to ensure the exchange of data information among devices. This work proposes an approach, based on Fuzzy Logic, that dynamically manages the amplitude of the signal, with which each node transmits, by processing the master-slave link quality measured and the master-slave distance. The main objective of this is to reduce both the impact of communication interferences induced and power consumption.

  10. A fuzzy behaviorist approach to sensor-based robot control

    Energy Technology Data Exchange (ETDEWEB)

    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.

  11. Neutral network and fuzzy logic based grate control; Roststyrning med neutrala naetverk och fuzzy logic

    Energy Technology Data Exchange (ETDEWEB)

    Ramstroem, Erik [TPS Termiska Processer AB, Nykoeping (Sweden)

    2002-04-01

    Grate-control is a complex task in many ways. The relations between controlled variables and the values they depend on are mostly unknown. Research projects are going on to create grate models based on physical laws. Those models are too complex for control implementation. The evaluation time is to long for control use. Another fundamental difficulty is that the relationships are none linear. That is, for a specific change in control value, the change in controlled value depends on the original size of control value, process disturbances and controlled values. There are extensive theories for linear process control. Non-linear control theory is used in robotic applications, but not in process and combustion control. The aim of grate control is to use as much of the grate area as possible, without having unburned material in ash. The outlined strategy is: To keep the position of the final bum out zone constant and its extension controlled. The control variables should be primary airflow, distribution of primary air, and fuel flow. Disturbances that should be measured are the fuel moisture content, the temperature of primary air and the grate temperature under the fuel bed. Technologies used are, fuzzy-logic and neural networks. A combination of booth could be used as well as any of them separately. A Fuzzy-logic controller acts as a computerised operator. Rules are specified with 'if - then' thesis. An example of that is: - if temperature is low, then close the valve The boundaries between the rules are made fuzzy. That makes it possible for the temperature to be just a bit low, which makes the valve open a bit. A lot of rules are created so that the controller knows what to do in every situation. Neural networks are sort of multi dimensional curves, with arbitrary degrees of freedom. The nets are used to predict future process values from measured ones. The model is evaluated from collected data. Parameters are adjusted for best correspondence between

  12. A fuzzy control technique for a magnetically levitated system

    Energy Technology Data Exchange (ETDEWEB)

    Lo Verso, G [C.N.R., Ce.Ri.S.E.P., Palermo (Italy); Trapanese, M [Dipt. di Ingegneria Elettrica, Univ. di Palermo (Italy)

    1996-12-31

    This paper presents the results of some analytical and numerical investigations on a control approach for magnetically leviated systems. This approach is based on fuzzy logic. It has been widely demonstrated that traditional control systems consent to maintain a stiff control on the air gap length. However, the traditional approaches could cause at very high speed, a vertical acceleration of the vehicle cabin larger than the maximum value currently allowed by the ISO standard. It is aim of this work to investigate the possibilities that a fuzzy controller offer in order to solve this problem. In order set up the controller, every mechanical degree of freedom is modelled in terms of some linguistic variables. These linguistic variables are described by several fuzzy sets. It must be noted that, doing so, the disturbances can be described in terms of fuzzy sets, too. A single-mass-model of the vehicle is considered in the paper. The features of the controller are numerically simulated under several types of disturbances and they are compared with a traditional control approach. It is shown how some parameters (especially the vertical acceleration) improve their behaviour. (orig.)

  13. Fuzzy Logic Based MPPT Controller for a PV System

    Directory of Open Access Journals (Sweden)

    Carlos Robles Algarín

    2017-12-01

    Full Text Available The output power of a photovoltaic (PV module depends on the solar irradiance and the operating temperature; therefore, it is necessary to implement maximum power point tracking controllers (MPPT to obtain the maximum power of a PV system regardless of variations in climatic conditions. The traditional solution for MPPT controllers is the perturbation and observation (P&O algorithm, which presents oscillation problems around the operating point; the reason why improving the results obtained with this algorithm has become an important goal to reach for researchers. This paper presents the design and modeling of a fuzzy controller for tracking the maximum power point of a PV System. Matlab/Simulink (MathWorks, Natick, MA, USA was used for the modeling of the components of a 65 W PV system: PV module, buck converter and fuzzy controller; highlighting as main novelty the use of a mathematical model for the PV module, which, unlike diode based models, only needs to calculate the curve fitting parameter. A P&O controller to compare the results obtained with the fuzzy control was designed. The simulation results demonstrated the superiority of the fuzzy controller in terms of settling time, power loss and oscillations at the operating point.

  14. Research on fuzzy PID control to electronic speed regulator

    Science.gov (United States)

    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.

  15. When intelligence is in control

    Energy Technology Data Exchange (ETDEWEB)

    Bellman, K.L.

    1996-12-31

    Each time a discipline redefines itself, I look at it as a sign of growth, because often such redefinition means that there is new theory, new methods, or new {open_quotes}disciples{close_quote} from other disciplines who are stretching, enlarging, and deepening the field. Such is the case with semiotics. Deeply entwined with the concepts of {open_quotes}intelligent systems{close_quotes}, {open_quotes}intelligent control{close_quotes}, and complex systems theory, semiotics struggles to develop representations, notations (systems of representations), and models (functionally-oriented sets of related representations) to study systems that may or may not be usefully described as employing representations, notations, and models themselves. That last, of course, is the main problem that semiotics faces. Semiotics, like psychology, philosophy, or any other self-referential discipline, is burdened by the eye attempting to study the eye or the mind studying the mind, or more to the point here, the modeler studying the modeling acts of others.

  16. Dynamic Intelligent Feedback Scheduling in Networked Control Systems

    Directory of Open Access Journals (Sweden)

    Hui-ying Chen

    2013-01-01

    Full Text Available For the networked control system with limited bandwidth and flexible workload, a dynamic intelligent feedback scheduling strategy is proposed. Firstly, a monitor is used to acquire the current available network bandwidth. Then, the new available bandwidth in the next interval is predicted by using LS_SVM approach. At the same time, the dynamic performance indices of all control loops are obtained with a two-dimensional fuzzy logic modulator. Finally, the predicted network bandwidth is dynamically allocated by the bandwidth manager and the priority allocator in terms of the loops' dynamic performance indices. Simulation results show that the sampling periods and priorities of control loops are adjusted timely according to the network workload condition and the dynamic performance of control loops, which make the system running in the optimal state all the time.

  17. Experimental analysis of fuzzy controlled energy efficient demand controlled ventilation economizer cycle variable air volume air conditioning system

    Directory of Open Access Journals (Sweden)

    Rajagopalan Parameshwaran

    2008-01-01

    Full Text Available In the quest for energy conservative building design, there is now a great opportunity for a flexible and sophisticated air conditioning system capable of addressing better thermal comfort, indoor air quality, and energy efficiency, that are strongly desired. The variable refrigerant volume air conditioning system provides considerable energy savings, cost effectiveness and reduced space requirements. Applications of intelligent control like fuzzy logic controller, especially adapted to variable air volume air conditioning systems, have drawn more interest in recent years than classical control systems. An experimental analysis was performed to investigate the inherent operational characteristics of the combined variable refrigerant volume and variable air volume air conditioning systems under fixed ventilation, demand controlled ventilation, and combined demand controlled ventilation and economizer cycle techniques for two seasonal conditions. The test results of the variable refrigerant volume and variable air volume air conditioning system for each techniques are presented. The test results infer that the system controlled by fuzzy logic methodology and operated under the CO2 based mechanical ventilation scheme, effectively yields 37% and 56% per day of average energy-saving in summer and winter conditions, respectively. Based on the experimental results, the fuzzy based combined system can be considered to be an alternative energy efficient air conditioning scheme, having significant energy-saving potential compared to the conventional constant air volume air conditioning system.

  18. Fuzzy logic controllers and chaotic natural convection loops

    International Nuclear Information System (INIS)

    Theler, German

    2007-01-01

    The study of natural circulation loops is a subject of special concern for the engineering design of advanced nuclear reactors, as natural convection provides an efficient and completely passive heat removal system. However, under certain circumstances thermal-fluid-dynamical instabilities may appear, threatening the reactor safety as a whole.On the other hand, fuzzy logic controllers provide an ideal framework to approach highly non-linear control problems. In the present work, we develop a software-based fuzzy logic controller and study its application to chaotic natural convection loops.We numerically analyse the linguistic control of the loop known as the Welander problem in such conditions that, if the controller were not present, the circulation flow would be non-periodic unstable.We also design a Taka gi-Sugeno fuzzy controller based on a fuzzy model of a natural convection loop with a toroidal geometry, in order to stabilize a Lorenz-chaotic behaviour.Finally, we show experimental results obtained in a rectangular natural circulation loop [es

  19. Optimization Settings in the Fuzzy Combined Mamdani PID Controller

    Science.gov (United States)

    Kudinov, Y. I.; Pashchenko, F. F.; Pashchenko, A. F.; Kelina, A. Y.; Kolesnikov, V. A.

    2017-11-01

    In the present work the actual problem of determining the optimal settings of fuzzy parallel proportional-integral-derivative (PID) controller is considered to control nonlinear plants that is not always possible to perform with classical linear PID controllers. In contrast to the linear fuzzy PID controllers there are no analytical methods of settings calculation. In this paper, we develop a numerical optimization approach to determining the coefficients of a fuzzy PID controller. Decomposition method of optimization is proposed, the essence of which was as follows. All homogeneous coefficients were distributed to the relevant groups, for example, three error coefficients, the three coefficients of the changes of errors and the three coefficients of the outputs P, I and D components. Consistently in each of such groups the search algorithm was selected that has determined the coefficients under which we receive the schedule of the transition process satisfying all the applicable constraints. Thus, with the help of Matlab and Simulink in a reasonable time were found the factors of a fuzzy PID controller, which meet the accepted limitations on the transition process.

  20. Study on intelligence fault diagnosis method for nuclear power plant equipment based on rough set and fuzzy neural network

    International Nuclear Information System (INIS)

    Liu Yongkuo; Xia Hong; Xie Chunli; Chen Zhihui; Chen Hongxia

    2007-01-01

    Rough set theory and fuzzy neural network are combined, to take full advantages of the two of them. Based on the reduction technology to knowledge of Rough set method, and by drawing the simple rule from a large number of initial data, the fuzzy neural network was set up, which was with better topological structure, improved study speed, accurate judgment, strong fault-tolerant ability, and more practical. In order to test the validity of the method, the inverted U-tubes break accident of Steam Generator and etc are used as examples, and many simulation experiments are performed. The test result shows that it is feasible to incorporate the fault intelligence diagnosis method based on rough set and fuzzy neural network in the nuclear power plant equipment, and the method is simple and convenience, with small calculation amount and reliable result. (authors)

  1. Artificial intelligence-based speed control of DTC induction motor drives - A comparative study

    Energy Technology Data Exchange (ETDEWEB)

    Gadoue, S.M.; Giaouris, D.; Finch, J.W. [School of Electrical, Electronic and Computer Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU (United Kingdom)

    2009-01-15

    The design of the speed controller greatly affects the performance of an electric drive. A common strategy to control an induction machine is to use direct torque control combined with a PI speed controller. These schemes require proper and continuous tuning and therefore adaptive controllers are proposed to replace conventional PI controllers to improve the drive's performance. This paper presents a comparison between four different speed controller design strategies based on artificial intelligence techniques; two are based on tuning of conventional PI controllers, the third makes use of a fuzzy logic controller and the last is based on hybrid fuzzy sliding mode control theory. To provide a numerical comparison between different controllers, a performance index based on speed error is assigned. All methods are applied to the direct torque control scheme and each control strategy has been tested for its robustness and disturbance rejection ability. (author)

  2. Robust algebraic image enhancement for intelligent control systems

    Science.gov (United States)

    Lerner, Bao-Ting; Morrelli, Michael

    1993-01-01

    Robust vision capability for intelligent control systems has been an elusive goal in image processing. The computationally intensive techniques a necessary for conventional image processing make real-time applications, such as object tracking and collision avoidance difficult. In order to endow an intelligent control system with the needed vision robustness, an adequate image enhancement subsystem capable of compensating for the wide variety of real-world degradations, must exist between the image capturing and the object recognition subsystems. This enhancement stage must be adaptive and must operate with consistency in the presence of both statistical and shape-based noise. To deal with this problem, we have developed an innovative algebraic approach which provides a sound mathematical framework for image representation and manipulation. Our image model provides a natural platform from which to pursue dynamic scene analysis, and its incorporation into a vision system would serve as the front-end to an intelligent control system. We have developed a unique polynomial representation of gray level imagery and applied this representation to develop polynomial operators on complex gray level scenes. This approach is highly advantageous since polynomials can be manipulated very easily, and are readily understood, thus providing a very convenient environment for image processing. Our model presents a highly structured and compact algebraic representation of grey-level images which can be viewed as fuzzy sets.

  3. Designing an Adaptive Nuero-Fuzzy Inference System for Evaluating the Business Intelligence System Implementation in Software Industry

    Directory of Open Access Journals (Sweden)

    Iman Raeesi Vanani

    2015-03-01

    Full Text Available The main goal of research is designing an adaptive nuero-fuzzy inference system for evaluating the implementation of business intelligence systems in software industry. Iranian software development organizations have been facing a lot of problems in case of implementing business intelligence systems. This system would be helpful in recognizing the conditions and prerequisites of success or failure. Organizations can recalculate the neuro-fuzzy system outputs with some considerations on various inputs to figure out which inputs have the most effect on the implementation outputs. By resolving the problems on inputs, organizations can achieve a better level of implementation success. The designed system has been trained by a data set and afterwards, it has been evaluated. The trained system has reached the error value of 0.08. Eventually, some recommendations have been provided for software development firms on the areas that might need more considerations and improvements.

  4. A hybrid fuzzy-ontology based intelligent system to determine level of severity and treatment recommendation for Benign Prostatic Hyperplasia.

    Science.gov (United States)

    Torshizi, Abolfazl Doostparast; Zarandi, Mohammad Hossein Fazel; Torshizi, Ghazaleh Doostparast; Eghbali, Kamyar

    2014-01-01

    This paper deals with application of fuzzy intelligent systems in diagnosing severity level and recommending appropriate therapies for patients having Benign Prostatic Hyperplasia. Such an intelligent system can have remarkable impacts on correct diagnosis of the disease and reducing risk of mortality. This system captures various factors from the patients using two modules. The first module determines severity level of the Benign Prostatic Hyperplasia and the second module, which is a decision making unit, obtains output of the first module accompanied by some external knowledge and makes an appropriate treatment decision based on its ontology model and a fuzzy type-1 system. In order to validate efficiency and accuracy of the developed system, a case study is conducted by 44 participants. Then the results are compared with the recommendations of a panel of experts on the experimental data. Then precision and accuracy of the results were investigated based on a statistical analysis. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  5. Simulasi Kecepatan Kendaraan dengan Menggunakan Logika Fuzzy

    OpenAIRE

    Lukas, Samuel; Aribowo, Arnold; Tjia, Yogih Suharta

    2008-01-01

    Artificial intelligence has been implemented widely. Many of household products are designed based on artificial intellegence concept. One of them is fuzzy logic system. This paper describes on how a fuzzy logic system can also be implemented in controling the speed of a car in the road. The fuzzy inference system was designed according to Tsukamoto inferencing method and for the defuzzyfication method is used weighted average method. There are three inputs for the system. The are distance b...

  6. Simulasi Kecepatan Kendaraan Dengan Menggunakan Logika Fuzzy

    OpenAIRE

    Lukas, Samuel; Aribowo, Arnold; Tjia, Yogih Suharta

    2009-01-01

    Artificial intelligence has been implemented widely. Many of household products are designed based on artificial intellegence concept. One of them is fuzzy logic system. This paper describes on how a fuzzy logic system can also be implemented in controling the speed of a car in the road.  The fuzzy inference system was designed according to Tsukamoto inferencing method and for the defuzzyfication method is used weighted average method. There are three inputs for the system. The are distance b...

  7. Optimized Fuzzy-Cuckoo Controller for Active Power Control of Battery Energy Storage System, Photovoltaic, Fuel Cell and Wind Turbine in an Isolated Micro-Grid

    OpenAIRE

    Mohsen Einan; Hossein Torkaman; Mahdi Pourgholi

    2017-01-01

    This paper presents a new control strategy for isolated micro-grids including wind turbines (WT), fuel cells (FC), photo-voltaic (PV) and battery energy storage systems (BESS). FC have been used in parallel with BESSs in order to increase their lifetime and efficiency. The changes in some parameters such as wind speed, sunlight, and consumption, lead to improper performance of droop. To overcome this challenge, a new intelligent method using a combination of fuzzy controller and cuckoo optimi...

  8. Adaptation in the fuzzy self-organising controller

    DEFF Research Database (Denmark)

    Jantzen, Jan; Poulsen, Niels Kjølstad

    2003-01-01

    This simulation study provides an analysis of the adaptation mechanism in the self-organising fuzzy controller, SOC. The approach is to apply a traditional adaptive control viewpoint. A simplified performance measure in the SOC controller is used in a loss function, and thus the MIT rule implies...... an update mechanism similar to the SOC update mechanism. Two simulations of proportionally controlled systems show the behaviour of the proportional gain as it adapts to a specified behaviour....

  9. Fuzzy logic control of vehicle suspensions with dry friction nonlinearity

    Indian Academy of Sciences (India)

    We design and investigate the performance of fuzzy logic-controlled (FLC) active suspensions on a nonlinear vehicle model with four degrees of freedom, without causing any degeneration in suspension working limits. Force actuators were mounted parallel to the suspensions. In this new approach, linear combinations of ...

  10. Fuzzy sliding mode controller for doubly fed induction motor speed ...

    African Journals Online (AJOL)

    The use of the nonlinear fuzzy sliding mode method provides very good performance for motor operation and robustness of the control law despite the external/internal perturbations. The chattering effects is eliminated by a particular function "sat" that presents a serious problem to applications of variable structure systems.

  11. Fuzzy controller for better tennis ball robot | Nguyen | Journal of ...

    African Journals Online (AJOL)

    This paper aims at designing a tennis ball robot as a training facility for tennis players. The robot is built with fuzzy controller which provides proper techniques for the players to gain practical experience as well as technical skills; thus, it can effectively serve the community and train athletes in the high-performance sport.

  12. Systems control with generalized probabilistic fuzzy-reinforcement learning

    NARCIS (Netherlands)

    Hinojosa, J.; Nefti, S.; Kaymak, U.

    2011-01-01

    Reinforcement learning (RL) is a valuable learning method when the systems require a selection of control actions whose consequences emerge over long periods for which input-output data are not available. In most combinations of fuzzy systems and RL, the environment is considered to be

  13. Fuzzy Logic Applied to an Oven Temperature Control System

    Directory of Open Access Journals (Sweden)

    Nagabhushana KATTE

    2011-10-01

    Full Text Available The paper describes the methodology of design and development of fuzzy logic based oven temperature control system. As simple fuzzy logic controller (FLC structure with an efficient realization and a small rule base that can be easily implemented in existing underwater control systems is proposed. The FLC has been designed using bell-shaped membership function for fuzzification, 49 control rules in its rule base and centre of gravity technique for defuzzification. Analog interface card with 16-bits resolution is designed to achieve higher precision in temperature measurement and control. The experimental results of PID and FLC implemented system are drawn for a step input and presented in a comparative fashion. FLC exhibits fast response and it has got sharp rise time and smooth control over conventional PID controller. The paper scrupulously discusses the hardware and software (developed using ‘C’ language features of the system.

  14. An intelligent active force control algorithm to control an upper extremity exoskeleton for motor recovery

    Science.gov (United States)

    Hasbullah Mohd Isa, Wan; Taha, Zahari; Mohd Khairuddin, Ismail; Majeed, Anwar P. P. Abdul; Fikri Muhammad, Khairul; Abdo Hashem, Mohammed; Mahmud, Jamaluddin; Mohamed, Zulkifli

    2016-02-01

    This paper presents the modelling and control of a two degree of freedom upper extremity exoskeleton by means of an intelligent active force control (AFC) mechanism. The Newton-Euler formulation was used in deriving the dynamic modelling of both the anthropometry based human upper extremity as well as the exoskeleton that consists of the upper arm and the forearm. A proportional-derivative (PD) architecture is employed in this study to investigate its efficacy performing joint-space control objectives. An intelligent AFC algorithm is also incorporated into the PD to investigate the effectiveness of this hybrid system in compensating disturbances. The Mamdani Fuzzy based rule is employed to approximate the estimated inertial properties of the system to ensure the AFC loop responds efficiently. It is found that the IAFC-PD performed well against the disturbances introduced into the system as compared to the conventional PD control architecture in performing the desired trajectory tracking.

  15. Robust fuzzy output feedback controller for affine nonlinear systems via T-S fuzzy bilinear model: CSTR benchmark.

    Science.gov (United States)

    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. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  16. Fuzzy PID Feedback Control of Piezoelectric Actuator with Feedforward Compensation

    Directory of Open Access Journals (Sweden)

    Ziqiang Chi

    2014-01-01

    Full Text Available Piezoelectric actuator is widely used in the field of micro/nanopositioning. However, piezoelectric hysteresis introduces nonlinearity to the system, which is the major obstacle to achieve a precise positioning. In this paper, the Preisach model is employed to describe the hysteresis characteristic of piezoelectric actuator and an inverse Preisach model is developed to construct a feedforward controller. Considering that the analytical expression of inverse Preisach model is difficult to derive and not suitable for practical application, a digital inverse model is established based on the input and output data of a piezoelectric actuator. Moreover, to mitigate the compensation error of the feedforward control, a feedback control scheme is implemented using different types of control algorithms in terms of PID control, fuzzy control, and fuzzy PID control. Extensive simulation studies are carried out using the three kinds of control systems. Comparative investigation reveals that the fuzzy PID control system with feedforward compensation is capable of providing quicker response and better control accuracy than the other two ones. It provides a promising way of precision control for piezoelectric actuator.

  17. Research on laser cladding control system based on fuzzy PID

    Science.gov (United States)

    Zhang, Chuanwei; Yu, Zhengyang

    2017-12-01

    Laser cladding technology has a high demand for control system, and the domestic laser cladding control system mostly uses the traditional PID control algorithm. Therefore, the laser cladding control system has a lot of room for improvement. This feature is suitable for laser cladding technology, Based on fuzzy PID three closed-loop control system, and compared with the conventional PID; At the same time, the laser cladding experiment and friction and wear experiment were carried out under the premise of ensuring the reasonable control system. Experiments show that compared with the conventional PID algorithm in fuzzy the PID algorithm under the surface of the cladding layer is more smooth, the surface roughness increases, and the wear resistance of the cladding layer is also enhanced.

  18. Implementation Of Fuzzy Automated Brake Controller Using TSK Algorithm

    Science.gov (United States)

    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.

  19. Self-tuning fuzzy logic nuclear reactor controller

    International Nuclear Information System (INIS)

    Alang-Rashid, N. K.; Heger, A.S.

    1994-01-01

    A method for self-timing of a fuzzy logic controller (FLC) based on the estimation of the optimum value of the centroids of the its output fuzzy sets is proposed. The method can be implemented on-line and does not modify the membership function and the control rules, thus preserving the description of control statements in their original forms. Results of simulation and actual tests show that the tuning method improves the FLCs performance in following desired reactor power level trajectories (simulation tests) and simple power up and power down experiments (simulation and actual tests). The FLC control rules were derived from control statements expressing the relations between error, rate of error change, and control rod duration and direction of movements

  20. Self-tuning fuzzy logic nuclear reactor controller

    Energy Technology Data Exchange (ETDEWEB)

    Alang-Rashid, N K; Heger, A S

    1994-12-31

    A method for self-timing of a fuzzy logic controller (FLC) based on the estimation of the optimum value of the centroids of the its output fuzzy sets is proposed. The method can be implemented on-line and does not modify the membership function and the control rules, thus preserving the description of control statements in their original forms. Results of simulation and actual tests show that the tuning method improves the FLCs performance in following desired reactor power level trajectories (simulation tests) and simple power up and power down experiments (simulation and actual tests). The FLC control rules were derived from control statements expressing the relations between error, rate of error change, and control rod duration and direction of movements.

  1. A new approach of active compliance control via fuzzy logic control for multifingered robot hand

    Science.gov (United States)

    Jamil, M. F. A.; Jalani, J.; Ahmad, A.

    2016-07-01

    Safety is a vital issue in Human-Robot Interaction (HRI). In order to guarantee safety in HRI, a model reference impedance control can be a very useful approach introducing a compliant control. In particular, this paper establishes a fuzzy logic compliance control (i.e. active compliance control) to reduce impact and forces during physical interaction between humans/objects and robots. Exploiting a virtual mass-spring-damper system allows us to determine a desired compliant level by understanding the behavior of the model reference impedance control. The performance of fuzzy logic compliant control is tested in simulation for a robotic hand known as the RED Hand. The results show that the fuzzy logic is a feasible control approach, particularly to control position and to provide compliant control. In addition, the fuzzy logic control allows us to simplify the controller design process (i.e. avoid complex computation) when dealing with nonlinearities and uncertainties.

  2. The Medical Microrobot Control System Design via Fuzzy Logic Application

    Directory of Open Access Journals (Sweden)

    A. S. Yuschenko

    2014-01-01

    Full Text Available The aim of the investigation is the development of the instruments and technologies for diagnostics and treatment of tube-like human’s organs such as blood vessels and intestines. The medical microrobots may be applied to move along the tube-like organs by the same way as a worm. Such microrobots had been presented in some works in Russia and abroad among them is a project of BMSTU. The control system of the robot has to adapt the movement process to the peculiarity of the biology environment. The safety of the application of robotic device inside the human body is the main requirement to the construction.An experimental model of microrobot has three segments which contracting successively to ensure progressive movement of the device. The diameter of the robot is smaller than the same of the blood vessel. So it is pressed to the internal cover of the vessel by the special planes to avoid the thrombosis of the vessel. Every segment of robot contain three contact elements, pressure sensors and a regulator to control the pressure of the elements to the internal surface of the vessel. Aboard the robot is a micro-video camera has been mounted to inform the surgeon of the situation inside the vessel and other micro-devices. The supporting plates carry tens metric sensors to control the contact forces. The driver of the robot is of hydraulic type with physiologic solution to avoid the danger of embolism.Microrobot is a part of the robotic system including also a hydro-driver mounted in the stationary part of the system and intelligent interface of the operator. The surgeon-operator has opportunity to observe the inner surface of the vessel by the sensors mounted aboard the robot and to control the robot movement along the vessel. The construction of the microrobot has to guarantee the stable position of the robot in the moving blood flow and its movement inside the vessel without any damage of the inner surface.The peculiarity of the microrobot

  3. Switch Reluctance Motor Control Based on Fuzzy Logic System

    Directory of Open Access Journals (Sweden)

    S. V. Aleksandrovsky

    2012-01-01

    Full Text Available Due to its intrinsic simplicity and reliability, the switched reluctance motor (SRM has now become a promising candidate for variable-speed drive applications as an alternative induction motor in various industrial application. However, the SRM has the disadvantage of nonlinear characteristic and control. It is suggested to use controller based on fuzzy logic system. Design of FLS controller and simulation model presented.

  4. Intelligent distributed voltage control system for smart grid application

    Energy Technology Data Exchange (ETDEWEB)

    Sajadi, Amirhossein [Warsaw Univ. of Technology (Poland); Ariatabar, Mitra [RWTH Aachen Univ. (Germany)

    2012-07-01

    Increasing penetration of the renewable energy source (RES) units in distribution networks particularly due to nonlinear and unpredictable nature of renewable units brings up new challenges in different aspects of electricity network, which leads to more complex power systems. Multi-agent system is consisting of agents which are capable to perceive environment that they are located in and to reacts with each other by communication infrastructure in order to achieve overall goals. In this paper an approach to control the voltage based on in the power distribution system is proposed and discussed. Therefore, a multi-agent system has been integrated with artificial intelligence to come up with fuzzy multi-agent based system. The proposed control scheme is deployed to a smart distribution system consisting distribution generation units, modelled in MATLAB/Simulink, to evaluate its effectiveness. The simulation results show how proposed system can regulate voltage in smart distribution feeders. (orig.)

  5. Review of Control Techniques for HVAC Systems—Nonlinearity Approaches Based on Fuzzy Cognitive Maps

    Directory of Open Access Journals (Sweden)

    Farinaz Behrooz

    2018-02-01

    Full Text Available Heating, Ventilating, and Air Conditioning (HVAC systems are the major energy-consuming devices in buildings. Nowadays, due to the high demand for HVAC system installation in buildings, designing an effective controller in order to decrease the energy consumption of the devices while meeting the thermal comfort demands in buildings are the most important goals of control designers. The purpose of this article is to investigate the different control methods for Heating, Ventilating, and Air Conditioning and Refrigeration (HVAC & R systems. The advantages and disadvantages of each control method are discussed and finally the Fuzzy Cognitive Map (FCM method is introduced as a new strategy for HVAC systems. The FCM method is an intelligent and advanced control technique to address the nonlinearity, Multiple-Input and Multiple-Output (MIMO, complexity and coupling effect features of the systems. The significance of this method and improvements by this method are compared with other methods.

  6. A fuzzy logic controller for feedwater regulation in pressurized water reactors

    International Nuclear Information System (INIS)

    Eryuerek, E.E.; Upadhyaya, B.R.; Alguindigue, I.E.

    1994-01-01

    Fuzzy control refers to the application of fuzzy logic theory to control systems. In this paper fuzzy controllers for steam generator water level control and pump speed control are presented, and their performance in the presence of perturbations is discussed. In order to test the robustness of the controllers, their performance is compared with the performance of model based adaptive controllers and traditional PID controllers. The control actions calculated by the fuzzy controllers is have the characteristic of quick and smooth control compared to the others

  7. Fuzzy Adaptation Algorithms’ Control for Robot Manipulators with Uncertainty Modelling Errors

    Directory of Open Access Journals (Sweden)

    Yongqing Fan

    2018-01-01

    Full Text Available A novel fuzzy control scheme with adaptation algorithms is developed for robot manipulators’ system. At the beginning, one adjustable parameter is introduced in the fuzzy logic system, the robot manipulators system with uncertain nonlinear terms as the master device and a reference model dynamic system as the slave robot system. To overcome the limitations such as online learning computation burden and logic structure in conventional fuzzy logic systems, a parameter should be used in fuzzy logic system, which composes fuzzy logic system with updated parameter laws, and can be formed for a new fashioned adaptation algorithms controller. The error closed-loop dynamical system can be stabilized based on Lyapunov analysis, the number of online learning computation burdens can be reduced greatly, and the different kinds of fuzzy logic systems with fuzzy rules or without any fuzzy rules are also suited. Finally, effectiveness of the proposed approach has been shown in simulation example.

  8. Control of a mechanical gripper with a fuzzy controller; Control de una garra robotizada mediante un controlador borroso

    Energy Technology Data Exchange (ETDEWEB)

    Alberdi, J.; Barcala, J.M.; Gamero, E.; Navarrete, J.J.

    1995-07-01

    A fuzzy logic system is used to control a mechanical gripper. System is based in a NLX230 fuzzy micro controller. Control rules are programmed by a 68020 microprocessor in the micro controller memory. Stress and its derived are used as feedback signals in the control. This system can adapt its effort to the mechanical resistance of the object between the fingers.

  9. Control of a mechanical gripper with a fuzzy controller; Control de una garra robotizada mediante un controlador borroso

    Energy Technology Data Exchange (ETDEWEB)

    Alberdi, J.; Barcala, J.M.; Gamero, E.; Navarrete, J.J.

    1995-07-01

    A fuzzy logic system is used to control a mechanical gripper. System is based in a NLX230 fuzzy micro controller. Control rules are programmed by a 68020 microprocessor in the micro controller memory. Stress and its derived are used as feedback signals in the control. This system can adapt its effort to the mechanical resistance of the object between the fingers. (Author)

  10. A Genetic Based Neuro-Fuzzy Controller System

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2014-01-01

    Recently, the mobile robots have great importance in the manufacturing processes. They are widely used for assembling processes, handling the dangerous components, moving the weighted things, etc. Designing the controller of the mobile robot is a very complex task. Many simple control systems used the neuro-fuzzy controller in the mobile robots. But, they faced with great complexity when moving in unstructured and dynamic environments. The proposed system introduces the uses of the genetic algorithm for optimizing the parameters of the neuro-fuzzy controller. So, the proposed system can improve the performance of the mobile robots. It has applied for a mobile robot used for moving the dangerous and critical materials in unstructured environment. Its results are compared with other traditional controller systems. The suggested system has proved its success for the real-time applications

  11. Evaluation-Function-based Model-free Adaptive Fuzzy Control

    Directory of Open Access Journals (Sweden)

    Agus Naba

    2016-12-01

    Full Text Available Designs of adaptive fuzzy controllers (AFC are commonly based on the Lyapunov approach, which requires a known model of the controlled plant. They need to consider a Lyapunov function candidate as an evaluation function to be minimized. In this study these drawbacks were handled by designing a model-free adaptive fuzzy controller (MFAFC using an approximate evaluation function defined in terms of the current state, the next state, and the control action. MFAFC considers the approximate evaluation function as an evaluative control performance measure similar to the state-action value function in reinforcement learning. The simulation results of applying MFAFC to the inverted pendulum benchmark verified the proposed scheme’s efficacy.

  12. Study of Inverted Pendulum Robot Using Fuzzy Servo Control Method

    Directory of Open Access Journals (Sweden)

    Dazhong Wang

    2012-09-01

    Full Text Available The inverted pendulum robot is a classical problem in controls. The inherit instabilities in the setup make it a natural target for a control system. Inverted pendulum robot is suitable to use for investigation and verification of various control methods for dynamic systems. Maintaining an equilibrium position of the pendulum pointing up is a challenge as this equilibrium position is unstable. As the inverted pendulum robot system is nonlinear it is well-suited to be controlled by fuzzy logic. In this paper, Lagrange method has been applied to develop the mathematical model of the system. The objective of the simulation to be shown using the fuzzy control method can stabilize the nonlinear system of inverted pendulum robot.

  13. Autonomous intelligent cruise control system

    Science.gov (United States)

    Baret, Marc; Bomer, Thierry T.; Calesse, C.; Dudych, L.; L'Hoist, P.

    1995-01-01

    Autonomous intelligent cruise control (AICC) systems are not only controlling vehicles' speed but acting on the throttle and eventually on the brakes they could automatically maintain the relative speed and distance between two vehicles in the same lane. And more than just for comfort it appears that these new systems should improve the safety on highways. By applying a technique issued from the space research carried out by MATRA, a sensor based on a charge coupled device (CCD) was designed to acquire the reflected light on standard-mounted car reflectors of pulsed laser diodes emission. The CCD is working in a unique mode called flash during transfer (FDT) which allows identification of target patterns in severe optical environments. It provides high accuracy for distance and angular position of targets. The absence of moving mechanical parts ensures high reliability for this sensor. The large field of view and the high measurement rate give a global situation assessment and a short reaction time. Then, tracking and filtering algorithms have been developed in order to select the target, on which the equipped vehicle determines its safety distance and speed, taking into account its maneuvering and the behaviors of other vehicles.

  14. A fuzzy controller with a robust learning function

    International Nuclear Information System (INIS)

    Tanji, Jun-ichi; Kinoshita, Mitsuo

    1987-01-01

    A self-organizing fuzzy controller is able to use linguistic decision rules of control strategy and has a strong adaptive property by virture of its rule learning function. While a simple linguistic description of the learning algorithm first introduced by Procyk, et al. has much flexibility for applications to a wide range of different processes, its detailed formulation, in particular with control stability and learning process convergence, is not clear. In this paper, we describe the formulation of an analytical basis for a self-organizing fuzzy controller by using a method of model reference adaptive control systems (MRACS) for which stability in the adaptive loop is theoretically proven. A detailed formulation is described regarding performance evaluation and rule modification in the rule learning process of the controller. Furthermore, an improved learning algorithm using adaptive rule is proposed. An adaptive rule gives a modification coefficient for a rule change estimating the effect of disturbance occurrence in performance evaluation. The effect of introducing an adaptive rule to improve the learning convergency is described by using a simple iterative formulation. Simulation tests are presented for an application of the proposed self-organizing fuzzy controller to the pressure control system in a Boiling Water Reactor (BWR) plant. Results with the tests confirm the improved learning algorithm has strong convergent properties, even in a very disturbed environment. (author)

  15. Design issues of a reinforcement-based self-learning fuzzy controller for petrochemical process control

    Science.gov (United States)

    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.

  16. Monitoring of surge tanks in hydroelectric power plants using fuzzy control; Ueberwachung von Wasserschloessern in Wasserkraftwerken mit Fuzzy-Control

    Energy Technology Data Exchange (ETDEWEB)

    Lin, J.C.

    2000-07-01

    Surge tanks are used to reduce pressure variations caused by fluid transients in high-head hydroelectric power plants. Occasionally load increases have to be limited to prevent the surge tank from draining due to excessive demands of flow. A control concept based on fuzzy logic was developed for governing the load changes of hydroelectric power plants. In order to achieve an optimal control behaviour and simultaneously to avoid the draining of surge tanks, the speed of load increases is automatically adjusted by a fuzzy conclusion depending on the height and the gradient of the water level in the surge tank, the reservoir level and the sum of load increases. The hydroelectric power plant Achensee of Tiroler Wasserkraftwerke AG in Austria is taken as an example to demonstrate the characteristics of the control concept. In comparison with a conventional control concept, the operation of load increases using the fuzzy concept proves to be more flexible and unrestricted. (orig.) [German] Ein Wasserschloss dient zur Verminderung von Druckschwankungen im Wasserfuehrungssystem von Hochdruckanlagen. Gelegentlich muss man die Lastaufnahme so beschraenken, dass das Wasserschloss nicht durch uebermaessige Wasserentnahme leerlaeuft. Fuer die Leistungsregelung eines Wasserkraftwerks wurde ein Konzept entwickelt, das auf der Fuzzy-Control in Verbindung mit der klassischen Regelung beruht. Um ein optimales Regelverhalten zu erhalten und gleichzeitig das Leerlaufen des Wasserschlosses zu vermeiden, wird die Geschwindigkeit der Lastaufnahme in Abhaengigkeit von der Hoehenkote und dem Gradienten des Wasserschlosspegels, dem Pegel des Oberwassers und der Groesse der geforderten Lasterhoehung automatisch eingestellt. Die Untersuchung erfolgt am Beispiel des Achenseekraftwerkes der Tiroler Wasserkraftwerke AG, Oesterreich. Im Vergleich mit einer konventionellen Regelung ergibt sich mit dem Fuzzy-Konzept eine flexiblere und freizuegigere Lastaufnahme. (orig.)

  17. Fuzzy-Genetic Optimal Control for Four Degreeof Freedom Robotic Arm Movement

    OpenAIRE

    V. K. Banga; R. Kumar; Y. Singh

    2009-01-01

    In this paper, we present optimal control for movement and trajectory planning for four degrees-of-freedom robot using Fuzzy Logic (FL) and Genetic Algorithms (GAs). We have evaluated using Fuzzy Logic (FL) and Genetic Algorithms (GAs) for four degree-of-freedom (4 DOF) robotics arm, Uncertainties like; Movement, Friction and Settling Time in robotic arm movement have been compensated using Fuzzy logic and Genetic Algorithms. The development of a fuzzy genetic optimizatio...

  18. Fuzzy algorithm for an automatic reactor power control in a PWR

    International Nuclear Information System (INIS)

    Hah, Yung Joon; Song, In Ho; Yu, Sung Sik; Choi, Jung In; Lee, Byong Whi

    1994-01-01

    A fuzzy algorithm is presented for automatic reactor power control in a pressurized water reactor. Automatic power shape control is complicated by the use of control rods because it is highly coupled with reactivity compensation. Thus, manual shape controls are usually employed even for the limited capability for the load - follow operation including frequency control. In an attempt to achieve automatic power shape control without any design modification of the core, a fuzzy power control algorithm is proposed. For the fuzzy control, the rule base is formulated based on a multi - input multi - output system. The minimum operation rule and the center of area method are implemented for the development of the fuzzy algorithm. The fuzzy power control algorithm has been applied to the Yonggwang Nuclear Unit 3. The simulation results show that the fuzzy control can be adapted as a practical control strategy for automatic reactor power control of the pressurized water reactor during the load - follow operation

  19. 2015 Chinese Intelligent Automation Conference

    CERN Document Server

    Li, Hongbo

    2015-01-01

    Proceedings of the 2015 Chinese Intelligent Automation Conference presents selected research papers from the CIAC’15, held in Fuzhou, China. The topics include adaptive control, fuzzy control, neural network based control, knowledge based control, hybrid intelligent control, learning control, evolutionary mechanism based control, multi-sensor integration, failure diagnosis, reconfigurable control, etc. Engineers and researchers from academia, industry and the government can gain valuable insights into interdisciplinary solutions in the field of intelligent automation.

  20. Systematic methods for the design of a class of fuzzy logic controllers

    Science.gov (United States)

    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

  1. A fuzzy logic approach to control anaerobic digestion.

    Science.gov (United States)

    Domnanovich, A M; Strik, D P; Zani, L; Pfeiffer, B; Karlovits, M; Braun, R; Holubar, P

    2003-01-01

    One of the goals of the EU-Project AMONCO (Advanced Prediction, Monitoring and Controlling of Anaerobic Digestion Process Behaviour towards Biogas Usage in Fuel Cells) is to create a control tool for the anaerobic digestion process, which predicts the volumetric organic loading rate (Bv) for the next day, to obtain a high biogas quality and production. The biogas should contain a high methane concentration (over 50%) and a low concentration of components toxic for fuel cells, e.g. hydrogen sulphide, siloxanes, ammonia and mercaptanes. For producing data to test the control tool, four 20 l anaerobic Continuously Stirred Tank Reactors (CSTR) are operated. For controlling two systems were investigated: a pure fuzzy logic system and a hybrid-system which contains a fuzzy based reactor condition calculation and a hierachial neural net in a cascade of optimisation algorithms.

  2. Design and Realization of Intelligent Flow Controller

    Directory of Open Access Journals (Sweden)

    Jianxiong Ye

    2014-09-01

    Full Text Available According to accurate flow rate control requirements in large irrigation zone, a fuzzy controller with dead-band is designed on the characteristics analysis and comparison of PID and Fuzzy. The setting values of water flow for gates are determined by real-time water level detection sensors, and the realistic value of discharged water and gate opening are detected out with relative sensors, simulation manifest that the specific control strategy can adjust the gate swiftly in circumstance of huge offset, and regulate the gate slightly in time of small bias, it is realized with Siemens S315 PLC (Programmable Logical Controller and has being working steadily for 2 years, the aim of regulation is performed properly.

  3. Study and simulation of a MPPT controller based on fuzzy logic controller for photovoltaic system

    Energy Technology Data Exchange (ETDEWEB)

    Belaidi, R.; Chikouche, A.; Fathi, M.; Mohand Kaci, G.; Smara, Z. [Unite de Developpement des Equipements Solaires (Algeria); Haddouche, A. [Universite Badji Mokhtar (Algeria)], E-mail: rachidi3434@yahoo.fr

    2011-07-01

    With the depletion of fossil fuels and the increasing concerns about the environment, renewable energies have become more and more attractive. Photovoltaic systems convert solar energy into electric energy through the use of photovoltaic cells. The aim of this paper is to compare the capacity of fuzzy logic and perturb and observe controllers in optimizing the control performance of photovoltaic systems. Simulations were performed using Matlab and Simulink and were analyzed to determine the effectiveness of both controllers and compare them. Results showed that the fuzzy controller has a better dynamic performance than the perturb and observe controller in terms of response time and damping characteristics. In addition, the fuzzy controller was found to better follow the maximum power point and to provide faster convergence and lower statistical error. This study demonstrated that the fuzzy controller gives a better performance than traditional controllers in optimizing the performance of photovoltaic systems.

  4. Expected value based fuzzy programming approach to solve integrated supplier selection and inventory control problem with fuzzy demand

    Science.gov (United States)

    Sutrisno; Widowati; Sunarsih; Kartono

    2018-01-01

    In this paper, a mathematical model in quadratic programming with fuzzy parameter is proposed to determine the optimal strategy for integrated inventory control and supplier selection problem with fuzzy demand. To solve the corresponding optimization problem, we use the expected value based fuzzy programming. Numerical examples are performed to evaluate the model. From the results, the optimal amount of each product that have to be purchased from each supplier for each time period and the optimal amount of each product that have to be stored in the inventory for each time period were determined with minimum total cost and the inventory level was sufficiently closed to the reference level.

  5. Artificial intelligence in robot control systems

    Science.gov (United States)

    Korikov, A.

    2018-05-01

    This paper analyzes modern concepts of artificial intelligence and known definitions of the term "level of intelligence". In robotics artificial intelligence system is defined as a system that works intelligently and optimally. The author proposes to use optimization methods for the design of intelligent robot control systems. The article provides the formalization of problems of robotic control system design, as a class of extremum problems with constraints. Solving these problems is rather complicated due to the high dimensionality, polymodality and a priori uncertainty. Decomposition of the extremum problems according to the method, suggested by the author, allows reducing them into a sequence of simpler problems, that can be successfully solved by modern computing technology. Several possible approaches to solving such problems are considered in the article.

  6. Feedforward Tracking Control of Flat Recurrent Fuzzy Systems

    International Nuclear Information System (INIS)

    Gering, Stefan; Adamy, Jürgen

    2014-01-01

    Flatness based feedforward control has proven to be a feasible solution for the problem of tracking control, which may be applied to a broad class of nonlinear systems. If a flat output of the system is known, the control is often based on a feedforward controller generating a nominal input in combination with a linear controller stabilizing the linearized error dynamics around the trajectory. We show in this paper that the very same idea may be incorporated for tracking control of MIMO recurrent fuzzy systems. Their dynamics is given by means of linguistic differential equations but may be converted into a hybrid system representation, which then serves as the basis for controller synthesis

  7. Feedforward Tracking Control of Flat Recurrent Fuzzy Systems

    Science.gov (United States)

    Gering, Stefan; Adamy, Jürgen

    2014-12-01

    Flatness based feedforward control has proven to be a feasible solution for the problem of tracking control, which may be applied to a broad class of nonlinear systems. If a flat output of the system is known, the control is often based on a feedforward controller generating a nominal input in combination with a linear controller stabilizing the linearized error dynamics around the trajectory. We show in this paper that the very same idea may be incorporated for tracking control of MIMO recurrent fuzzy systems. Their dynamics is given by means of linguistic differential equations but may be converted into a hybrid system representation, which then serves as the basis for controller synthesis.

  8. Fuzzy Control of Tidal volume, Respiration number and Pressure value

    OpenAIRE

    Hasan Guler; Fikret Ata

    2010-01-01

    In this study, control of tidal volume, respiration number and pressure value which are arrived to patient at mechanical ventilator device which is used in intensive care units were performed with fuzzy logic controller. The aim of this system is to reduce workload of aneshesiologist. By calculating tidal volume, respiration number and pressure value, the error Pe(k) between reference pressure value (Pref) and pressure of gas given ill person (Phasta) and error change rate ;#948;Pe(k) were co...

  9. Instrumentation, Control, and Intelligent Systems

    Energy Technology Data Exchange (ETDEWEB)

    2005-09-01

    Abundant and affordable energy is required for U.S. economic stability and national security. Advanced nuclear power plants offer the best near-term potential to generate abundant, affordable, and sustainable electricity and hydrogen without appreciable generation of greenhouse gases. To that end, Idaho National Laboratory (INL) has been charged with leading the revitalization of nuclear power in the U.S. The INL vision is to become the preeminent nuclear energy laboratory with synergistic, world-class, multi-program capabilities and partnerships by 2015. The vision focuses on four essential destinations: (1) Be the preeminent internationally-recognized nuclear energy research, development, and demonstration laboratory; (2) Be a major center for national security technology development and demonstration; (3) Be a multi-program national laboratory with world-class capabilities; (4) Foster academic, industry, government, and international collaborations to produce the needed investment, programs, and expertise. Crucial to that effort is the inclusion of research in advanced instrumentation, control, and intelligent systems (ICIS) for use in current and advanced power and energy security systems to enable increased performance, reliability, security, and safety. For nuclear energy plants, ICIS will extend the lifetime of power plant systems, increase performance and power output, and ensure reliable operation within the system's safety margin; for national security applications, ICIS will enable increased protection of our nation's critical infrastructure. In general, ICIS will cost-effectively increase performance for all energy security systems.

  10. Instrumentation, Control, and Intelligent Systems

    International Nuclear Information System (INIS)

    Not Available

    2005-01-01

    Abundant and affordable energy is required for U.S. economic stability and national security. Advanced nuclear power plants offer the best near-term potential to generate abundant, affordable, and sustainable electricity and hydrogen without appreciable generation of greenhouse gases. To that end, Idaho National Laboratory (INL) has been charged with leading the revitalization of nuclear power in the U.S. The INL vision is to become the preeminent nuclear energy laboratory with synergistic, world-class, multi-program capabilities and partnerships by 2015. The vision focuses on four essential destinations: (1) Be the preeminent internationally-recognized nuclear energy research, development, and demonstration laboratory; (2) Be a major center for national security technology development and demonstration; (3) Be a multi-program national laboratory with world-class capabilities; (4) Foster academic, industry, government, and international collaborations to produce the needed investment, programs, and expertise. Crucial to that effort is the inclusion of research in advanced instrumentation, control, and intelligent systems (ICIS) for use in current and advanced power and energy security systems to enable increased performance, reliability, security, and safety. For nuclear energy plants, ICIS will extend the lifetime of power plant systems, increase performance and power output, and ensure reliable operation within the system's safety margin; for national security applications, ICIS will enable increased protection of our nation's critical infrastructure. In general, ICIS will cost-effectively increase performance for all energy security systems

  11. Modeling and control of an unstable system using probabilistic fuzzy inference system

    Directory of Open Access Journals (Sweden)

    Sozhamadevi N.

    2015-09-01

    Full Text Available A new type Fuzzy Inference System is proposed, a Probabilistic Fuzzy Inference system which model and minimizes the effects of statistical uncertainties. The blend of two different concepts, degree of truth and probability of truth in a unique framework leads to this new concept. This combination is carried out both in Fuzzy sets and Fuzzy rules, which gives rise to Probabilistic Fuzzy Sets and Probabilistic Fuzzy Rules. Introducing these probabilistic elements, a distinctive probabilistic fuzzy inference system is developed and this involves fuzzification, inference and output processing. This integrated approach accounts for all of the uncertainty like rule uncertainties and measurement uncertainties present in the systems and has led to the design which performs optimally after training. In this paper a Probabilistic Fuzzy Inference System is applied for modeling and control of a highly nonlinear, unstable system and also proved its effectiveness.

  12. The implementation of intelligent home controller

    Science.gov (United States)

    Li, Biqing; Li, Zhao

    2018-04-01

    This paper mainly talks about the working way of smart home terminal controller and the design of hardware and software. Controlling the lights and by simulating the lamp and the test of the curtain, destroy the light of lamp ON-OFF and the curtain's UP-DOWN by simulating the lamp and the test of the cuetain. Through the sensor collects the ambient information and sends to the network, such as light, temperature and humidity. Besides, it can realise the control of intelligent home control by PCS. Terminal controller of intelligent home which is based on ZiBee technology has into the intelligent home system, it provides people with convenient, safe and intelligent household experience.

  13. Vibrations control of light rail transportation vehicle via PID type fuzzy controller using parameters adaptive method

    OpenAIRE

    METİN, Muzaffer; GÜÇLÜ, Rahmi

    2014-01-01

    In this study, a conventional PID type fuzzy controller and parameter adaptive fuzzy controller are designed to control vibrations actively of a light rail transport vehicle which modeled as 6 degree-of-freedom system and compared performances of these two controllers. Rail vehicle model consists of a passenger seat and its suspension system, vehicle body, bogie, primary and secondary suspensions and wheels. The similarity between mathematical model and real system is shown by compar...

  14. Implementation Of Automatic Wiper Speed Control And Headlight Modes Control Systems Using Fuzzy Logic

    OpenAIRE

    ThetKoKo; ZawMyoTun; Hla Myo Tun

    2015-01-01

    Abstract This research paper describes the design and simulation of the automatic wiper speed and headlight modes controllers using fuzzy logic. This proposed system consists of a fuzzy logic controller to control a cars wiper speed and headlight modes. The automatic wiper system detects the rain and its intensity. And according to the rain intensity the wiper speed is automatically controlled. Headlight modes automatically changes either from low beam mode to high beam mode or form high beam...

  15. Fuzzy controller for real time supervision of nuclear power reactor

    International Nuclear Information System (INIS)

    Bala Subramanian, R.

    2012-01-01

    Generally nuclear energy provides about 60% of the whole electricity production. A modulation of the nuclear power plants must be able to respond to the demand on the network. The pressurized water nuclear reactor has to yield correctly a load set point. Fundamentally, two parameters are concerned in leading this task to a successful conclusion: the power axial-offset and the control rods position. The focus of this study is the automation of the control of the power axial-offset by adding soluble boron and by minimizing the volume flows through the water pump. It is also important to take into consideration the liquid waste volume. Water or boron is injected into the reactor primary circuit. At the present time this task is still performed manually by an operator, for all previous attempts to automate it failed. That device, sketchily described in the paper, gave rise to the development of a real-time fuzzy controller for the power axial-offset and the control rods insertion in a pressurized water reactor (PWR). The fuzzy controller, which is the main subject of the paper, expresses more naturally the human expertise, thus avoiding the previous issue of empirical tunings. It was implemented in simulation using Matlab-Simulink on a Sun workstation. Two realistic tests discussed show that the fuzzy controller runs as efficiently as an expert operator does

  16. Neural and Fuzzy Adaptive Control of Induction Motor Drives

    International Nuclear Information System (INIS)

    Bensalem, Y.; Sbita, L.; Abdelkrim, M. N.

    2008-01-01

    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

  17. Introduction to Fuzzy Set Theory

    Science.gov (United States)

    Kosko, Bart

    1990-01-01

    An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.

  18. An automatic tuning method of a fuzzy logic controller for nuclear reactors

    International Nuclear Information System (INIS)

    Ramaswamy, P.; Lee, K.Y.; Edwards, R.M.

    1993-01-01

    The design and evaluation by simulation of an automatically tuned fuzzy logic controller is presented. Typically, fuzzy logic controllers are designed based on an expert's knowledge of the process. However, this approach has its limitations in the fact that the controller is hard to optimize or tune to get the desired control action. A method to automate the tuning process using a simplified Kalman filter approach is presented for the fuzzy logic controller to track a suitable reference trajectory. Here, for purposes of illustration an optimal controller's response is used as a reference trajectory to determine automatically the rules for the fuzzy logic controller. To demonstrate the robustness of this design approach, a nonlinear six-delayed neutron group plant is controlled using a fuzzy logic controller that utilizes estimated reactor temperatures from a one-delayed neutron group observer. The fuzzy logic controller displayed good stability and performance robustness characteristics for a wide range of operation

  19. Navigation Algorithm Using Fuzzy Control Method in Mobile Robotics

    Directory of Open Access Journals (Sweden)

    Cviklovič Vladimír

    2016-03-01

    Full Text Available The issue of navigation methods is being continuously developed globally. The aim of this article is to test the fuzzy control algorithm for track finding in mobile robotics. The concept of an autonomous mobile robot EN20 has been designed to test its behaviour. The odometry navigation method was used. The benefits of fuzzy control are in the evidence of mobile robot’s behaviour. These benefits are obtained when more physical variables on the base of more input variables are controlled at the same time. In our case, there are two input variables - heading angle and distance, and two output variables - the angular velocity of the left and right wheel. The autonomous mobile robot is moving with human logic.

  20. Flocking of quad-rotor UAVs with fuzzy control.

    Science.gov (United States)

    Mao, Xiang; Zhang, Hongbin; Wang, Yanhui

    2018-03-01

    This paper investigates the flocking problem of quad-rotor UAVs. Considering the actual situations, we derived a new simplified quad-rotor UAV model which is more reasonable. Based on the model, the T-S fuzzy model of attitude dynamic equation and the corresponding T-S fuzzy feedback controller are discussed. By introducing a double-loop control construction, we adjust its attitude to realize the position control. Then a flocking algorithm is proposed to achieve the flocking of the quad-rotor UAVs. Compared with the flocking algorithm of the mass point model, we dealt with the collision problem of the quad-rotor UAVs. In order to improve the airspace utilization, a more compact configuration called quasi e-lattice is constructed to guarantee the compact flight of the quad-rotor UAVs. Finally, numerical simulations are provided to illustrate the effectiveness of the obtained theoretical results. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  1. Fuzzy Logic Supervised Teleoperation Control for Mobile Robot

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The supervised teleoperation control is presented for a mobile robot to implement the tasks by using fuzzy logic. The teleoperation control system includes joystick based user interaction mechanism, the high level instruction set and fuzzy logic behaviors integrated in a supervised autonomy teleoperation control system for indoor navigation. These behaviors include left wall following, right wall following, turn left, turn right, left obstacle avoidance, right obstacle avoidance and corridor following based on ultrasonic range finders data. The robot compares the instructive high level command from the operator and relays back a suggestive signal back to the operator in case of mismatch between environment and instructive command. This strategy relieves the operator's cognitive burden, handle unforeseen situations and uncertainties of environment autonomously. The effectiveness of the proposed method for navigation in an unstructured environment is verified by experiments conducted on a mobile robot equipped with only ultrasonic range finders for environment sensing.

  2. Design of a Tele-Control Electrical Vehicle System Using a Fuzzy Logic Control

    Directory of Open Access Journals (Sweden)

    M. Boukhnifer

    2012-11-01

    Full Text Available This paper presents a fuzzy logic design of a tele-control electrical vehicle system. We showed that the application of fuzzy logic control allows the stability of tele-vehicle system in spite of communication delays between the operator and the vehicle. A robust bilateral controller design using fuzzy logic frameworks was proposed. This approach allows a convenient means to trade off robustness and stability for a pre-specified time-delay margin. Both the performance and robustness of the proposed method were demonstrated by simulation results for a constant time delay between the operator and the electrical vehicle system.

  3. Design of a Polynomial Fuzzy Observer Controller With Sampled-Output Measurements for Nonlinear Systems Considering Unmeasurable Premise Variables

    OpenAIRE

    Liu, Chuang; Lam, H. K.

    2015-01-01

    In this paper, we propose a polynomial fuzzy observer controller for nonlinear systems, where the design is achieved through the stability analysis of polynomial-fuzzy-model-based (PFMB) observer-control system. The polynomial fuzzy observer estimates the system states using estimated premise variables. The estimated states are then employed by the polynomial fuzzy controller for the feedback control of nonlinear systems represented by the polynomial fuzzy model. The system stability of the P...

  4. Intelligent Control of UPFC for Enhancing Transient Stability on Multi-Machine Power Systems

    Directory of Open Access Journals (Sweden)

    Hassan Barati

    2010-01-01

    Full Text Available One of the benefit of FACTS devices is increase of stability in power systems with control active and reactive power at during the fault in power system. Although, the power system stabilizers (PSSs have been one of the most common controls used to damp out oscillations, this device may not produce enough damping especially to inter-area mode and therefore, there is an increasing interest in using FACTS devices to aid in damping of these oscillations. In This paper, UPFC is used for damping oscillations and to enhance the transient stability performance of power systems. The controller parameters are designed using an efficient version of the Takagi-Sugeno fuzzy control scheme. The function based Takagi-Sugeno-Kang (TSK fuzzy controller uses. For optimization parameters of fuzzy PI controller, the GA, PSO and HGAPSO algorithms are used. The computer simulation results, the effect of UPFC with conventional PI controller, fuzzy PI controller and intelligent controllers (GA, PSO and HGAPSO for damping the local-mode and inter-area mode of under large and small disturbances in the four-machine two-area power system evaluated and compared.

  5. Design of a self-adaptive fuzzy PID controller for piezoelectric ceramics micro-displacement system

    Science.gov (United States)

    Zhang, Shuang; Zhong, Yuning; Xu, Zhongbao

    2008-12-01

    In order to improve control precision of the piezoelectric ceramics (PZT) micro-displacement system, a self-adaptive fuzzy Proportional Integration Differential (PID) controller is designed based on the traditional digital PID controller combining with fuzzy control. The arithmetic gives a fuzzy control rule table with the fuzzy control rule and fuzzy reasoning, through this table, the PID parameters can be adjusted online in real time control. Furthermore, the automatic selective control is achieved according to the change of the error. The controller combines the good dynamic capability of the fuzzy control and the high stable precision of the PID control, adopts the method of using fuzzy control and PID control in different segments of time. In the initial and middle stage of the transition process of system, that is, when the error is larger than the value, fuzzy control is used to adjust control variable. It makes full use of the fast response of the fuzzy control. And when the error is smaller than the value, the system is about to be in the steady state, PID control is adopted to eliminate static error. The problems of PZT existing in the field of precise positioning are overcome. The results of the experiments prove that the project is correct and practicable.

  6. Maximizing Function through Intelligent Robot Actuator Control

    Data.gov (United States)

    National Aeronautics and Space Administration — Maximizing Function through Intelligent Robot Actuator Control Successful missions to Mars and beyond will only be possible with the support of high-performance...

  7. Intelligent Electronic Speed Controller, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — This project intends to design and develop an Intelligent Electronic Speed Controller (IESC) for use on Unmanned Aerial Vehicles (UAVs). The IESC will advance the...

  8. Intelligent control systems: an introduction with examples

    National Research Council Canada - National Science Library

    Hangos, K. M; Lakner, Rozália; Gerzson, Miklós

    2001-01-01

    ... The structure of the knowledge base 1.2 The reasoning algorithm 1.3 Conflict resolution 31 31 32 33 36 viiviii INTELLIGENT CONTROL SYSTEMS 2. 3. 4. 5. 1.4 Explanation of the reasoning Forward r...

  9. Research on intelligent algorithm of electro - hydraulic servo control system

    Science.gov (United States)

    Wang, Yannian; Zhao, Yuhui; Liu, Chengtao

    2017-09-01

    In order to adapt the nonlinear characteristics of the electro-hydraulic servo control system and the influence of complex interference in the industrial field, using a fuzzy PID switching learning algorithm is proposed and a fuzzy PID switching learning controller is designed and applied in the electro-hydraulic servo controller. The designed controller not only combines the advantages of the fuzzy control and PID control, but also introduces the learning algorithm into the switching function, which makes the learning of the three parameters in the switching function can avoid the instability of the system during the switching between the fuzzy control and PID control algorithms. It also makes the switch between these two control algorithm more smoother than that of the conventional fuzzy PID.

  10. Artificial intelligence-based computer modeling tools for controlling slag foaming in electric arc furnaces

    Science.gov (United States)

    Wilson, Eric Lee

    Due to increased competition in a world economy, steel companies are currently interested in developing techniques that will allow for the improvement of the steelmaking process, either by increasing output efficiency or by improving the quality of their product, or both. Slag foaming is one practice that has been shown to contribute to both these goals. However, slag foaming is highly dynamic and difficult to model or control. This dissertation describes an effort to use artificial intelligence-based tools (genetic algorithms, fuzzy logic, and neural networks) to both model and control the slag foaming process. Specifically, a neural network is trained and tested on slag foaming data provided by a steel plant. This neural network model is then controlled by a fuzzy logic controller, which in turn is optimized by a genetic algorithm. This tuned controller is then installed at a steel plant and given control be a more efficient slag foaming controller than what was previously used by the steel plant.

  11. Design of a fuzzy logic based controller for neutron power regulation

    International Nuclear Information System (INIS)

    Velez D, D.

    2000-01-01

    This work presents a fuzzy logic controller design for neutron power control, from its source to its full power level, applied to a nuclear reactor model. First, we present the basic definitions on fuzzy sets as generalized definitions of the crisp (non fuzzy) set theory. Likewise, we define the basic operations on fuzzy sets (complement, union, and intersection), and the operations on fuzzy relations such as projection and cylindrical extension operations. Furthermore, some concepts of the fuzzy control theory, such as the main modules of the typical fuzzy controller structure and its internal variables, are defined. After the knowledge base is obtained by simulation of the reactor behavior, where the controlled system is modeled by a simple nonlinear reactor model, this model is used to infer a set of fuzzy rules for the reactor response to different insertions of reactivity. The reduction of the response time, using fuzzy rule based controllers on this reactor, is possible by adjusting the output membership functions, by selecting fuzzy rule sets, or by increasing the number of crisp inputs to the fuzzy controller. System characteristics, such as number of rules, response times, and safety parameter values, were considered in the evaluation of each controller merits. Different fuzzy controllers are designed to attain the desired power level, to maintain a constant level for long periods of time, and to keep the reactor away from a shutdown condition. The basic differences among the controllers are the number of crisp inputs and the novel implementation of a crisp power level-based selection of different sets of output membership functions. Simulation results highlight, mainly: (1) A decrease of the response variations at low power level, and (2) a decrease in the time required to attain the desired neutron power. Finally, we present a comparative study of different fuzzy control algorithms applied to a nuclear model. (Author)

  12. Intelligent controller of a flexible hybrid robot machine for ITER assembly and maintenance

    International Nuclear Information System (INIS)

    Al-saedi, Mazin I.; Wu, Huapeng; Handroos, Heikki

    2014-01-01

    Highlights: • Studying flexible multibody dynamic of hybrid parallel robot. • Investigating fuzzy-PD controller to control a hybrid flexible hydraulically driven robot. • Investigating ANFIS-PD controller to control a hybrid flexible robot. Compare to traditional PID this method gives better performance. • Using the equilibrium of reaction forces between the parallel and serial parts of hybrid robot to control the serial part hydraulically driven. - Abstract: The assembly and maintenance of International Thermonuclear Experimental Reactor (ITER) vacuum vessel (VV) is highly challenging since the tasks performed by the robot involve welding, material handling, and machine cutting from inside the VV. To fulfill the tasks in ITER application, this paper presents a hybrid redundant manipulator with four DOFs provided by serial kinematic axes and six DOFs by parallel mechanism. Thus, in machining, to achieve greater end-effector trajectory tracking accuracy for surface quality, a robust control of the actuators for the flexible link has to be deduced. In this paper, the intelligent control of a hydraulically driven parallel robot part based on the dynamic model and two control schemes have been investigated: (1) fuzzy-PID self tuning controller composed of the conventional PID control and with fuzzy logic; (2) adaptive neuro-fuzzy inference system-PID (ANFIS-PID) self tuning of the gains of the PID controller, which are implemented independently to control each hydraulic cylinder of the parallel robot based on rod position predictions. The obtained results of the fuzzy-PID and ANFIS-PID self tuning controller can reduce more tracking errors than the conventional PID controller. Subsequently, the serial component of the hybrid robot can be analyzed using the equilibrium of reaction forces at the universal joint connections of the hexa-element. To achieve precise positional control of the end effector for maximum precision machining, the hydraulic cylinder should

  13. Intelligent controller of a flexible hybrid robot machine for ITER assembly and maintenance

    Energy Technology Data Exchange (ETDEWEB)

    Al-saedi, Mazin I., E-mail: mazin.al-saedi@lut.fi; Wu, Huapeng; Handroos, Heikki

    2014-10-15

    Highlights: • Studying flexible multibody dynamic of hybrid parallel robot. • Investigating fuzzy-PD controller to control a hybrid flexible hydraulically driven robot. • Investigating ANFIS-PD controller to control a hybrid flexible robot. Compare to traditional PID this method gives better performance. • Using the equilibrium of reaction forces between the parallel and serial parts of hybrid robot to control the serial part hydraulically driven. - Abstract: The assembly and maintenance of International Thermonuclear Experimental Reactor (ITER) vacuum vessel (VV) is highly challenging since the tasks performed by the robot involve welding, material handling, and machine cutting from inside the VV. To fulfill the tasks in ITER application, this paper presents a hybrid redundant manipulator with four DOFs provided by serial kinematic axes and six DOFs by parallel mechanism. Thus, in machining, to achieve greater end-effector trajectory tracking accuracy for surface quality, a robust control of the actuators for the flexible link has to be deduced. In this paper, the intelligent control of a hydraulically driven parallel robot part based on the dynamic model and two control schemes have been investigated: (1) fuzzy-PID self tuning controller composed of the conventional PID control and with fuzzy logic; (2) adaptive neuro-fuzzy inference system-PID (ANFIS-PID) self tuning of the gains of the PID controller, which are implemented independently to control each hydraulic cylinder of the parallel robot based on rod position predictions. The obtained results of the fuzzy-PID and ANFIS-PID self tuning controller can reduce more tracking errors than the conventional PID controller. Subsequently, the serial component of the hybrid robot can be analyzed using the equilibrium of reaction forces at the universal joint connections of the hexa-element. To achieve precise positional control of the end effector for maximum precision machining, the hydraulic cylinder should

  14. A Fuzzy Control System for Inductive Video Games

    OpenAIRE

    Lara-Alvarez, Carlos; Mitre-Hernandez, Hugo; Flores, Juan; Fuentes, Maria

    2017-01-01

    It has been shown that the emotional state of students has an important relationship with learning; for instance, engaged concentration is positively correlated with learning. This paper proposes the Inductive Control (IC) for educational games. Unlike conventional approaches that only modify the game level, the proposed technique also induces emotions in the player for supporting the learning process. This paper explores a fuzzy system that analyzes the players' performance and their emotion...

  15. SPEED CONTROL OF DC MOTOR ON LOAD USING FUZZY LOGIC ...

    African Journals Online (AJOL)

    This paper presents the development of a fuzzy logic controller for the driver DC motor in the lube oil system of the H25 Hitachi gas turbine generator. The turbine generator is required to run at an operating pressure of 1.5bar with the low and the high pressure trip points being 0.78 bar and 1.9 bar respectively. However, the ...

  16. Fuzzy logic controller for weaning neonates from mechanical ventilation.

    Science.gov (United States)

    Hatzakis, G E; Davis, G M

    2002-01-01

    Weaning from mechanical ventilation is the gradual detachment from any ventilatory support till normal spontaneous breathing can be fully resumed. To date, we have developed a fuzzy logic controller for weaning COPD adults using pressure support ventilation (PS). However, adults and newborns differ in the pathophysiology of lung disease. We therefore used our fuzzy logic-based weaning platform to develop modularized components for weaning newborns with lung disease. Our controller uses the heart rate (HR), respiratory rate (RR), tidal volume (VT) and oxygen saturation (SaO2) and their trends deltaHR/deltat, deltaVT/deltat and deltaSaO2/deltat to evaluate, respectively, the Current and Trend weaning status of the newborn. Through appropriate fuzzification of these vital signs, Current and Trend weaning status can quantitatively determine the increase/decrease in the synchronized intermittent mandatory ventilation (SIMV) setting. The post-operative weaning courses of 10 newborns, 82+/-162 days old, were assessed at 2-hour intervals for 68+/-39 days. The SIMV levels, proposed by our algorithm, were matched to those levels actually applied. For 60% of the time both values coincided. For the remaining 40%, our algorithm suggested lower SIMV support than what was applied. The Area Under the Curve for integrated ventilatory support over time was 1203+/-846 for standard ventilatory strategies and 1152+/-802 for fuzzy controller. This suggests that the algorithm, approximates the actual weaning progression, and may advocate a more aggressive strategy. Moreover, the core of the fuzzy controller facilitates adaptation for body size and diversified disease patterns and sets the premises as an infant-weaning tool.

  17. Performance analysis of PM synchronous motor using fuzzy logic and self tuning fuzzy PI speed controls

    International Nuclear Information System (INIS)

    Karakaya, A.; Karakas, E.

    2008-01-01

    Permanent Magnet Synchronous Motors have nonlinear characteristics whose dynamics changes with time. In spite of this structure the permanent magnet synchronous motor has answered engineering problems in industry such as motion control which need high torque values. This paper obtains a nonlinear mathematical model for Permanent Magnet Synchronous Motor and realizes stimulation of the obtained model in the Matlab/Simulink program. Motor parameters are determined by an experimental set-up and they are used in the motor model. Speed control of motor model is made with Fuzzy Logic and Self Tuning logic PI controllers. Using the speed graphs obtained, rise time, overshoot, steady-state error and settling time are analyzed and controller performances are compared. (author)

  18. Stability Analysis of Positive Polynomial Fuzzy-Model-Based Control Systems with Time Delay under Imperfect Premise Matching

    OpenAIRE

    Li, Xiaomiao; Lam, Hak Keung; Song, Ge; Liu, Fucai

    2017-01-01

    This paper deals with the stability and positivity analysis of polynomial-fuzzy-model-based ({PFMB}) control systems with time delay, which is formed by a polynomial fuzzy model and a polynomial fuzzy controller connected in a closed loop, under imperfect premise matching. To improve the design and realization flexibility, the polynomial fuzzy model and the polynomial fuzzy controller are allowed to have their own set of premise membership functions. A sum-of-squares (SOS)-based stability ana...

  19. Horizontal and Vertical Rule Bases Method in Fuzzy Controllers

    Directory of Open Access Journals (Sweden)

    Sadegh Aminifar

    2013-01-01

    Full Text Available Concept of horizontal and vertical rule bases is introduced. Using this method enables the designers to look for main behaviors of system and describes them with greater approximations. The rules which describe the system in first stage are called horizontal rule base. In the second stage, the designer modulates the obtained surface by describing needed changes on first surface for handling real behaviors of system. The rules used in the second stage are called vertical rule base. Horizontal and vertical rule bases method has a great roll in easing of extracting the optimum control surface by using too lesser rules than traditional fuzzy systems. This research involves with control of a system with high nonlinearity and in difficulty to model it with classical methods. As a case study for testing proposed method in real condition, the designed controller is applied to steaming room with uncertain data and variable parameters. A comparison between PID and traditional fuzzy counterpart and our proposed system shows that our proposed system outperforms PID and traditional fuzzy systems in point of view of number of valve switching and better surface following. The evaluations have done both with model simulation and DSP implementation.

  20. Intelligent Traffic Light Based on PLC Control

    Science.gov (United States)

    Mei, Lin; Zhang, Lijian; Wang, Lingling

    2017-11-01

    The traditional traffic light system with a fixed control mode and single control function is contradicted with the current traffic section. The traditional one has been unable to meet the functional requirements of the existing flexible traffic control system. This paper research and develop an intelligent traffic light called PLC control system. It uses PLC as control core, using a sensor module for receiving real-time information of vehicles, traffic control mode for information to select the traffic lights. Of which control mode is flexible and changeable, and it also set the countdown reminder to improve the effectiveness of traffic lights, which can realize the goal of intelligent traffic diversion, intelligent traffic diversion.

  1. Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network

    Directory of Open Access Journals (Sweden)

    Seng-Chi Chen

    2014-01-01

    Full Text Available Studies on active magnetic bearing (AMB systems are increasing in popularity and practical applications. Magnetic bearings cause less noise, friction, and vibration than the conventional mechanical bearings; however, the control of AMB systems requires further investigation. The magnetic force has a highly nonlinear relation to the control current and the air gap. This paper proposes an intelligent control method for positioning an AMB system that uses a neural fuzzy controller (NFC. The mathematical model of an AMB system comprises identification followed by collection of information from this system. A fuzzy logic controller (FLC, the parameters of which are adjusted using a radial basis function neural network (RBFNN, is applied to the unbalanced vibration in an AMB system. The AMB system exhibited a satisfactory control performance, with low overshoot, and produced improved transient and steady-state responses under various operating conditions. The NFC has been verified on a prototype AMB system. The proposed controller can be feasibly applied to AMB systems exposed to various external disturbances; demonstrating the effectiveness of the NFC with self-learning and self-improving capacities is proven.

  2. Car-Like Mobile Robot Oriented Positioning by Fuzzy Controllers

    Directory of Open Access Journals (Sweden)

    Noureddine Ouadah

    2008-11-01

    Full Text Available In this paper, fuzzy logic controllers (FLC are used to implement an efficient and accurate positioning of an autonomous car-like mobile robot, respecting final orientation. To accomplish this task, called "Oriented Positioning", two FLC have been developed: robot positioning controller (RPC and robot following controller (RFC. Computer simulation results illustrate the effectiveness of the proposed technique. Finally, real-time experiments have been made on an autonomous car-like mobile robot called "Robucar", developed to perform people transportation. Obtained results from experiments demonstrate the effectiveness of the proposed control strategy.

  3. Car-Like Mobile Robot Oriented Positioning by Fuzzy Controllers

    Directory of Open Access Journals (Sweden)

    Noureddine Ouadah

    2008-09-01

    Full Text Available In this paper, fuzzy logic controllers (FLC are used to implement an efficient and accurate positioning of an autonomous car-like mobile robot, respecting final orientation. To accomplish this task, called “Oriented Positioning”, two FLC have been developed: robot positioning controller (RPC and robot following controller (RFC. Computer simulation results illustrate the effectiveness of the proposed technique. Finally, real-time experiments have been made on an autonomous car-like mobile robot called “Robucar”, developed to perform people transportation. Obtained results from experiments demonstrate the effectiveness of the proposed control strategy.

  4. Buck supplies output voltage ripple reduction using fuzzy control

    Directory of Open Access Journals (Sweden)

    Nicu BIZON

    2007-12-01

    Full Text Available Using the PWM control for switching power supplies the peaks EMI noise appear at the switching frequency and its harmonics. Using randomize or chaotic PWM control techniques in these systems the power spectrum is spread out in all frequencies band spectral emissions, but with a bigger ripple in the output voltage. The proposed nonlinear feedback control method, which induces chaos, is based by fuzzy rules that minimize the output voltage ripple. The feasibility and effectiveness of this relative simple method is shown by simulation. A comparison with the previous control method is included, too.

  5. An intelligent control strategy based on ANFIS techniques in order to improve the performance of a low-cost unmanned aerial vehicle vision system

    OpenAIRE

    Marichal, G. N.; Hernández, A.; Olivares Méndez, Miguel Ángel; Acosta, L.; Campoy Cervera, Pascual

    2010-01-01

    In this paper, an intelligent control approach based on Neuro-Fuzzy systems is presented. A model of a low-cost vision platform for an unmanned aerial system is taken in the study. A simulation platform including this low-cost vision system and the influence of the helicopter vibrations over this system is shown. The intelligent control approach has been inserted in this simulation platform. Several trials taking these Neuro-Fuzzy systems as a fundamental part of the control strategy have bee...

  6. Fuzzy Logic Based Behavior Fusion for Navigation of an Intelligent Mobile Robot

    Institute of Scientific and Technical Information of China (English)

    李伟; 陈祖舜; 等

    1996-01-01

    This paper presents a new method for behavior fusion control of a mobile robot in uncertain environments.Using behavior fusion by fuzzy logic,a mobile robot is able to directly execute its motion according to range information about environments,acquired by ultrasonic sensors,without the need for trajectory planning.Based on low-level behavior control,an efficient strategy for integrating high-level global planning for robot motion can be formulated,since,in most applications,some information on environments is prior knowledge.A global planner,therefore,only to generate some subgoal positions rather than exact geometric paths.Because such subgoals can be easily removed from or added into the plannes,this strategy reduces computational time for global planning and is flexible for replanning in dynamic environments.Simulation results demonstrate that the proposed strategy can be applied to robot motion in complex and dynamic environments.

  7. CLOSED LOOP CONTROL OF EMBEDDED Z-SOURCE INVERTER WITH FUZZY CONTROLLER FOR SOLAR PV APPLICATIONS

    OpenAIRE

    Midde Mahesh*, K. Leleedhar Rao

    2017-01-01

    This paper proposes the use of Embedded Z –source inverter system with fuzzy controller for Solar Photo Voltaic (PV) applications with adjustable speed drives. Closed loop operation FUZZY control strategies of EZSI system are proposed. EZSI produces the same voltage gain as Z-source inverter (ZSI) but due to the DC sources embedded within the X- shaped impedance network, it has the added advantage of inherent source filtering capability and also reduced capacitor sizing. This can be achiev...

  8. DESCRIBING FUNCTION METHOD FOR PI-FUZZY CONTROLLED SYSTEMS STABILITY ANALYSIS

    Directory of Open Access Journals (Sweden)

    Stefan PREITL

    2004-12-01

    Full Text Available The paper proposes a global stability analysis method dedicated to fuzzy control systems containing Mamdani PI-fuzzy controllers with output integration to control SISO linear / linearized plants. The method is expressed in terms of relatively simple steps, and it is based on: the generalization of the describing function method for the considered fuzzy control systems to the MIMO case, the approximation of the describing functions by applying the least squares method. The method is applied to the stability analysis of a class of PI-fuzzy controlled servo-systems, and validated by considering a case study.

  9. Fuzzy model-based servo and model following control for nonlinear systems.

    Science.gov (United States)

    Ohtake, Hiroshi; Tanaka, Kazuo; Wang, Hua O

    2009-12-01

    This correspondence presents servo and nonlinear model following controls for a class of nonlinear systems using the Takagi-Sugeno fuzzy model-based control approach. First, the construction method of the augmented fuzzy system for continuous-time nonlinear systems is proposed by differentiating the original nonlinear system. Second, the dynamic fuzzy servo controller and the dynamic fuzzy model following controller, which can make outputs of the nonlinear system converge to target points and to outputs of the reference system, respectively, are introduced. Finally, the servo and model following controller design conditions are given in terms of linear matrix inequalities. Design examples illustrate the utility of this approach.

  10. State-feedback control of fuzzy discrete-event systems.

    Science.gov (United States)

    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.

  11. Quasi-adaptive fuzzy heating control of solar buildings

    Energy Technology Data Exchange (ETDEWEB)

    Gouda, M.M. [Faculty of Industrial Education, Cairo (Egypt); Danaher, S. [University of Northumbria, Newcastle upon Tyne, (United Kingdom). School of Engineering; Underwood, C.P. [University of Northumbria, Newcastle upon Tyne (United Kingdom). School of Built Environment and Sustainable Cities Research Institute

    2006-12-15

    Significant progress has been made on maximising passive solar heat gains to building spaces in winter. Control of the space heating in these applications is complicated due to the lagging influence of the useful solar heat gain coupled with the wide range of construction materials and heating system choices. Additionally, and in common with most building control applications, there is a need to develop control solutions that permit simple and transparent set-up and commissioning procedures. This paper addresses the development and testing of a quasi-adaptive fuzzy logic control method that addresses these issues. The controller is developed in two steps. A feed-forward neural network is used to predict the internal air temperature, in which a singular value decomposition (SVD) algorithm is used to remove the highly correlated data from the inputs of the neural network to reduce the network structure. The fuzzy controller is then designed to have two inputs: the first input being the error between the set-point temperature and the internal air temperature and the second the predicted future internal air temperature. The controller was implemented in real-time using a test cell with controlled ventilation and a modulating electric heating system. Results, compared with validated simulations of conventionally controlled heating, confirm that the proposed controller achieves superior tracking and reduced overheating when compared with the conventional method of control. (author)

  12. Control-technical optimization of topped denitrification through fuzzy control; Regelungstechnische Optimierung der vorgeschalteten Denitrifikation durch Anwendung von Fuzzy-Control

    Energy Technology Data Exchange (ETDEWEB)

    Meyer, U. [Hydroplan Ingenieurgesellschaft, Worms (Germany); Poepel, H.J. [Technische Univ. Darmstadt (Germany). Inst. WAR - Wasserversorgung, Abwassertechnik, Abfalltechnik, Umwelt- und Raumplanung

    1999-07-01

    The present paper describes complex fuzzy systems for controlling nitrogen elimination at plants with topped denitrification. For the design and testing of the fuzzy systems, dynamic simulation calculations and experimental tests were carried out in a semi-technical pilot plant. The controlling fuzzy systems indicate the supposed oxygen values for individual tank areas and the most appropriate partitioning of the activated sludge tank (anoxic/aerobic zone) as a function of the input quantities used. It is established that, with the more flexible control behaviour, a more stable nitrogen elimination and, at the same time, a cut in the amount of air transferred to the system can be attained in comparison with a conventional control. (orig.) [German] Im Rahmen dieses Beitrags werden komplexe Fuzzy-Systeme zur Regelung der Stickstoffelimination in Anlagen mit vorgeschalteter Denitrifikation vorgestellt. Zum Entwurf und zur Erprobung der Fuzzy-Systeme wurden dynamische Simulationsrechnungen und experimentelle Untersuchungen an einer halbtechnischen Versuchsanlage durchgefuehrt. Die uebergeordneten Fuzzy-Systeme geben in Abhaengigkeit der verwendeten Eingangsgroessen Sauerstoffsollwerte fuer die einzelnen Beckenbereiche und die jeweils guenstigste Aufteilung des Belebungsbeckens (anoxische/aerobe Zone) vor. Die Ergebnisse zeigen, dass durch das flexible Reglerverhalten eine stabilere Stickstoffelimination und gleichzeitig eine Einsparung an eingetragener Luftmenge im Vergleich zu einem konventionellen Regelsystem erreicht werden kann. (orig.)

  13. Real Time Implementation of a DC Motor Speed Control by Fuzzy Logic Controller and PI Controller Using FPGA

    Directory of Open Access Journals (Sweden)

    G. Sakthivel

    2010-10-01

    Full Text Available Fuzzy logic control has met with growing interest in many motor control applications due to its non-linearity, handling features and independence of plant modelling. The hardware implementation of fuzzy logic controller (FLC on FPGA is very important because of the increasing number of fuzzy applications requiring highly parallel and high speed fuzzy processing. Implementation of a fuzzy logic controller and conventional PI controller on an FPGA using VHDL for DC motor speed control is presented in this paper. The proposed scheme is to improve tracking performance of D.C. motor as compared to the conventional (PI control strategy .This paper describes the hardware implementation of two inputs (error and change in error, one output fuzzy logic controller based on PI controller and conventional PI controller using VHDL. Real time implementation FLC and conventional PI controller is made on Spartan-3A DSP FPGA (XC3SD1800A FPGA for the speed control of DC motor. It is observed that fuzzy logic based controllers give better responses than the conventional PI controller for the speed control of dc motor.

  14. H∞ Control of Polynomial Fuzzy Systems: A Sum of Squares Approach

    OpenAIRE

    Bomo W. Sanjaya; Bambang Riyanto Trilaksono; Arief Syaichu-Rohman

    2014-01-01

    This paper proposes the control design ofa nonlinear polynomial fuzzy system with H∞ performance objective using a sum of squares (SOS) approach. Fuzzy model and controller are represented by a polynomial fuzzy model and controller. The design condition is obtained by using polynomial Lyapunov functions that not only guarantee stability but also satisfy the H∞ performance objective. The design condition is represented in terms of an SOS that can be numerically solved via the SOSTOOLS. A simul...

  15. Fuzzy stochastic damage mechanics (FSDM based on fuzzy auto-adaptive control theory

    Directory of Open Access Journals (Sweden)

    Ya-jun Wang

    2012-06-01

    Full Text Available In order to fully interpret and describe damage mechanics, the origin and development of fuzzy stochastic damage mechanics were introduced based on the analysis of the harmony of damage, probability, and fuzzy membership in the interval of [0,1]. In a complete normed linear space, it was proven that a generalized damage field can be simulated through β probability distribution. Three kinds of fuzzy behaviors of damage variables were formulated and explained through analysis of the generalized uncertainty of damage variables and the establishment of a fuzzy functional expression. Corresponding fuzzy mapping distributions, namely, the half-depressed distribution, swing distribution, and combined swing distribution, which can simulate varying fuzzy evolution in diverse stochastic damage situations, were set up. Furthermore, through demonstration of the generalized probabilistic characteristics of damage variables, the cumulative distribution function and probability density function of fuzzy stochastic damage variables, which show β probability distribution, were modified according to the expansion principle. The three-dimensional fuzzy stochastic damage mechanical behaviors of the Longtan rolled-concrete dam were examined with the self-developed fuzzy stochastic damage finite element program. The statistical correlation and non-normality of random field parameters were considered comprehensively in the fuzzy stochastic damage model described in this paper. The results show that an initial damage field based on the comprehensive statistical evaluation helps to avoid many difficulties in the establishment of experiments and numerical algorithms for damage mechanics analysis.

  16. Control of a Quadrotor Using a Smart Self-Tuning Fuzzy PID Controller

    Directory of Open Access Journals (Sweden)

    Deepak Gautam

    2013-11-01

    Full Text Available This paper deals with the modelling, simulation-based controller design and path planning of a four rotor helicopter known as a quadrotor. All the drags, aerodynamic, coriolis and gyroscopic effect are neglected. A Newton-Euler formulation is used to derive the mathematical model. A smart self-tuning fuzzy PID controller based on an EKF algorithm is proposed for the attitude and position control of the quadrotor. The PID gains are tuned using a self-tuning fuzzy algorithm. The self-tuning of fuzzy parameters is achieved based on an EKF algorithm. A smart selection technique and exclusive tuning of active fuzzy parameters is proposed to reduce the computational time. Dijkstra's algorithm is used for path planning in a closed and known environment filled with obstacles and/or boundaries. The Dijkstra algorithm helps avoid obstacle and find the shortest route from a given initial position to the final position.

  17. Optimized Fuzzy-Cuckoo Controller for Active Power Control of Battery Energy Storage System, Photovoltaic, Fuel Cell and Wind Turbine in an Isolated Micro-Grid

    Directory of Open Access Journals (Sweden)

    Mohsen Einan

    2017-08-01

    Full Text Available This paper presents a new control strategy for isolated micro-grids including wind turbines (WT, fuel cells (FC, photo-voltaic (PV and battery energy storage systems (BESS. FC have been used in parallel with BESSs in order to increase their lifetime and efficiency. The changes in some parameters such as wind speed, sunlight, and consumption, lead to improper performance of droop. To overcome this challenge, a new intelligent method using a combination of fuzzy controller and cuckoo optimization algorithm (COA techniques for active power controllers in isolated networks is proposed. In this paper, COA is compared with genetic algorithm (GA and particles swarm optimization algorithm (PSO. In order to show efficiency of the proposed controller, this optimal controller has been compared with droop, optimized droop, and conventional fuzzy methods, the dynamic analysis of the island is implemented to assess the behavior of isolated generations accurately and simulation results are reported.

  18. Stability Analysis of a Type of Takagi-Sugeno PI Fuzzy Control Systems Using Circle Criterion

    Directory of Open Access Journals (Sweden)

    Kairui Cao

    2011-04-01

    Full Text Available A type of Takagi-Sugeno (T-S Proportional-Integral (PI fuzzy controllers is studied. The T-S PI fuzzy controller is formed by a T-S Proportional-Derivative (PD fuzzy controller connected with an integrator. In this particular structure, the T-S PD fuzzy controller is a weighted sum of some linear PD sub-controllers. The mathematical properties of our T-S PI fuzzy controller are also investigated. Based on these properties, the global asymptotic stability of the fuzzy control systems, in which the T-S PI fuzzy controllers are employed, are analyzed by using the well-known circle criterion. A sufficient condition with an elegant graphical interpretation in the frequency domain is further derived to guarantee the global asymptotic stability of the above fuzzy control systems. Finally, two numerical examples are provided to demonstrate how to deploy this method in analyzing the T-S PI fuzzy control systems in the frequency domain with the aid of some simple graphs.

  19. Fuzzy Approximate Model for Distributed Thermal Solar Collectors Control

    KAUST Repository

    Elmetennani, Shahrazed

    2014-07-01

    This paper deals with the problem of controlling concentrated solar collectors where the objective consists of making the outlet temperature of the collector tracking a desired reference. The performance of the novel approximate model based on fuzzy theory, which has been introduced by the authors in [1], is evaluated comparing to other methods in the literature. The proposed approximation is a low order state representation derived from the physical distributed model. It reproduces the temperature transfer dynamics through the collectors accurately and allows the simplification of the control design. Simulation results show interesting performance of the proposed controller.

  20. Fuzzy Coordinated PI Controller: Application to the Real-Time Pressure Control Process

    Directory of Open Access Journals (Sweden)

    N. Kanagaraj

    2008-01-01

    Full Text Available This paper presents the real-time implementation of a fuzzy coordinated classical PI control scheme for controlling the pressure in a pilot pressure tank system. The fuzzy system has been designed to track the variation parameters in a feedback loop and tune the classical controller to achieve a better control action for load disturbances and set point changes. The error and process inputs are chosen as the inputs of fuzzy system to tune the conventional PI controller according to the process condition. This online conventional controller tuning technique will reduce the human involvement in controller tuning and increase the operating range of the conventional controller. The proposed control algorithm is experimentally implemented for the real-time pressure control of a pilot air tank system and validated using a high-speed 32-bit ARM7 embedded microcontroller board (ATMEL AT91M55800A. To demonstrate the performance of the fuzzy coordinated PI control scheme, results are compared with a classical PI and PI-type fuzzy control method. It is observed that the proposed controller structure is able to quickly track the parameter variation and perform better in load disturbances and also for set point changes.

  1. F-15 IFCS: Intelligent Flight Control System

    Science.gov (United States)

    Bosworth, John

    2007-01-01

    This viewgraph presentation describes the F-15 Intelligent Flight Control System (IFCS). The goals of this project include: 1) Demonstrate revolutionary control approaches that can efficiently optimize aircraft performance in both normal and failure conditions; and 2) Demonstrate advance neural network-based flight control technology for new aerospace systems designs.

  2. Fuzzy Sarsa with Focussed Replacing Eligibility Traces for Robust and Accurate Control

    Science.gov (United States)

    Kamdem, Sylvain; Ohki, Hidehiro; Sueda, Naomichi

    Several methods of reinforcement learning in continuous state and action spaces that utilize fuzzy logic have been proposed in recent years. This paper introduces Fuzzy Sarsa(λ), an on-policy algorithm for fuzzy learning that relies on a novel way of computing replacing eligibility traces to accelerate the policy evaluation. It is tested against several temporal difference learning algorithms: Sarsa(λ), Fuzzy Q(λ), an earlier fuzzy version of Sarsa and an actor-critic algorithm. We perform detailed evaluations on two benchmark problems : a maze domain and the cart pole. Results of various tests highlight the strengths and weaknesses of these algorithms and show that Fuzzy Sarsa(λ) outperforms all other algorithms tested for a larger granularity of design and under noisy conditions. It is a highly competitive method of learning in realistic noisy domains where a denser fuzzy design over the state space is needed for a more precise control.

  3. Stabilization of nonlinear systems using sampled-data output-feedback fuzzy controller based on polynomial-fuzzy-model-based control approach.

    Science.gov (United States)

    Lam, H K

    2012-02-01

    This paper investigates the stability of sampled-data output-feedback (SDOF) polynomial-fuzzy-model-based control systems. Representing the nonlinear plant using a polynomial fuzzy model, an SDOF fuzzy controller is proposed to perform the control process using the system output information. As only the system output is available for feedback compensation, it is more challenging for the controller design and system analysis compared to the full-state-feedback case. Furthermore, because of the sampling activity, the control signal is kept constant by the zero-order hold during the sampling period, which complicates the system dynamics and makes the stability analysis more difficult. In this paper, two cases of SDOF fuzzy controllers, which either share the same number of fuzzy rules or not, are considered. The system stability is investigated based on the Lyapunov stability theory using the sum-of-squares (SOS) approach. SOS-based stability conditions are obtained to guarantee the system stability and synthesize the SDOF fuzzy controller. Simulation examples are given to demonstrate the merits of the proposed SDOF fuzzy control approach.

  4. H∞ Control of Polynomial Fuzzy Systems: A Sum of Squares Approach

    Directory of Open Access Journals (Sweden)

    Bomo W. Sanjaya

    2014-07-01

    Full Text Available This paper proposes the control design ofa nonlinear polynomial fuzzy system with H∞ performance objective using a sum of squares (SOS approach. Fuzzy model and controller are represented by a polynomial fuzzy model and controller. The design condition is obtained by using polynomial Lyapunov functions that not only guarantee stability but also satisfy the H∞ performance objective. The design condition is represented in terms of an SOS that can be numerically solved via the SOSTOOLS. A simulation study is presented to show the effectiveness of the SOS-based H∞ control designfor nonlinear polynomial fuzzy systems.

  5. Evaluation of a Multi-Variable Self-Learning Fuzzy Logic Controller ...

    African Journals Online (AJOL)

    In spite of the usefulness of fuzzy control, its main drawback comes from lack of a systematic control design methodology. The most challenging aspect of the design of a fuzzy logic controller is the elicitation of the control rules for its rule base. In this paper, a scheme capable of elicitation of acceptable rules for multivariable ...

  6. Achieving transparency and adaptivity in fuzzy control framework : an application to power transformers predictive overload system

    NARCIS (Netherlands)

    Acampora, G.; Loia, V.; Ippolito, L.; Siano, P.

    2004-01-01

    From a technologic point of view, the problem of fuzzy control deals with the real implementation of a controller on a specific hardware. Today, the market of micro-controller offers different solutions able to implement a fuzzy controller varying from application domains to programming language

  7. Application of an Intelligent Fuzzy Regression Algorithm in Road Freight Transportation Modeling

    Directory of Open Access Journals (Sweden)

    Pooya Najaf

    2013-07-01

    Full Text Available Road freight transportation between provinces of a country has an important effect on the traffic flow of intercity transportation networks. Therefore, an accurate estimation of the road freight transportation for provinces of a country is so crucial to improve the rural traffic operation in a large scale management. Accordingly, the focused case study database in this research is the information related to Iran’s provinces in the year 2008. Correlation between road freight transportation with variables such as transport cost and distance, population, average household income and Gross Domestic Product (GDP of each province is calculated. Results clarify that the population is the most effective factor in the prediction of provinces’ transported freight. Linear Regression Model (LRM is calibrated based on the population variable, and afterwards Fuzzy Regression Algorithm (FRA is generated on the basis of the LRM. The proposed FRA is an intelligent modified algorithm with an accurate prediction and fitting ability. This methodology can be significantly useful in macro-level planning problems where decreasing prediction error values is one of the most important concerns for decision makers. In addition, Back-Propagation Neural Network (BPNN is developed to evaluate the prediction capability of the models and to be compared with FRA. According to the final results, the modified FRA estimates road freight transportation values more accurately than the BPNN and LRM. Finally, in order to predict the road freight transportation values, the reliability of the calibrated models is analyzed using the information of the year 2009. Results show higher reliability for the proposed modified FRA.

  8. Design of fuzzy learning control systems for steam generator water level control

    International Nuclear Information System (INIS)

    Park, Gee Yong

    1996-02-01

    A fuzzy learning algorithm is developed in order to construct the useful control rules and tune the membership functions in the fuzzy logic controller used for water level control of nuclear steam generator. The fuzzy logic controllers have shown to perform better than conventional controllers for ill-defined or complex processes such as nuclear steam generator. Whereas the fuzzy logic controller does not need a detailed mathematical model of a plant to be controlled, its structure is to be made on the basis of the operator's linguistic information experienced from the plant operations. It is not an easy work and also there is no systematic way to translate the operator's linguistic information into quantitative information. When the linguistic information of operators is incomplete, tuning the parameters of fuzzy controller is to be performed for better control performance. It is the time and effort consuming procedure that controller designer has to tune the structure of fuzzy logic controller for optimal performance. And if the number of control inputs is many and the rule base is constructed in multidimensional space, it is very difficult for a controller designer to tune the fuzzy controller structure. Hence, the difficulty in putting the experimental knowledge into quantitative (or numerical) data and the difficulty in tuning the rules are the major problems in designing fuzzy logic controller. In order to overcome the problems described above, a learning algorithm by gradient descent method is included in the fuzzy control system such that the membership functions are tuned and the necessary rules are created automatically for good control performance. For stable learning in gradient descent method, the optimal range of learning coefficient not to be trapped and not to provide too slow learning speed is investigated. With the optimal range of learning coefficient, the optimal value of learning coefficient is suggested and with this value, the gradient

  9. Embedded intelligent adaptive PI controller for an electromechanical system.

    Science.gov (United States)

    El-Nagar, Ahmad M

    2016-09-01

    In this study, an intelligent adaptive controller approach using the interval type-2 fuzzy neural network (IT2FNN) is presented. The proposed controller consists of a lower level proportional - integral (PI) controller, which is the main controller and an upper level IT2FNN which tuning on-line the parameters of a PI controller. The proposed adaptive PI controller based on IT2FNN (API-IT2FNN) is implemented practically using the Arduino DUE kit for controlling the speed of a nonlinear DC motor-generator system. The parameters of the IT2FNN are tuned on-line using back-propagation algorithm. The Lyapunov theorem is used to derive the stability and convergence of the IT2FNN. The obtained experimental results, which are compared with other controllers, demonstrate that the proposed API-IT2FNN is able to improve the system response over a wide range of system uncertainties. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  10. Fuzzy control applied to nuclear power plant pressurizer system

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira, Mauro V.; Almeida, Jose C.S., E-mail: mvitor@ien.gov.b, E-mail: jcsa@ien.gov.b [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil)

    2011-07-01

    In a pressurized water reactor (PWR) nuclear power plants (NPPs) the pressure control in the primary loop is very important for keeping the reactor in a safety condition and improve the generation process efficiency. The main component responsible for this task is the pressurizer. The pressurizer pressure control system (PPCS) utilizes heaters and spray valves to maintain the pressure within an operating band during steady state conditions, and limits the pressure changes, during transient conditions. Relief and safety valves provide overpressure protection for the reactor coolant system (RCS) to ensure system integrity. Various protective reactor trips are generated if the system parameters exceed safe bounds. Historically, a proportional-integral derivative (PID) controller is used in PWRs to keep the pressure in the set point, during those operation conditions. The purpose of this study has two main goals: first is to develop a pressurizer model based on artificial neural networks (ANNs); second is to develop a fuzzy controller for the PWR pressurizer pressure, and compare its performance with the P controller. Data from a simulator PWR plant was used to test the ANN and the controllers as well. The reference simulator is a Westinghouse 3-loop PWR plant with a total thermal output of 2785 MWth. The simulation results show that the pressurizer ANN model response are in reasonable agreement with the simulated power plant, and the fuzzy controller built in this study has better performance compared to the P controller. (author)

  11. Fuzzy control applied to nuclear power plant pressurizer system

    International Nuclear Information System (INIS)

    Oliveira, Mauro V.; Almeida, Jose C.S.

    2011-01-01

    In a pressurized water reactor (PWR) nuclear power plants (NPPs) the pressure control in the primary loop is very important for keeping the reactor in a safety condition and improve the generation process efficiency. The main component responsible for this task is the pressurizer. The pressurizer pressure control system (PPCS) utilizes heaters and spray valves to maintain the pressure within an operating band during steady state conditions, and limits the pressure changes, during transient conditions. Relief and safety valves provide overpressure protection for the reactor coolant system (RCS) to ensure system integrity. Various protective reactor trips are generated if the system parameters exceed safe bounds. Historically, a proportional-integral derivative (PID) controller is used in PWRs to keep the pressure in the set point, during those operation conditions. The purpose of this study has two main goals: first is to develop a pressurizer model based on artificial neural networks (ANNs); second is to develop a fuzzy controller for the PWR pressurizer pressure, and compare its performance with the P controller. Data from a simulator PWR plant was used to test the ANN and the controllers as well. The reference simulator is a Westinghouse 3-loop PWR plant with a total thermal output of 2785 MWth. The simulation results show that the pressurizer ANN model response are in reasonable agreement with the simulated power plant, and the fuzzy controller built in this study has better performance compared to the P controller. (author)

  12. Multi input-output fuzzy logic smart controller for a residential hybrid solar-wind-storage energy system

    International Nuclear Information System (INIS)

    Derrouazin, A.; Aillerie, M.; Mekkakia-Maaza, N.; Charles, J.-P.

    2017-01-01

    Highlights: • We present a fuzzy smart controller for hybrid renewable and conventional energy system. • The rules are based on human intelligence and implemented in the smart controller. • Efficient tracking capability of the proposed controller is proofed in this paper by an example. • Excess produced renewable energy is converted to hydrogen for household use . • Considerable electric grid energy saving is highlighted in the proposed controller system. - Abstract: This study concerns the conception and development of an efficient multi input-output fuzzy logic smart controller, to manage the energy flux of a sustainable hybrid power system, based on renewable power sources, integrating solar panels and a wind turbine associated with storage, applied to a typical residential habitat. In the suggested topology, the energy surplus is redirected to an electrolysis system to produce hydrogen suitable for household utilities. To assume a constant access to electricity in case of consumption peak, connection to the grid is also considered as an exceptional rescue resource. The objective of the presented controller is to exploit instantaneously the produced renewable electric energy and insure savings of electric grid energy. The proposed multi input-output fuzzy logic smart controller has been achieved and verified, outcome switches command signals are discussed and the renewable energy system integration ratio is highlighted.

  13. Application of a fuzzy control algorithm with improved learning speed to nuclear steam generator level control

    International Nuclear Information System (INIS)

    Park, Gee Yong; Seong, Poong Hyun

    1994-01-01

    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 control actions of the plant operator or other controllers. Learning process in fuzzy control algorithm is to find the optimal values of parameters, which consist of the membership functions and the rule base, by gradient descent method. Learning speed of gradient descent is significantly improved in this work with the addition of modified momentum. This control algorithm is applied to the steam generator level control by computer simulations. The simulation results confirm the good performance of this control algorithm for level control and show that the fuzzy learning algorithm has the generalization capability for the relation of inputs and outputs and it also has the excellent capability of disturbance rejection

  14. Induction machine Direct Torque Control system based on fuzzy adaptive control

    Science.gov (United States)

    Li, Shi-ping; Yu, Yan; Jiao, Zhen-gang; Gu, Shu-sheng

    2009-07-01

    Direct Torque Control technology is a high-performance communication control method, it uses the space voltage vector method, and then to the inverter switch state control, to obtain high torque dynamic performance. But none of the switching states is able to generate the exact voltage vector to produce the desired changes in torque and flux in most of the switching instances. This causes a high ripple in torque. To solve this problem, a fuzzy implementation of Direct Torque Control of Induction machine is presented here. Error of stator flux, error of motor electromagnetic torque and position of angle of flux are taken as fuzzy variables. In order to further solve nonlinear problem of variation parameters in direct torque control system, the paper proposes a fuzzy parameter PID adaptive control method which is suitable for the direct torque control of an asynchronous motor. The generation of its fuzzy control is obtained by analyzing and optimizing PID control step response and combining expert's experience. For this reason, it carries out fuzzy work to PID regulator of motor speed to achieve to regulate PID parameters. Therefore the control system gets swifter response velocity, stronger robustness and higher precision of velocity control. The computer simulated results verify the validity of this novel method.

  15. Study of the intelligent control robustness with respect to radiations induced faults

    International Nuclear Information System (INIS)

    Cheynet, Ph.

    1999-01-01

    The so-called intelligent control techniques, such as Artificial Neural Networks and Fuzzy Logic, are considered as being potentially robust. Their digital implementation gives compact and powerful solutions to some problems difficult to be tackled by classical techniques. Such approaches might be used for applications working in harsh environment (nuclear and space). The aim of this thesis is to study the robustness of artificial neural networks and fuzzy logic against Single Event Upset faults, in order to evaluate their viability and their efficiency for onboard spacecraft processes. A set of experiments have been performed on a neural network and a fuzzy controller, both implementing real space applications: texture analysis from satellite images and wheel control of a martian rover. An original method, allowing to increase the recognition rate of any artificial neural network has been developed and used on the studied network. Digital architectures implementing the two studied techniques in this thesis, have been boarded on two scientific satellites. One is in flight since one year, the other will be launched in the end of 1999. Obtained results, both from software simulations, hardware fault injections or particle accelerator tests, show that intelligent control techniques have a significant robustness against Single Event Upset faults. Data issued from the flight experiment confirm these properties, showing that some onboard spacecraft processes can be reliably executed by digital artificial neural networks. (author)

  16. Application of robust fuzzy control in power control of nuclear reactor

    International Nuclear Information System (INIS)

    Liu Lei; Luan Xiuchun; Jin Guangyuan; Yu Tao; Rao Su

    2013-01-01

    Robust-fuzzy controller based on T-S fuzzy model was designed for real-time controlling of nuclear reactor power and adapting to the load changing of power grid. Local controller was designed by means of state feedback technique, and the global controller was designed by parallel distributed compensation (PDC) method. The result of solving linear matrix inequalities (LMI) proves that this controller is stable. The simulation shows that the nuclear power can be well controlled in three typical conditions by this controller. (authors)

  17. Development of intelligent supervisory control system

    International Nuclear Information System (INIS)

    Takizawa, Y.; Fukumoto, A.; Makino, M.; Takiguchi, S.

    1994-01-01

    The objective of the development of an intelligent supervisory control system for next generation plants is enhancement of the operational reliability by applying the recent outcome of artificial intelligence and computer technologies. This system consists of the supervisory control and monitoring for automatic operation, the equipment operation support for historical data management and for test scheduling, the operators' decision making support for accidental plant situations and the human-friendly interface of these support functions. The verification test results showed the validity of the functions realized by this system for the next generation control room. (author)

  18. Intelligent distributed control for nuclear power plants

    International Nuclear Information System (INIS)

    Klevans, E.H.

    1991-01-01

    In September of 1989 work began on the DOE University Program grant DE-FG07-89ER12889. The grant provides support for a three year project to develop and demonstrate Intelligent Distributed Control (IDC) for Nuclear Power Plants. The body of this First Annual Technical Progress report summarizes the first year tasks while the appendices provide detailed information presented at conference meetings. One major addendum report, authored by M.A. Schultz, describes the ultimate goals and projected structure of an automatic distributed control system for EBR-2. The remaining tasks of the project develop specific implementations of various components required to demonstrate the intelligent distributed control concept

  19. An intelligent control scheme for precise tip-motion control in atomic force microscopy.

    Science.gov (United States)

    Wang, Yanyan; Hu, Xiaodong; Xu, Linyan

    2016-01-01

    The paper proposes a new intelligent control method to precisely control the tip motion of the atomic force microscopy (AFM). The tip moves up and down at a high rate along the z direction during scanning, requiring the utilization of a rapid feedback controller. The standard proportional-integral (PI) feedback controller is commonly used in commercial AFMs to enable topography measurements. The controller's response performance is determined by the set of the proportional (P) parameter and the integral (I) parameter. However, the two parameters cannot be automatically altered simultaneously according to the scanning speed and the surface topography during continuors scanning, leading to an inaccurate measurement. Thus a new intelligent controller combining the fuzzy controller and the PI controller is put forward in the paper. The new controller automatically selects the most appropriate PI parameters to achieve a fast response rate on basis of the tracking errors. In the experimental setup, the new controller is realized with a digital signal process (DSP) system, implemented in a conventional AFM system. Experiments are carried out by comparing the new method with the standard PI controller. The results demonstrate that the new method is more robust and effective for the precise tip motion control, corresponding to the achievement of a highly qualified image by shortening the response time of the controller. © Wiley Periodicals, Inc.

  20. Active control of flexible structures using a fuzzy logic algorithm

    Science.gov (United States)

    Cohen, Kelly; Weller, Tanchum; Ben-Asher, Joseph Z.

    2002-08-01

    This study deals with the development and application of an active control law for the vibration suppression of beam-like flexible structures experiencing transient disturbances. Collocated pairs of sensors/actuators provide active control of the structure. A design methodology for the closed-loop control algorithm based on fuzzy logic is proposed. First, the behavior of the open-loop system is observed. Then, the number and locations of collocated actuator/sensor pairs are selected. The proposed control law, which is based on the principles of passivity, commands the actuator to emulate the behavior of a dynamic vibration absorber. The absorber is tuned to a targeted frequency, whereas the damping coefficient of the dashpot is varied in a closed loop using a fuzzy logic based algorithm. This approach not only ensures inherent stability associated with passive absorbers, but also circumvents the phenomenon of modal spillover. The developed controller is applied to the AFWAL/FIB 10 bar truss. Simulated results using MATLAB© show that the closed-loop system exhibits fairly quick settling times and desirable performance, as well as robustness characteristics. To demonstrate the robustness of the control system to changes in the temporal dynamics of the flexible structure, the transient response to a considerably perturbed plant is simulated. The modal frequencies of the 10 bar truss were raised as well as lowered substantially, thereby significantly perturbing the natural frequencies of vibration. For these cases, too, the developed control law provides adequate settling times and rates of vibrational energy dissipation.

  1. Intelligent process control operator aid -- An artificial intelligence approach

    International Nuclear Information System (INIS)

    Sharma, D.D.; Miller, D.D.; Hajek, B.; Chandrasekaran, B.

    1986-01-01

    This paper describes an approach for designing intelligent process and power plant control operator aids. It is argued that one of the key aspects of an intelligent operator aid is the capability for dynamic procedure synthesis with incomplete definition of initial state, unknown goal states, and the dynamic world situation. The dynamic world state is used to determine the goal, select appropriate plan steps from prespecified procedures to achieve the goal, control the execution of the synthesized plan, and provide for dynamic recovery from failure often using a goal hierarchy. The dynamic synthesis of a plan requires integration of various problems solving capabilities such as plan generation, plan synthesis, plan modification, and failure recovery from a plan. The programming language for implementing the DPS framework provides a convenient tool for developing applications. An application of the DPS approach to a Nuclear Power Plant emergency procedure synthesis is also described. Initial test results indicate that the approach is successful in dynamically synthesizing the procedures. The authors realize that the DPS framework is not a solution for all control tasks. However, many existing process and plant control problems satisfy the requirements discussed in the paper and should be able to benefit from the framework described

  2. Adaptive Fractional Fuzzy Sliding Mode Control for Multivariable Nonlinear Systems

    Directory of Open Access Journals (Sweden)

    Junhai Luo

    2014-01-01

    Full Text Available This paper presents a robust adaptive fuzzy sliding mode control method for a class of uncertain nonlinear systems. The fractional order calculus is employed in the parameter updating stage. The underlying stability analysis as well as parameter update law design is carried out by Lyapunov based technique. In the simulation, two examples including a comparison with the traditional integer order counterpart are given to show the effectiveness of the proposed method. The main contribution of this paper consists in the control performance is better for the fractional order updating law than that of traditional integer order.

  3. Improved Rotor Speed Brushless DC Motor Using Fuzzy Controller

    Directory of Open Access Journals (Sweden)

    Jafar Mostafapour

    2015-04-01

    Full Text Available A brushless DC (BLDC Motors have advantages over brushed, Direct current (DC Motors and , Induction motor (IM. They have better speed verses torque characteristics, high dynamic response, high efficiency, long operating life, noiseless operation, higher speed ranges, and rugged construction. Also, torque delivered to motor size is higher, making it useful in application where space and weight are critical factors. With these advantages BLDC motors find wide spread application in automotive appliance, aerospace medical, and instrumentation and automation industries This paper can be seen as fuzzy controllers compared to PI control BLDC motor rotor speed has improved significantly and beter result can be achieve.

  4. T-S Fuzzy Modelling and H∞ Attitude Control for Hypersonic Gliding Vehicles

    Directory of Open Access Journals (Sweden)

    Weidong Zhang

    2017-01-01

    Full Text Available This paper addresses the T-S fuzzy modelling and H∞ attitude control in three channels for hypersonic gliding vehicles (HGVs. First, the control-oriented affine nonlinear model has been established which is transformed from the reentry dynamics. Then, based on Taylor’s expansion approach and the fuzzy linearization approach, the homogeneous T-S local modelling technique for HGVs is proposed. Given the approximation accuracy and controller design complexity, appropriate fuzzy premise variables and operating points of interest are selected to construct the T-S homogeneous submodels. With so-called fuzzy blending, the original plant is transformed into the overall T-S fuzzy model with disturbance. By utilizing Lyapunov functional approach, a state feedback fuzzy controller has been designed based on relaxed linear matrix inequality (LMI conditions to stable the original plants with a prescribed H∞ performance of disturbance. Finally, numerical simulations are performed to demonstrate the effectiveness of the proposed H∞ T-S fuzzy controller for the original attitude dynamics; the superiority of the designed T-S fuzzy controller compared with other local controllers based on the constructed fuzzy model is shown as well.

  5. Control of Rotary Cranes Using Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    Amjed A. Al-mousa

    2003-01-01

    Full Text Available Rotary cranes (tower cranes are common industrial structures that are used in building construction, factories, and harbors. These cranes are usually operated manually. With the size of these cranes becoming larger and the motion expected to be faster, the process of controlling them has become difficult without using automatic control methods. In general, the movement of cranes has no prescribed path. Cranes have to be run under different operating conditions, which makes closed-loop control attractive.

  6. Spacecraft attitude control using neuro-fuzzy approximation of the optimal controllers

    Science.gov (United States)

    Kim, Sung-Woo; Park, Sang-Young; Park, Chandeok

    2016-01-01

    In this study, a neuro-fuzzy controller (NFC) was developed for spacecraft attitude control to mitigate large computational load of the state-dependent Riccati equation (SDRE) controller. The NFC was developed by training a neuro-fuzzy network to approximate the SDRE controller. The stability of the NFC was numerically verified using a Lyapunov-based method, and the performance of the controller was analyzed in terms of approximation ability, steady-state error, cost, and execution time. The simulations and test results indicate that the developed NFC efficiently approximates the SDRE controller, with asymptotic stability in a bounded region of angular velocity encompassing the operational range of rapid-attitude maneuvers. In addition, it was shown that an approximated optimal feedback controller can be designed successfully through neuro-fuzzy approximation of the optimal open-loop controller.

  7. Adaptive fuzzy sliding-mode control for multi-input multi-output chaotic systems

    International Nuclear Information System (INIS)

    Poursamad, Amir; Markazi, Amir H.D.

    2009-01-01

    This paper describes an adaptive fuzzy sliding-mode control algorithm for controlling unknown or uncertain, multi-input multi-output (MIMO), possibly chaotic, dynamical systems. The control approach encompasses a fuzzy system and a robust controller. The fuzzy system is designed to mimic an ideal sliding-mode controller, and the robust controller compensates the difference between the fuzzy controller and the ideal one. The parameters of the fuzzy system, as well as the uncertainty bound of the robust controller, are tuned adaptively. The adaptive laws are derived in the Lyapunov sense to guarantee the asymptotic stability and tracking of the controlled system. The effectiveness of the proposed method is shown by applying it to some well-known chaotic systems.

  8. To the Problem of Electromechanical Interaction in Elevators with Controlled Electric Drive and Fuzzy Speed Controller

    Directory of Open Access Journals (Sweden)

    A. S. Koval

    2010-01-01

    Full Text Available The paper considers problems concerning electromechanical interaction in elevators with an adjustable asynchronous electric drive equipped with the vector control systems under direct torque control and direct torque control with pulse-width modulator. A mathematical description of electromechanical elevator system with due account of nonlinearity of the worm gear is given in the paper. The paper presents a simplified circuit design of a control system with a fuzzy speed controller. It has been established that the factor of electromechanical interaction in electromechanical system with the adjustable asynchronous electric drive and an fuzzy speed controller is within the range which corresponds to existence of the essential electromechanical interaction.

  9. Modeling and Control of Multivariable Process Using Intelligent Techniques

    Directory of Open Access Journals (Sweden)

    Subathra Balasubramanian

    2010-10-01

    Full Text Available For nonlinear dynamic systems, the first principles based modeling and control is difficult to implement. In this study, a fuzzy controller and recurrent fuzzy controller are developed for MIMO process. Fuzzy logic controller is a model free controller designed based on the knowledge about the process. In fuzzy controller there are two types of rule-based fuzzy models are available: one the linguistic (Mamdani model and the other is Takagi–Sugeno model. Of these two, Takagi-Sugeno model (TS has attracted most attention. The fuzzy controller application is limited to static processes due to their feedforward structure. But, most of the real-time processes are dynamic and they require the history of input/output data. In order to store the past values a memory unit is needed, which is introduced by the recurrent structure. The proposed recurrent fuzzy structure is used to develop a controller for the two tank heating process. Both controllers are designed and implemented in a real time environment and their performance is compared.

  10. FUZZY LOGIC CONTROL OF ELECTRIC MOTORS AND MOTOR DRIVES: FEASIBILITY STUDY

    Science.gov (United States)

    The report gives results of a study (part 1) of fuzzy logic motor control (FLMC). The study included: 1) reviews of existing applications of fuzzy logic, of motor operation, and of motor control; 2) a description of motor control schemes that can utilize FLMC; 3) selection of a m...

  11. Model-based fuzzy control solutions for a laboratory Antilock Braking System

    DEFF Research Database (Denmark)

    Precup, Radu-Emil; Spataru, Sergiu; Rǎdac, Mircea-Bogdan

    2010-01-01

    This paper gives two original model-based fuzzy control solutions dedicated to the longitudinal slip control of Antilock Braking System laboratory equipment. The parallel distributed compensation leads to linear matrix inequalities which guarantee the global stability of the fuzzy control systems...

  12. Design Genetic Algorithm Optimization Education Software Based Fuzzy Controller for a Tricopter Fly Path Planning

    Science.gov (United States)

    Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao

    2016-01-01

    In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…

  13. Fuzzy logic controller for crude oil levels at Escravos Tank Farm ...

    African Journals Online (AJOL)

    Fuzzy logic controller (FLC) for crude oil flow rates and tank levels was designed for monitoring flow and tank level management at Escravos Tank Farm in Nigeria. The fuzzy control system incorporated essence of expert knowledge required to handle the tasks. Proportional Integral Derivative (PID) control of crude flow ...

  14. A systematic approach for fine-tuning of fuzzy controllers applied to WWTPs

    DEFF Research Database (Denmark)

    Ruano, M.V.; Ribes, J.; Sin, Gürkan

    2010-01-01

    A systematic approach for fine-tuning fuzzy controllers has been developed and evaluated for an aeration control system implemented in a WWTR The challenge with the application of fuzzy controllers to WWTPs is simply that they contain many parameters, which need to be adjusted for different WWTP ...

  15. Sensitivity-based self-learning fuzzy logic control for a servo system

    NARCIS (Netherlands)

    Balenovic, M.

    1998-01-01

    Describes an experimental verification of a self-learning fuzzy logic controller (SLFLC). 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 proposed controller has been

  16. Fuzzy-Neural Automatic Daylight Control System

    Directory of Open Access Journals (Sweden)

    Grif H. Şt.

    2011-12-01

    Full Text Available The paper presents the design and the tuning of a CMAC controller (Cerebellar Model Articulation Controller implemented in an automatic daylight control application. After the tuning process of the controller, the authors studied the behavior of the automatic lighting control system (ALCS in the presence of luminance disturbances. The luminance disturbances were produced by the authors in night conditions and day conditions as well. During the night conditions, the luminance disturbances were produced by turning on and off a halogen desk lamp. During the day conditions the luminance disturbances were produced in two ways: by daylight contributions changes achieved by covering and uncovering a part of the office window and by turning on and off a halogen desk lamp. During the day conditions the luminance disturbances, produced by turning on and off the halogen lamp, have a smaller amplitude than those produced during the night conditions. The luminance disturbance during the night conditions was a helpful tool to select the proper values of the learning rate for CMAC controller. The luminance disturbances during the day conditions were a helpful tool to demonstrate the right setting of the CMAC controller.

  17. Enhanced situation awareness and decision making for an intelligent reconfigurable reactor power controller

    International Nuclear Information System (INIS)

    Kenney, S.J.; Edwards, R.M.

    1996-01-01

    A Learning Automata based intelligent reconfigurable controller has been adapted for use as a reactor power controller to achieve improved reactor temperature performance. The intelligent reconfigurable controller is capable of enforcing either a classical or an optimal reactor power controller based on control performance feedback. Four control performance evaluation measures: dynamically estimated average quadratic temperature error, power, rod reactivity and rod reactivity rate were developed to provide feedback to the control decision component of the intelligent reconfigurable controller. Fuzzy Logic and Neural Network controllers have been studied for inclusion in the bank of controllers that form the intermediate level of an enhanced intelligent reconfigurable reactor power controller (IRRPC). The increased number of alternatives available to the supervisory level of the IRRPC requires enhanced situation awareness. Additional performance measures have been designed and a method for synthesizing them into a single indication of the overall performance of the currently enforced reactor power controller has been conceptualized. Modification of the reward/penalty scheme implemented in the existing IRRPC to increase the quality of the supervisory level decision process has been studied. The logogen model of human memory (Morton, 1969) and individual controller design information could be used to allocate reward to the most appropriate controller. Methods for allocating supervisory level attention were also studied with the goal of maximizing learning rate

  18. Four Degree Freedom Robot Arm with Fuzzy Neural Network Control

    Directory of Open Access Journals (Sweden)

    Şinasi Arslan

    2013-01-01

    Full Text Available In this study, the control of four degree freedom robot arm has been realized with the computed torque control method.. It is usually required that the four jointed robot arm has high precision capability and good maneuverability for using in industrial applications. Besides, high speed working and external applied loads have been acting as important roles. For those purposes, the computed torque control method has been developed in a good manner that the robot arm can track the given trajectory, which has been able to enhance the feedback control together with fuzzy neural network control. The simulation results have proved that the computed torque control with the neural network has been so successful in robot control.

  19. Adaptive neuro-fuzzy inference system based automatic generation control

    Energy Technology Data Exchange (ETDEWEB)

    Hosseini, S.H.; Etemadi, A.H. [Department of Electrical Engineering, Sharif University of Technology, Tehran (Iran)

    2008-07-15

    Fixed gain controllers for automatic generation control are designed at nominal operating conditions and fail to provide best control performance over a wide range of operating conditions. So, to keep system performance near its optimum, it is desirable to track the operating conditions and use updated parameters to compute control gains. A control scheme based on artificial neuro-fuzzy inference system (ANFIS), which is trained by the results of off-line studies obtained using particle swarm optimization, is proposed in this paper to optimize and update control gains in real-time according to load variations. Also, frequency relaxation is implemented using ANFIS. The efficiency of the proposed method is demonstrated via simulations. Compliance of the proposed method with NERC control performance standard is verified. (author)

  20. Supervisory System and Multivariable Control Applying Weighted Fuzzy-PID Logic in an Alcoholic Fermentation Process

    Directory of Open Access Journals (Sweden)

    Márcio Mendonça

    2015-10-01

    Full Text Available In this work, it is analyzed a multivariate system control of an alcoholic fermentation process with no minimum phase. The control is made with PID classic controllers associated with a supervisory system based on Fuzzy Systems. The Fuzzy system, a priori, send set-points to PID controllers, but also adds protection functions, such as if the biomass valued is at zero or very close. The Fuzzy controller changes the campaign to prevent or mitigate the paralyzation of the process. Three control architectures based on Fuzzy Control Systems are presented and compared in performance with classic control in different campaigns. The third architecture, in particular, adds an adaptive function. A brief summary of Fuzzy theory and correlated works will be presented. And, finally simulations results, conclusions and future works end the article.

  1. Feasibility analysis of fuzzy logic control for ITER Poloidal field (PF) AC/DC converter system

    Energy Technology Data Exchange (ETDEWEB)

    Hassan, Mahmood Ul; Fu, Peng [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); University of Science and Technology of China (China); Song, Zhiquan, E-mail: zhquansong@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); Chen, Xiaojiao [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); University of Science and Technology of China (China); Zhang, Xiuqing [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); Humayun, Muhammad [Shanghai Jiaotong University (China)

    2017-05-15

    Highlights: • The implementation of the Fuzzy controller for the ITER PF converter system is presented. • The comparison of the FLC and PI simulation are investigated. • The FLC single and parallel bridge operation are presented. • Fuzzification and Defuzzification algorithms are presented using FLC controller. - Abstract: This paper describes the feasibility analysis of the fuzzy logic control to increase the performance of the ITER poloidal field (PF) converter systems. A fuzzy-logic-based controller is designed for ITER PF converter system, using the traditional PI controller and Fuzzy controller (FC), the dynamic behavior and transient response of the PF converter system are compared under normal operation by analysis and simulation. The analysis results show that the fuzzy logic control can achieve better operation performance than PI control.

  2. Two-Dimensional Fuzzy Sliding Mode Control of a Field-Sensed Magnetic Suspension System

    Directory of Open Access Journals (Sweden)

    Jen-Hsing Li

    2014-01-01

    Full Text Available This paper presents the two-dimensional fuzzy sliding mode control of a field-sensed magnetic suspension system. The fuzzy rules include both the sliding manifold and its derivative. The fuzzy sliding mode control has advantages of the sliding mode control and the fuzzy control rules are minimized. Magnetic suspension systems are nonlinear and inherently unstable systems. The two-dimensional fuzzy sliding mode control can stabilize the nonlinear systems globally and attenuate chatter effectively. It is adequate to be applied to magnetic suspension systems. New design circuits of magnetic suspension systems are proposed in this paper. ARM Cortex-M3 microcontroller is utilized as a digital controller. The implemented driver, sensor, and control circuits are simpler, more inexpensive, and effective. This apparatus is satisfactory for engineering education. In the hands-on experiments, the proposed control scheme markedly improves performances of the field-sensed magnetic suspension system.

  3. Intelligent Control Framework for the Feeding System in the Biomass Power Plant

    Directory of Open Access Journals (Sweden)

    Sun Jin

    2015-01-01

    Full Text Available This paper proposes an intelligent control framework for biomass drying process with flue gases based on FLC (fuzzy logic controller and CAN (Controller Area Network bus. In the operation of a biomass drying process, in order to get the biomass with the set-point low moisture content dried by waste high temperature flue gases, it is necessary to intelligent control for the biomass flow rate. Use of an experiment with varied materials at different initial moisture contents enables acquisition of the biomass flow rates as initial setting values. Set the error between actual straw moisture content and set-point, and rate of change of error as two inputs. the biomass flow rate can be acquired by the fuzzy logic computing as the output. Since the length of dryer is more than twenty meters, the integration by the CAN bus can ensure real-time reliable data acquisition and processing. The control framework for biomass drying process can be applied to a variety of biomass, such as, cotton stalk, corn stalk, rice straw, wheat straw, sugar cane. It has strong potential for practical applications because of its advantages on intelligent providing the set-point low moisture content of biomass feedstock for power generation equipment.

  4. Performance of Globally Linearized Controller and Two Region Fuzzy Logic Controller on a Nonlinear Process

    Directory of Open Access Journals (Sweden)

    N. Jaya

    2008-10-01

    Full Text Available In this work, a design and implementation of a Conventional PI controller, single region fuzzy logic controller, two region fuzzy logic controller and Globally Linearized Controller (GLC for a two capacity interacting nonlinear process is carried out. The performance of this process using single region FLC, two region FLC and GLC are compared with the performance of conventional PI controller about an operating point of 50 %. It has been observed that GLC and two region FLC provides better performance. Further, this procedure is also validated by real time experimentation using dSPACE.

  5. Energy Management of Dual-Source Propelled Electric Vehicle using Fuzzy Controller Optimized via Genetic Algorithm

    DEFF Research Database (Denmark)

    Khoobi, Saeed; Halvaei, Abolfazl; Hajizadeh, Amin

    2016-01-01

    Energy and power distribution between multiple energy sources of electric vehicles (EVs) is the main challenge to achieve optimum performance from EV. Fuzzy inference systems are powerful tools due to nonlinearity and uncertainties of EV system. Design of fuzzy controllers for energy management...... of EV relies too much on the expert experience and it may lead to sub-optimal performance. This paper develops an optimized fuzzy controller using genetic algorithm (GA) for an electric vehicle equipped with two power bank including battery and super-capacitor. The model of EV and optimized fuzzy...

  6. Contribution to intelligent vehicle platoon control

    OpenAIRE

    Zhao , Jin

    2010-01-01

    This PhD thesis is dedicated to the control strategies for intelligent vehicle platoon in highway with the main aims of alleviating traffic congestion and improving traffic safety. After a review of the different existing automated driving systems, the vehicle longitudinal and lateral dynamic models are derived. Then, the longitudinal control and lateral control strategies are studied respectively. At first, the longitudinal control system is designed to be hierarchical with an upper level co...

  7. Modeling and simulation of adaptive Neuro-fuzzy based intelligent system for predictive stabilization in structured overlay networks

    Directory of Open Access Journals (Sweden)

    Ramanpreet Kaur

    2017-02-01

    Full Text Available Intelligent prediction of neighboring node (k well defined neighbors as specified by the dht protocol dynamism is helpful to improve the resilience and can reduce the overhead associated with topology maintenance of structured overlay networks. The dynamic behavior of overlay nodes depends on many factors such as underlying user’s online behavior, geographical position, time of the day, day of the week etc. as reported in many applications. We can exploit these characteristics for efficient maintenance of structured overlay networks by implementing an intelligent predictive framework for setting stabilization parameters appropriately. Considering the fact that human driven behavior usually goes beyond intermittent availability patterns, we use a hybrid Neuro-fuzzy based predictor to enhance the accuracy of the predictions. In this paper, we discuss our predictive stabilization approach, implement Neuro-fuzzy based prediction in MATLAB simulation and apply this predictive stabilization model in a chord based overlay network using OverSim as a simulation tool. The MATLAB simulation results present that the behavior of neighboring nodes is predictable to a large extent as indicated by the very small RMSE. The OverSim based simulation results also observe significant improvements in the performance of chord based overlay network in terms of lookup success ratio, lookup hop count and maintenance overhead as compared to periodic stabilization approach.

  8. Indirect fuzzy adaptive control of a class of SISO nonlinear systems

    International Nuclear Information System (INIS)

    Laboid, S.; Boucherit, M.S.

    2006-01-01

    This paper presents an adaptive fuzzy control scheme for a class of continuous-time single-input single-output nonlinear systems with unknown dynamics and disturbance. Within this scheme, the fuzzy systems are employed to approximate the unknown system's dynamics. The proposed controller is composed of a well-defined adaptive fuzzy control term that uses the adaptive fuzzy approximation errors and disturbance. Based on a Lyapunov synthesis method, it is shown that the proposed adaptive control scheme guarantees the convergence of the tracking error to zero and the global boundedness of all signals in the closed-loop system. Moreover, the proposed controller allows initialization by zero of all adjusted parameters in the fuzzy approximators, and does not require the knowledge of the lower bound of the control gain and upper bounds of the approximation errors and disturbance. Simulation results performed on an inverted pendulum system are given to point out the good performance of the developed adaptive controller. (author)

  9. An intelligent system based on fuzzy probabilities for medical diagnosis – a study in aphasia diagnosis

    Directory of Open Access Journals (Sweden)

    Majid Moshtagh Khorasani

    2009-04-01

    Full Text Available

    • BACKGROUND: Aphasia diagnosis is particularly challenging due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with  mprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease.
    • METHODS: Fuzzy probability is proposed here as the basic framework for handling the uncertainties in medical diagnosis and particularly aphasia diagnosis. To efficiently construct this fuzzy probabilistic mapping, statistical analysis is performed that constructs input membership functions as well as determines an effective set of input features.
    • RESULTS: Considering the high sensitivity of performance measures to different distribution of testing/training sets, a statistical t-test of significance is applied to compare fuzzy approach results with NN  esults as well as author’s earlier work using fuzzy logic. The proposed fuzzy probability estimator approach clearly provides better diagnosis for both classes of data sets. Specifically, for the first and second type of fuzzy probability classifiers, i.e. spontaneous speech and comprehensive model, P-values are 2.24E-08 and 0.0059, espectively, strongly rejecting the null hypothesis.
    • CONCLUSIONS: The technique is applied and compared on both comprehensive and spontaneous speech test data for diagnosis of four Aphasia types: Anomic, Broca, Global and Wernicke. Statistical analysis confirms that the proposed approach can significantly improve accuracy using fewer Aphasia features.
    • KEYWORDS: Aphasia, fuzzy probability, fuzzy logic, medical diagnosis, fuzzy rules.

  10. Local Model Predictive Control for T-S Fuzzy Systems.

    Science.gov (United States)

    Lee, Donghwan; Hu, Jianghai

    2017-09-01

    In this paper, a new linear matrix inequality-based model predictive control (MPC) problem is studied for discrete-time nonlinear systems described as Takagi-Sugeno fuzzy systems. A recent local stability approach is applied to improve the performance of the proposed MPC scheme. At each time k , an optimal state-feedback gain that minimizes an objective function is obtained by solving a semidefinite programming problem. The local stability analysis, the estimation of the domain of attraction, and feasibility of the proposed MPC are proved. Examples are given to demonstrate the advantages of the suggested MPC over existing approaches.

  11. Fuzzy Networked Control Systems Design Considering Scheduling Restrictions

    Directory of Open Access Journals (Sweden)

    H. Benítez-Pérez

    2012-01-01

    known a priory but from a dynamic real-time behavior. To do so, the use of priority dynamic Priority exchange scheduling is performed. The objective of this paper is to show a way to tackle multiple time delays that are bounded and the dynamic response from real-time scheduling approximation. The related control law is designed considering fuzzy logic approximation for nonlinear time delays coupling, where the main advantage is the integration of this behavior through extended state space representation keeping certain linear and bounded behavior and leading to a stable situation during events presentation by guaranteeing stability through Lyapunov.

  12. Improved Polynomial Fuzzy Modeling and Controller with Stability Analysis for Nonlinear Dynamical Systems

    OpenAIRE

    Hamed Kharrati; Sohrab Khanmohammadi; Witold Pedrycz; Ghasem Alizadeh

    2012-01-01

    This study presents an improved model and controller for nonlinear plants using polynomial fuzzy model-based (FMB) systems. To minimize mismatch between the polynomial fuzzy model and nonlinear plant, the suitable parameters of membership functions are determined in a systematic way. Defining an appropriate fitness function and utilizing Taylor series expansion, a genetic algorithm (GA) is used to form the shape of membership functions in polynomial forms, which are afterwards used in fuzzy m...

  13. Simplified Fuzzy Control for Flux-Weakening Speed Control of IPMSM Drive

    Directory of Open Access Journals (Sweden)

    M. J. Hossain

    2011-01-01

    Full Text Available This paper presents a simplified fuzzy logic-based speed control scheme of an interior permanent magnet synchronous motor (IPMSM above the base speed using a flux-weakening method. In this work, nonlinear expressions of d-axis and q-axis currents of the IPMSM have been derived and subsequently incorporated in the control algorithm for the practical purpose in order to implement fuzzy-based flux-weakening strategy to operate the motor above the base speed. The fundamentals of fuzzy logic algorithms as related to motor control applications are also illustrated. A simplified fuzzy speed controller (FLC for the IPMSM drive has been designed and incorporated in the drive system to maintain high performance standards. The efficacy of the proposed simplified FLC-based IPMSM drive is verified by simulation at various dynamic operating conditions. The simplified FLC is found to be robust and efficient. Laboratory test results of proportional integral (PI controller-based IPMSM drive have been compared with the simulated results of fuzzy controller-based flux-weakening IPMSM drive system.

  14. Simulation of the fuzzy-smith control system for the high temperature gas cooled reactor

    International Nuclear Information System (INIS)

    Li Deheng; Xu Xiaolin; Zheng Jie; Guo Renjun; Zhang Guifen

    1997-01-01

    The Fuzzy-Smith pre-estimate controller to solve the control of the big delay system is developed, accompanied with the development of the mathematical model of the 10 MW high temperature gas cooled test reactor (HTR-10) and the design of its control system. The simulation results show the Fuzzy-Smith pre-estimate controller has the advantages of both fuzzy control and Smith pre-estimate controller; it has better compensation to the delay and better adaptability to the parameter change of the control object. So it is applicable to the design of the control system for the high temperature gas cooled reactor

  15. Constant Cutting Force Control for CNC Machining Using Dynamic Characteristic-Based Fuzzy Controller

    Directory of Open Access Journals (Sweden)

    Hengli Liu

    2015-01-01

    Full Text Available This paper presents a dynamic characteristic-based fuzzy adaptive control algorithm (DCbFACA to avoid the influence of cutting force changing rapidly on the machining stability and precision. The cutting force is indirectly obtained in real time by monitoring and extraction of the motorized spindle current, the feed speed is fuzzy adjusted online, and the current was used as a feedback to control cutting force and maintain the machining process stable. Different from the traditional fuzzy control methods using the experience-based control rules, and according to the complex nonlinear characteristics of CNC machining, the power bond graph method is implemented to describe the dynamic characteristics of process, and then the appropriate variation relations are achieved between current and feed speed, and the control rules are optimized and established based on it. The numerical results indicated that DCbFACA can make the CNC machining process more stable and improve the machining precision.

  16. A GA-fuzzy automatic generation controller for interconnected power system

    CSIR Research Space (South Africa)

    Boesack, CD

    2011-10-01

    Full Text Available This paper presents a GA-Fuzzy Automatic Generation Controller for large interconnected power systems. The design of Fuzzy Logic Controllers by means of expert knowledge have typically been the traditional design norm, however, this may not yield...

  17. Fuzzeval: A Fuzzy Controller-Based Approach in Adaptive Learning for Backgammon Game

    DEFF Research Database (Denmark)

    Heinze, Mikael; Ortiz-Arroyo, Daniel; Larsen, Henrik Legind

    2005-01-01

    In this paper we investigate the effectiveness of applying fuzzy controllers to create strong computer player programs in the domain of backgammon. Fuzzeval, our proposed mechanism, consists of a fuzzy controller that dynamically evaluates the perceived strength of the board configurations it re-...

  18. The Absolute Stability Analysis in Fuzzy Control Systems with Parametric Uncertainties and Reference Inputs

    Science.gov (United States)

    Wu, Bing-Fei; Ma, Li-Shan; Perng, Jau-Woei

    This study analyzes the absolute stability in P and PD type fuzzy logic control systems with both certain and uncertain linear plants. Stability analysis includes the reference input, actuator gain and interval plant parameters. For certain linear plants, the stability (i.e. the stable equilibriums of error) in P and PD types is analyzed with the Popov or linearization methods under various reference inputs and actuator gains. The steady state errors of fuzzy control systems are also addressed in the parameter plane. The parametric robust Popov criterion for parametric absolute stability based on Lur'e systems is also applied to the stability analysis of P type fuzzy control systems with uncertain plants. The PD type fuzzy logic controller in our approach is a single-input fuzzy logic controller and is transformed into the P type for analysis. In our work, the absolute stability analysis of fuzzy control systems is given with respect to a non-zero reference input and an uncertain linear plant with the parametric robust Popov criterion unlike previous works. Moreover, a fuzzy current controlled RC circuit is designed with PSPICE models. Both numerical and PSPICE simulations are provided to verify the analytical results. Furthermore, the oscillation mechanism in fuzzy control systems is specified with various equilibrium points of view in the simulation example. Finally, the comparisons are also given to show the effectiveness of the analysis method.

  19. Less Conservative ℋ∞ Fuzzy Control for Discrete-Time Takagi-Sugeno Systems

    Directory of Open Access Journals (Sweden)

    Leonardo Amaral Mozelli

    2011-01-01

    Full Text Available New analysis and control design conditions of discrete-time fuzzy systems are proposed. Using fuzzy Lyapunov's functions and introducing slack variables, less conservative conditions are obtained. The controller guarantees system stabilization and ℋ∞ performance. Numerical tests and a practical experiment in Chua's circuit are presented to show the effectiveness.

  20. Intelligent control for large-scale variable speed variable pitch wind turbines

    Institute of Scientific and Technical Information of China (English)

    Xinfang ZHANG; Daping XU; Yibing LIU

    2004-01-01

    Large-scale wind turbine generator systems have strong nonlinear multivariable characteristics with many uncertain factors and disturbances.Automatic control is crucial for the efficiency and reliability of wind turbines.On the basis of simplified and proper model of variable speed variable pitch wind turbines,the effective wind speed is estimated using extended Kalman filter.Intelligent control schemes proposed in the paper include two loops which operate in synchronism with each other.At below-rated wind speed,the inner loop adopts adaptive fuzzy control based on variable universe for generator torque regulation to realize maximum wind energy capture.At above-rated wind speed, a controller based on least square support vector machine is proposed to adjust pitch angle and keep rated output power.The simulation shows the effectiveness of the intelligent control.