Designing adaptive fuzzy controller for nonlinear systems
NASA Astrophysics Data System (ADS)
Zhou, Bo; Shi, Aiguo; Cai, Feng; Zhang, Yongsheng; Yang, Baozhang
2003-09-01
The objective of this paper is to achieve model reference adaptive fuzzy control for a nonlinear dynamical system. An adaptive fuzzy autopilot for ship course-keeping is developed. The influence of sea current and wave disturbances on course-keeping performance is also considered as random noises. Simulation results are presented.
Hybrid adaptive fuzzy identification and control of nonlinear systems
Mehrdad Hojati; Saeed Gazor
2002-01-01
We present a combined direct and indirect adaptive control scheme for adjusting an adaptive fuzzy controller, and adaptive fuzzy identification model parameters. First, using adaptive fuzzy building blocks, with a common set of parameters, we design and study an adaptive controller and an adaptive identification model that have been proposed for a general class of uncertain structure nonlinear dynamic systems.
Decentralized adaptive fuzzy control of robot manipulators.
Jin, Y
1998-01-01
This paper develops a decentralized adaptive fuzzy control scheme for robot manipulators via a combination of genetic algorithm and gradient method. The controller for each link consists of a feedforward fuzzy torque-computing system and a feedback fuzzy PD system. The feedforward fuzzy system is trained and optimized off-line by an improved genetic algorithm, that is to say, not only the parameters but also the structure of the fuzzy system are self-organized. Because genetic algorithm can operate successfully without the system model, no exact inverse dynamics of the robot system are required. The feedback fuzzy PD system, on the other hand, is tuned on-line using gradient method. In this way, the proportional and derivative gains are adjusted properly to keep the closed-loop system stable. The proposed controller has the following merits: (1) it needs no exact dynamics of the robot systems and the computation is time-saving because of the simple structure of the fuzzy systems; and (2) the controller is insensitive to various dynamics and payload uncertainties in robot systems. These are demonstrated by analyses of the computational complexity and various computer simulations. PMID:18255921
Genetic algorithms in adaptive fuzzy control
NASA Technical Reports Server (NTRS)
Karr, C. Lucas; Harper, Tony R.
1992-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.
An adaptive fuzzy-neural controller for multivariable system
Jun Xiao; Jizhong Xiao; Xinhe Xu; Ning Xi
2005-01-01
This paper presents an adaptive fuzzy-neural controller for multivariable system which incorporates the advantage of fuzzy logic and neural network. Inverted pendulum is well known as a multivariable and nonlinear system. And it is very difficult to design and realize a single stage fuzzy controller for the problem of multivariable system as inverted pendulum. After the description of the research
Adaptive fuzzy sliding mode control of nonlinear system
Byungkook Yoo; Woonchul Ham
1998-01-01
In this paper, the fuzzy approximator and sliding mode control (SMC) scheme are considered. We propose two methods of adaptive SMC schemes that the fuzzy logic systems (approximators) are used to approximate the unknown system functions in designing the SMC of nonlinear system. In the first method, a fuzzy logic system is utilized to approximate the unknown function f of
IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 5, NO. 2, MAY 1997 167 Adaptive Fuzzy Control: Experiments
IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 5, NO. 2, MAY 1997 167 Adaptive Fuzzy Control: Experiments. For an introduction, see [4][6]. 2 The FMRLC has also been successfully implemented on a rotational inverted pendulum
Adaptive neuro-fuzzy control of a flexible manipulator
Lianfang Tian; Curtis Collins
2005-01-01
This paper describes an adaptive neuro-fuzzy control system for controlling a flexible manipulator with variable payload. The controller proposed in this paper is comprised of a fuzzy logic controller (FLC) in the feedback configuration and two dynamic recurrent neural networks in the forward path. A dynamic recurrent identification network (RIN) is used to identify the output of the manipulator system,
An adaptive fuzzy controller for permanent-magnet AC servo drives
Le-Huy, H. [Laval Univ., Ste-Foy, Quebec (Canada). Dept. of Electrical Engineering
1995-12-31
This paper presents a theoretical study on a model-reference adaptive fuzzy logic controller for vector-controlled permanent-magnet ac servo drives. In the proposed system, fuzzy logic is used to implement the direct controller as well as the adaptation mechanism. The operation of the direct fuzzy controller and the fuzzy logic based adaptation mechanism is studied. The control performance of the adaptive fuzzy controller is evaluated by simulation for various operating conditions. The results are compared with that provided by a non-adaptive fuzzy controller. The implementation of proposed adaptive fuzzy controller is discussed.
K. Kouzi; M.-S. Nait-Said
2005-01-01
This paper describes a simple but powerful and robust fuzzy tuning procedure for on-line adapting the scaling factors of a fuzzy logic controller (FLC) for the high performance drives induction motor (IM). This fuzzy adaptation does not require the knowledge of the system model. The improvements and the performances of the proposed controller based on IM drive are investigated and
K. Kouzi; L. Mokrani; M.-S. Nait-Said
2003-01-01
This paper presents a new design of fuzzy logic controller with fuzzy adapted gains (FLC with FAG) for speed regulation of an indirect field-oriented induction motor (IM). Firstly the speed regulation by classical fuzzy logic controller (FLC) is presented. Secondly speed regulation based on suggested FLC with FAG is proposed. The improvements and the performances of the proposed controller based
Adaptive Fuzzy Control of a Direct Drive Motor
NASA Technical Reports Server (NTRS)
Medina, E.; Kim, Y. T.; Akbaradeh-T., M. -R.
1997-01-01
This paper presents a state feedback adaptive control method for position and velocity control of a direct drive motor. The proposed control scheme allows for integrating heuristic knowledge with mathematical knowledge of a system. It performs well even when mathematical model of the system is poorly understood. The controller consists of an adaptive fuzzy controller and a supervisory controller. The supervisory controller requires only knowledge of the upper bound and lower bound of the system parameters. The fuzzy controller is based on fuzzy basis functions and states of the system. The adaptation law is derived based on the Lyapunov function which ensures that the state of the system asymptotically approaches zero. The proposed controller is applied to a direct drive motor with payload and parameter uncertainty, and the effectiveness is verified by simulation results.
Adaptive Fuzzy Control of a Direct Drive Motor: Experimental Aspects
NASA Technical Reports Server (NTRS)
Medina, E.; Akbarzadeh-T, M.-R.; Kim, Y. T.
1998-01-01
This paper presents a state feedback adaptive control method for position and velocity control of a direct drive motor. The proposed control scheme allows for integrating heuristic knowledge with mathematical knowledge of a system. It performs well even when mathematical model of the system is poorly understood. The controller consists of an adaptive fuzzy controller and a supervisory controller. The supervisory controller requires only knowledge of the upper bound and lower bound of the system parameters. The fuzzy controller is based on fuzzy basis functions and states of the system. The adaptation law is derived based on the Lyapunov function which ensures that the state of the system asymptotically approaches zero. The proposed controller is applied to a direct drive motor with payload and parameter uncertainty, and the effectiveness is experimentally verified. The real-time performance is compared with simulation results.
ADAPTIVE FUZZY CONTROL FOR UNDERWATER HYDRAULIC MANIPULATORS
Leonardo Bittencourt Testi; Bruno Cardozo dos Santos; Max Suell Dutra
2004-01-01
Underwater hydraulic manipulators are usually systems hard to be modeled and present strong non-linearities in its dynamics behavior. These types of manipulators are operated, nowadays, in a master-slave configuration with simple control algorithms performing tasks in hazardous and unstructured environments. In such conditions only low accuracy simple tasks can be performed. This paper presents the application of a special fuzzy
Fuzzy Self-Adaptive PID Controller for Freeway Ramp Metering
Tao Jiang; Xinrong Liang
2009-01-01
Aiming at the nonlinear and time-varying characteristics of freeway traffic system, a fuzzy self-adaptive PID controller is designed and applied to freeway ramp metering in this paper. A traffic flow model to describe the freeway flow process is firstly built. Based on the model and in conjunction with nonlinear feedback theory, a fuzzy-PID ramp controller is then designed. The ramp
Li, Yangmin
Dynamic Modeling and Adaptive Neural-Fuzzy Control for Nonholonomic Mobile Manipulators Moving on a Slope 1 Dynamic Modeling and Adaptive Neural-Fuzzy Control for Nonholonomic Mobile Manipulators Moving on a slope makes them even more complex. Neural networks (NNs) and fuzzy logic systems have been widely used
IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 10, NO. 5, OCTOBER 2002 583 Adaptive Neural/Fuzzy Control (in the form of TakagiSugeno fuzzy systems or radial basis function neural networks) are used approximation structures with universal approx- imation properties (such as neural networks or fuzzy systems
Robust Adaptive Fuzzy Tracking Control of Stochastic Neuron Systems
Huiyan Li; Jiang Wang; Qitao Jin; Bin Deng; Xile Wei; Yanqiu Che
2012-01-01
Nerve cells communicate by generating and transmitting action potentials. Annihilation of neural oscillation by functional electrical stimulation is a promising treatment modality in neural diseases. In this paper, a robust adaptive fuzzy tracking control is proposed for stochastic Hodgkin-Huxley (HH) neuron systems to generate a desired reference response in spite of environmental noises, uncertain initial values, and diffusion currents from
Fuzzy Adaptive Control for Intelligent Autonomous Space Exploration Problems
NASA Technical Reports Server (NTRS)
Esogbue, Augustine O.
1998-01-01
The principal objective of the research reported here is the re-design, analysis and optimization of our newly developed neural network fuzzy adaptive controller model for complex processes capable of learning fuzzy control rules using process data and improving its control through on-line adaption. The learned improvement is according to a performance objective function that provides evaluative feedback; this performance objective is broadly defined to meet long-range goals over time. Although fuzzy control had proven effective for complex, nonlinear, imprecisely-defined processes for which standard models and controls are either inefficient, impractical or cannot be derived, the state of the art prior to our work showed that procedures for deriving fuzzy control, however, were mostly ad hoc heuristics. The learning ability of neural networks was exploited to systematically derive fuzzy control and permit on-line adaption and in the process optimize control. The operation of neural networks integrates very naturally with fuzzy logic. The neural networks which were designed and tested using simulation software and simulated data, followed by realistic industrial data were reconfigured for application on several platforms as well as for the employment of improved algorithms. The statistical procedures of the learning process were investigated and evaluated with standard statistical procedures (such as ANOVA, graphical analysis of residuals, etc.). The computational advantage of dynamic programming-like methods of optimal control was used to permit on-line fuzzy adaptive control. Tests for the consistency, completeness and interaction of the control rules were applied. Comparisons to other methods and controllers were made so as to identify the major advantages of the resulting controller model. Several specific modifications and extensions were made to the original controller. Additional modifications and explorations have been proposed for further study. Some of these are in progress in our laboratory while others await additional support. All of these enhancements will improve the attractiveness of the controller as an effective tool for the on line control of an array of complex process environments.
Neural and Fuzzy Adaptive Control of Induction Motor Drives
Bensalem, Y.; Sbita, L.; Abdelkrim, M. N.
2008-06-12
This paper proposes an adaptive neural network speed control scheme for an induction motor (IM) drive. The proposed scheme consists of an adaptive neural network identifier (ANNI) and an adaptive neural network controller (ANNC). For learning the quoted neural networks, a back propagation algorithm was used to automatically adjust the weights of the ANNI and ANNC in order to minimize the performance functions. Here, the ANNI can quickly estimate the plant parameters and the ANNC is used to provide on-line identification of the command and to produce a control force, such that the motor speed can accurately track the reference command. By combining artificial neural network techniques with fuzzy logic concept, a neural and fuzzy adaptive control scheme is developed. Fuzzy logic was used for the adaptation of the neural controller to improve the robustness of the generated command. The developed method is robust to load torque disturbance and the speed target variations when it ensures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the IM designed controller.
-Output Adaptive Fuzzy/Neural Control Ra´ul Ord´o~nez and Kevin M. Passino Abstract-- In this letter, stable direct--Direct adaptive control, fuzzy control, indirect adaptive control, MIMO nonlinear systems, neural control. I. INTRODUCTION FUZZY systems and neural networks-based control methodologies have emerged in recent years
Adaptive process control using fuzzy logic and genetic algorithms
NASA Technical Reports Server (NTRS)
Karr, C. L.
1993-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Hany M. Hasanien; S. M. Muyeen; Junji Tamura
2010-01-01
This paper presents a novel adaptive neuro-fuzzy controller applies on transverse flux linear motor for controlling its speed. The proposed controller presents fuzzy logic controller with self tuning scaling factors based on artificial neural network structure. It has two input variables and one control output variable. Firstly the fuzzy logic control rules are described then NN architecture is represented to
Hao Li; Qiuyun Mo; Zhilin Zhao
2010-01-01
A novel direct torque control strategy, using genetic algorithm on-line to optimize the fuzzy PI controller, is proposed. In this approach, according to speed error and its first time derivative, the proportional coefficient Kp and integral coefficient Ki can be on-line adjusted by fuzzy adaptive PI speed regulation, and the fuzzy logic adapter parameters are optimized by genetic algorithm to
Global asymptotic stabilization using adaptive fuzzy PD control.
Pan, Yongping; Yu, Haoyong; Sun, Tairen
2015-03-01
It is well-known that standard adaptive fuzzy control (AFC) can only guarantee uniformly ultimately bounded stability due to inherent fuzzy approximation errors (FAEs). This paper proves that standard AFC with proportional-derivative (PD) control can guarantee global asymptotic stabilization even in the presence of FAEs for a class of uncertain affine nonlinear systems. Variable-gain PD control is designed to globally stabilize the plant. An optimal FAE is shown to be bounded by the norm of the plant state vector multiplied by a globally invertible and nondecreasing function, which provides a pivotal property for stability analysis. Without discontinuous control compensation, the closed-loop system achieves global and partially asymptotic stability in the sense that all plant states converge to zero. Compared with previous adaptive approximation-based global/asymptotic stabilization approaches, the major advantage of our approach is that global stability and asymptotic stabilization are achieved concurrently by a much simpler control law. Illustrative examples have further verified the theoretical results. PMID:25122847
An ART-based fuzzy controller for the adaptive navigation of a quadruped robot
Xuedong Chen; Keigo Watanabe; Kazuo Kiguchi; Kiyotaka Izumi
2002-01-01
An adaptive-resonance theory (ART)-based fuzzy controller is presented for the adaptive navigation of a quadruped robot in cluttered environments, by incorporating the capability of ART in stable category recognition into fuzzy-logic control for selecting the adequate rule base. The environment category and navigation mechanism are first described for the quadruped robot. The ART-based fuzzy controller, including an ART-based environment recognizer,
FPGA-Realization of Adaptive Fuzzy Controller for the Linear X-Y Table
Ying-shieh Kung; Le Thi Van Anh; Shin-hung Jou
2008-01-01
This study presents an adaptive fuzzy controller for linear X-Y table based on FPGA (Field Programmable Gate Array) technology. The linear X-Y table is driven by two PMLSMs (Permanent Magnet Linear Synchronous Motors). Firstly, a mathematic modeled for PMLSM drive is defined. Secondly, to increase the performance of the PMLSM drive system, an AFC (Adaptive Fuzzy Controller) constructed by a
Fuzzy adaptive process control of resistance spot welding with a current reference model
Xingqiao Chen; K. Araki
1997-01-01
This paper describes a fuzzy adaptive process control scheme for resistance spot welding and proposes a new method identifying dynamic welding resistance to estimate the spot welding process. An optimal current reference model is also founded, which would modify its output according to the stages in the spot welding process. Then a fuzzy adaptive real time process control system is
A New Robust Adaptive-Fuzzy Control Method Applied to Quadrotor Helicopter Stabilization
C. Coza; C. J. B. Macnab
2006-01-01
A new method for adaptive-fuzzy control achieves stabilization of a quadrotor helicopter in the presence of sinusoidal wind disturbance. Techniques traditionally used in adaptive control for robust parameter updates may not be sufficient for fuzzy schemes. In particular, e-modification may result in the fuzzy-membership centers drifting to large values when persistent oscillations are present in the input. These large values
Development of adaptive UPFC supplementary fuzzy controller for power system stability enhancement
T. S. Chung; Xiaodong Yang; D. Z. Fang; C. Y. Chung
2004-01-01
In this paper, a fuzzy logic supplementary controller with gain-varied characteristic is developed for unified power flow controller (UPFC). The attractive feature of the controller is that the gain factor can be adaptively tuned in the control process according to the damping effect. The fuzzy-logic controller is mainly equipped with two fuzzy-logic units with one to switch the UPFC modulation
Robust model reference adaptive control of nonlinear systems using fuzzy systems
CHAIO-SHIUNG CHEN; WEN-LIANG CHEN
1996-01-01
The paper presents a model reference adaptive control architecture for a class of nonlinear dynamic systems, which are either ill-defined or rather complex. The architecture employs fuzzy systems to model the unknown plant nonlinearity. Then an adaptive law is constructed based on these fuzzy systems. Global asymptotic stability of the algorithm is established in the Lyapunov sense and is shown
Adaptive fuzzy control of DC motors using state and output feedback
Gerasimos G. Rigatos
2009-01-01
Conventional PID of state feedback controllers for DC motors have poor performance when changes of the motor or load dynamics take place. To handle this shortcoming adaptive fuzzy control of DC motors is proposed. Neuro-fuzzy networks are used to approximate the unknown motor dynamics. The information needed to generate the control signal comes from feedback of the full state vector
NASA Technical Reports Server (NTRS)
Kopasakis, George
1997-01-01
Performance Seeking Control attempts to find the operating condition that will generate optimal performance and control the plant at that operating condition. In this paper a nonlinear multivariable Adaptive Performance Seeking Control (APSC) methodology will be developed and it will be demonstrated on a nonlinear system. The APSC is comprised of the Positive Gradient Control (PGC) and the Fuzzy Model Reference Learning Control (FMRLC). The PGC computes the positive gradients of the desired performance function with respect to the control inputs in order to drive the plant set points to the operating point that will produce optimal performance. The PGC approach will be derived in this paper. The feedback control of the plant is performed by the FMRLC. For the FMRLC, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for the effective tuning of the FMRLC controller.
A new adaptive configuration of PID type fuzzy logic controller.
Fereidouni, Alireza; Masoum, Mohammad A S; Moghbel, Moayed
2015-05-01
In this paper, an adaptive configuration for PID type fuzzy logic controller (FLC) is proposed to improve the performances of both conventional PID (C-PID) controller and conventional PID type FLC (C-PID-FLC). The proposed configuration is called adaptive because its output scaling factors (SFs) are dynamically tuned while the controller is functioning. The initial values of SFs are calculated based on its well-tuned counterpart while the proceeding values are generated using a proposed stochastic hybrid bacterial foraging particle swarm optimization (h-BF-PSO) algorithm. The performance of the proposed configuration is evaluated through extensive simulations for different operating conditions (changes in reference, load disturbance and noise signals). The results reveal that the proposed scheme performs significantly better over the C-PID controller and the C-PID-FLC in terms of several performance indices (integral absolute error (IAE), integral-of-time-multiplied absolute error (ITAE) and integral-of-time-multiplied squared error (ITSE)), overshoot and settling time for plants with and without dead time. PMID:25530256
Adaptive Fuzzy Sliding Mode Method-Based Position and Anti-swing Control for Overhead Cranes
Lingzhi Cao; Lei Liu
2011-01-01
In this paper, an indirect adaptive fuzzy sliding mode control approach is proposed for anti-swing and position control of overhead cranes taking into account the uncertainty of load mass. The approach employs a sliding surface that couples the trolley motion and load swing dynamics to regulate the trolley position and guarantee the stability of swing dynamics, and applies the fuzzy
Adaptive fuzzy-neural-network control for maglev transportation system.
Wai, Rong-Jong; Lee, Jeng-Dao
2008-01-01
A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies. PMID:18269938
Adaptive fuzzy controller for vehicle active suspensions with particle swarm optimization
NASA Astrophysics Data System (ADS)
Cao, Jiangtao; Li, Ping; Liu, Honghai; Brown, David
2008-10-01
With the particle swarm optimal (PSO) algorithm, an adaptive fuzzy logic controller (AFC) based on interval fuzzy membership functions is proposed for vehicle non-linear active suspension systems. The interval membership functions (IMFs) are utilized in the AFC design to deal with not only non-linearity and uncertainty caused from irregular road inputs and immeasurable disturbance, but also the potential uncertainty of expert's knowledge and experience. The adaptive strategy is designed to self-tune the active force between the lower bounds and upper bounds of interval fuzzy outputs. A case study based on a quarter active suspension model has demonstrated that the proposed adaptive fuzzy controller significantly outperforms conventional fuzzy controllers of an active suspension and a passive suspension.
A. Halvai Niasar; H. Moghbeli; R. Kazemi
2003-01-01
This paper, investigates the traction control of an electric vehicle (EV) that is equipped with two motor drives. A new yaw moment control scheme via an Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed. The ANFIS is an attractive compromise between the adaptability of a neural network and interpretability of a fuzzy inference system. In a 2WD EV, because of independent
Zhang, Ying-Bi
1997-01-01
In this thesis, I propose a fuzzy adaptive connection admission control (CAC) approach for real-time applications in ATM-Based heterogeneous networks (ABHN) where ATM serves as a backbone that connects different LANs by interface devices. This type...
Design of an adaptive fuzzy sliding mode controller for uncertain anti-ship missiles
Zhou Shao-lei; Xue Yu-ting; Fan Zuo-e; He Peng-cheng
2010-01-01
Adaptive fuzzy sliding mode control strategy for the overload control of anti-ship missiles using Lyapunov's stability theory is proposed. The internal dynamics of the system is stabilized based on the output-redefinition technique firstly, then an adaptive controller is adopted to attenuate the unknown parameters and uncertainties; and a sliding mode controller is applied to guarantee the robustness of the system.
Fuzzy-adapted recursive sliding-mode controller design for a nuclear power plant control
Zhengyu Huang; Robert M. Edwards; Kwang Y. Lee
2004-01-01
In this paper, a multi-input multi-output fuzzy-adapted recursive sliding-mode controller (FARSMC) is designed for an advanced boiling water reactor (ABWR) nuclear power plant, to control reactor pressure, reactor water level and turbine power. The FARSMC is intended to replace the existing conventional controllers for the power range of 70% to 100% rated power. The controller has a recursive form that
Performance improvement of a microbial fuel cell based on adaptive fuzzy control.
Fan, Liping; Li, Chong; Boshnakov, Kosta
2014-05-01
Microbial fuel cells have been obtaining more and more attention with the associated abilities of continuous electrical power supply and wastewater treatment. Because of its complicated reaction mechanism and its inherent characteristics of time varying, uncertainty, strong coupling and nonlinearity, there are complex control challenges in microbial fuel cells. In this paper, an adaptive fuzzy control scheme is proposed for the microbial fuel cell system to achieve constant voltage output under different loads. A main fuzzy controller is used to track the set value, and an auxiliary fuzzy controller is applied to adjust the factors of the main controller. Simulation results show that the output voltage can track the given value well. The proposed adaptive fuzzy controller can give better steady-state behavior and faster response, and it improves the running performance of the microbial fuel cell. PMID:24816708
Study on application of adaptive fuzzy control and neural network in the automatic leveling system
NASA Astrophysics Data System (ADS)
Xu, Xiping; Zhao, Zizhao; Lan, Weiyong; Sha, Lei; Qian, Cheng
2015-04-01
This paper discusses the adaptive fuzzy control and neural network BP algorithm in large flat automatic leveling control system application. The purpose is to develop a measurement system with a flat quick leveling, Make the installation on the leveling system of measurement with tablet, to be able to achieve a level in precision measurement work quickly, improve the efficiency of the precision measurement. This paper focuses on the automatic leveling system analysis based on fuzzy controller, Use of the method of combining fuzzy controller and BP neural network, using BP algorithm improve the experience rules .Construct an adaptive fuzzy control system. Meanwhile the learning rate of the BP algorithm has also been run-rate adjusted to accelerate convergence. The simulation results show that the proposed control method can effectively improve the leveling precision of automatic leveling system and shorten the time of leveling.
Li, Yongming; Tong, Shaocheng; Li, Tieshan
2015-01-01
In this paper, an adaptive fuzzy decentralized output feedback control design is presented for a class of interconnected nonlinear pure-feedback systems. The considered nonlinear systems contain unknown nonlinear uncertainties and the states are not necessary to be measured directly. Fuzzy logic systems are employed to approximate the unknown nonlinear functions, and then a fuzzy state observer is designed and the estimations of the immeasurable state variables are obtained. Based on the adaptive backstepping dynamic surface control design technique, an adaptive fuzzy decentralized output feedback control scheme is developed. It is proved that all the variables of the resulting closed-loop system are semi-globally uniformly ultimately bounded, and also that the observer and tracking errors are guaranteed to converge to a small neighborhood of the origin. Some simulation results and comparisons with the existing results are provided to illustrate the effectiveness and merits of the proposed approach. PMID:25051573
Experimental comparative analysis of conventional, fuzzy logic, and adaptive fuzzy logic controllers
Fouad Mrad; Ghassan Deeb
1999-01-01
Conventional control depends on the mathematical model of the plant being controlled. When this model is uncertain, intelligent controllers promise better performance. We aim in this paper to compare conventional control to fuzzy logic control (FLC) experimentally. This will be achieved by constructing a hardware station comprising a plant and implementing different control algorithms for the same load conditions or
Aalborg Universitet Methanol Reformer System Modeling and Control using an Adaptive Neuro-Fuzzy
Andreasen, Søren Juhl
Aalborg Universitet Methanol Reformer System Modeling and Control using an Adaptive Neuro., & Sahlin, S. L. (2012). Methanol Reformer System Modeling and Control using an Adaptive Neuro Neuro-Fuzzy Inference System approach Kristian K. Justesen, John Andersen, Mikkel P. Ehmsen, Søren J
An adaptation method for fuzzy logic controllers in lateral vehicle guidance
Thomas Hessburg; Masayoshi Tomizuka
1995-01-01
A model reference adaptive fuzzy logic controller (MRAFLC) is designed and simulated for a full-sized test vehicle to achieve control of the lateral motion of the vehicle. The structure of the FLC is modularized as a feedback and preview rule base. The parameters of the FLC are tuned automatically using the MRAFLC adaptive tuning scheme. The goal is to make
Fuzzy control of magnetic bearings
NASA Technical Reports Server (NTRS)
Feeley, J. J.; Niederauer, G. M.; Ahlstrom, D. J.
1991-01-01
The use of an adaptive fuzzy control algorithm implemented on a VLSI chip for the control of a magnetic bearing was considered. The architecture of the adaptive fuzzy controller is similar to that of a neural network. The performance of the fuzzy controller is compared to that of a conventional controller by computer simulation.
Tipper, David
IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 16, NO. 5, SEPTEMBER 2005 1147 Fuzzy-Based Adaptive control at an acceptable level. Index Terms--Adaptive bandwidth allocation, fuzzy control, quality Member, IEEE, and David Tipper, Senior Member, IEEE Abstract--This paper presents the use of adaptive
NASA Astrophysics Data System (ADS)
Wu, Zhenhui; Dong, Chaoyang
2006-11-01
Because of nonlinearity and strong coupling of reaction-jet and aerodynamics compound control missile, a missile autopilot design method based on adaptive fuzzy sliding mode control (AFSMC) is proposed in this paper. The universal approximation ability of adaptive fuzzy system is used to approximate the nonlinear function in missile dynamics equation during the flight of high angle of attack. And because the sliding mode control is robustness to external disturbance strongly, the sliding mode surface of the error system is constructed to overcome the influence of approximation error and external disturbance so that the actual overload can track the maneuvering command with high precision. Simulation results show that the missile autopilot designed in this paper not only can track large overload command with higher precision than traditional method, but also is robust to model uncertainty and external disturbance strongly.
Adaptive neuro-fuzzy control of ionic polymer metal composite actuators
NASA Astrophysics Data System (ADS)
Thinh, Nguyen Truong; Yang, Young-Soo; Oh, Il-Kwon
2009-06-01
An adaptive neuro-fuzzy controller was newly designed to overcome the degradation of the actuation performance of ionic polymer metal composite actuators that show highly nonlinear responses such as a straightening-back problem under a step excitation. An adaptive control algorithm with the merits of fuzzy logic and neural networks was applied for controlling the tip displacement of the ionic polymer metal composite actuators. The reference and actual displacements and the change of the error with the electrical inputs were recorded to generate the training data. These data were used for training the adaptive neuro-fuzzy controller to find the membership functions in the fuzzy control algorithm. Software simulation and real-time experiments were conducted by using the Simulink and dSPACE environments. Present results show that the current adaptive neuro-fuzzy controller can be successfully applied to the reliable control of the ionic polymer metal composite actuator for which the performance degrades under long-time actuation.
Adaptive control of a class of nonlinear systems with fuzzy logic
Chun-Yi Su; Yury Stepanenko
1994-01-01
An adaptive tracking control architecture is proposed for a class of continuous-time nonlinear dynamic systems, for which an explicit linear parameterization of the uncertainty in the dynamics is either unknown or impossible. The architecture employs fuzzy systems, which are expressed as a series expansion of basis functions, to adaptively compensate for the plant nonlinearities. Global asymptotic stability of the algorithm
Wai, Rong-Jong; Yang, Zhi-Wei
2008-10-01
This paper focuses on the development of adaptive fuzzy neural network control (AFNNC), including indirect and direct frameworks for an n-link robot manipulator, to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances, and parameter variations. In order to cope with this problem, an indirect AFNNC (IAFNNC) scheme and a direct AFNNC (DAFNNC) strategy are investigated without the requirement of prior system information. In these model-free control topologies, a continuous-time Takagi-Sugeno (T-S) dynamic fuzzy model with online learning ability is constructed to represent the system dynamics of an n-link robot manipulator. In the IAFNNC, an FNN estimator is designed to tune the nonlinear dynamic function vector in fuzzy local models, and then, the estimative vector is used to indirectly develop a stable IAFNNC law. In the DAFNNC, an FNN controller is directly designed to imitate a predetermined model-based stabilizing control law, and then, the stable control performance can be achieved by only using joint position information. All the IAFNNC and DAFNNC laws and the corresponding adaptive tuning algorithms for FNN weights are established in the sense of Lyapunov stability analyses to ensure the stable control performance. Numerical simulations and experimental results of a two-link robot manipulator actuated by dc servomotors are given to verify the effectiveness and robustness of the proposed methodologies. In addition, the superiority of the proposed control schemes is indicated in comparison with proportional-differential control, fuzzy-model-based control, T-S-type FNN control, and robust neural fuzzy network control systems. PMID:18784015
Adaptive fuzzy logic controller for feed drives of a CNC machine tool
Sungchul Jee; Yoram Koren
2004-01-01
This paper introduces an adaptive fuzzy logic controller (AFLC) for precision contour machining which adjusts both input and output membership functions simultaneously. The parameters of the proposed controller are self-tuned in real-time according to a continuous measurement of the performance of the controller itself and estimated disturbance values. The adjustment of the membership functions are restricted within a stable range
Faridoon Shabaninia; Reza Khorshidi
2005-01-01
We present an algorithm of supervisory control with fuzzy logic and adaptive control of a low Earth orbit (LEO) satellite with single-spin stabilization. The error generated by an adaptive control signal can be computed with only the reference dynamics of the model and the damping. A fuzzy logic algorithm is presented that acts as a supervisory control and estimates the
Active control of friction-induced self-excited vibration using adaptive fuzzy systems
NASA Astrophysics Data System (ADS)
Wang, Y. F.; Wang, D. H.; Chai, T. Y.
2011-08-01
Vibration caused by friction is harmful to engineering systems. Understanding the mechanism of such a physical phenomenon and developing some strategies to effectively control the vibration have both theoretical and practical significance. Based on our previous work, this paper deals with a problem of active compensation control of friction-induced self-excited vibration using adaptive fuzzy systems. Comparative studies on control performance are carried out, where a class of adaptive compensation control schemes with various friction models are applied to control a motion dynamics with friction. It is observed that our proposed modeling and control techniques are powerful to eliminate the limit cycle and the steady-state error. Furthermore, robustness of the proposed controller with respect to external disturbances is discussed. Simulation results show that the active controller with adaptive fuzzy friction compensation outperforms other active controllers with compensation terms characterized by three well-known friction models.
Adaptive fuzzy control of underactuated robotic systems with the use of differential flatness theory
NASA Astrophysics Data System (ADS)
Rigatos, Gerasimos G.
2013-10-01
An adaptive fuzzy controller is designed for a class of underactuated nonlinear robotic manipulators, under the constraint that the system's model is unknown. The control algorithm aims at satisfying the H? tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the robotic system into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neuro-fuzzy networks. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H? tracking performance. The efficiency of the proposed adaptive fuzzy control scheme is checked in the case of a 2-DOF planar robotic manipulator that has the structure of a closed-chain mechanism.
NASA Astrophysics Data System (ADS)
Phu, Do Xuan; Shah, Kruti; Choi, Seung-Bok
2014-06-01
This paper presents a new adaptive fuzzy controller and its implementation for the damping force control of a magnetorheological (MR) fluid damper in order to validate the effectiveness of the control performance. An interval type 2 fuzzy model is built, and then combined with modified adaptive control to achieve the desired damping force. In the formulation of the new adaptive controller, an enhanced iterative algorithm is integrated with the fuzzy model to decrease the time of calculation (D Wu 2013 IEEE Trans. Fuzzy Syst. 21 80-99) and the control algorithm is synthesized based on the {{H}^{\\infty }} tracking technique. In addition, for the verification of good control performance of the proposed controller, a cylindrical MR damper which can be applied to the vibration control of a washing machine is designed and manufactured. For the operating fluid, a recently developed plate-like particle-based MR fluid is used instead of a conventional MR fluid featuring spherical particles. To highlight the control performance of the proposed controller, two existing adaptive fuzzy control algorithms proposed by other researchers are adopted and altered for a comparative study. It is demonstrated from both simulation and experiment that the proposed new adaptive controller shows better performance of damping force control in terms of response time and tracking accuracy than the existing approaches.
Djukanovic, M.B.; Calovic, M.S.; Vesovic, B.V.; Sobajic, D.J.
1997-12-01
This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order non-linear hydrogenerator model.
Song, Zhankui; Sun, Kaibiao
2014-01-01
A novel adaptive backstepping sliding mode control (ABSMC) law with fuzzy monitoring strategy is proposed for the tracking-control of a kind of nonlinear mechanical system. The proposed ABSMC scheme combining the sliding mode control and backstepping technique ensure that the occurrence of the sliding motion in finite-time and the trajectory of tracking-error converge to equilibrium point. To obtain a better perturbation rejection property, an adaptive control law is employed to compensate the lumped perturbation. Furthermore, we introduce fuzzy monitoring strategy to improve adaptive capacity and soften the control signal. The convergence and stability of the proposed control scheme are proved by using Lyaponov's method. Finally, numerical simulations demonstrate the effectiveness of the proposed control scheme. PMID:24059943
Flatness-based embedded adaptive fuzzy control of spark ignited engines
NASA Astrophysics Data System (ADS)
Rigatos, Gerasimos; Siano, Pierluigi; Arsie, Ivan
2014-10-01
The paper proposes a differential flatness theory-based adaptive fuzzy controller for spark-ignited (SI) engines. The system's dynamic model is considered to be completely unknown. By applying a change of variables (diffeomorphism) that is based on differential flatness theory the engine's dynamic model is written in the linear canonical (Brunovsky) form. After transforming the SI-engine model into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. These nonlinear terms are approximated with the use of neuro-fuzzy networks while a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. Moreover, using Lyapunov stability analysis it is shown that the adaptive fuzzy control scheme succeeds H? tracking performance, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. The efficiency of the proposed adaptive fuzzy control scheme is checked through simulation experiments.
-Tolerant Adaptive Fuzzy/Neural Control for a Turbine Engine Yixin Diao and Kevin M. Passino, Senior Member, IEEE scheme is designed based on stable adaptive fuzzy/neural con- trol, where its on-line learning, fuzzy systems, neural networks. I. INTRODUCTION MOTIVATED by the growing need for high levels of system
Development of an adaptive neuro-fuzzy method for supply air pressure control in HVAC system
Wu Jian; Cai Wenjian
2000-01-01
An adaptive neuro-fuzzy (ANF) method is developed for the supply air pressure control loop of a heating, ventilation and air-conditioning (HVAC) system. Although a well-tuned PID controller performs well around normal working points, its tolerance to process parameter variations is severely affected due to the nature of PID controllers. The ANF controller developed overcomes this weakness. The controller design involves
Flatness-based embedded adaptive fuzzy control of turbocharged diesel engines
NASA Astrophysics Data System (ADS)
Rigatos, Gerasimos; Siano, Pierluigi; Arsie, Ivan
2014-10-01
In this paper nonlinear embedded control for turbocharged Diesel engines is developed with the use of Differential flatness theory and adaptive fuzzy control. It is shown that the dynamic model of the turbocharged Diesel engine is differentially flat and admits dynamic feedback linearization. It is also shown that the dynamic model can be written in the linear Brunovsky canonical form for which a state feedback controller can be easily designed. To compensate for modeling errors and external disturbances an adaptive fuzzy control scheme is implemanted making use of the transformed dynamical system of the diesel engine that is obtained through the application of differential flatness theory. Since only the system's output is measurable the complete state vector has to be reconstructed with the use of a state observer. It is shown that a suitable learning law can be defined for neuro-fuzzy approximators, which are part of the controller, so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed observer-based adaptive fuzzy control scheme results in H? tracking performance.
Adaptive fuzzy control for inter-vehicle gap keeping
José Eugenio Naranjo; Carlos González; Jesús Reviejo; Ricardo García Rosa; Teresa De Pedro
2003-01-01
There is a broad range of diverse technologies under the generic topic of intelligent transportation systems (ITS) that holds the answer to many of the transportation problems. In this paper, one approach to ITS is presented. One of the most important research topics in this field is adaptive cruise control (ACC). The main features of this kind of controller are
Shahnazi, Reza
2015-01-01
An adaptive fuzzy output feedback controller is proposed for a class of uncertain MIMO nonlinear systems with unknown input nonlinearities. The input nonlinearities can be backlash-like hysteresis or dead-zone. Besides, the gains of unknown input nonlinearities are unknown nonlinear functions. Based on universal approximation theorem, the unknown nonlinear functions are approximated by fuzzy systems. The proposed method does not need the availability of the states and an observer based on strictly positive real (SPR) theory is designed to estimate the states. An adaptive robust structure is used to cope with fuzzy approximation error and external disturbances. The semi-global asymptotic stability of the closed-loop system is guaranteed via Lyapunov approach. The applicability of the proposed method is also shown via simulations. PMID:25104646
Li, Yongming; Tong, Shaocheng; Li, Tieshan
2014-11-25
In this paper, a composite adaptive fuzzy output-feedback control approach is proposed for a class of single-input and single-output strict-feedback nonlinear systems with unmeasured states and input saturation. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a fuzzy state observer is designed to estimate the unmeasured states. By utilizing the designed fuzzy state observer, a serial--parallel estimation model is established. Based on adaptive backstepping dynamic surface control technique and utilizing the prediction error between the system states observer model and the serial--parallel estimation model, a new fuzzy controller with the composite parameters adaptive laws are developed. It is proved that all the signals of the closed-loop system are bounded and the system output can follow the given bounded reference signal. A numerical example and simulation comparisons with previous control methods are provided to show the effectiveness of the proposed approach. PMID:25438335
Flight test results of the fuzzy logic adaptive controller-helicopter (FLAC-H)
NASA Astrophysics Data System (ADS)
Wade, Robert L.; Walker, Gregory W.
1996-05-01
The fuzzy logic adaptive controller for helicopters (FLAC-H) demonstration is a cooperative effort between the US Army Simulation, Training, and Instrumentation Command (STRICOM), the US Army Aviation and Troop Command, and the US Army Missile Command to demonstrate a low-cost drone control system for both full-scale and sub-scale helicopters. FLAC-H was demonstrated on one of STRICOM's fleet of full-scale rotary-winged target drones. FLAC-H exploits fuzzy logic in its flight control system to provide a robust solution to the control of the helicopter's dynamic, nonlinear system. Straight forward, common sense fuzzy rules governing helicopter flight are processed instead of complex mathematical models. This has resulted in a simplified solution to the complexities of helicopter flight. Incorporation of fuzzy logic reduced the cost of development and should also reduce the cost of maintenance of the system. An adaptive algorithm allows the FLAC-H to 'learn' how to fly the helicopter, enabling the control system to adjust to varying helicopter configurations. The adaptive algorithm, based on genetic algorithms, alters the fuzzy rules and their related sets to improve the performance characteristics of the system. This learning allows FLAC-H to automatically be integrated into a new airframe, reducing the development costs associated with altering a control system for a new or heavily modified aircraft. Successful flight tests of the FLAC-H on a UH-1H target drone were completed in September 1994 at the White Sands Missile Range in New Mexico. This paper discuses the objective of the system, its design, and performance.
Adaptive Fuzzy Hysteresis Band Current Controller for Four-Wire Shunt Active Filter
NASA Astrophysics Data System (ADS)
Hamoudi, F.; Chaghi, A.; Amimeur, H.; Merabet, E.
2008-06-01
This paper presents an adaptive fuzzy hysteresis band current controller for four-wire shunt active power filters to eliminate harmonics and to compensate reactive power in distribution systems in order to keep currents at the point of common coupling sinusoidal and in phase with the corresponding voltage and the cancel neutral current. The conventional hysteresis band known for its robustness and its advantage in current controlled applications is adapted with a fuzzy logic controller to change the bandwidth according to the operating point in order to keep the frequency modulation at tolerable limits. The algorithm used to identify the reference currents is based on the synchronous reference frame theory (dq?). Finally, simulation results using Matlab/Simulink are given to validate the proposed control.
Fuzzy-rule-based Adaptive Resource Control for Information Sharing in P2P Networks
NASA Astrophysics Data System (ADS)
Wu, Zhengping; Wu, Hao
With more and more peer-to-peer (P2P) technologies available for online collaboration and information sharing, people can launch more and more collaborative work in online social networks with friends, colleagues, and even strangers. Without face-to-face interactions, the question of who can be trusted and then share information with becomes a big concern of a user in these online social networks. This paper introduces an adaptive control service using fuzzy logic in preference definition for P2P information sharing control, and designs a novel decision-making mechanism using formal fuzzy rules and reasoning mechanisms adjusting P2P information sharing status following individual users' preferences. Applications of this adaptive control service into different information sharing environments show that this service can provide a convenient and accurate P2P information sharing control for individual users in P2P networks.
A fuzzy adaptive learning control network with on-line structure and parameter learning.
Lin, C J
1996-11-01
This paper addresses a general connectionist model, called Fuzzy Adaptive Learning Control Network (FALCON), for the realization of a fuzzy logic control system. An on-line supervised structure/parameter learning algorithm is proposed for constructing the FALCON dynamically. It combines the backpropagation learning scheme for parameter learning and the fuzzy ART algorithm for structure learning. The supervised learning algorithm has some important features. First of all, it partitions the input state space and output control space using irregular fuzzy hyperboxes according to the distribution of training data. In many existing fuzzy or neural fuzzy control systems, the input and output spaces are always partitioned into "grids". As the number of input/output variables increase, the number of partitioned grids will grow combinatorially. To avoid the problem of combinatorial growing of partitioned grids in some complex systems, the proposed learning algorithm partitions the input/output spaces in a flexible way based on the distribution of training data. Second, the proposed learning algorithm can create and train the FALCON in a highly autonomous way. In its initial form, there is no membership function, fuzzy partition, and fuzzy logic rule. They are created and begin to grow as the first training pattern arrives. The users thus need not give it any a priori knowledge or even any initial information on these. In some real-time applications, exact training data may be expensive or even impossible to obtain. To solve this problem, a Reinforcement Fuzzy Adaptive Learning Control Network (RFALCON) is further proposed. The proposed RFALCON is constructed by integrating two FALCONs, one FALCON as a critic network, and the other as an action network. By combining temporal difference techniques, stochastic exploration, and a proposed on-line supervised structure/parameter learning algorithm, a reinforcement structure/parameter learning algorithm is proposed, which can construct a RFALCON dynamically through a reward/penalty signal. The ball and beam balancing system is presented to illustrate the performance and applicability of the proposed models and learning algorithms. PMID:9040059
Robust tracking control of 4-SPS(PS) type parallel manipulator via adaptive fuzzy logic approach
Dachang Zhu; Cai Jinbao; Yuefa Fang
2008-01-01
The dynamic behavior of electro-hydraulic driven parallel manipulators is highly nonlinear system, the nonlinear behavior arising from load friction as well as the valve flow-pressure drop relationship. This paper is concerned with the robust tracking control of electro-hydraulic driven parallel manipulators with the model uncertainties. An adaptive fuzzy controller is used to estimate the uncertainties of electro-hydraulic system, including the
Adaptive-Neuro-Fuzzy-Based Sensorless Control of a Smart-Material Actuator
Ali Sadighi; Won-jong Kim
2011-01-01
In this paper, adaptive-neuro-fuzzy-based sensorless control of a smart-material actuator is presented. The smart ma- terial that we used to develop a novel type of linear actuator is Terfenol-D. The peristaltic motion in the actuator is generated by inducing a traveling magnetic field inside the Terfenol-D element. The sensorless control of the actuator is based on an observation illustrating a
An adaptive fuzzy logic: fuzzy squared
D. Molinari; Zei Shuk Park; M. Otha
1995-01-01
This paper introduces a “continuous logic” constructed as a weighted sum of fuzzy AND and OR logics, called fuzzy squared. In order to provide adaptability to it, a parameter must be selected. A network model and a modified genetic algorithm are considered to meet the fuzzy squared logic needs. Simulations demonstrate that this logic can learn the behavior of a
Functional Based Adaptive and Fuzzy Sliding Controller for Non-Autonomous Active Suspension System
NASA Astrophysics Data System (ADS)
Huang, Shiuh-Jer; Chen, Hung-Yi
In this paper, an adaptive sliding controller is developed for controlling a vehicle active suspension system. The functional approximation technique is employed to substitute the unknown non-autonomous functions of the suspension system and release the model-based requirement of sliding mode control algorithm. In order to improve the control performance and reduce the implementation problem, a fuzzy strategy with online learning ability is added to compensate the functional approximation error. The update laws of the functional approximation coefficients and the fuzzy tuning parameters are derived from the Lyapunov theorem to guarantee the system stability. The proposed controller is implemented on a quarter-car hydraulic actuating active suspension system test-rig. The experimental results show that the proposed controller suppresses the oscillation amplitude of the suspension system effectively.
Luo, Shaohua
2014-09-01
This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaos of PMSM and show the effectiveness and robustness of the proposed method. PMID:25273215
NASA Astrophysics Data System (ADS)
Luo, Shaohua
2014-09-01
This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaos of PMSM and show the effectiveness and robustness of the proposed method.
Fuzzy neural network-based adaptive control for a class of uncertain nonlinear stochastic systems.
Chen, C L Philip; Liu, Yan-Jun; Wen, Guo-Xing
2014-05-01
This paper studies an adaptive tracking control for a class of nonlinear stochastic systems with unknown functions. The considered systems are in the nonaffine pure-feedback form, and it is the first to control this class of systems with stochastic disturbances. The fuzzy-neural networks are used to approximate unknown functions. Based on the backstepping design technique, the controllers and the adaptation laws are obtained. Compared to most of the existing stochastic systems, the proposed control algorithm has fewer adjustable parameters and thus, it can reduce online computation load. By using Lyapunov analysis, it is proven that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded in probability and the system output tracks the reference signal to a bounded compact set. The simulation example is given to illustrate the effectiveness of the proposed control algorithm. PMID:24132033
Adaptive fuzzy controller for thermal comfort inside the air-conditioned automobile chamber
Tong, L.; Yu, B.; Chen, Z.; Yang, K.
1999-07-01
In order to meet the passengers' demand for thermal comfort, the adaptive fuzzy logic control design methodology is applied for the automobile airconditioner system. In accordance with the theory of air flow and heat transfer, the air temperature field inside the airconditioned automobile chamber is simulated by a set of simplified half-empirical formula. Then, instead of PMV (Predicted Mean Vote) criterion, RIV (Real Individual Vote) criterion is adopted as the base of the control for passengers' thermal comfort. The proposed controller is applied to the air temperature regulation at the individual passenger position. The control procedure is based on partitioning the state space of the system into cell-groups and fuzzily quantificating the state space into these cells. When the system model has some parameter perturbation, the controller can also adjust its control parameters to compensate for the perturbation and maintain the good performance. The learning procedure shows its ideal effect in both computer simulation and experiments. The final results demonstrate the ideal performance of this adaptive fuzzy controller.
Disturbance road adaptive driving control of power-assisted wheelchair using fuzzy inference.
Seki, Hirokazu; Kiso, Atsushi
2011-01-01
This paper describes a novel driving control scheme of electric power-assisted wheelchairs for assistive driving on various large disturbance roads. The "electric power-assisted wheelchair" which assists the driving force by electric motors is expected to be widely used as a mobility support system for elderly people and disabled people; however, there are lots of large disturbance roads such as uphill roads and rough roads and operators need to row the hand-rims with the larger power load on such roads in order to obtain the enough driving velocity. For example the wheelchair might move backward on uphill roads due to the driving torque shortage. Therefore this study proposes a fuzzy algorithm based adaptive control scheme in order to realize the assistive driving without the operator's power load on large disturbance roads. The proposed fuzzy rules are designed from the driving distance information and the control parameters are inferred by the fuzzy algorithm. The assisted torque can be adjusted so that the enough distance and velocity are kept even on large disturbance roads. Driving experimental results are provided to verify the effectiveness of the proposed control system. PMID:22254627
Chang, Yeong-Hwa; Chan, Wei-Shou
2014-02-01
This paper presents a new robust adaptive control method for a class of nonlinear systems subject to uncertainties. The proposed approach is based on an adaptive dynamic surface control, where the system uncertainties are approximately modeled by interval type-2 fuzzy neural networks. In this paper, the robust stability of the closed-loop system is guaranteed by the Lyapunov theorem, and all error signals are shown to be uniformly ultimately bounded. In addition to simulations, the proposed method is applied to a real ball-and-beam system for performance evaluations. To highlight the system robustness, different initial settings of ball-and-beam parameters are considered. Simulation and experimental results indicate that the proposed control scheme has superior responses, compared to conventional dynamic surface control. PMID:23757550
Adaptive fuzzy logic control of a DC-DC boost converter with large parametric and load uncertainties
Hicham Chaoui; Suruz Miah; Pierre Sicard
2010-01-01
In this manuscript, we propose an adaptive fuzzy logic controller for a DC-DC boost converter with parametric and load uncertainties. The control strategy aims to achieve accurate voltage tracking with unknown dynamics, highly parameter and load variations, and no current sensing. Therefore, robustness to uncertainties of large magnitudes is achieved without the inner current control loop, which reduces the number
Chemachema, Mohamed
2012-12-01
A direct adaptive control algorithm, based on neural networks (NN) is presented for a class of single input single output (SISO) nonlinear systems. The proposed controller is implemented without a priori knowledge of the nonlinear systems; and only the output of the system is considered available for measurement. Contrary to the approaches available in the literature, in the proposed controller, the updating signal used in the adaptive laws is an estimate of the control error, which is directly related to the NN weights instead of the tracking error. A fuzzy inference system (FIS) is introduced to get an estimate of the control error. Without any additional control term to the NN adaptive controller, all the signals involved in the closed loop are proven to be exponentially bounded and hence the stability of the system. Simulation results demonstrate the effectiveness of the proposed approach. PMID:23037773
Application of fuzzy adaptive control to a MIMO nonlinear time-delay pump-valve system.
Lai, Zhounian; Wu, Peng; Wu, Dazhuan
2015-07-01
In this paper, a control strategy to balance the reliability against efficiency is introduced to overcome the common off-design operation problem in pump-valve systems. The pump-valve system is a nonlinear multi-input-multi-output (MIMO) system with time delays which cannot be accurately measured but can be approximately modeled using Bernoulli Principle. A fuzzy adaptive controller is applied to approximate system parameters and achieve the control of delay-free model since the system model is inaccurate and the direct feedback linearization method cannot be applied. An extended Smith predictor is introduced to compensate time delays of the system using the inaccurate system model. The experiment is carried out to verify the effectiveness of the control strategy whose results show that the control performance is well achieved. PMID:25681018
A neural fuzzy controller learning by fuzzy error propagation
NASA Technical Reports Server (NTRS)
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.
Universal Approximation of Mamdani Fuzzy Controllers and Fuzzy Logical Controllers
NASA Technical Reports Server (NTRS)
Yuan, Bo; Klir, George J.
1997-01-01
In this paper, we first distinguish two types of fuzzy controllers, Mamdani fuzzy controllers and fuzzy logical controllers. Mamdani fuzzy controllers are based on the idea of interpolation while fuzzy logical controllers are based on fuzzy logic in its narrow sense, i.e., fuzzy propositional logic. The two types of fuzzy controllers treat IF-THEN rules differently. In Mamdani fuzzy controllers, rules are treated disjunctively. In fuzzy logic controllers, rules are treated conjunctively. Finally, we provide a unified proof of the property of universal approximation for both types of fuzzy controllers.
2005-01-01
Engineering Applications of Artificial Intelligence 18 (2005) 317334 Stable auto-tuning of hybrid adaptive fuzzy/neural controllers for nonlinear systems Hazem N. Nounoua,Ã, Kevin M. Passinob a Department of Electrical Engineering, United Arab Emirates University, P.O. Box 17555, Al-Ain, UAE b Department
Efe, Mehmet Önder
@ieee.org Abstract: Adaptive neuro-fuzzy inference systems exhibit both the numeric power of neural networks, which is a suitable combination of neural networks and fuzzy inference systems, can exhibit the above in an Adaptive Neuro Fuzzy Inference System M. Onder Efe Bogazici University, Electrical and Electronic
Fuzzy Control Strategies in Human Operator and Sport Modeling
Ivancevic, Tijana T; Markovic, Sasa
2009-01-01
The motivation behind mathematically modeling the human operator is to help explain the response characteristics of the complex dynamical system including the human manual controller. In this paper, we present two different fuzzy logic strategies for human operator and sport modeling: fixed fuzzy-logic inference control and adaptive fuzzy-logic control, including neuro-fuzzy-fractal control. As an application of the presented fuzzy strategies, we present a fuzzy-control based tennis simulator.
Fuzzy Logic Programming and Fuzzy Control
Giangiacomo Gerla
2005-01-01
The paper concerns fuzzy logic programming. As an example, we show that is not restrictive to confine ourselves to fuzzy Herbrand interpretations in giving a semantics for fuzzy programs. Also, we show that the resulting apparatus gives a unifying theoretical framework for fuzzy control.
Adaptive fuzzy control for mobile robot obstacle avoidance based on virtual line path tracking
Baoguo Li; Chunxi Zhang
2006-01-01
In unknown environments, a mobile robot moves toward a target point by tracking a virtual line path that connects the start point and the target point. Errors from wheel diameters, assembly and gearing may bring uncertain disturbance to the velocity of the mobile robot and enlarge the path tracking error. An adaptive fuzzy logic system is used to approximate the
Chih-lyang Hwang; Li-jui Chang
2008-01-01
In this paper, a navigation system is developed. The system includes path tracking and obstacle avoidance apparatus for a car-like wheeled robot (CLWR) within an Internet-based smart-space (IBSS) using fuzzy-neural adaptive control (FNAC). Two distributed charge-coupled device (CCD) cameras are installed to capture both the dynamic pose of the CLWR and the obstacle. Based on the control authority of these
Chen, Hung-Yi; Liang, Jin-Wei; Wu, Jia-Wei
2013-01-01
This paper presents an intelligent control strategy to overcome nonlinear and time-varying characteristics of a diaphragm-type pneumatic vibration isolator (PVI) system. By combining an adaptive rule with fuzzy and sliding-mode control, the method has online learning ability when it faces the system's nonlinear and time-varying behaviors during an active vibration control process. Since the proposed scheme has a simple structure, it is easy to implement. To validate the proposed scheme, a composite control which adopts both chamber pressure and payload velocity as feedback signal is implemented. During experimental investigations, sinusoidal excitation at resonance and random-like signal are input on a floor base to simulate ground vibration. Performances obtained from the proposed scheme are compared with those obtained from passive system and PID scheme to illustrate the effectiveness of the proposed intelligent control. PMID:23820746
NASA Astrophysics Data System (ADS)
Phu, Do Xuan; Shin, Do Kyun; Choi, Seung-Bok
2015-08-01
This paper presents a new adaptive fuzzy controller featuring a combination of two different control methodologies: H infinity control technique and sliding mode control. It is known that both controllers are powerful in terms of high performance and robust stability. However, both control methods require an accurate dynamic model to design a state variable based controller in order to maintain their advantages. Thus, in this work a fuzzy control method which does not require an accurate dynamic model is adopted and two control methodologies are integrated to maintain the advantages even in an uncertain environment of the dynamic system. After a brief explanation of the interval type 2 fuzzy logic, a new adaptive fuzzy controller associated with the H infinity control and sliding mode control is formulated on the basis of Lyapunov stability theory. Subsequently, the formulated controller is applied to vibration control of a vehicle seat equipped with magnetorheological fluid damper (MR damper in short). An experimental setup for realization of the proposed controller is established and vibration control performances such as acceleration at the driver’s seat are evaluated. In addition, in order to demonstrate the effectiveness of the proposed controller, a comparative work with two existing controllers is undertaken. It is shown through simulation and experiment that the proposed controller can provide much better vibration control performance than the two existing controllers.
AutoTuning Fuzzy PID Control of a Pendubot System
Chia-Ju Wu; Tsong-Li Lee; Yu-Yi Fu; Chia-Ju Li-Chun Lai
2007-01-01
The goal of this paper is to design a fuzzy proportional-integral-derivative (PID) controller to swing up the pendubot and maintain it in an unstable inverted equilibrium position. Different from PID controllers with fixed gains, the fuzzy PID controller is expressed in terms of fuzzy rules such that the PID gains are adaptive and the fuzzy PID controller has more flexibility
Adapting Fuzzy Formal Concept Analysis for Fuzzy Description Logics
Baader, Franz
Adapting Fuzzy Formal Concept Analysis for Fuzzy Description Logics Felix Distel Institute@tcs.inf.tu-dresden.de Abstract. Fuzzy Logics have been applied successfully within both For- mal Concept Analysis and Description Logics. Especially in the latter field, Fuzzy Logics have been gaining significant momentum during
Adaptive fuzzy PID temperature control system based on single-chip computer for the autoclave
NASA Astrophysics Data System (ADS)
Zhang, F.; Wang, J.; Fu, S. L.; He, Z. T.; Li, X. P.
2008-12-01
The autoclave is one of main preparation equipments of crystal preparation by hydrothermal method. The preparation temperature will seriously influence crystals quality and crystals size at high temperature, how to measure and control precisely the autoclave temperature can be of real significance. The characteristic of hysteresis, nonlinearity and difficulty to acquire the precise mathematical model existing in the temperature control of the autoclave was researched. The general PID controller adopted usually in the autoclave temperature control system is hard to improve temperature control performance. Based on the advantages of fuzzy controller that does not depend on the precise mathematical model and the stabilization of PID controller, single-chip computer integrated fuzzy PID control algorithm is adopted, and the temperature system is designed, the foundational working principle was discussed. The control system includes SCM (AT89C52), temperature sensor, A/D converter circuit and corresponding circuit and interface, can make the autoclave temperature measure and control accurately. The system hardware includes main circuit, thyristor drive circuit, audible and visual alarm circuit, watchdog circuit, clock circuit, keyboard and display circuit so on, which can achieve gathering, analyzing, comparing and controlling the autoclave temperature parameter. The program of control system includes the treatment and collection of temperature data, the dynamic display program, the fuzzy PID control system, the audible and visual alarm program, et al, and the system's main software, which includes initialization, key-press processing, input processing, display, and the fuzzy PID control program was analyzed. The results showed that the fuzzy PID control system makes the adjustment time of temperature decreased and the precision of temperature control improved, the quality and the crystals size of the preparation crystals can achieve the expect experiment results.
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.
1992-01-01
Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.
Automatic Design of Hierarchical Fuzzy Controllers Using Genetic Algorithms
Hoffmann, Frank
to hidden neurons in neural networks. The architecture of the hierarchical fuzzy controller is shown in Fig controlling a dynamic system. Besides adaptive fuzzy systems and neuro fuzzy systems, genetic algorithms (GAAutomatic Design of Hierarchical Fuzzy Controllers Using Genetic Algorithms Frank Ho mann, Gerd P
Embedded Control Systems Research Lab Fuzzy Membership Function
Simon, Dan
Embedded Control Systems Research Lab Fuzzy Membership Function Optimization for System Conference June 4, 2006 #12;Embedded Control Systems Research Lab Overview Real system Fuzzy logic system inputs + Adaptation algorithm #12;Embedded Control Systems Research Lab Overview 1. Introduction 2
NASA Astrophysics Data System (ADS)
Fallah-Ghalhary, G.-A.; Habibi Nokhandan, M.; Mousavi Baygi, M.
2009-04-01
This paper aims to study the relationship between climatic large-scale synoptic patterns and rainfall in Khorasan Razavi Province. The adaptive neural-fuzzy inference system was used in this study to predict rainfall in the period between April and June in Khorasan Razavi Province. We first analyzed the relationship between average regional rainfall and the changes in synoptic patterns including sea-level pressure, sea-level pressure difference, sea-level temperature, temperature difference between sea level and 1000-mb level, the temperature of 700-mb level, the thickness between 500 and 1000-mb levels, the relative humidity of 300-mb level and precipitable water. In the selection of these regions, which include a number of locations in the Persian Gulf, the Oman Sea, the Black Sea, the Caspian, the Mediterranean, the Adriatic, the Red Sea, the Eden Gulf, the Atlantic, the Indian Ocean, and Siberia, we have examined the effect of synoptic patterns in these regions on the rainfall in the northeast region of Iran. Then, the adaptive neural-fuzzy inference system in the period 1970 -1997 has been taught. Finally, the rainfall in the period 1998-2007 has been predicted. The results show that the adaptive neural-fuzzy inference system can predict the rainfall with reasonable accuracy in 90 percent of the years
Adaptive Fuzzy Systems for Multichannel Signal Processing
Plataniotis, Konstantinos N.
Adaptive Fuzzy Systems for Multichannel Signal Processing KONSTANTINOS N. PLATANIOTIS, MEMBER, IEEE perspec- tives, such as transform domain filtering, classical least-square approaches, neural networks filter designs. The strong potential of fuzzy adaptive filters for multichannel signal applica- tions
Liu, Zhi; Wang, Fang; Zhang, Yun; Chen, Xin; Chen, C L Philip
2014-10-01
This paper focuses on an input-to-state practical stability (ISpS) problem of nonlinear systems which possess unmodeled dynamics in the presence of unstructured uncertainties and dynamic disturbances. The dynamic disturbances depend on the states and the measured output of the system, and its assumption conditions are relaxed compared with the common restrictions. Based on an input-driven filter, fuzzy logic systems are directly used to approximate the unknown and desired control signals instead of the unknown nonlinear functions, and an integrated backstepping technique is used to design an adaptive output-feedback controller that ensures robustness with respect to unknown parameters and uncertain nonlinearities. This paper, by applying the ISpS theory and the generalized small-gain approach, shows that the proposed adaptive fuzzy controller guarantees the closed-loop system being semi-globally uniformly ultimately bounded. A main advantage of the proposed controller is that it contains only three adaptive parameters that need to be updated online, no matter how many states there are in the systems. Finally, the effectiveness of the proposed approach is illustrated by two simulation examples. PMID:25222716
Xia, Yonghui; Yang, Zijiang; Han, Maoan
2009-07-01
This paper considers the lag synchronization (LS) issue of unknown coupled chaotic delayed Yang-Yang-type fuzzy neural networks (YYFCNN) with noise perturbation. Separate research work has been published on the stability of fuzzy neural network and LS issue of unknown coupled chaotic neural networks, as well as its application in secure communication. However, there have not been any studies that integrate the two. Motivated by the achievements from both fields, we explored the benefits of integrating fuzzy logic theories into the study of LS problems and applied the findings to secure communication. Based on adaptive feedback control techniques and suitable parameter identification, several sufficient conditions are developed to guarantee the LS of coupled chaotic delayed YYFCNN with or without noise perturbation. The problem studied in this paper is more general in many aspects. Various problems studied extensively in the literature can be treated as special cases of the findings of this paper, such as complete synchronization (CS), effect of fuzzy logic, and noise perturbation. This paper presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed adaptive scheme. This research also demonstrates the effectiveness of application of the proposed adaptive feedback scheme in secure communication by comparing chaotic masking with fuzziness with some previous studies. Chaotic signal with fuzziness is more complex, which makes unmasking more difficult due to the added fuzzy logic. PMID:19497816
Fuzzy antiswing crane controllers
Yoon, Ji Sup; Park, Byung Suk; Lee, Jae Sol; Park, Hyun Soo
1994-12-31
Crane operation for handling a heavy load inherently causes a swinging motion at the load due to acceleration or deceleration of the crane. This swing not only diminishes handling safety but also makes the load-handling time longer because the swing should be fully damped before proceeding to the next step of operation. Recently, the iron and steel industries have demanded new transportation technologies for the efficient and safe handling of heavy material such as hot coils. The Nuclear Environment Management Center (NEMAC) subsidiary institute of Korea Atomic Energy Research Institute (KAERI) has developed several types of antiswing controllers for this demand. One of these controllers, which adopts fuzzy logic, has been transferred to these industries, including POSCO, the largest iron and steel company in Korea, and several other commercial product companies. In this paper, design procedures of the fuzzy controller and its implementation results are described. The fuzzy controller consists of three sequential stages in which fuzzy variables are automatically changed as follows: (1) The fuzzy acceleration controller is designed to rapidly reach the desired initial velocity. As input fuzzy variables, the velocity error (between the desired velocity and the actual velocity) and the distance error (difference between the desired distance at the acceleration stage and actual distance) are adopted. (2) The fuzzy antiswing controller is designed to rapidly damp out the swing angle. In this controller the error and error change between the actual swing angle of the load and the pre-specified swing angle are adopted as input variables. (3) The fuzzy stop position controller is designed for precise positioning of the crane while damping out the residual swings before and after crane stops. In this controller all of the preceding variables, velocity, position, and swing errors are adopted as input fuzzy variables.
Huang, Yi-Shao; Liu, Wel-Ping; Wu, Min; Wang, Zheng-Wu
2014-09-01
This paper presents a novel observer-based decentralized hybrid adaptive fuzzy control scheme for a class of large-scale continuous-time multiple-input multiple-output (MIMO) uncertain nonlinear systems whose state variables are unmeasurable. The scheme integrates fuzzy logic systems, state observers, and strictly positive real conditions to deal with three issues in the control of a large-scale MIMO uncertain nonlinear system: algorithm design, controller singularity, and transient response. Then, the design of the hybrid adaptive fuzzy controller is extended to address a general large-scale uncertain nonlinear system. It is shown that the resultant closed-loop large-scale system keeps asymptotically stable and the tracking error converges to zero. The better characteristics of our scheme are demonstrated by simulations. PMID:24975565
An adaptive model-based neuro-fuzzy-fractal controller for biochemical reactors in the food industry
P. Melin; O. Castillo
1998-01-01
We describe a computer program based on the use of neural networks and fuzzy logic for controlling bacteria growth in biochemical reactors for the food industry. This computer program is an implementation of a new method for control using neural networks techniques and a new method for automated mathematical modelling using fuzzy logic techniques. Biochemical processes are often highly non-linear
Indirect Adaptive Fuzzy Power System Stabilizer
NASA Astrophysics Data System (ADS)
Saoudi, Kamel; Bouchama, Ziad; Harmas, Mohamed Naguib; Zehar, Khaled
2008-06-01
A power system stabilizer based on adaptive fuzzy technique is presented. The design of a fuzzy logic power system stabilizer (FLPSS) requires the collection of fuzzy IF-THEN rules which are used to initialize an adaptive fuzzy power system AFPSS. The rule-base can be then tuned on-line so that the stabilizer can adapt to the different operating conditions occurring in the power system. The adaptation laws are developed based on a Lyapunov synthesis approach. Assessing the validity of this technique simulation of a power system is conducted and results are discussed.
Arc welding fuzzy control using neural net supervisor
A. Bigand; K. Messaadi
1996-01-01
The traditional automatic voltage controller for arc welding is limited for robust and optimal welding. An adaptive fuzzy controller for real-time welding control has been developed, to replace the controller when parameters of the plant are varying. Robustness and design of this adaptive fuzzy controller is described. An automatic rule generation using feedforward neural network technique is defined
Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang
2014-01-01
Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3-9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved. PMID:24790547
Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang
2014-01-01
Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved. PMID:24790547
Hamid R. Berenji
\\u000a Fuzzy Set Theory, introduced by Zadeh in 1965 [77], has been the subject of much controversy and debate. In recent years, it has found many applications in a variety of fields.\\u000a Among the most successful applications of this theory has been the area of Fuzzy Logic Control (FLC) initiated by the work\\u000a of Mamdani and Assilian [36]. FLC has had
Fuzzy model based control: stability, robustness, and performance issues
Tor A. Johansen
1994-01-01
A nonlinear controller based on a fuzzy model of MIMO dynamical systems is described and analyzed. The fuzzy model is based on a set of ARX models that are combined using a fuzzy inference mechanism. The controller is a discrete-time nonlinear decoupler, which is analyzed both for the adaptive and the fixed parameter cases. A detailed stability analysis is carried
Fuzzy Logic Programming and Fuzzy Control Giangiacomo Gerla
Gerla, Giangiacomo
1 Fuzzy Logic Programming and Fuzzy Control by Giangiacomo Gerla Department of Mathematics and Computer Science Via S. Allende, 84081, Baronissi (Salerno) Italy Abstract. The paper concerns fuzzy logic a unifying theoretical framework for fuzzy control. Keywords: Fuzzy logic programming, Herbrand
Incremental Tuning of Fuzzy Controllers by Means of an Evolution Strategy
Hoffmann, Frank
that adapts the knowledge base of a fuzzy logic controller. The evolution strategy adopted in this paper or logical models. Recently numerous researchers explored the integra- tionofevolutionaryalgorithmswith fuzzy design or optimization of fuzzy logic controllers either by adapting the fuzzy membership functions
The Role of Fuzzy Logic Control in Evolutionary Robotics
Hoffmann, Frank
- jective, soft computing employs a variety of methodologies, fuzzy logic, neural networks, probabilistic applications of genetic-fuzzy systems in detail, adapting a wall-following behavior of a mobile robotThe Role of Fuzzy Logic Control in Evolutionary Robotics Frank Ho mann Electrical Engineering
The control of indoor thermal comfort conditions: introducing a fuzzy adaptive controller
Francesco Calvino; Maria La Gennusa; Gianfranco Rizzo; Gianluca Scaccianoce
2004-01-01
The control and the monitoring of indoor thermal conditions represents a pre-eminent task with the aim of ensuring suitable working and living spaces to people. Especially in industrialised countries, in fact, several rules and standards have been recently released in order of providing technicians with suitable design tools and effective indexes and parameters for the checking of the indoor microclimate.
Díaz, Vicente Hernández; Martínez, José-Fernán; Martínez, Néstor Lucas; Del Toro, Raúl M
2015-01-01
The solutions to cope with new challenges that societies have to face nowadays involve providing smarter daily systems. To achieve this, technology has to evolve and leverage physical systems automatic interactions, with less human intervention. Technological paradigms like Internet of Things (IoT) and Cyber-Physical Systems (CPS) are providing reference models, architectures, approaches and tools that are to support cross-domain solutions. Thus, CPS based solutions will be applied in different application domains like e-Health, Smart Grid, Smart Transportation and so on, to assure the expected response from a complex system that relies on the smooth interaction and cooperation of diverse networked physical systems. The Wireless Sensors Networks (WSN) are a well-known wireless technology that are part of large CPS. The WSN aims at monitoring a physical system, object, (e.g., the environmental condition of a cargo container), and relaying data to the targeted processing element. The WSN communication reliability, as well as a restrained energy consumption, are expected features in a WSN. This paper shows the results obtained in a real WSN deployment, based on SunSPOT nodes, which carries out a fuzzy based control strategy to improve energy consumption while keeping communication reliability and computational resources usage among boundaries. PMID:26393612
A Fuzzy System for Adaptive Network Routing A. Pasupuleti*
Shenoy, Nirmala
A Fuzzy System for Adaptive Network Routing A. Pasupuleti* , A.V. Mathew*, N. Shenoy** and S. A In this paper we propose an adaptive routing algorithm in which the link cost are dynamically assigned using With an ever-increasing demand for good communication network services, network control techniques play a vital
NASA Astrophysics Data System (ADS)
Ronilaya, Ferdian; Miyauchi, Hajime
2014-10-01
This paper presents a new implementation of a parameter adaptive PID-type fuzzy controller (PAPIDfc) for a grid-supporting inverter of battery to alleviate frequency fluctuations in a wind-diesel power system. A variable speed wind turbine that drives a permanent magnet synchronous generator is assumed for demonstrations. The PAPIDfc controller is built from a set of control rules that adopts the droop method and uses only locally measurable frequency signal. The output control signal is determined from the knowledge base and the fuzzy inference. The input-derivative gain and the output-integral gain of the PAPIDfc are tuned online. To ensure safe battery operating limits, we also propose a protection scheme called intelligent battery protection (IBP). Several simulation experiments are performed by using MATLAB®/SimPowersystems™. Next, to verify the scheme's effectiveness, the simulation results are compared with the results of conventional controllers. The results demonstrate the effectiveness of the PAPIDfc scheme to control a grid-supporting inverter of the battery in the reduction of frequency fluctuations.
NASA Astrophysics Data System (ADS)
Ajay Kumar, M.; Srikanth, N. V.
2014-03-01
In HVDC Light transmission systems, converter control is one of the major fields of present day research works. In this paper, fuzzy logic controller is utilized for controlling both the converters of the space vector pulse width modulation (SVPWM) based HVDC Light transmission systems. Due to its complexity in the rule base formation, an intelligent controller known as adaptive neuro fuzzy inference system (ANFIS) controller is also introduced in this paper. The proposed ANFIS controller changes the PI gains automatically for different operating conditions. A hybrid learning method which combines and exploits the best features of both the back propagation algorithm and least square estimation method is used to train the 5-layer ANFIS controller. The performance of the proposed ANFIS controller is compared and validated with the fuzzy logic controller and also with the fixed gain conventional PI controller. The simulations are carried out in the MATLAB/SIMULINK environment. The results reveal that the proposed ANFIS controller is reducing power fluctuations at both the converters. It also improves the dynamic performance of the test power system effectively when tested for various ac fault conditions.
A Car-Steering Model Based on an Adaptive Neuro-Fuzzy Controller
NASA Astrophysics Data System (ADS)
Amor, Mohamed Anis Ben; Oda, Takeshi; Watanabe, Shigeyoshi
This paper is concerned with the development of a car-steering model for traffic simulation. Our focus in this paper is to propose a model of the steering behavior of a human driver for different driving scenarios. These scenarios are modeled in a unified framework using the idea of target position. The proposed approach deals with the driver’s approximation and decision-making mechanisms in tracking a target position by means of fuzzy set theory. The main novelty in this paper lies in the development of a learning algorithm that has the intention to imitate the driver’s self-learning from his driving experience and to mimic his maneuvers on the steering wheel, using linear networks as local approximators in the corresponding fuzzy areas. Results obtained from the simulation of an obstacle avoidance scenario show the capability of the model to carry out a human-like behavior with emphasis on learned skills.
Neuro-fuzzy control of vertical vibrations in railcars using magnetorheological dampers
Atray, Vipul Sunil
2002-01-01
-fuzzy controller. A pair of magnetorheological (MR) dampers is designed and installed in a rail truck. Two neuro-fuzzy systems, Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neuro Fuzzy Controller (NEFCON), are used to emulate behavior of the MR dampers...
Fuzzy logic in control systems: Fuzzy logic controller. I, II
NASA Technical Reports Server (NTRS)
Lee, Chuen Chien
1990-01-01
Recent advances in the theory and applications of fuzzy-logic controllers (FLCs) are examined in an analytical review. The fundamental principles of fuzzy sets and fuzzy logic are recalled; the basic FLC components (fuzzification and defuzzification interfaces, knowledge base, and decision-making logic) are described; and the advantages of FLCs for incorporating expert knowledge into a control system are indicated. Particular attention is given to fuzzy implication functions, the interpretation of sentence connectives (and, also), compositional operators, and inference mechanisms. Applications discussed include the FLC-guided automobile developed by Sugeno and Nishida (1985), FLC hardware systems, FLCs for subway trains and ship-loading cranes, fuzzy-logic chips, and fuzzy computers.
Theodoridis, Dimitrios; Boutalis, Yiannis; Christodoulou, Manolis
2010-04-01
The indirect adaptive regulation of unknown nonlinear dynamical systems with multiple inputs and states (MIMS) under the presence of dynamic and parameter uncertainties, is considered in this paper. The method is based on a new neuro-fuzzy dynamical systems description, which uses the fuzzy partitioning of an underlying fuzzy systems outputs and high order neural networks (HONN's) associated with the centers of these partitions. Every high order neural network approximates a group of fuzzy rules associated with each center. The indirect regulation is achieved by first identifying the system around the current operation point, and then using its parameters to device the control law. Weight updating laws for the involved HONN's are provided, which guarantee that, under the presence of both parameter and dynamic uncertainties, both the identification error and the system states reach zero, while keeping all signals in the closed loop bounded. The control signal is constructed to be valid for both square and non square systems by using a pseudoinverse, in Moore-Penrose sense. The existence of the control signal is always assured by employing a novel method of parameter hopping instead of the conventional projection method. The applicability is tested on well known benchmarks. PMID:20411596
Fuzzy logic in control systems: fuzzy logic controller. II
C. C. Lee
1990-01-01
For pt.I see ibid., vol.20, no.2, p.404-18, 1990. The basic aspects of the FLC (fuzzy logic controller) decision-making logic are examined. Several issues, including the definitions of a fuzzy implication, compositional operators, the interpretations of the sentence connectives `and' and `also', and fuzzy inference mechanisms, are investigated. Defuzzification strategies, are discussed. Some of the representative applications of the FLC, from
2002-01-01
of neural networks or fuzzy systems. On-line approximation-based stable adaptive neural/fuzzy control: Fault-tolerant control; Adaptive control; Neural/fuzzy control; Fault diagnosis; Nonlinear systems 1Control Engineering Practice 10 (2002) 801817 Intelligent fault-tolerant control using adaptive
Current projects in Fuzzy Control
NASA Technical Reports Server (NTRS)
Sugeno, Michio
1990-01-01
Viewgraphs on current projects in fuzzy control are presented. Three projects on helicopter flight control are discussed. The projects are (1) radio control by oral instructions; (2) automatic autorotation entry in engine failure; and (3) unmanned helicopter for sea rescue.
Jin, Yaochu
to the artificial neural network is the fuzzy system. It has been shown that the fuzzy systems are also universal 1998 47 Decentralized Adaptive Fuzzy Control of Robot Manipulators Yaochu Jin Abstract--This paper develops a decentralized adaptive fuzzy control scheme for robot manipulators via a combination of genetic
Fuzzy adaptive filters, with application to nonlinear channel equalization
Li-Xin Wang; Jerry M. Mendel
1993-01-01
Two fuzzy adaptive filters are developed: one uses a recursive-least-squares (RLS) adaptation algorithm, and the other uses a least-mean-square (LMS) adaptation algorithm. The RLS fuzzy adaptive filter is constructed through the following four steps: (1) define fuzzy sets in the filter input space Rn whose membership functions cover U; (2) construct a set of fuzzy IF-THEN rules which either come
Fundamentals of Fuzzy Logic Control — Fuzzy Sets, Fuzzy Rules and Defuzzifications
Ying Bai; Dali Wang
A review of the fundamentals of fuzzy sets, fuzzy rules and fuzzy inference systems is provided in this chapter. Beginning\\u000a with crisp or classical sets and their operations, we derived fuzzy sets and their operations. Classical set membership functions\\u000a and fuzzy membership functions are discussed in detail following set theory. Fuzzy rules are described using an air conditioner\\u000a control example.
Fuzzy logic control of a model airplane
Jon C. Ervin; Sema E. Alptekin
1998-01-01
Common uses of fuzzy logic control today are found in relatively simple applications in products. These applications only scratch the surface of the potential of fuzzy logic control in complex mechatronic systems. In order to demonstrate the utility of fuzzy logic in complex device control, a fuzzy logic fly-by-wire system has been developed for a model airplane. The model airplane
Adaptive Fuzzy Systems in Computational Intelligence
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.
1996-01-01
In recent years, the interest in computational intelligence techniques, which currently includes neural networks, fuzzy systems, and evolutionary programming, has grown significantly and a number of their applications have been developed in the government and industry. In future, an essential element in these systems will be fuzzy systems that can learn from experience by using neural network in refining their performances. The GARIC architecture, introduced earlier, is an example of a fuzzy reinforcement learning system which has been applied in several control domains such as cart-pole balancing, simulation of to Space Shuttle orbital operations, and tether control. A number of examples from GARIC's applications in these domains will be demonstrated.
Modeling-Error Based Adaptive Fuzzy Sliding Mode Control for Trajectory-Tracking of Nonlinear
Efe, Mehmet Önder
systems have been successfully applied to control complex or ill-defined processes whose mathematical control for uncertain nonlinear systems is still a challenging problem. Sliding mode control (SMC) is well suffers from a well known problem - chattering due to the high gain and high-speed switching control
Design of a wireless controller for an automotive actuator based on PID-Fuzzy Logic
S. Cai; M. Becherif; M. Wack; M. Y. Ayad; A. Kebairi
2011-01-01
In this paper, an adaptive PID-Fuzzy Logic con- troller is proposed in order to control the angular displacement of an automobile Pierburg mechatronic actuator and achieve a wireless network control. This new PID-fuzzy logic controller topology takes advantage of the classical PID controller and the fuzzy logic technique which can improve the angular displacement precision and reduce the time delay
A neuro-fuzzy based parameter identification of an indirect vector-controlled induction motor drive
L. R. Valdenebro; J. R. Hernandez; E. Bim
1999-01-01
In this paper an adaptive field oriented control of induction motor drive is proposed. The adaptive scheme uses a neuro-fuzzy approach for the identification of the rotor time constant, which is used to adjust the estimate of the slip angular speed. First, a fuzzy logic estimator was developed and tuned, then the fuzzy estimator was implemented by a dynamic backpropagation
Adaptive fuzzy controller for vehicle active suspensions with particle swarm optimization
NASA Astrophysics Data System (ADS)
Cao, Jiangtao; Li, Ping; Liu, Honghai; Brown, David
2008-10-01
In this study, the probability density function (PDF) control method has been developed to deal with the random tracking error for a class of robotic manipulator that are subjected to non-Gaussian noises. The control aim is that the shape of the PDF of the tracking error is made as close as possible to the desired PDF. The ILC frame about PDF control approach of manipulators system with non-Gaussian noises has been proposed and a recursive optimization solution batch-by-batch has been developed. In each batch, nonlinear closed-loop error dynamics is considered. In addition, the convergence condition of the tracking control algorithm has been analyzed. Finally, a simulation is given to illustrate the efficiency of the proposed approach.
Low speed control of a DC motor driving a mechanical system with fuzzy adaptive compensation
Hyun, Dongyoon
1997-01-01
and lubricated sliding junctions. For experiments, an IBM PC, a DSPACE DSP board, SE uLM and Real Time Workshop are used. All three control systems can achieve such a very low sustainable speed as 0.005 rad/sec without stick-slip oscillations, which appear when...
Tuning fuzzy logic controllers by genetic algorithms
Francisco Herrera; Manuel Lozano; José L. Verdegay
1995-01-01
The performance of a fuzzy logic controller depends on its control rules and membership functions. Hence, it is very important to adjust these parameters to the process to be controlled. A method is presented for tuning fuzzy control rules by genetic algorithms to make the fuzzy logic control systems behave as closely as possible to the operator or expert behavior
Fuzzy coordinator in control problems
NASA Technical Reports Server (NTRS)
Rueda, A.; Pedrycz, W.
1992-01-01
In this paper a hierarchical control structure using a fuzzy system for coordination of the control actions is studied. The architecture involves two levels of control: a coordination level and an execution level. Numerical experiments will be utilized to illustrate the behavior of the controller when it is applied to a nonlinear plant.
Expert system driven fuzzy control application to power reactors
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.
Expert system driven fuzzy control application to power reactors
Tsoukalas, L.H.; Berkan, R.C.; Upadhyaya, B.R.; Uhrig, R.E.
1990-12-31
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.
EL-SIM: a Development Environment for Neuro-Fuzzy Intelligent Controllers
Reyneri, Leonardo
EL-SIM: a Development Environment for Neuro-Fuzzy Intelligent Controllers M. Chiaberge , G. Di Bene are commonly integrated into the neuro-fuzzy ap- proach, which has proven well adapted to non- linear control- tectures by appropriately combining and opti- mizing them. However, although the neuro-fuzzy approach alone
Position control of ionic polymer metal composite actuator based on neuro-fuzzy system
NASA Astrophysics Data System (ADS)
Nguyen, Truong-Thinh; Yang, Young-Soo; Oh, Il-Kwon
2009-07-01
This paper describes the application of Neuro-Fuzzy techniques for controlling an IPMC cantilever configuration under water to improve tracking ability for an IPMC actuator. The controller was designed using an Adaptive Neuro-Fuzzy Controller (ANFC). The measured input data based including the tip-displacements and electrical signals have been recorded for generating the training in the ANFC. These data were used for training the ANFC to adjust the membership functions in the fuzzy control algorithm. The comparison between actual and reference values obtained from the ANFC gave satisfactory results, which showed that Adaptive Neuro-Fuzzy algorithm is reliable in controlling IPMC actuator. In addition, experimental results show that the ANFC performed better than the pure fuzzy controller (PFC). Present results show that the current adaptive neuro-fuzzy controller can be successfully applied to the real-time control of the ionic polymer metal composite actuator for which the performance degrades under long-term actuation.
Fuzzy control system for a mobile robot
Hai Quan Dai; Dalton, G.R.; Tulenko, J. (Univ. of Florida, Gainesville (United States))
1992-01-01
Since the first fuzzy logic control system was proposed by Mamdani, many studies have been carried out on industrial process and real-time controls. The key problem for the application of fuzzy logic control is to find a suitable set of fuzzy control rules. Three common modes of deriving fuzzy control rules are often distinguished and mentioned: (1) expert experience and knowledge; (2) modeling operator control actions; and (3) modeling a process. In cases where an operator's skill is important, it is very useful to derive fuzzy control rules by modeling an operator's control actions. It is possible to model an operator's control behaviors in terms of fuzzy implications using the input-output data concerned with his/her control actions. The authors use the model obtained in this way as the basis for a fuzzy controller. The authors use a finite number of fuzzy or approximate control rules. To control a robot in a cluttered reactor environment, it is desirable to combine all the methods. In this paper, the authors describe a general algorithm for a mobile robot control system with fuzzy logic reasoning. They discuss the way that knowledge of fuzziness will be represented in this control system. They also describe a simulation program interface to the K2A Cybermation mobile robot to be used to demonstrate the control system.
H? tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach
Bor-Sen Chen; Ching-Hsiang Lee; Yeong-Chan Chang
1996-01-01
A fuzzy logic controller equipped with a training (adaptive) algorithm is proposed in this work to achieve H? tracking performance for a class of uncertain (model free) nonlinear single-input single-output (SISO) systems with external disturbances. An attempt is also made to create a bridge between two important control design techniques, i.e., H? control design and fuzzy control design, so as
Fuzzy logic controllers are universal approximators
J. L. Castro
1995-01-01
In this paper, we consider a fundamental theoretical question on why does fuzzy control have such a good performance for a wide variety of practical problems. We try to answer this fundamental question by proving that for each fixed fuzzy logic belonging to a wide class of fuzzy logics, and for each fixed type of membership function belonging to a
A fuzzy-tuned adaptive Kalman filter
Painter, John H.; Young Hwan Lho
1993-12-01
are not continuous, but occur at isolated times, with periods of sta- tionarity in between changes. A solution to this case, developed by Eggers [5], is to make a stochastic estimate of the required covariances. The Kalman gains, g1 and 92, etc. are functions...(k). Then the error covariance matrix is given by P(k) = E{e(k)eT(k)} = E{(x(k) - x(k))(x(k) - x(k))T} The block diagram for equation 1.3 is depicted in Fig. 2 (1.4) Figure 2: Fuzzy-Tuned Adaptive Kalinan Filter Eggers? covariance estimators [5] were biased...
Fuzzy logic based robotic controller
NASA Technical Reports Server (NTRS)
Attia, F.; Upadhyaya, M.
1994-01-01
Existing Proportional-Integral-Derivative (PID) robotic controllers rely on an inverse kinematic model to convert user-specified cartesian trajectory coordinates to joint variables. These joints experience friction, stiction, and gear backlash effects. Due to lack of proper linearization of these effects, modern control theory based on state space methods cannot provide adequate control for robotic systems. In the presence of loads, the dynamic behavior of robotic systems is complex and nonlinear, especially where mathematical modeling is evaluated for real-time operators. Fuzzy Logic Control is a fast emerging alternative to conventional control systems in situations where it may not be feasible to formulate an analytical model of the complex system. Fuzzy logic techniques track a user-defined trajectory without having the host computer to explicitly solve the nonlinear inverse kinematic equations. The goal is to provide a rule-based approach, which is closer to human reasoning. The approach used expresses end-point error, location of manipulator joints, and proximity to obstacles as fuzzy variables. The resulting decisions are based upon linguistic and non-numerical information. This paper presents a solution to the conventional robot controller which is independent of computationally intensive kinematic equations. Computer simulation results of this approach as obtained from software implementation are also discussed.
Fuzzy logic control and optimization system
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.
METHODOLOGY OF FUZZY CONTROL: AN INTRODUCTION
Kreinovich, Vladik
how to control them: we can, to some extent, control weather, we can control pollution, etcMETHODOLOGY OF FUZZY CONTROL: AN INTRODUCTION H.T. NGUYEN Department of Mathematical Sciences New@cs.utep.edu Abstract. Fuzzy control is a methodology that translates the experience of human operators, experience
Adaptive Output Tracking of Transverse Flux Machines Using Neuro-Fuzzy Approach
A. Babazadeh; H. R. Karimi
2006-01-01
This paper deals with adaptive output tracking of a transverse flux permanent magnet machine as a nonlinear system with unknown nonlinearities by utilizing Takagi-Sugeno type neuro-fuzzy networks. The technique of feedback linearization and H control are used to design an adaptive control law for compensating the unknown nonlinear parts, such the effect of cogging torque, as a disturbance on the
D Kolokotsa; D Tsiavos; G. S Stavrakakis; K Kalaitzakis; E Antonidakis
2001-01-01
The aim of this paper is to present and evaluate control strategies for adjustment and preservation of air quality, thermal and visual comfort for buildings’ occupants while, simultaneously, energy consumption reduction is achieved. Fuzzy PID, fuzzy PD and adaptive fuzzy PD control methods are applied. The inputs to any controller are: the PMV index affecting thermal comfort, the CO2 concentration
Incorporating Adaptive Local Information Into Fuzzy Clustering for Image Segmentation.
Liu, Guoying; Zhang, Yun; Wang, Aimin
2015-11-01
Fuzzy c-means (FCM) clustering with spatial constraints has attracted great attention in the field of image segmentation. However, most of the popular techniques fail to resolve misclassification problems due to the inaccuracy of their spatial models. This paper presents a new unsupervised FCM-based image segmentation method by paying closer attention to the selection of local information. In this method, region-level local information is incorporated into the fuzzy clustering procedure to adaptively control the range and strength of interactive pixels. First, a novel dissimilarity function is established by combining region-based and pixel-based distance functions together, in order to enhance the relationship between pixels which have similar local characteristics. Second, a novel prior probability function is developed by integrating the differences between neighboring regions into the mean template of the fuzzy membership function, which adaptively selects local spatial constraints by a tradeoff weight depending upon whether a pixel belongs to a homogeneous region or not. Through incorporating region-based information into the spatial constraints, the proposed method strengthens the interactions between pixels within the same region and prevents over smoothing across region boundaries. Experimental results over synthetic noise images, natural color images, and synthetic aperture radar images show that the proposed method achieves more accurate segmentation results, compared with five state-of-the-art image segmentation methods. PMID:26186787
Fuzzy PID control of Stewart platform
Yang Bo; Pei Zhongcai; Tang Zhiyong
2011-01-01
Stewart platform equipped with asymmetric hydraulic cylinder controlled by symmetric valve has features such as nonlinearity, time variance and strong interference, which lead to various difficulties in control process. Fuzzy control algorithm is an in-time regulating parameters algorithm, which has the ability to emulate the behavior of a human operator. Fuzzy logic control shows its priority in real-time nonlinear system
NASA Astrophysics Data System (ADS)
Kim, Hunmo
In the brake systems, it is important to reduce the rear brake pressure in order to secure the safety of the vehicle in braking. So, there was some research that reduced and controlled the rear brake pressure exactly like a L. S. P. V and a E. L. S. P. V. However, the previous research has some weaknesses: the L. S. P. V is a mechanical system and its brake efficiency is lower than the efficiency of E. L. S. P. V. But, the cost of E. L. S. P. V is very higher so its application to the vehicle is very difficult. Additionally, when a fail appears in the circuit which controls the valves, the fail results in some wrong operation of the valves. But, the previous researchers didn't take the effect of fail into account. Hence, the efficiency of them is low and the safety of the vehicle is not confirmed. So, in this paper we develop a new economical pressure modulator that exactly controls brake pressure and confirms the safety of the vehicle in any case using a direct adaptive fuzzy controller.
Fuzzy predictive control for nitrogen removal in biological wastewater treatment
wastewater is too low, full denitrification is difficult to obtain and an additional source of organic carbon of an improved model for denitrification, the use of benchmark (i.e. thoroughly tested and standardised) input saving and discharge compliance. Keywords Adaptive control; denitrification; fuzzy control; model
Modeling and fuzzy control of the resistance spot welding process
Xingqiao Chen; Keiiji Araki; T. Mizuno
1997-01-01
Establishes a welding energy model and describes a fuzzy adaptive process control scheme for a resistance spot welding system. For a welding system, it is not easy to measure online the output, the nugget size, by usual measuring methods. It is also difficult to control the welding process in real time. The paper proposes a method using input welding energy
Fuzzy control of a fluidized bed dryer
Taprantzis, A.V.; Siettos, C.I.; Bafas, G.V. [National Technical Univ., Athens (Greece). Dept. of Chemical Engineering
1997-05-01
Fluidized bed dryers are utilized in almost every area of drying applications and therefore improved control strategies are always of great interest. The nonlinear character of the process, exhibited in the mathematical model and the open loop analysis, implies that a fuzzy logic controller is appropriate because, in contrast with conventional control schemes, fuzzy control inherently compensates for process nonlinearities and exhibits more robust behavior. In this study, a fuzzy logic controller is proposed; its design is based on a heuristic approach and its performance is compared against a conventional PI controller for a variety of responses. It is shown that the fuzzy controller exhibits a remarkable dynamic behavior, equivalent if not better than the PI controller, for a wide range of disturbances. In addition, the proposed fuzzy controller seems to be less sensitive to the nonlinearities of the process, achieves energy savings and enables MIMO control.
A fuzzy logic controller for autonomous vehicle control
Vinson, Yale Patrick
1995-01-01
using current fuzzy control theory. Then, a Base Model is presented and evaluated for its utility for the vehicle following problem. Similarly, the Fuzzy Automobile Control Software (FACS) system is developed and evaluated using the same criteria...
Analysis of inventory difference using fuzzy controllers
Zardecki, A.
1994-08-01
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.
Fuzzy Regulator Design for Wind Turbine Yaw Control
Koulouras, Grigorios
2014-01-01
This paper proposes the development of an advanced fuzzy logic controller which aims to perform intelligent automatic control of the yaw movement of wind turbines. The specific fuzzy controller takes into account both the wind velocity and the acceptable yaw error correlation in order to achieve maximum performance efficacy. In this way, the proposed yaw control system is remarkably adaptive to the existing conditions. In this way, the wind turbine is enabled to retain its power output close to its nominal value and at the same time preserve its yaw system from pointless movement. Thorough simulation tests evaluate the proposed system effectiveness. PMID:24693237
Multistage fuzzy load frequency control using PSO
H. Shayeghi; A. Jalili; H. A. Shayanfar
2008-01-01
In this paper, a particle swarm optimization (PSO) based multi-stage fuzzy (PSOMSF) controller is proposed for solution of the load frequency control (LFC) problem in a restructured power system that operate under deregulation based on the bilateral policy scheme. In this strategy the control is tuned on line from the knowledge base and fuzzy inference, which request fewer sources and
Fuzzy logic control for camera tracking system
NASA Technical Reports Server (NTRS)
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.
A fuzzy classifier system for process control
NASA Technical Reports Server (NTRS)
Karr, C. L.; Phillips, J. C.
1994-01-01
A fuzzy classifier system that discovers rules for controlling a mathematical model of a pH titration system was developed by researchers at the U.S. Bureau of Mines (USBM). Fuzzy classifier systems successfully combine the strengths of learning classifier systems and fuzzy logic controllers. Learning classifier systems resemble familiar production rule-based systems, but they represent their IF-THEN rules by strings of characters rather than in the traditional linguistic terms. Fuzzy logic is a tool that allows for the incorporation of abstract concepts into rule based-systems, thereby allowing the rules to resemble the familiar 'rules-of-thumb' commonly used by humans when solving difficult process control and reasoning problems. Like learning classifier systems, fuzzy classifier systems employ a genetic algorithm to explore and sample new rules for manipulating the problem environment. Like fuzzy logic controllers, fuzzy classifier systems encapsulate knowledge in the form of production rules. The results presented in this paper demonstrate the ability of fuzzy classifier systems to generate a fuzzy logic-based process control system.
Refining fuzzy logic controllers with machine learning
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.
1994-01-01
In this paper, we describe the GARIC (Generalized Approximate Reasoning-Based Intelligent Control) architecture, which learns from its past performance and modifies the labels in the fuzzy rules to improve performance. It uses fuzzy reinforcement learning which is a hybrid method of fuzzy logic and reinforcement learning. This technology can simplify and automate the application of fuzzy logic control to a variety of systems. GARIC has been applied in simulation studies of the Space Shuttle rendezvous and docking experiments. It has the potential of being applied in other aerospace systems as well as in consumer products such as appliances, cameras, and cars.
Robust Fuzzy Controllers Using FPGAs
NASA Technical Reports Server (NTRS)
Monroe, Author Gene S., Jr.
2007-01-01
Electro-mechanical device controllers typically come in one of three forms, proportional (P), Proportional Derivative (PD), and Proportional Integral Derivative (PID). Two methods of control are discussed in this paper; they are (1) the classical technique that requires an in-depth mathematical use of poles and zeros, and (2) the fuzzy logic (FL) technique that is similar to the way humans think and make decisions. FL controllers are used in multiple industries; examples include control engineering, computer vision, pattern recognition, statistics, and data analysis. Presented is a study on the development of a PD motor controller written in very high speed hardware description language (VHDL), and implemented in FL. Four distinct abstractions compose the FL controller, they are the fuzzifier, the rule-base, the fuzzy inference system (FIS), and the defuzzifier. FL is similar to, but different from, Boolean logic; where the output value may be equal to 0 or 1, but it could also be equal to any decimal value between them. This controller is unique because of its VHDL implementation, which uses integer mathematics. To compensate for VHDL's inability to synthesis floating point numbers, a scale factor equal to 10(sup (N/4) is utilized; where N is equal to data word size. The scaling factor shifts the decimal digits to the left of the decimal point for increased precision. PD controllers are ideal for use with servo motors, where position control is effective. This paper discusses control methods for motion-base platforms where a constant velocity equivalent to a spectral resolution of 0.25 cm(exp -1) is required; however, the control capability of this controller extends to various other platforms.
Stability of fuzzy linguistic control systems
Gholamreza Langari; M. Tomizuka
1990-01-01
A new approach to the stability analysis of fuzzy linguistic control (FLC) systems is presented. Specifically, it is shown that the direct method of Lyapunov can be used to determine sufficient conditions for global stability of a broad class of fuzzy control schemes. Moreover, a measure of robustness is proposed that can be used to evaluate and possibly redesign a
Fuzzy logic control for parallel hybrid vehicles
Niels J. Schouten; Mutasim A. Salman; Naim A. Kheir
2002-01-01
In this paper, a fuzzy logic controller is developed for hybrid vehicles with parallel configuration. Using the driver command, the state of charge of the energy storage, and the motor\\/generator speed, a set of rules have been developed, in a fuzzy controller, to effectively determine the split between the two powerplants: electric motor and internal combustion engine. The underlying theme
Fuzzy controller for wall-climbing microrobots
Jun Xiao; Jizhong Z. Xiao; Ning Xi; R. Lal Tummala; Ranjan Mukherjee
2004-01-01
This paper presents a fuzzy control system that incorporates sensing, control and planning to improve the performance of the wall-climbing microrobots in unstructured environments. After introduction of the robot system, a task reference method is proposed which is based on a fuzzy multisensor data fusion scheme. The method provides a novel mechanism to efficiently integrate task scheduling, action planning and
A new methodology for designing a fuzzy logic controller
Han-Xiong Li; H. B. Gatland
1995-01-01
A new methodology is proposed for designing a fuzzy logic controller (FLC). A phase plane is used to bridge the gap between the time-response and rule base. The rule base can be easily built using the general dynamics of the process, and then readily updated to contain the delayed information for reducing the deadtime effects of the process. An adaptive
Fuzzy-model-based hybrid predictive control.
Núñez, Alfredo; Sáez, Doris; Oblak, Simon; Skrjanc, Igor
2009-01-01
In this paper we present a method of hybrid predictive control (HPC) based on a fuzzy model. The identification methodology for a nonlinear system with discrete state-space variables based on combining fuzzy clustering and principal component analysis is proposed. The fuzzy model is used for HPC design, where the optimization problem is solved by the use of genetic algorithms (GAs). An illustrative experiment on a hybrid tank system is conducted to demonstrate the benefits of the proposed approach. PMID:19027897
Fuzzy Systems in Instrumentation: Fuzzy Control Emil M. Petriu and Graham Eatherley
Petriu, Emil M.
@trix.genie.uottawa.ca Abstract - This paper presents a short overview o f fuzzy control and discusses fuzzy partition i n and trailer is finally presented. I. INTRODUCTION The basic idea of "fuzzy logic control" (FLC) was suggested important and vislble application today is in a realm not anticipated when fuzzy logic was conceived
Improvement on fuzzy controller design techniques
NASA Technical Reports Server (NTRS)
Wang, Paul P.
1993-01-01
This paper addresses three main issues, which are somewhat interrelated. The first issue deals with the classification or types of fuzzy controllers. Careful examination of the fuzzy controllers designed by various engineers reveals distinctive classes of fuzzy controllers. Classification is believed to be helpful from different perspectives. The second issue deals with the design according to specifications, experiments related to the tuning of fuzzy controllers, according to the specification, will be discussed. General design procedure, hopefully, can be outlined in order to ease the burden of a design engineer. The third issue deals with the simplicity and limitation of the rule-based IF-THEN logical statements. The methodology of fuzzy-constraint network is proposed here as an alternative to the design practice at present. It is our belief that predicate calculus and the first order logic possess much more expressive power.
Control Augmentation Using Fuzzy Logic Control
NASA Astrophysics Data System (ADS)
Kato, Akio; Inukai, Daisuke
Control to improve control characteristics of aicraft, CA (Control Augmentation), is used to realize the desirable motion of aircraft corresponding to pilot's control action. C* criterion is an important factor for the pilot's preferred longitudinal motion. The time history of C* corresponding to step input is specified to be within the upper and lower envelope, and that near the center of the envelope is best for the pilot's easy control. In this research, the control laws for control augmentation of small supersonic aircraft were designed using fuzzy logic control to obtain the C* response near the center of the envelope. The evaluation of the designed control laws showed good performance in all flight conditions. Here, the control laws were varied by only one scaling factor for dynamic pressure. Therefore, the gain schedules by Mach number and angle of attack, which are necessary for supersonic aircraft in which the control laws were designed by model following optimal control, are not necessary. This proves that fuzzy logic control is an effective and flexible method when applied to control laws for control augmentation of aircraft.
Coordination of Excitation and Governing Control Based on Fuzzy Logic
Coordination of Excitation and Governing Control Based on Fuzzy Logic Taiyou Yong, Robert H. In this paper, we present a fuzzy logic based method for the excitation control and governing control. Fuzzy, Transient stability, Fuzzy logic, Excitation and governing control. 1. Introduction Power system stability
Matrix Expression of Logic and Fuzzy Control
Daizhan Cheng; Hongsheng Qi
2005-01-01
This paper gives a matrix expression of logic. Under matrix expression a general description of the logical operations is proposed, which is very convenient in logical inference. Then based on matrix expression the logic operators have been extended to multi-valued logic, which provides a foundation for fuzzy systems. Finally, the logic-based fuzzy control is considered.
Optimizing a fuzzy logic controller for reactive navigation
NASA Astrophysics Data System (ADS)
Castellano, G.; Stella, Ettore; Attolico, Giovanni; Distante, Arcangelo
1997-01-01
Low-level navigation for autonomous vehicles can be accomplished efficiently by a behavioral-based approach that involves the simultaneous execution of independent sub-tasks seen as primitive behaviors. Each behavior maps sensory data into control commands in a reactive way, with no need of internal representations. A useful tool for realizing such a direct mapping is fuzzy logic, that allows the production of control rules by either manual programming or automatic learning. In prospect of implementing an articulated control system including all the low-level behaviors of navigation, this paper focuses on the problem of obtaining an efficient and robust fuzzy controller performing a single behavior and presents a method for minimizing the number of rules of a fuzzy controller developed for driving a TRC Labmate based vehicle along the wall on its right-hand side. Fuzzy rules, that map ultrasonic sensor readings onto steering velocity values, are learned automatically from training data collected during operator-driven runs of the vehicle. In addition, we address the problem of defining an appropriate performance function, that may be useful for evaluating the influence of the rule base reduction on the overall behavior of the vehicle during navigation, but also for estimating the quality of a control rule, in order to adapt rules on- line. Results of an experimental comparison between the original fuzzy wall-follower and its optimized version are reported.
Control Augmentation Using Fuzzy Logic Control
NASA Astrophysics Data System (ADS)
Kato, Akio; Inukai, Daisuke
Overall control to improve the control characteristics of an aircraft, CA (Control Augmentation), is used to realize the desirable motion of the aircraft in relation to the pilot’s control action. C? criterion is an important factor for the pilot’s preferred longitudinal motion. The time history of C? corresponding to the step input is specified within the upper and lower envelope, and it is desirable to be near the center of the envelope. In this research, the control laws of control augmentation for small supersonic aircraft were designed with the use of fuzzy logic control to obtain the C? response near the center of the envelope. The evaluation of the designed control laws showed good performance in all flight conditions. Here the control laws were varied by only one scaling factor for dynamic pressure. This means that virtually no gain schedules by the Mach number and the angle of attack are necessary. This paper shows that fuzzy logic control is an effective and flexible method when applied to control laws for the control augmentation of aircraft.
Implementation of a fuzzy logic/neural network multivariable controller
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.
Synthesis of nonlinear control strategies from fuzzy logic control algorithms
NASA Technical Reports Server (NTRS)
Langari, Reza
1993-01-01
Fuzzy control has been recognized as an alternative to conventional control techniques in situations where the plant model is not sufficiently well known to warrant the application of conventional control techniques. Precisely what fuzzy control does and how it does what it does is not quite clear, however. This important issue is discussed and in particular it is shown how a given fuzzy control scheme can resolve into a nonlinear control law and that in those situations the success of fuzzy control hinges on its ability to compensate for nonlinearities in plant dynamics.
This paper presents a fuzzy control approach for improving convergence time in stochastic global
Neumaier, Arnold
optimization method. VFSR is particularly well suited to applications involving neuro-fuzzy systems and neural network training, taking into account its superior performance and simplicity. ASA (Adaptive Simulated1 Abstract This paper presents a fuzzy control approach for improving convergence time
Fuzzy controllers in nuclear material accounting
Zardecki, A.
1994-10-01
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.
An Exploration of the Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling
Aickelin, Uwe
combines the fuzzy logic qualitative approach and adaptive neural network capabil- ities towards betterAn Exploration of the Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling Survival;An Exploration of the Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling Survival Hazlina
Adaptive fuzzy logic-based velocity observer for servo motor drives
Feng-Chieh Lin; Sheng-Ming Yang
2003-01-01
As the position transducers commonly used in industry do not inherently measure an instantaneous velocity, signal processing is generally required to improve the accuracy of velocity estimation at each sampling instant. This estimated signal is then used as the velocity feedback for the velocity loop control in servo motor drives. In this paper, an adaptive fuzzy logic-based observer is proposed
Decentralized fuzzy control of multiple nonholonomic vehicles
Driessen, B.J.; Feddema, J.T.; Kwok, K.S.
1997-09-01
This work considers the problem of controlling multiple nonholonomic vehicles so that they converge to a scent source without colliding with each other. Since the control is to be implemented on simple 8-bit microcontrollers, fuzzy control rules are used to simplify a linear quadratic regulator control design. The inputs to the fuzzy controllers for each vehicle are the (noisy) direction to the source, the distance to the closest neighbor vehicle, and the direction to the closest vehicle. These directions are discretized into four values: Forward, Behind, Left, and Right, and the distance into three values: Near, Far, Gone. The values of the control at these discrete values are obtained based on the collision-avoidance repulsive forces and the change of variables that reduces the motion control problem of each nonholonomic vehicle to a nonsingular one with two degrees of freedom, instead of three. A fuzzy inference system is used to obtain control values for inputs between the small number of discrete input values. Simulation results are provided which demonstrate that the fuzzy control law performs well compared to the exact controller. In fact, the fuzzy controller demonstrates improved robustness to noise.
Fuzzy pre-compensated fuzzy self-tuning fuzzy PID controller of 3 DOF planar robot manipulators
A. F. Amer; E. A. Sallam; W. M. Elawady
2010-01-01
Control of an industrial robot includes nonlinearities, uncertainties and external perturbations that should be considered in the design of control laws. Proportional-integral-derivative (PID)-type fuzzy controller is a well-known conventional motion control strategy for manipulators which ensures global asymptotic stability. To enhance the PID-type fuzzy controller performance for the control of rigid planar robot manipulators, in this paper, a fuzzy pre-compensation
Adaptation of Fuzzy Inferencing: A Survey Payman Arabshahi, Robert J. Marks II, and Russell Reed
Arabshahi, Payman
of the membership functions and the parameters of the fuzzy AND and OR operations. In this paper, an overview by input membership functions and processed by a fuzzy logic interpretation of a set of fuzzy rulesAdaptation of Fuzzy Inferencing: A Survey Payman Arabshahi, Robert J. Marks II, and Russell Reed
Variable-order fuzzy fractional PID controller.
Liu, Lu; Pan, Feng; Xue, Dingyu
2015-03-01
In this paper, a new tuning method of variable-order fractional fuzzy PID controller (VOFFLC) is proposed for a class of fractional-order and integer-order control plants. Fuzzy logic control (FLC) could easily deal with parameter variations of control system, but the fractional-order parameters are unable to change through this way and it has confined the effectiveness of FLC. Therefore, an attempt is made in this paper to allow all the five parameters of fractional-order PID controller vary along with the transformation of system structure as the outputs of FLC, and the influence of fractional orders ? and ? on control systems has been investigated to make the fuzzy rules for VOFFLC. Four simulation results of different plants are shown to verify the availability of the proposed control strategy. PMID:25440947
Buck\\/boost converter control with fuzzy logic approach
Bor-Ren Lin; Chihchiang Hua
1993-01-01
In this paper, the application of fuzzy control to DC-DC power converters operating at finite switching frequency is studied. Several control methods currently used for buck\\/boost power converters are compared to fuzzy converter control. Simulation results for several control methods are presented. The simulations show that the fuzzy control method has better dynamic performance and less steady state error
The fuzzy logic of visuomotor control1 Arthur Prochazka
Prochazka, Arthur
The fuzzy logic of visuomotor control1 Arthur Prochazka Abstract: Biological sensorimotor control animals use to control movement. It is argued that the concepts of fuzzy logic control provide a useful to the future study of interneuronal systems. Key words: fuzzy logic, behavioural set, reflex control
Vibration control of vehicle suspension system using adaptive critic-based neurofuzzy controller
R. Vatankhah; M. Rahaeifard; A. Alasty
2009-01-01
This paper presents an active suspension system for passenger cars, using adaptive critic-based neurofuzzy controller. The model is described by a system with seven degrees of freedom. The car is subjected to excitation from a rode surface and wheel unbalance. The main superiority of the proposed controller over previous analogous fuzzy logic controller designed approaches, e.g., genetic fuzzy logic controller,
Intelligent control based on fuzzy logic and neural net theory
NASA Technical Reports Server (NTRS)
Lee, Chuen-Chien
1991-01-01
In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.
Design and simulation of self-tuning PID-type fuzzy adaptive control for an expert HVAC system
Servet Soyguder; Mehmet Karaköse; Hasan Alli
2009-01-01
The modelling, numerical simulation and intelligent control of an expert HVAC (heating, ventilating and air-conditioning) system having two different zones with variable flow-rate were performed by considering the ambient temperature in this study. The sub-models of the system were obtained by deriving heat transfer equations of heat loss of two zones by conduction and convection, cooling unit and fan. All
Fuzzy control of a quarter-car suspension system
Salah G. Foda
2000-01-01
This paper describes the design of a fuzzy logic controller for a quarter-car model. The car suspension system with designed fuzzy logic controller is simulated to obtain the desired ride performance under various road conditions. Simulation results indicate the feasibility of the designed controller. The designed fuzzy logic controller is simple and suitable for real time implementation
Experiment Study on Fuzzy Vibration Control of Solar Panel
NASA Astrophysics Data System (ADS)
Li, Dongxu X.; Xu, Rui; Jiang, Jiangjian P.
Some flexible appendages of spacecraft are cantilever plate structures, such as solar panels. These structures usually have very low damping ratios, high dimensional order, low modal frequencies and parameter uncertainties in dynamics. Their unwanted vibrations will be caused unavoidably, and harmful to the spacecraft. To solve this problem, the dynamic equations of the solar panel with piezoelectric patches are derived, and an accelerometer based fuzzy controller is designed. In order to verify the effectiveness of the vibration control algorithms, experiment research was conducted on a piezoelectric adaptive composite honeycomb cantilever panel. The experiment results demonstrate that the accelerometer-based fuzzy vibration control method can suppress the vibration of the solar panel effectively, the first bending mode damping ratio of the controlled system increase to 1.64%, and that is 3.56 times of the uncontrolled system.
Lin, Tsau Young
Neural Networks, Qualitative-Fuzzy Logic and Granular Adaptive Systems T. Y. Lin Department, adaptive system. 1. INTRODUCTION Neural networks (NN) and fuzzy logic (FL) are well known-Though traditional neural networks and fuzzy logic are powerful universal approximators, however without some
Design and analysis of an adaptive fuzzy power system stabilizer
Hoang, P.; Tomsovic, K. [Washington State Univ., Pullman, WA (United States). School of Electrical Engineering and Computer Science] [Washington State Univ., Pullman, WA (United States). School of Electrical Engineering and Computer Science
1996-06-01
Power system stabilizers (PSS) must be capable of providing appropriate stabilization signals over a broad range of operating conditions and disturbances. Traditional PSS rely on robust linear design methods. In an attempt to cover a wider range of operating conditions, expert or rule-based controllers have also been proposed. Recently, fuzzy logic as a novel robust control design method has shown promising results. The emphasis in fuzzy control design centers around uncertainties in system parameters and operating conditions. Such an emphasis is of particular relevance as the difficulty of accurately modelling the connected generation is expected to increase under power industry deregulation. Fuzzy logic controllers are based on empirical control rules. In this paper, a systematic approach to fuzzy logic control design is proposed. Implementation for a specific machine requires specification of performance criteria. This performance criteria translates into three controller parameters which can be calculated off-line or computed in real-time in response to system changes. The robustness of the controller is emphasized. Small signal and transient analysis methods are discussed. This work is directed at developing robust stabilizer design and analysis methods appropriate when fuzzy logic is applied.
Fuzzy Control for the Swing-Up of the Inverted Pendulum System
NASA Astrophysics Data System (ADS)
Wu, Yu; Zhu, Peiyi
The nonlinear inverted-pendulum system is an unstable and non-minimum phase system. It is often used to be the controlled target to test the qualities of the controllers like PID, optimal LQR, Neural network, adaptive, and fuzzy logic controller, etc. This paper will describe a new fuzzy controller for an inverted pendulum system. In this case, a fuzzy controller followed with a state space controller was implemented for control. It is achieved to design a control condition for the pendulum to swing up in one direction only because that the movement of throwing a bowling ball can only from one side to the unstable equilibrium point. Simulation and experimental results show that the fuzzy control can swing up the single inverted pendulum in short time with well stability and strong robustness.
Fuzzy logic resource manager: real-time adaptation and self-organization
NASA Astrophysics Data System (ADS)
Smith, James F., III
2004-08-01
A fuzzy logic expert system has been developed that automatically allocates electronic attack (EA) resources distributed over different platforms in real-time. Genetic algorithm based optimization is conducted to determine the form of the membership functions for the fuzzy root concepts. The resource manager (RM) is made up of five parts, the isolated platform model, the multi-platform decision tree, the fuzzy EA model, the fuzzy parameter selection tree and the fuzzy strategy tree. The platforms are each controlled by their own copy of the RM allowing them to automatically work together, i.e., they self-organize through communication without the need of a central commander. A group of platforms working together will automatically show desirable forms of emergent behavior, i.e., they will exhibit desirable behavior that was never explicitly programmed into them. This is important since it is impossible to have a rule covering every possible situation that might be encountered. An example of desirable emergent behavior is discussed as well as a method using a co-evolutionary war game for eliminating undesirable forms of behavior. The RM"s ability to adapt to changing situations is enhanced by the fuzzy parameter selection tree. Tree structure is discussed as well as various related examples.
A neural fuzzy control system with structure and parameter learning
Chin-Teng Lin
1995-01-01
A general connectionist model, called neural fuzzy control network (NFCN), is proposed for the realization of a fuzzy logic control system. The proposed NFCN is a feedforward multilayered network which integrates the basic elements and functions of a traditional fuzzy logic controller into a connectionist structure which has distributed learning abilities. The NFCN can be constructed from supervised training examples
Coordinated control of AFS and ESP based on fuzzy logic method
Guo Jianhua; Chu Liang; Zhou Feikun; Yao Liang
2011-01-01
In this paper, coordinated vehicle dynamic control system of AFS\\/ESP is proposed to improve vehicle handling and ride performance. The SMC methodology which is deduced form the linear 2DOF vehicle model is used to design the AFS subsystem controller. The model reference adaptive algorithm is used to derive the control law for ESP controller. A fuzzy logic control strategy is
APPROXIMATE REASONING TO UNIFY NORM-BASED AND IMPLICATION-BASED FUZZY CONTROL
Gerla, Giangiacomo
. Abstract. In Gerla [2000] a fuzzy logic in narrow sense is proposed as a theoretical framework, the information carried on by a system of fuzzy IF-THEN rules is represented by a fuzzy theory in a fuzzy logic-based fuzzy control can be represented by a suitable fuzzy theory in fuzzy logic. This is achieved
Intelligent fuzzy controller of a quadrotor
Matilde Santos; Victoria López; Franciso Morata
2010-01-01
The aim of this work is to describe an intelligent system based on fuzzy logic that is developed to control a quadrotor. A quadrotor is a helicopter with four rotors, that make the vehicle more stable but more complex to model and to control. The quadrotor has been used as a testing platform in the last years for various universities
Fuzzy learning control for antiskid braking systems
Jeffery R. Layne; Kevin M. Passino; Stephen Yurkovich
1993-01-01
Although antiskid braking systems (ABS) are designed to optimize braking effectiveness while maintaining steerability, their performance often degrades under harsh road conditions (e.g. icy\\/snowy roads). The use of the fuzzy model reference learning control (FMRLC) technique for maintaining adequate performance even under such adverse road conditions is proposed. This controller utilizes a learning mechanism that observes the plant outputs and
Fuzzy Sequential Control Based On Petri Nets
J. C. Pascal; R. Valette; D. Andreu
1992-01-01
Programmable Logic Controllers are able to directly implement control sequences specified by means of standard languages such as Grafcet or formal models such as Petri nets. In case of simple regulation problems it could be of particular interest to introduce a notion of fuzzy events. Such an event corresponds to a continuous evolution from one state to another and results
Genetic algorithm based fuzzy control of spacecraft autonomous rendezvous
NASA Technical Reports Server (NTRS)
Karr, C. L.; Freeman, L. M.; Meredith, D. L.
1990-01-01
The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy logic based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective decision. High performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.
Fuzzy Modal Control Applied to Smart Composite Structure
NASA Astrophysics Data System (ADS)
Koroishi, E. H.; Faria, A. W.; Lara-Molina, F. A.; Steffen, V., Jr.
2015-07-01
This paper proposes an active vibration control technique, which is based on Fuzzy Modal Control, as applied to a piezoelectric actuator bonded to a composite structure forming a so-called smart composite structure. Fuzzy Modal Controllers were found to be well adapted for controlling structures with nonlinear behavior, whose characteristics change considerably with respect to time. The smart composite structure was modelled by using a so called mixed theory. This theory uses a single equivalent layer for the discretization of the mechanical displacement field and a layerwise representation of the electrical field. Temperature effects are neglected. Due to numerical reasons it was necessary to reduce the size of the model of the smart composite structure so that the design of the controllers and the estimator could be performed. The role of the Kalman Estimator in the present contribution is to estimate the modal states of the system, which are used by the Fuzzy Modal controllers. Simulation results illustrate the effectiveness of the proposed vibration control methodology for composite structures.
Fuzzy Economizer control using a Prolog-C inference engine
Belur, Raghuveer R.
1993-01-01
This research is in two parts: I. Develop a generic tool to perform fuzzy inference on a wide class of systems.Thisis done using Prolog and C. 2.Develop a hierarchical control scheme using this fuzzy inference mechanism ...
Position control of a servopneumatic system using fuzzy compensation
Sathyanarayana, Sreenivas
2000-01-01
The position control of a servopneumatic system in the presence of stick-slip type of friction is investigated. A cost effective, model-free fuzzy compensation scheme is proposed. The fuzzy compensation scheme compensates ...
Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques
Michael A. Lee; Hideyuki Takagi
1993-01-01
This paper proposes using fuzzy logic techniques to dynamically control parameter settings of ge- netic algorithms (GAs). We describe the Dy- namic Parametric GA: a GA that uses a fuzzy knowledge-based system to control GA parame- ters. We then introduce a technique for automati- cally designing and tuning the fuzzy knowledge- base system using GAs. Results from initial experiments show
Fuzzy control and multimedia with examples from law enforcement
Susan Hackwood
1995-01-01
We present an extension of fuzzy controllers to include multimedia rules, i.e., rules which do not include verbal or numerical descriptors. We describe the structure and construction of such a multimedia fuzzy controller. In particular, we describe an empirical but unbiased methodology to measure, from human subjects, distances in feature space and hence determine fuzzy memberships. We also propose a
Stability of fuzzy control systems with bounded uncertain delays
Zhang Yi; Pheng Ann Heng
2002-01-01
Global exponential stability of fuzzy control systems with delays is studied. These delays in the fuzzy control systems are assumed to be any uncertain bounded continuous functions. Stability of systems with uncertain delays is interesting since in practical applications it is not easy to know the delays exactly. Conditions for global exponential stability of free fuzzy systems with uncertain delays
Research of irrigation control system based on fuzzy neural network
Guifen Chen; Lisong Yue
2011-01-01
Saving water is vital for water-saving irrigation project. The paper proposed an intelligent irrigation control system and combined fuzzy system and neural network to intelligent control system. Aimed at water-saving irrigation technology, the new method not only can offset the deficiency of neural network in processing the fuzzy data, but also can effectively resolve the disability of fuzzy logic on
Fuzzy Control/Space Station automation
NASA Technical Reports Server (NTRS)
Gersh, Mark
1990-01-01
Viewgraphs on fuzzy control/space station automation are presented. Topics covered include: Space Station Freedom (SSF); SSF evolution; factors pointing to automation & robotics (A&R); astronaut office inputs concerning A&R; flight system automation and ground operations applications; transition definition program; and advanced automation software tools.
FUZZY LOGIC CONTROL OF AC INDUCTION MOTORS
The paper discusses the fuzzy logic control (FLC) of electric motors, being investigated under the sponsorship of the U.S. EPA to reduce energy consumption when motors are operated at less than rated speeds and loads. lectric motors use 60% of the electrical energy generated in t...
A Laboratory Testbed for Embedded Fuzzy Control
ERIC Educational Resources Information Center
Srivastava, S.; Sukumar, V.; Bhasin, P. S.; Arun Kumar, D.
2011-01-01
This paper presents a novel scheme called "Laboratory Testbed for Embedded Fuzzy Control of a Real Time Nonlinear System." The idea is based upon the fact that project-based learning motivates students to learn actively and to use their engineering skills acquired in their previous years of study. It also fosters initiative and focuses students'…
Fuzzy PD+I and Fuzzy PID controllers design for a nonlinear quarter car suspension system
Sajad Tabatabaei; Alireza Barzegar; Mokhtar Sha Sadeghi; Pegah Roosta
2010-01-01
In this paper, Fuzzy PID and Fuzzy PD+I controller are designed for a nonlinear quarter car suspension system. The main aim of these controllers is to decrease vertical car body displacement and control signal, therefore provides more comfortable conditions for passengers. Simulation results show excellent efficiency of the proposed controller.
Force control of a tri-layer conducting polymer actuator using optimized fuzzy logic control
NASA Astrophysics Data System (ADS)
Itik, Mehmet; Sabetghadam, Mohammadreza; Alici, Gursel
2014-12-01
Conducting polymers actuators (CPAs) are potential candidates for replacing conventional actuators in various fields, such as robotics and biomedical engineering, due to their advantageous properties, which includes their low cost, light weight, low actuation voltage and biocompatibility. As these actuators are very suitable for use in micro-nano manipulation and in injection devices in which the magnitude of the force applied to the target is of crucial importance, the force generated by CPAs needs to be accurately controlled. In this paper, a fuzzy logic (FL) controller with a Mamdani inference system is designed to control the blocking force of a trilayer CPA with polypyrrole electrodes, which operates in air. The particle swarm optimization (PSO) method is employed to optimize the controller’s membership function parameters and therefore enhance the performance of the FL controller. An adaptive neuro-fuzzy inference system model, which can capture the nonlinear dynamics of the actuator, is utilized in the optimization process. The optimized Mamdani FL controller is then implemented on the CPA experimentally, and its performance is compared with a non-optimized fuzzy controller as well as with those obtained from a conventional PID controller. The results presented indicate that the blocking force at the tip of the CPA can be effectively controlled by the optimized FL controller, which shows excellent transient and steady state characteristics but increases the control voltage compared to the non-optimized fuzzy controllers.
Generalizations of fuzzy linguistic control points in geometric design
NASA Astrophysics Data System (ADS)
Sallehuddin, M. H.; Wahab, A. F.; Gobithaasan, R. U.
2014-07-01
Control points are geometric primitives that play an important role in designing the geometry curve and surface. When these control points are blended with some basis functions, there are several geometric models such as Bezier, B-spline and NURBS(Non-Uniform Rational B-Spline) will be produced. If the control points are defined by the theory of fuzzy sets, then fuzzy geometric models are produced. But the fuzzy geometric models can only solve the problem of uncertainty complex. This paper proposes a new definition of fuzzy control points with linguistic terms. When the fuzzy control points with linguistic terms are blended with basis functions, then a fuzzy linguistic geometric model is produced. This paper ends with some numerical examples illustrating linguistic control attributes of fuzzy geometric models.
The Fuzzy-PI mix control for the briquette production
Lan Xizhu [China Univ. of Mining and Technology, Beijing (China); Yang Hongjun [Hebi Coal Administrative Bureau, Henan (China)
1998-12-31
The paper applies the Fuzzy-PI mix control to the briquette production, a new kind of Fuzzy-PI controller is developed combining the Fuzzy control principle with classic control theory, and the pressure control system for the briquette production is also developed. The simulation research on the above system has been done, which was compared with the traditional PID control system. The simulation result shows: the Fuzzy-PI control system gives satisfactory effect in the field of the response speed, control accuracy and control performance, and moreover, the system has better robustness.
Experimental fuzzy logic active vibration control
M. K. Joujou; Fouad Mrad; Ahamad Smaili
2008-01-01
This paper presents analytical and experimental investigations on the elastodynamic control of a four-bar mechanism system with a flexible coupler link retrofitted with a collocated piezo electric sensor actuator pair using P-like fuzzy logic control (FLC). A major advantage of FLC is that the controller can be designed without having a model of the system. The four-bar mechanism will be
Adaptive Neuro-Fuzzy Extended Kalman Filtering for Robot Localization
Havangi, Ramazan; Teshnehlab, Mohammad
2010-01-01
Extended Kalman Filter (EKF) has been a popular approach to localization a mobile robot. However, the performance of the EKF and the quality of the estimation depends on the correct a priori knowledge of process and measurement noise covariance matrices (Qk and Rk, respectively). Imprecise knowledge of these statistics can cause significant degradation in performance. This paper proposed the development of an Adaptive Neuro- Fuzzy Extended Kalman Filtering (ANFEKF) for localization of robot. The Adaptive Neuro-Fuzzy attempts to estimate the elements of Qk and Rk matrices of the EKF algorithm, at each sampling instant when measurement update step is carried out. The ANFIS supervises the performance of the EKF with the aim of reducing the mismatch between the theoretical and actual covariance of the innovation sequences. The free parameters of ANFIS are trained using the steepest gradient descent (SD) to minimize the differences of the actual value of the covariance of the residual with its theoretical value as...
PI and fuzzy logic controllers for shunt Active Power Filter--a report.
P, Karuppanan; Mahapatra, Kamala Kanta
2012-01-01
This paper presents a shunt Active Power Filter (APF) for power quality improvements in terms of harmonics and reactive power compensation in the distribution network. The compensation process is based only on source current extraction that reduces the number of sensors as well as its complexity. A Proportional Integral (PI) or Fuzzy Logic Controller (FLC) is used to extract the required reference current from the distorted line-current, and this controls the DC-side capacitor voltage of the inverter. The shunt APF is implemented with PWM-current controlled Voltage Source Inverter (VSI) and the switching patterns are generated through a novel Adaptive-Fuzzy Hysteresis Current Controller (A-F-HCC). The proposed adaptive-fuzzy-HCC is compared with fixed-HCC and adaptive-HCC techniques and the superior features of this novel approach are established. The FLC based shunt APF system is validated through extensive simulation for diode-rectifier/R-L loads. PMID:21982358
FUSION ARTMAP: AN ADAPTIVE FUZZY NETWORK FOR MULTI-CHANNEL CLASSIFICATION
Grossberg, Stephen
and Neural Systems 111 Cl\\rllmington Street Boston. MA 02215 #12;Fusion ARTMAP: An Adaptive Fuzzy NetworkFUSION ARTMAP: AN ADAPTIVE FUZZY NETWORK FOR MULTI-CHANNEL CLASSIFICATION YousifR. Asfour, Gail A§ Center for Adaptive Systemsand Department of Cognitive and Neural Systems Boston University, 111
Learning fuzzy logic controller for reactive robot behaviours
Dongbing Gu; Huosheng Hu; Libor Spacek
2003-01-01
Fuzzy logic plays an important role in the design of reactive robot behaviours. This paper presents a learning approach to the development of a fuzzy logic controller based on the delayed rewards from the real world. The delayed rewards are apportioned to the individual fuzzy rules by using reinforcement Q-learning. The efficient exploration of a solution space is one of
Adaptive critic autopilot design of bank-to-turn missiles using fuzzy basis function networks.
Lin, Chuan-Kai
2005-04-01
A new adaptive critic autopilot design for bank-to-turn missiles is presented. In this paper, the architecture of adaptive critic learning scheme contains a fuzzy-basis-function-network based associative search element (ASE), which is employed to approximate nonlinear and complex functions of bank-to-turn missiles, and an adaptive critic element (ACE) generating the reinforcement signal to tune the associative search element. In the design of the adaptive critic autopilot, the control law receives signals from a fixed gain controller, an ASE and an adaptive robust element, which can eliminate approximation errors and disturbances. Traditional adaptive critic reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic environment, however, the proposed tuning algorithm can significantly shorten the learning time by online tuning all parameters of fuzzy basis functions and weights of ASE and ACE. Moreover, the weight updating law derived from the Lyapunov stability theory is capable of guaranteeing both tracking performance and stability. Computer simulation results confirm the effectiveness of the proposed adaptive critic autopilot. PMID:15828650
Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong
2015-01-01
In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896
Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong
2015-01-01
In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896
Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm
NASA Technical Reports Server (NTRS)
Mitra, Sunanda; Pemmaraju, Surya
1992-01-01
Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.
Kosko, Bart
cost: It con- verts fuzzy systems to radial-basis-function neural networks or Manuscript received in Adaptive Function Approximation Sanya Mitaim and Bart Kosko Abstract--The shape of if-part fuzzy sets the resulting adaptive fuzzy systems approximate a battery of test functions. No one set shape emerges
Fuzzy logic controller for longwall mining shield alignment
Banta, L.; Parthasarathy, M.; Mucino, V.H. [West Virginia Univ., Morgantown, WV (United States)
1995-05-01
At West Virginia University, we are working on an application of longwall mining methods to the problem of hazardous waste containment. This application requires stringent control of the position of each shield, including control of its orientation with respect to the face. This problem will be handled by using European style shields which include lateral hydraulic cylinders coupling adjacent shields. In Europe, this system is used to control lateral creep during face advance maneuvers. We propose to adapt the system to control shield position and orientation. We have developed a shield position control system based on the use of Fuzzy Logic which is accurate, flexible and computationally much simpler than an equivalent controller using traditional methods. The controller is of general interest because the mechanism is similar to many other common applications requiring multi-actuator control. The controller was developed by combining a formal kinematic analysis of the mechanism with computer simulation of the closed loop system. This paper describes the problem, the approach taken to the development of the fuzzy rule set and the results of the simulation studies used to test the performance of the controller.
Fuzzy Current-Mode Control and Stability Analysis
NASA Technical Reports Server (NTRS)
Kopasakis, George
2000-01-01
In this paper a current-mode control (CMC) methodology is developed for a buck converter by using a fuzzy logic controller. Conventional CMC methodologies are based on lead-lag compensation with voltage and inductor current feedback. In this paper the converter lead-lag compensation will be substituted with a fuzzy controller. A small-signal model of the fuzzy controller will also be developed in order to examine the stability properties of this buck converter control system. The paper develops an analytical approach, introducing fuzzy control into the area of CMC.
FPGA implementation of fuzzy wall-following control
Med Slim Masmoudi; Insop Song; Fakerddine Karray; Mohamed Masmoudi; N. Derbel
2004-01-01
The objective of this study concerns the design and implementation of a complete intelligent mechatronic system. The basic idea uses the concept of car maneuvers; control (fuzzy logic controller) and sensor-based behaviors together merged to implement the wall-following control algorithm. The fuzzy logic control algorithm (FLC) was considered as the heart of the controller due to the advantage of its
Maximum entropy approach to fuzzy control
NASA Technical Reports Server (NTRS)
Ramer, Arthur; Kreinovich, Vladik YA.
1992-01-01
For the same expert knowledge, if one uses different &- and V-operations in a fuzzy control methodology, one ends up with different control strategies. Each choice of these operations restricts the set of possible control strategies. Since a wrong choice can lead to a low quality control, it is reasonable to try to loose as few possibilities as possible. This idea is formalized and it is shown that it leads to the choice of min(a + b,1) for V and min(a,b) for &. This choice was tried on NASA Shuttle simulator; it leads to a maximally stable control.
Pipelined recurrent fuzzy neural networks for nonlinear adaptive speech prediction.
Stavrakoudis, Dimitris G; Theocharis, John B
2007-10-01
A class of pipelined recurrent fuzzy neural networks (PRFNNs) is proposed in this paper for nonlinear adaptive speech prediction. The PRFNNs are modular structures comprising a number of modules that are interconnected in a chained form. Each module is implemented by a small-scale recurrent fuzzy neural network (RFNN) with internal dynamics. Due to module nesting, the PRFNNs offer a number of desirable attributes, including decomposition of the modeling task, enhanced temporal processing capabilities, and multistage dynamic fuzzy inference. Tuning of the PRFNN adaptable parameters is accomplished by a series of gradient descent methods with different weighting of the modules and the decoupled extended Kalman filter (DEKF) algorithm, based on weight grouping. Extensive experimentation is carried out to evaluate the performance of the PRFNNs on the speech prediction platform. Comparative analysis shows that the PRFNNs outperform the single-RFNN models in terms of the prediction gains that are obtained and computational efficiency. Furthermore, PRFNNs provide considerably better performance compared to pipelined recurrent neural networks, for models with similar model complexity. PMID:17926711
Harmonic Control Based on Fuzzy Logic
NASA Astrophysics Data System (ADS)
Wu, Shihong; Dang, Gang; Wang, Jun; Li, Xiaohui; Zhang, Zhixia; Jiang, Fengli
Proliferation of nonlinear loads in power systems has increased harmonic pollution and deteriorated power quality. Passive filtering has typically been the standard technology for harmonic and reactive power compensation .With the advancements in power electronics, active filtering is being more widely considered given its flexibility and precise control. However, cost, complexity, and reliability are considered the major drawbacks of active filters. In this paper a new fuzzy logic is introduced to control the harmonic in the power system, which has more advantages such as simplicity, ease of application, flexibility, speed and ability to deal with imprecision and uncertainties .The introduction of fuzzy logic can not only reduce harmonic,but also correct the power factor.
Nonlinear Fuzzy Hybrid Control of Spacecraft
NASA Technical Reports Server (NTRS)
Mason, Paul A. C.; Crassidis, John L.; Markley, F. Landis
1999-01-01
This paper describes a new approach for intelligent control of a spacecraft with large angle maneuvers. This new approach, based on fuzzy logic, determines the required torque to achieve a robust, high performance attitude response. This scheme extends the robustness, performance and portability of the existing linear or nonlinear attitude controllers. Formulations are presented for attitude-control but can be extended to other applications. A simulation study, which uses the new control strategy to stabilize the motion of the Microwave Anisotropy Probe spacecraft in the presence of disturbances and saturations, demonstrates the merits of the proposed scheme.
Fuzzy Multicriteria Decision Analysis for Adaptive Watershed Management
NASA Astrophysics Data System (ADS)
Chang, N.
2006-12-01
The dramatic changes of societal complexity due to intensive interactions among agricultural, industrial, and municipal sectors have resulted in acute issues of water resources redistribution and water quality management in many river basins. Given the fact that integrated watershed management is more a political and societal than a technical challenge, there is a need for developing a compelling method leading to justify a water-based land use program in some critical regions. Adaptive watershed management is viewed as an indispensable tool nowadays for providing step-wise constructive decision support that is concerned with all related aspects of the water consumption cycle and those facilities affecting water quality and quantity temporally and spatially. Yet the greatest challenge that decision makers face today is to consider how to leverage ambiguity, paradox, and uncertainty to their competitive advantage of management policy quantitatively. This paper explores a fuzzy multicriteria evaluation method for water resources redistribution and subsequent water quality management with respect to a multipurpose channel-reservoir system--the Tseng- Wen River Basin, South Taiwan. Four fuzzy operators tailored for this fuzzy multicriteria decision analysis depict greater flexibility in representing the complexity of various possible trade-offs among management alternatives constrained by physical, economic, and technical factors essential for adaptive watershed management. The management strategies derived may enable decision makers to integrate a vast number of internal weirs, water intakes, reservoirs, drainage ditches, transfer pipelines, and wastewater treatment facilities within the basin and bring up the permitting issue for transboundary diversion from a neighboring river basin. Experience gained indicates that the use of different types of fuzzy operators is highly instructive, which also provide unique guidance collectively for achieving the overarching goals of sustainable development on a regional scale.
Precise tracking control of robot manipulator using fuzzy logic
P. Sumathi
2005-01-01
This paper describes a fuzzy position control scheme designed for a three-link manipulator. The proposed control scheme is based on nonlinear dynamic model derived using Lagrange-Euler formulation. This fuzzy controller controls the position of each link independently and provides compensation for gravity acting on the third link. Computer simulation results on three link robot manipulators are presented to show the
Fuzzy controllers and fuzzy expert systems: industrial applications of fuzzy technology
NASA Astrophysics Data System (ADS)
Bonissone, Piero P.
1995-06-01
We will provide a brief description of the field of approximate reasoning systems, with a particular emphasis on the development of fuzzy logic control (FLC). FLC technology has drastically reduced the development time and deployment cost for the synthesis of nonlinear controllers for dynamic systems. As a result we have experienced an increased number of FLC applications. In a recently published paper we have illustrated some of our efforts in FLC technology transfer, covering projects in turboshaft aircraft engine control, stream turbine startup, steam turbine cycling optimization, resonant converter power supply control, and data-induced modeling of the nonlinear relationship between process variable in a rolling mill stand. These applications will be illustrated in the oral presentation. In this paper, we will compare these applications in a cost/complexity framework, and examine the driving factors that led to the use of FLCs in each application. We will emphasize the role of fuzzy logic in developing supervisory controllers and in maintaining explicit the tradeoff criteria used to manage multiple control strategies. Finally, we will describe some of our FLC technology research efforts in automatic rule base tuning and generation, leading to a suite of programs for reinforcement learning, supervised learning, genetic algorithms, steepest descent algorithms, and rule clustering.
Sanyal, Sugata
or efficiency. In this paper, two machine-learning paradigms, Artificial Neural Networks and Fuzzy Inference of misuse and anomaly based detection techniques in combination with neural networks to make it adaptiveAdaptive Neuro-Fuzzy Intrusion Detection Systems Sampada Chavan, Khusbu Shah, Neha Dave
Active structural control by fuzzy logic rules: An introduction
Tang, Yu; Wu, Kung C.
1996-12-31
A zeroth level introduction to fuzzy logic control applied to the active structural control to reduce the dynamic response of structures subjected to earthquake excitations is presented. It is hoped that this presentation will increase the attractiveness of the methodology to structural engineers in research as well as in practice. The basic concept of the fuzzy logic control are explained by examples and by diagrams with a minimum of mathematics. The effectiveness and simplicity of the fuzzy logic control is demonstrated by a numerical example in which the response of a single- degree-of-freedom system subjected to earthquake excitations is controlled by making use of the fuzzy logic controller. In the example, the fuzzy rules are first learned from the results obtained from linear control theory; then they are fine tuned to improve their performance. It is shown that the performance of fuzzy logic control surpasses that of the linear control theory. The paper shows that linear control theory provides experience for fuzzy logic control, and fuzzy logic control can provide better performance; therefore, two controllers complement each other.
Active structural control by fuzzy logic rules: An introduction
Tang, Y.
1995-07-01
An introduction to fuzzy logic control applied to the active structural control to reduce the dynamic response of structures subjected to earthquake excitations is presented. It is hoped that this presentation will increase the attractiveness of the methodology to structural engineers in research as well as in practice. The basic concept of the fuzzy logic control are explained by examples and by diagrams with a minimum of mathematics. The effectiveness and simplicity of the fuzzy logic control is demonstrated by a numerical example in which the response of a single-degree-of-freedom system subjected to earthquake excitations is controlled by making use of the fuzzy logic controller. In the example, the fuzzy rules are first learned from the results obtained from linear control theory; then they are fine tuned to improve their performance. It is shown that the performance of fuzzy logic control surpasses that of the linear control theory. The paper shows that linear control theory provides experience for fuzzy logic control, and fuzzy logic control can provide better performance; therefore, two controllers complement each other.
Wastewater neutralization control based in fuzzy logic: Simulation results
Garrido, R.; Adroer, M.; Poch, M. [Univ. de Girona (Spain)] [Univ. de Girona (Spain)
1997-05-01
Neutralization is a technique widely used as a part of wastewater treatment processes. Due to the importance of this technique, extensive study has been devoted to its control. However, industrial wastewater neutralization control is a procedure with a lot of problems--nonlinearity of the titration curve, variable buffering, changes in loading--and despite the efforts devoted to this subject, the problem has not been totally solved. in this paper, the authors present the development of a controller based in fuzzy logic (FLC). In order to study its effectiveness, it has been compared, by simulation, with other advanced controllers (using identification techniques and adaptive control algorithms using reference models) when faced with various types of wastewater with different buffer capacity or when changes in the concentration of the acid present in the wastewater take place. Results obtained show that FLC could be considered as a powerful alternative for wastewater neutralization processes.
Intelligent fuzzy controller for event-driven real time systems
NASA Technical Reports Server (NTRS)
Grantner, Janos; Patyra, Marek; Stachowicz, Marian S.
1992-01-01
Most of the known linguistic models are essentially static, that is, time is not a parameter in describing the behavior of the object's model. In this paper we show a model for synchronous finite state machines based on fuzzy logic. Such finite state machines can be used to build both event-driven, time-varying, rule-based systems and the control unit section of a fuzzy logic computer. The architecture of a pipelined intelligent fuzzy controller is presented, and the linguistic model is represented by an overall fuzzy relation stored in a single rule memory. A VLSI integrated circuit implementation of the fuzzy controller is suggested. At a clock rate of 30 MHz, the controller can perform 3 MFLIPS on multi-dimensional fuzzy data.
Learning and tuning fuzzy logic controllers through reinforcements
Hamid R. Berenji; Pratap Khedkar
1992-01-01
A method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. It is shown that: the generalized approximate-reasoning-based intelligent control (GARIC) architecture learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; introduces a new conjunction operator in computing the rule strengths
Neural network implementation of a fuzzy logic controller
G. S. Buja; F. Todesco
1994-01-01
Fuzzy logic is an attractive technique for plant control but suffers from a heavy computation burden. A solution to this problem is proposed here and consists of implementing a fuzzy logic controller in a neural network. The solution is applied to the speed control of a DC motor drive and is validated by experimental results
Control of a fluidized bed combustor using fuzzy logic
Koffman, S.J. [Purdue Univ., West Lafayette, IN (United States). School of Mechanical Engineering; Brown, R.C. [Iowa State Univ., Ames, IA (United States). Dept. of Mechanical Engineering; Fullmer, R.R. [Utah State Univ., Logan, UT (United States). Dept. of Mechanical and Aerospace Engineering
1996-01-01
Fuzzy logic--an artificial intelligence technique--can be employed to exploit the wealth of information human experts have learned about complex systems while attempting to control them. This information is usually of a qualitative nature that is unusable by rigid conventional control techniques. Fuzzy logic, uses as a control method, manipulates linguistically expressed, heuristic knowledge from a human expert to derive control actions for a described system. As an alternative approach to classical controls, fuzzy logic is examined for start-up control and normal regulation of a bubbling fluidized bed combustor. To validate the fuzzy logic approach, the fuzzy controller is compared to a classical proportional and integral (PI) controller, commonly used in industrial applications, designed by Ziegler-Nichols tuning.
Modal control of a plate using a fuzzy logic controller
NASA Astrophysics Data System (ADS)
Sharma, Manu; Singh, S. P.; Sachdeva, B. L.
2007-08-01
This paper presents fuzzy logic based independent modal space control (IMSC) and fuzzy logic based modified independent modal space control (MIMSC) of vibration. The rule base of the controller consists of nine rules, which have been derived based upon simple human reasoning. Input to the controller consists of the first two modal displacements and velocities of the structure and the output of the controller is the modal force to be applied by the actuator. Fuzzy logic is used in such a way that the actuator is never called to apply effort which is beyond safe limits and also the operator is saved from calculating control gains. The proposed fuzzy controller is experimentally tested for active vibration control of a cantilevered plate. A piezoelectric patch is used as a sensor to sense vibrations of the plate and another piezoelectric patch is used as an actuator to control vibrations of the plate. For analytical formulation, a finite element method based upon Hamilton's principle is used to model the plate. For experimentation, the first two modes of the plate are observed using a Kalman observer. Real-time experiments are performed to control the first mode, the second mode and both modes simultaneously. Experiments are also performed to control the first mode by IMSC, the second mode by IMSC and both modes simultaneously by MIMSC. It is found that for the same decibel reduction in the first mode, the voltage applied by the fuzzy logic based controller is less than that applied by IMSC. While controlling the second mode by IMSC, a considerable amount of spillover is observed in the first mode and region just after the second mode, whereas while controlling the second mode by fuzzy logic, spillover effects are much smaller. While controlling two modes simultaneously, with a single sensor/actuator pair, appreciable resonance control is observed both with fuzzy logic based MIMSC as well as with direct MIMSC, but there is a considerable amount of spillover in the off-resonance region. This may be due to the sub-optimal location and/or an insufficient number of actuators. So, another smart plate with two piezoelectric actuators and one piezoelectric sensor is considered. Piezoelectric patches are fixed in an area where modal strains are high. With this configuration of the smart plate, experiments are conducted to control the first three modes of the plate and it is found that spillover effects are greatly reduced.
Design of fuzzy system by NNs and realization of adaptability
NASA Technical Reports Server (NTRS)
Takagi, Hideyuki
1993-01-01
The issue of designing and tuning fuzzy membership functions by neural networks (NN's) was started by NN-driven Fuzzy Reasoning in 1988. NN-driven fuzzy reasoning involves a NN embedded in the fuzzy system which generates membership values. In conventional fuzzy system design, the membership functions are hand-crafted by trial and error for each input variable. In contrast, NN-driven fuzzy reasoning considers several variables simultaneously and can design a multidimensional, nonlinear membership function for the entire subspace.
SaFIN: a self-adaptive fuzzy inference network.
Tung, Sau Wai; Quek, Chai; Guan, Cuntai
2011-12-01
There are generally two approaches to the design of a neural fuzzy system: 1) design by human experts, and 2) design through a self-organization of the numerical training data. While the former approach is highly subjective, the latter is commonly plagued by one or more of the following major problems: 1) an inconsistent rulebase; 2) the need for prior knowledge such as the number of clusters to be computed; 3) heuristically designed knowledge acquisition methodologies; and 4) the stability-plasticity tradeoff of the system. This paper presents a novel self-organizing neural fuzzy system, named Self-Adaptive Fuzzy Inference Network (SaFIN), to address the aforementioned deficiencies. The proposed SaFIN model employs a new clustering technique referred to as categorical learning-induced partitioning (CLIP), which draws inspiration from the behavioral category learning process demonstrated by humans. By employing the one-pass CLIP, SaFIN is able to incorporate new clusters in each input-output dimension when the existing clusters are not able to give a satisfactory representation of the incoming training data. This not only avoids the need for prior knowledge regarding the number of clusters needed for each input-output dimension, but also allows SaFIN the flexibility to incorporate new knowledge with old knowledge in the system. In addition, the self-automated rule formation mechanism proposed within SaFIN ensures that it obtains a consistent resultant rulebase. Subsequently, the proposed SaFIN model is employed in a series of benchmark simulations to demonstrate its efficiency as a self-organizing neural fuzzy system, and excellent performances have been achieved. PMID:22020678
Fuzzy control for active suspensions
Fernando J. D’Amato; Daniel E. Viassolo
2000-01-01
A methodology for the design of active car suspension systems is presented. The goal is to minimize vertical car body acceleration, for passenger comfort, and to avoid hitting suspension limits, for component lifetime preservation. A controller consisting of two control loops is proposed to attain this goal. The inner loop controls a nonlinear hydraulic actuator to achieve tracking of a
NASA Astrophysics Data System (ADS)
Lin, J.; Zheng, Y. B.
2012-07-01
The main goal of this paper is to develop a novel approach for vibration control on a piezoelectric rotating truss structure. This study will analyze the dynamics and control of a flexible structure system with multiple degrees of freedom, represented in this research as a clamped-free-free-free truss type plate rotated by motors. The controller has two separate feedback loops for tracking and damping, and the vibration suppression controller is independent of position tracking control. In addition to stabilizing the actual system, the proposed proportional-derivative (PD) control, based on genetic algorithm (GA) to seek the primary optimal control gain, must supplement a fuzzy control law to ensure a stable nonlinear system. This is done by using an intelligent fuzzy controller based on adaptive neuro-fuzzy inference system (ANFIS) with GA tuning to increase the efficiency of fuzzy control. The PD controller, in its assisting role, easily stabilized the linear system. The fuzzy controller rule base was then constructed based on PD performance-related knowledge. Experimental validation for such a structure demonstrates the effectiveness of the proposed controller. The broad range of problems discussed in this research will be found useful in civil, mechanical, and aerospace engineering, for flexible structures with multiple degree-of-freedom motion.
Stabilization ball and beam by fuzzy logic control strategy
NASA Astrophysics Data System (ADS)
Asadi, Houshyar; Mohammadi, Arash; Oladazimi, Maysam
2011-12-01
Fuzzy logic controller is a controller for designing the challenging nonlinear control systems by If-Then laws that is like human intelligence and it increase the accuracy of the control action .This paper present a success control function using a Fuzzy System approach which is to control the Ball-Beam balance system, throughout modeling, simulation, and implementation. First we applied fuzzy logic for system which means for the outer loop a fuzzy logic controller is designed and for the inner loop of a ball and beam system a PD controller is implemented. We design a traditional PID controller and pole placement controller for the beam position in order to compare the performance of these three types of controllers; thus FLC found to give better transient and steady state results and there is less overshoot in compare with classical PID and pole placement controller. Simulation results are presented to show the better performance of the ball and beam using these controllers.
Stabilization ball and beam by fuzzy logic control strategy
NASA Astrophysics Data System (ADS)
Asadi, Houshyar; Mohammadi, Arash; Oladazimi, Maysam
2012-01-01
Fuzzy logic controller is a controller for designing the challenging nonlinear control systems by If-Then laws that is like human intelligence and it increase the accuracy of the control action .This paper present a success control function using a Fuzzy System approach which is to control the Ball-Beam balance system, throughout modeling, simulation, and implementation. First we applied fuzzy logic for system which means for the outer loop a fuzzy logic controller is designed and for the inner loop of a ball and beam system a PD controller is implemented. We design a traditional PID controller and pole placement controller for the beam position in order to compare the performance of these three types of controllers; thus FLC found to give better transient and steady state results and there is less overshoot in compare with classical PID and pole placement controller. Simulation results are presented to show the better performance of the ball and beam using these controllers.
Coordinated signal control for arterial intersections using fuzzy logic
NASA Astrophysics Data System (ADS)
Kermanian, Davood; Zare, Assef; Balochian, Saeed
2013-09-01
Every day growth of the vehicles has become one of the biggest problems of urbanism especially in major cities. This can waste people's time, increase the fuel consumption, air pollution, and increase the density of cars and vehicles. Fuzzy controllers have been widely used in many consumer products and industrial applications with success over the past two decades. This article proposes a comprehensive model of urban traffic network using state space equations and then using Fuzzy Logic Tool Box and SIMULINK Program MATLAB a fuzzy controller in order to optimize and coordinate signal control at two intersections at an arterial road. The fuzzy controller decides to extend, early cut or terminate a signal phase and phase sequence to ensure smooth flow of traffic with minimal waiting time and length of queue. Results show that the performance of the proposed traffic controller at novel fuzzy model is better that of conventional controllers under normal and abnormal traffic conditions.
Switching force\\/position fuzzy control of robotic manipulator
Jasmin Velagic; Azra Adzemovic; J. Ibrahimagic
2003-01-01
In this paper a switching force\\/position control system of robot manipulator by using fuzzy logic is proposed. This system contains a fuzzy control system. The first aim of this paper is achievement of a good end-effector trajectory tracking performance when the manipulator moves in free space. The second is considering the system behavior when contact forces arise. The objective is
Wide-range nuclear reactor temperature control using automatically tuned fuzzy logic controller
Ramaswamy, P.; Edwards, R.M.; Lee, K.Y. (Pennsylvania State Univ., University Park (United States))
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.
An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller
ERIC Educational Resources Information Center
Mamdani, E. H.; Assilian, S.
1975-01-01
This paper describes an experiment on the "linguistic" synthesis of a controller for a model industrial plant (a steam engine). Fuzzy logic is used to convert heuristic control rules stated by a human operator into an automatic control strategy. (Author)
A fuzzy logic controller for an autonomous mobile robot
NASA Technical Reports Server (NTRS)
Yen, John; Pfluger, Nathan
1993-01-01
The ability of a mobile robot system to plan and move intelligently in a dynamic system is needed if robots are to be useful in areas other than controlled environments. An example of a use for this system is to control an autonomous mobile robot in a space station, or other isolated area where it is hard or impossible for human life to exist for long periods of time (e.g., Mars). The system would allow the robot to be programmed to carry out the duties normally accomplished by a human being. Some of the duties that could be accomplished include operating instruments, transporting objects, and maintenance of the environment. The main focus of our early work has been on developing a fuzzy controller that takes a path and adapts it to a given environment. The robot only uses information gathered from the sensors, but retains the ability to avoid dynamically placed obstacles near and along the path. Our fuzzy logic controller is based on the following algorithm: (1) determine the desired direction of travel; (2) determine the allowed direction of travel; and (3) combine the desired and allowed directions in order to determine a direciton that is both desired and allowed. The desired direction of travel is determined by projecting ahead to a point along the path that is closer to the goal. This gives a local direction of travel for the robot and helps to avoid obstacles.
A new fuzzy control approach to voltage profile enhancement for power systems
Su, C.T.; Lin, C.T. [National Chung Cheng Univ., Chiayi (Taiwan, Province of China). Inst. of Electrical Engineering
1996-08-01
This paper presents a new approach using fuzzy set theory for voltage and reactive power control of power systems. The purpose is to enhance voltage security of an electric power system. The violation bus voltage and the controlling variables are translated into fuzzy set notations to formulate the relation between voltage violation level and controlling ability of controlling devices. A feasible solution set is first attained using the min-operation of fuzzy sets, then the optimal solution is fast determined employing the max-operation. A modified IEEE 30-bus test system is used to demonstrate the application of the proposed approach. Simulation results show that the approach is efficient and has good flexibility and adaptability for voltage-reactive power control.
Sensorless induction spindle motor drive using fuzzy neural network speed controller
Faa-Jeng Lin; Jyh-Chyang Yu; Mao-Sheng Tzeng
2001-01-01
A sensorless induction spindle motor drive using synchronous PWM and dead-time compensator with fuzzy neural network (FNN) speed controller is proposed in this study for advanced spindle motor applications. First, the operating principles of a new type synchronous PWM technique are described in detail. Then, a speed observer based on the model reference adaptive system (MRAS) theory is adopted to
Hierarchical fuzzy control of low-energy building systems
Yu, Zhen; Dexter, Arthur
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)
Vehicle yaw stability control using the fuzzy-logic controller
Bin Li; Daofei Li; Fan Yu
2007-01-01
The yaw stability of a vehicle is crucial to vehicle safety in steering manoeuvres. In this paper, a fuzzy-logic controller is designed for improving vehicle yaw stability by corrective yaw moment generated from differential braking so that the yaw rate and body sideslip angle can trace their desired values. An 8-DOF vehicle model with nonlinear tire characteristic is developed to
Decoupling control by hierarchical fuzzy sliding-mode controller
Chih-Min Lin; Yi-Jen Mon
2005-01-01
A design method using hierarchical fuzzy sliding-mode (HFSM) decoupling control is proposed to achieve system stability and favorable decoupling performance for a class of nonlinear systems. In this approach, the nonlinear system is decoupled into several subsystems and the state response of each subsystem can be designed to be governed by a corresponding sliding surface. Then the whole system is
Development of a self-tuning fuzzy logic controller
Huang, S.H.; Nelson, R.M.
1999-07-01
To avoid the laborious task of modifying control rule sets for fuzzy logic controllers, a novel model-based self-tuning strategy has been developed. The performance of this advanced fuzzy logic controller is measured and analyzed in a linguistic plane. An optimal performance trajectory functions as the control model. The self-tuning strategy improves the performance automatically until it converges to a predetermined optimal global criterion. The experimental results indicate that the actual performance trajectory of the advanced fuzzy controller with the self-tuning strategy has reached the optimal criterion.
Fuzzy position control of hydraulic robots with valve deadbands
N. Sepehri; T. Corbet; P. D. Lawrence
1995-01-01
The development of a real-time fuzzy-logic controller for a class of industrial hydraulic robots is described. The main element of the controller is a PD-type fuzzy control technique which utilizes a simple set of membership functions and rules to meet the basic control requirements of such robots. The controller, although effective, is shown to produce steady-state errors. The steady-state error
Design of Fuzzy Logic Controllers by Fuzzy c-Means Clustering
Watcharachai Wiriyasuttiwong; Kajornsak Kantapanit
In this paper, the use of Fuzzy c-means clustering algorithm in the design of membership functions and fuzzy rules of a fuzry logic controller.are described. In the design procedure, an auto- tuning PID controller was used to operate an example plant which is a model of the air-conditioning system, and the plant operating data were collected.The fuzry c-partition of the
Ali Jafarian Abianeh; Hew Wooi Ping
2010-01-01
In this paper, a fuzzy logic based direct torque control (DTC) scheme with optimum number of input membership functions and fuzzy inference rules is presented. The proposed direct torque fuzzy control (DTFC) algorithm is based on classical DTC while the hysteresis controllers are replaced with a single Mamdani type of fuzzy controller. As a result of such a modification, considerable
Adaptive Control Strategies for Flexible Robotic Arm
NASA Technical Reports Server (NTRS)
Bialasiewicz, Jan T.
1996-01-01
The control problem of a flexible robotic arm has been investigated. The control strategies that have been developed have a wide application in approaching the general control problem of flexible space structures. The following control strategies have been developed and evaluated: neural self-tuning control algorithm, neural-network-based fuzzy logic control algorithm, and adaptive pole assignment algorithm. All of the above algorithms have been tested through computer simulation. In addition, the hardware implementation of a computer control system that controls the tip position of a flexible arm clamped on a rigid hub mounted directly on the vertical shaft of a dc motor, has been developed. An adaptive pole assignment algorithm has been applied to suppress vibrations of the described physical model of flexible robotic arm and has been successfully tested using this testbed.
Fuzzy attitude control for a nanosatellite in leo orbit
NASA Astrophysics Data System (ADS)
Calvo, Daniel; Laverón-Simavilla, Ana; Lapuerta, Victoria; Aviles, Taisir
Fuzzy logic controllers are flexible and simple, suitable for small satellites Attitude Determination and Control Subsystems (ADCS). In this work, a tailored fuzzy controller is designed for a nanosatellite and is compared with a traditional Proportional Integrative Derivative (PID) controller. Both control methodologies are compared within the same specific mission. The orbit height varies along the mission from injection at around 380 km down to a 200 km height orbit, and the mission requires pointing accuracy over the whole time. Due to both the requirements imposed by such a low orbit, and the limitations in the power available for the attitude control, a robust and efficient ADCS is required. For these reasons a fuzzy logic controller is implemented as the brain of the ADCS and its performance and efficiency are compared to a traditional PID. The fuzzy controller is designed in three separated controllers, each one acting on one of the Euler angles of the satellite in an orbital frame. The fuzzy memberships are constructed taking into account the mission requirements, the physical properties of the satellite and the expected performances. Both methodologies, fuzzy and PID, are fine-tuned using an automated procedure to grant maximum efficiency with fixed performances. Finally both methods are probed in different environments to test their characteristics. The simulations show that the fuzzy controller is much more efficient (up to 65% less power required) in single maneuvers, achieving similar, or even better, precision than the PID. The accuracy and efficiency improvement of the fuzzy controller increase with orbit height because the environmental disturbances decrease, approaching the ideal scenario. A brief mission description is depicted as well as the design process of both ADCS controllers. Finally the validation process and the results obtained during the simulations are described. Those results show that the fuzzy logic methodology is valid for small satellites' missions benefiting from a well-developed artificial intelligence theory.
Adaptability of a Discrete PSO Algorithm applied to the Traveling Salesman Problem with Fuzzy Data
Ludwig, Simone
Adaptability of a Discrete PSO Algorithm applied to the Traveling Salesman Problem with Fuzzy Data-known optimization method, Particle Swarm Optimization (PSO), when solving a fuzzy problem. The discrete PSO in order to study the impact of uncertain information in the quality of the results provided by PSO
Soft Computing Paradigms for Hybrid Fuzzy Controllers: Experiments and applications*
Fernandez, Thomas
with the aid of these soft computing paradigms are presented. 2 Neuro-Fuzzy System Neural networks exhibit model. The neuro-fuzzy control architec- ture uses the two neural networks to modify the param- eters) University of New Mexico, Albuquerque, NM 87131, USA E-mail: akbazar@eece.unm.edu Abstract Neural Networks
Fuzzy logic control of a solar power plant
Francisco R. Rubio; Manuel Berenguel; Eduardo F. Camacho
1995-01-01
This paper presents an application of fuzzy logic control to the distributed collector field of a solar power plant. The major characteristic of a solar power plant is that the primary energy source, solar radiation, cannot be manipulated. Solar radiation varies throughout the day, causing changes in plant dynamics and strong perturbations in the process. A special subclass of fuzzy
Coordination of Distributed Fuzzy Behaviors in Mobile Robot Control
NASA Technical Reports Server (NTRS)
Tunstel, E.
1995-01-01
This presentation describes an approach to behavior coordination and conflict resolution within the context of a hierarchical architecture of fuzzy behaviors. Coordination is achieved using weighted decision-making based on behavioral degrees of applicability. This strategy is appropriate for fuzzy control of systems that can be represented by hierarchical or decentralized structures.
Modal-space reference-model-tracking fuzzy control of earthquake excited structures
NASA Astrophysics Data System (ADS)
Park, Kwan-Soon; Ok, Seung-Yong
2015-01-01
This paper describes an adaptive modal-space reference-model-tracking fuzzy control technique for the vibration control of earthquake-excited structures. In the proposed approach, the fuzzy logic is introduced to update optimal control force so that the controlled structural response can track the desired response of a reference model. For easy and practical implementation, the reference model is constructed by assigning the target damping ratios to the first few dominant modes in modal space. The numerical simulation results demonstrate that the proposed approach successfully achieves not only the adaptive fault-tolerant control system against partial actuator failures but also the robust performance against the variations of the uncertain system properties by redistributing the feedback control forces to the available actuators.
Fuzzy Sliding-Mode Control of Active Suspensions
Nurkan Yagiz; Yuksel Hacioglu; Yener Taskin
2008-01-01
In this paper, a robust fuzzy sliding-mode controller for active suspensions of a nonlinear half-car model is introduced. First, a nonchattering sliding-mode control is presented. Then, this control method is combined with a single-input-single-output fuzzy logic controller to improve its performance. The negative value of the ratio between the derivative of error and error is the input and the slope
Fuzzy logic controllers: A knowledge-based system perspective
NASA Technical Reports Server (NTRS)
Bonissone, Piero P.
1993-01-01
Over the last few years we have seen an increasing number of applications of Fuzzy Logic Controllers. These applications range from the development of auto-focus cameras, to the control of subway trains, cranes, automobile subsystems (automatic transmissions), domestic appliances, and various consumer electronic products. In summary, we consider a Fuzzy Logic Controller to be a high level language with its local semantics, interpreter, and compiler, which enables us to quickly synthesize non-linear controllers for dynamic systems.
Optimization of fuzzy controller for minimum time response
Farrukh Nagi; Logah Perumal
2009-01-01
Minimum time control is desired for non-linear spacecraft–satellite attitude systems to save on-board thruster fuel. The regulatory time depends on the initial attitude of the satellite. Bang–bang control of satellite attitude can be accomplished with fuzzy logic controller using largest of Maxima defuzzification technique. In case of fuzzy controller, earlier studies show that minimum response time depends on the span
Fuzzy terminal sliding-mode controller for robotic manipulators
Yun-Cheng Huang; Tzuu-Hseng S. Li
2005-01-01
In this paper, a new fuzzy terminal sliding-mode controller (FTSMC) is developed for robotic manipulators. A terminal sliding mode controller can drive the system tracking errors to converge to zero in finite time and the closed-loop system is infinitely stable. The FTSMC, incorporating the fuzzy logic controller and the terminal sliding-mode controller, is designed to retain the advantages of the
Distributed traffic signal control using fuzzy logic
NASA Technical Reports Server (NTRS)
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.
Approach to Synchronization Control of Magnetic Bearings Using Fuzzy Logic
NASA Technical Reports Server (NTRS)
Yang, Li-Farn
1996-01-01
This paper presents a fuzzy-logic approach to the synthesis of synchronization control for magnetically suspended rotor system. The synchronization control enables a whirling rotor to undergo synchronous motion along the magnetic bearing axes; thereby avoiding the gyroscopic effect that degrade the stability of rotor systems when spinning at high speed. The control system features a fuzzy controller acting on the magnetic bearing device, in which the fuzzy inference system trained through fuzzy rules to minimize the differential errors between four bearing axes so that an error along one bearing axis can affect the overall control loop for the motion synchronization. Numerical simulations of synchronization control for the magnetically suspended rotor system are presented to show the effectiveness of the present approach.
Fuzzy logic applications to expert systems and control
NASA Technical Reports Server (NTRS)
Lea, Robert N.; Jani, Yashvant
1991-01-01
A considerable amount of work on the development of fuzzy logic algorithms and application to space related control problems has been done at the Johnson Space Center (JSC) over the past few years. Particularly, guidance control systems for space vehicles during proximity operations, learning systems utilizing neural networks, control of data processing during rendezvous navigation, collision avoidance algorithms, camera tracking controllers, and tether controllers have been developed utilizing fuzzy logic technology. Several other areas in which fuzzy sets and related concepts are being considered at JSC are diagnostic systems, control of robot arms, pattern recognition, and image processing. It has become evident, based on the commercial applications of fuzzy technology in Japan and China during the last few years, that this technology should be exploited by the government as well as private industry for energy savings.
CONTRIBUTED Adaptive Stochastic Control
Powell, Warren B.
); approximate dynamic programming (ADP); control systems; Smart Grid I. INTRODUCTION Autonomous control systems for the Smart Grid, are more difficult than those required to control indoor and site-specific systems (e such an adaptive sto- chastic control (ASC) system for load and source manage- ment of real-time Smart Grid
Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters
Qilian Liang; Jerry M. Mendel
2000-01-01
Presents a kind of adaptive filter: type-2 fuzzy adaptive filter (FAF); one that is realized using an unnormalized type-2 Takagi-Sugeno-Kang (TSK) fuzzy logic system (FLS). We apply this filter to equalization of a nonlinear time-varying channel and demonstrate that it can implement the Bayesian equalizer for such a channel, has a simple structure, and provides fast inference. A clustering method
Neuro-fuzzy control of an MDOF building with a magnetorheological damper using acceleration feedback
Schurter, Kyle Christopher
2000-01-01
Parameter specification of a fuzzy inference system (HS) with the aid of artificial neural networks allows the creation of complex, multi-dimensional models that are computationally efficient and numerically robust. An adaptive neuro-fuzzy inference...
Young-Moon Park; Un-Chul Moon; Kwang Y. Lee
1995-01-01
The paper proposes a complete design method for an online self-organizing fuzzy logic controller without using any plant model. By mimicking the human learning process, the control algorithm finds control rules of a system for which little knowledge has been known. In a conventional fuzzy logic control, knowledge on the system supplied by an expert is required in developing control
Power control of SAFE reactor using fuzzy logic
NASA Astrophysics Data System (ADS)
Irvine, Claude
2002-01-01
Controlling the 100 kW SAFE (Safe Affordable Fission Engine) reactor consists of design and implementation of a fuzzy logic process control system to regulate dynamic variables related to nuclear system power. The first phase of development concentrates primarily on system power startup and regulation, maintaining core temperature equilibrium, and power profile matching. This paper discusses the experimental work performed in those areas. Nuclear core power from the fuel elements is simulated using resistive heating elements while heat rejection is processed by a series of heat pipes. Both axial and radial nuclear power distributions are determined from neuronic modeling codes. The axial temperature profile of the simulated core is matched to the nuclear power profile by varying the resistance of the heating elements. The SAFE model establishes radial temperature profile equivalence by establishing 32 control zones as the nodal coordinates. Control features also allow for slow warm up, since complete shutoff can occur in the heat pipes if heat-source temperatures drop/rise below a certain minimum value, depending on the specific fluid and gas combination in the heat pipe. The entire system is expected to be self-adaptive, i.e., capable of responding to long-range changes in the space environment. Particular attention in the development of the fuzzy logic algorithm shall ensure that the system process remains at set point, virtually eliminating overshoot on start-up and during in-process disturbances. The controller design will withstand harsh environments and applications where it might come in contact with water, corrosive chemicals, radiation fields, etc. .
A Comparison of Non-stationary, Type-2 and Dual Surface Fuzzy Control
Aickelin, Uwe
,uxa,jmg]@cs.nott.ac.uk Abstract--Type-1 fuzzy logic has frequently been used in control systems. However this method is sometimes and fuzzy logic to automate system controllers. The underpinning technique of fuzzy logic was originally introduced by Zadeh in his seminal paper [1]. In this paper, various types of fuzzy set are used including
Youmin Liu; Yunjie Wu; Dapeng Tian
2010-01-01
In this paper, a novel servo turning table control method based on optimal fuzzy reasoning and disturbance observer (DOB) was studied. Optimal fuzzy reasoning contains thought of optimization and feedback, and a fuzzy-PID controller using optimal fuzzy reasoning can make the reasoning process more reasonable and enhance the robustness to some extent. However, when a large external disturbance exists or
Hybrid Kalman filter-fuzzy logic adaptive multisensor data fusion architectures
P. Jorge Escamilla-Ambrosio; Neil Mort
2003-01-01
In this work the recently developed fuzzy logic-based adaptive Kalman filter (FL-AKF) is used to build adaptive centralized, decentralized, and federated Kalman filters for adaptive multisensor data fusion (AMSDF). The adaptation carried out is in the sense of adaptively adjusting the measurement noise covariance matrix of each local FL-AKF to fit the actual statistics of the noise profiles present in
Nonlinear rescaling of control values simplifies fuzzy control
NASA Technical Reports Server (NTRS)
Vanlangingham, H.; Tsoukkas, A.; Kreinovich, V.; Quintana, C.
1993-01-01
Traditional control theory is well-developed mainly for linear control situations. In non-linear cases there is no general method of generating a good control, so we have to rely on the ability of the experts (operators) to control them. If we want to automate their control, we must acquire their knowledge and translate it into a precise control strategy. The experts' knowledge is usually represented in non-numeric terms, namely, in terms of uncertain statements of the type 'if the obstacle is straight ahead, the distance to it is small, and the velocity of the car is medium, press the brakes hard'. Fuzzy control is a methodology that translates such statements into precise formulas for control. The necessary first step of this strategy consists of assigning membership functions to all the terms that the expert uses in his rules (in our sample phrase these words are 'small', 'medium', and 'hard'). The appropriate choice of a membership function can drastically improve the quality of a fuzzy control. In the simplest cases, we can take the functions whose domains have equally spaced endpoints. Because of that, many software packages for fuzzy control are based on this choice of membership functions. This choice is not very efficient in more complicated cases. Therefore, methods have been developed that use neural networks or generic algorithms to 'tune' membership functions. But this tuning takes lots of time (for example, several thousands iterations are typical for neural networks). In some cases there are evident physical reasons why equally space domains do not work: e.g., if the control variable u is always positive (i.e., if we control temperature in a reactor), then negative values (that are generated by equal spacing) simply make no sense. In this case it sounds reasonable to choose another scale u' = f(u) to represent u, so that equal spacing will work fine for u'. In the present paper we formulate the problem of finding the best rescaling function, solve this problem, and show (on a real-life example) that after an optimal rescaling, the un-tuned fuzzy control can be as good as the best state-of-art traditional non-linear controls.
Ge, Shuzhi Sam
, adaptive fuzzy control was investigated in [2]. Multi- layer-neural-network-based indirect adaptive controlIEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 20, NO. 3, MARCH 2009 483 Adaptive Neural Network and Shuzhi Sam Ge, Fellow, IEEE Abstract--In this paper, adaptive neural network (NN) tracking control
Simulation and design of fuzzy sliding-mode controller for ship heading-tracking
NASA Astrophysics Data System (ADS)
Yuan, Lei; Wu, Hansong
2011-03-01
In considering the characteristic of a rudder, the maneuvers of a ship were described by an unmatched uncertain nonlinear mathematic model with unknown virtual control coefficient and parameter uncertainties. In order to solve the uncertainties in the ship heading control, specifically the controller singular and paramount re-estimation problem, a new multiple sliding-mode adaptive fuzzy control algorithm was proposed by combining Nussbaum gain technology, the approximation property of fuzzy logic systems, and a multiple sliding-mode control algorithm. Based on the Lyapunov function, it was proven in theory that the controller made all signals in the nonlinear system of unmatched uncertain ship motion uniformly bounded, with tracking errors converging to zero. Simulation results show that the demonstrated controller design can track a desired course fast and accurately. It also exhibits strong robustness peculiarity in relation to system uncertainties and disturbances.
Chow, Mo-Yuen
of fuzzy logic and neural networks, a better understanding of the heuristics underlying the motor fault de presents two neural fuzzy (NN/FZ) inference systems, namely, Fuzzy Adaptive Learning Control/Decision Network (FALCON) and Adaptive Network Based Fuzzy Inference System (ANFIS), with applications to induction
Jong Sun Ko; Myung Joong Youn
1998-01-01
A new simple control for the robust position control of a brushless direct drive (BLDD) motor using fuzzy logic controller (FLC) is presented. The integral-proportional (IP) position controller plus fuzzy logic speed controller is employed to obtain the robust BLDD motor system, which is approximately linearized using the field-orientation method for an AC servo. The speed FLC for a BLDD
Fuzzy control of parabolic antenna with backlash compensation
NASA Astrophysics Data System (ADS)
Ahmed, Mohammed; Noor, Samsul Bahari B. Mohd
2015-05-01
A fuzzy logic based controller (FLC) was proposed for position control of a parabolic dish antenna system with the major aim of eradicating the effect backlash disturbance which may be present in the system. The disturbance is nonlinear and is capable of generating steady state positional errors. Simulation results obtained using SIMULINK/MATLAB 2012a were compared with those obtained when the controller was proportional-derivative controller (PDC). The fuzzy controller portrays that it has the capability of reducing the noise due to backlash and possibly others more than the proportional-derivative controller.
NASA Astrophysics Data System (ADS)
Yuan, Lei; Wu, Han-Song
2010-12-01
A terminal sliding mode fuzzy control based on multiple sliding surfaces was proposed for ship course tracking steering, which takes account of rudder characteristics and parameter uncertainty. In order to solve the problem, the controller was designed by employing the universal approximation property of fuzzy logic system, the advantage of Nussbaum function, and using multiple sliding mode control algorithm based on the recursive technique. In the last step of designing, a nonsingular terminal sliding mode was utilized to drive the last state of the system to converge in a finite period of time, and high-order sliding mode control law was designed to eliminate the chattering and make the system robust. The simulation results showed that the controller designed here could track a desired course fast and accurately. It also exhibited strong robustness peculiarly to system, and had better adaptive ability than traditional PID control algorithms.
Fuzzy logic control of the building structure with CLEMR dampers
NASA Astrophysics Data System (ADS)
Zhang, Xiang-Cheng; Xu, Zhao-Dong; Huang, Xing-Huai; Zhu, Jun-Tao
2013-04-01
The semi-active control technology has been paid more attention in the field of structural vibration control due to its high controllability, excellent control effect and low power requirement. When semi-active control device are used for vibration control, some challenges must be taken into account, such as the reliability and the control strategy of the device. This study presents a new large tonnage compound lead extrusion magnetorheological (CLEMR) damper, whose mathematical model is introduced to describe the variation of damping force with current and velocity. Then a current controller based on the fuzzy logic control strategy is designed to determine control currents of the CLEMR dampers rapidly. A ten-floor frame structure with CLEMR dampers using the fuzzy logic control strategy is built and calculated by using MATLAB. Calculation results show that CLEMR dampers can reduce the seismic responses of structures effectively. Calculation results of the fuzzy logic control strategy are compared with those of the semi-active limit Hrovat control structure, the passive-off control structure, and the uncontrolled structure. Comparison results show that the fuzzy logic control strategy can determine control currents of CLEMR dampers quickly and can reduce seismic responses of the structures more effectively than the passive-off control strategy and the uncontrolled structure.
Implementation of a new fuzzy vector control of induction motor.
Rafa, Souad; Larabi, Abdelkader; Barazane, Linda; Manceur, Malik; Essounbouli, Najib; Hamzaoui, Abdelaziz
2014-05-01
The aim of this paper is to present a new approach to control an induction motor using type-1 fuzzy logic. The induction motor has a nonlinear model, uncertain and strongly coupled. The vector control technique, which is based on the inverse model of the induction motors, solves the coupling problem. Unfortunately, in practice this is not checked because of model uncertainties. Indeed, the presence of the uncertainties led us to use human expertise such as the fuzzy logic techniques. In order to maintain the decoupling and to overcome the problem of the sensitivity to the parametric variations, the field-oriented control is replaced by a new block control. The simulation results show that the both control schemes provide in their basic configuration, comparable performances regarding the decoupling. However, the fuzzy vector control provides the insensitivity to the parametric variations compared to the classical one. The fuzzy vector control scheme is successfully implemented in real-time using a digital signal processor board dSPACE 1104. The efficiency of this technique is verified as well as experimentally at different dynamic operating conditions such as sudden loads change, parameter variations, speed changes, etc. The fuzzy vector control is found to be a best control for application in an induction motor. PMID:24629620
Evolutionary Learning of a Fuzzy Control Rule Base for an Autonomous Vehicle
Hoffmann, Frank
-physik.uni-kiel.de Abstract This paper presents a hybrid learning method in which fuzzy logic controllers (FLC) are au ago Lotfi Zadeh founded the princi- ples of fuzzy logic with his seminal paper on fuzzy sets ZAD65 this objective, soft computing suggests the combination of fuzzy logic (FL), neural networks (NN) and genetic
Implementation of Multi-valued Fuzzy Behavior Control for Robot Navigation in Cluttered
Collins, Emmanuel
of the individual behaviors. This paper presents an architectural design of a fuzzy behavior based system, resulting in the development of reactive fuzzy behavior methods that use fuzzy logic controllers, which can handle uncertainty in the robot information [5], [6], [7], [8], [9], [10], [11], [12] . Fuzzy logic also
Power Network Control Using Neuro-Fuzzy Term-Rewriting Adam Steele Ashley Morris
Steele, Adam
Power Network Control Using Neuro-Fuzzy Term-Rewriting Adam Steele Ashley Morris DePaul University in a uniform fashion. We will show how implementing a neuro- fuzzy solution will not only provide a more and Simulation, Fuzzy CLIPS, Neuro-Fuzzy Introduction Power networks are a classic example of a large-scale re
Neuro-fuzzy Learning of Strategies for Optimal Control Problems Kaivan Kamali1
Neuro-fuzzy Learning of Strategies for Optimal Control Problems Kaivan Kamali1 , Lijun Jiang2 of neuro-fuzzy systems which yields reusable knowledge in the form of fuzzy if-then rules. Ex- perimental-then rules acquired by training a neuro-fuzzy system can solve similar weight selection problems. 1
Intelligent Network Control Using Neuro-Fuzzy Term-Rewriting Adam Steele Ashley Morris
Steele, Adam
Intelligent Network Control Using Neuro-Fuzzy Term-Rewriting Adam Steele Ashley Morris De in a uniform fashion. We will show how implementing a neuro- fuzzy solution will not only provide a more and Simulation, Fuzzy CLIPS, Neuro-Fuzzy Introduction Power networks are a classic example of a large-scale re
MATLAB simulink model of fuzzy logic controller with PSS and its performance analysis
K. Gowrishankar; M. D. Masud Khan
2012-01-01
In this paper, the operation of a Fuzzy Logic Controller with PSS controller is analysed in simulink environment. The development of a Fuzzy Controller with power system stabilizer in order to maintain stability and enhance the performance of a power system is described widely. The application of the Fuzzy Controller with PSS controller is investigated by means of simulation studies
Advance of Systematic Design Methods on Fuzzy Control
Zhang, J.; Chen, Y.
2006-01-01
, Shenzhen, China Co ntrol Systems for Energy Efficiency and Comfort, Vol. V-2-5 Advance of Systematic Design Methods on Fuzzy Control1 Jili Zhang Yongpan Chen Ph.D. Professor Doctoral Candidate School of Municipal & Environmental... is the transfer of characteristics coefficient. This method need not calculate total fuzzy implication relationship, and simplify inference calculations greatly. But also inference result is the same to CRI. Based on CEI, Peizhuang Wang and Hongmin Zhang[21...
Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System.
Hosseini, Monireh Sheikh; Zekri, Maryam
2012-01-01
Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated. PMID:23493054
A Numerical Optimization Approach for Tuning Fuzzy Logic Controllers
NASA Technical Reports Server (NTRS)
Woodard, Stanley E.; Garg, Devendra P.
1998-01-01
This paper develops a method to tune fuzzy controllers using numerical optimization. The main attribute of this approach is that it allows fuzzy logic controllers to be tuned to achieve global performance requirements. Furthermore, this approach allows design constraints to be implemented during the tuning process. The method tunes the controller by parameterizing the membership functions for error, change-in-error and control output. The resulting parameters form a design vector which is iteratively changed to minimize an objective function. The minimal objective function results in an optimal performance of the system. A spacecraft mounted science instrument line-of-sight pointing control is used to demonstrate results.
Morteza Mohammadzaheri; Ley Chen
2007-01-01
In this research, the fuzzy control of the yaw angle of a model helicopter is studied, particularly, in order to reduce the overshoot which can be a serious problem in high inertia systems. Initially, a Sugeno-type controller is designed. This controller provides quick convergence and keeps the control input in a permitted range .Moreover, a good stability is offered by
The Application of the Fuzzy Controller Based on PLC in Sewage Disposal System
Kang Sun; Yan-min Song; Guo-chuan Feng
2009-01-01
In order to solve the problems such as long time-delay, non-linear and the difficulty to establish an accurate mathematical model emerged in industrial field, we combine advanced intelligent control method with traditional automated devices, and put forward a Fuzzy Controller in accordance with the principle of fuzzy control and the characteristics of PLC. The Fuzzy Controller realized on the basis
Composite fuzzy sliding mode control of nonlinear singularly perturbed systems.
Nagarale, Ravindrakumar M; Patre, B M
2014-05-01
This paper deals with the robust asymptotic stabilization for a class of nonlinear singularly perturbed systems using the fuzzy sliding mode control technique. In the proposed approach the original system is decomposed into two subsystems as slow and fast models by the singularly perturbed method. The composite fuzzy sliding mode controller is designed for stabilizing the full order system by combining separately designed slow and fast fuzzy sliding mode controllers. The two-time scale design approach minimizes the effect of boundary layer system on the full order system. A stability analysis allows us to provide sufficient conditions for the asymptotic stability of the full order closed-loop system. The simulation results show improved system performance of the proposed controller as compared to existing methods. The experimentation results validate the effectiveness of the proposed controller. PMID:24636524
Application of genetic algorithms to tuning fuzzy control systems
NASA Technical Reports Server (NTRS)
Espy, Todd; Vombrack, Endre; Aldridge, Jack
1993-01-01
Real number genetic algorithms (GA) were applied for tuning fuzzy membership functions of three controller applications. The first application is our 'Fuzzy Pong' demonstration, a controller that controls a very responsive system. The performance of the automatically tuned membership functions exceeded that of manually tuned membership functions both when the algorithm started with randomly generated functions and with the best manually-tuned functions. The second GA tunes input membership functions to achieve a specified control surface. The third application is a practical one, a motor controller for a printed circuit manufacturing system. The GA alters the positions and overlaps of the membership functions to accomplish the tuning. The applications, the real number GA approach, the fitness function and population parameters, and the performance improvements achieved are discussed. Directions for further research in tuning input and output membership functions and in tuning fuzzy rules are described.
Fuzzy Control of Flexible-Link Manipulators: A Review
NASA Technical Reports Server (NTRS)
Akbarzadeh-T, M.-R.; Quintana, S.; Jamshidi, M.
1998-01-01
Several recent research efforts are reviewed here which have applied fuzzy logic in control of flexible-link manipulators. A flexible robot is a distributed parameter system represented by complex nonlinear dynamics, its actuator and the control parameters are non-colocated, and lastly, unstructured/unknown parameters play a significant role in model dynamics of a flexible robot operating in the real world. As a result, control of flexible robots is considered a promising area for application of intelligent control methodologies such as fuzzy logic, genetic algorithms, and neural networks.
Implementation of fuzzy control approach for MIMO robotics systems
Youcef Touati; Yacine Amirat
2004-01-01
This paper proposes an effective approach of fuzzy logic controller (FLC) design and optimization methodology for Cartesian robot control. The FLC is based on a Takagi Sugeno (TS) model. It consists on MISO-controllers subsets decomposition. The FLC optimization methodology is implemented offline and proceeds in three phases: A first set of rules is extracted automatically from training data using rapid
J. SOLTANI; Y. ABDOLMALEKI; M. HAJIAN
In this paper, a speed sensorless induction motor drive is introduced which is direct vector controlled in a universal field-oriented (UFO) reference frame. This chosen reference frame is easily linked with direct and indirect rotor, stator and air gap field orientation control schemes of the induction machine (IM) drives using a stator to rotor virtual turn ratio. Based on partial
Minimum-time Control of Satellite Attitude using a Fuzzy Logic Controller
SURIYA THONGCHET; SUWAT KUNTANAPREEDA
A solution of satellite attitude control using a fuzzy logic as a bang-bang controller is presented in this paper. The control objective is to orient the satellite to a desired final state within minimum time and using minimum number of thruster on\\/off cycles. By properly selecting the span of each fuzzy membership functions, we are able to achieve a better
Intelligent Control of Hydro Power Sets Based on Fuzzy Model Reference Learning Control
Liao Zhong; You Lai Jian
2010-01-01
Intelligent control based on fuzzy model reference learning control (FMRLC) is being applied to hydraulic power sets, which are characterized by their nonlinearity, time variability and non-minimum phase free property, after having first studied principles of and methods used by FMRLC, and analyzed its capability of adjusting and correcting fuzzy rules. A FMRLC controller for a hydraulic power set was
L. Barazane; P. Sicard; R. Ouiguini
2009-01-01
In this article, a control design concept using fuzzy sets for an induction motor is presented. The aim of the proposed modelling approach is to provide a fuzzy set-based representation of the cascade sliding mode control of an induction motor fed by PWM voltage source inverter, which operates in a fixed reference frame. For this purpose, a new decoupled and
Adaptive neural-based fuzzy modeling for biological systems.
Wu, Shinq-Jen; Wu, Cheng-Tao; Chang, Jyh-Yeong
2013-04-01
The inverse problem of identifying dynamic biological networks from their time-course response data set is a cornerstone of systems biology. Hill and Michaelis-Menten model, which is a forward approach, provides local kinetic information. However, repeated modifications and a large amount of experimental data are necessary for the parameter identification. S-system model, which is composed of highly nonlinear differential equations, provides the direct identification of an interactive network. However, the identification of skeletal-network structure is challenging. Moreover, biological systems are always subject to uncertainty and noise. Are there suitable candidates with the potential to deal with noise-contaminated data sets? Fuzzy set theory is developed for handing uncertainty, imprecision and complexity in the real world; for example, we say "driving speed is high" wherein speed is a fuzzy variable and high is a fuzzy set, which uses the membership function to indicate the degree of a element belonging to the set (words in Italics to denote fuzzy variables or fuzzy sets). Neural network possesses good robustness and learning capability. In this study we hybrid these two together into a neural-fuzzy modeling technique. A biological system is formulated to a multi-input-multi-output (MIMO) Takagi-Sugeno (T-S) fuzzy system, which is composed of rule-based linear subsystems. Two kinds of smooth membership functions (MFs), Gaussian and Bell-shaped MFs, are used. The performance of the proposed method is tested with three biological systems. PMID:23376801
Applications of fuzzy logic to control and decision making
NASA Technical Reports Server (NTRS)
Lea, Robert N.; Jani, Yashvant
1991-01-01
Long range space missions will require high operational efficiency as well as autonomy to enhance the effectivity of performance. Fuzzy logic technology has been shown to be powerful and robust in interpreting imprecise measurements and generating appropriate control decisions for many space operations. Several applications are underway, studying the fuzzy logic approach to solving control and decision making problems. Fuzzy logic algorithms for relative motion and attitude control have been developed and demonstrated for proximity operations. Based on this experience, motion control algorithms that include obstacle avoidance were developed for a Mars Rover prototype for maneuvering during the sample collection process. A concept of an intelligent sensor system that can identify objects and track them continuously and learn from its environment is under development to support traffic management and proximity operations around the Space Station Freedom. For safe and reliable operation of Lunar/Mars based crew quarters, high speed controllers with ability to combine imprecise measurements from several sensors is required. A fuzzy logic approach that uses high speed fuzzy hardware chips is being studied.
Design and performance comparison of fuzzy logic based tracking controllers
NASA Technical Reports Server (NTRS)
Lea, Robert N.; Jani, Yashvant
1992-01-01
Several camera tracking controllers based on fuzzy logic principles have been designed and tested in software simulation in the software technology branch at the Johnson Space Center. The fuzzy logic based controllers utilize range measurement and pixel positions from the image as input parameters and provide pan and tilt gimble rate commands as output. Two designs of the rulebase and tuning process applied to the membership functions are discussed in light of optimizing performance. Seven test cases have been designed to test the performance of the controllers for proximity operations where approaches like v-bar, fly-around and station keeping are performed. The controllers are compared in terms of responsiveness, and ability to maintain the object in the field-of-view of the camera. Advantages of the fuzzy logic approach with respect to the conventional approach have been discussed in terms of simplicity and robustness.
Full design of fuzzy controllers using genetic algorithms
NASA Technical Reports Server (NTRS)
Homaifar, Abdollah; Mccormick, ED
1992-01-01
This paper examines the applicability of genetic algorithms (GA) in the complete design of fuzzy logic controllers. While GA has been used before in the development of rule sets or high performance membership functions, the interdependence between these two components dictates that they should be designed together simultaneously. GA is fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. We show the application of this new method to the development of a cart controller.
Full design of fuzzy controllers using genetic algorithms
NASA Technical Reports Server (NTRS)
Homaifar, Abdollah; Mccormick, ED
1992-01-01
This paper examines the applicability of genetic algorithms in the complete design of fuzzy logic controllers. While GA has been used before in the development of rule sets or high performance membership functions, the interdependence between these two components dictates that they should be designed together simultaneously. GA is fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. We show the application of this new method to the development of a cart controller.
Fuzzy logic control for active bus suspension system
NASA Astrophysics Data System (ADS)
Turkkan, Mujde; Yagiz, Nurkan
2013-02-01
In this study an active controller is presented for vibration suppression of a full-bus suspension model that use air spring. Since the air spring on the full-bus model may face different working conditions, auxiliary chambers have been designed. The vibrations, caused by the irregularities of the road surfaces, are tried to be suppressed via a multi input-single output fuzzy logic controller. The effect of changes in the number of auxiliary chambers on the vehicle vibrations is also investigated. The numerical results demonstrate that the presented fuzzy logic controller improves both ride comfort and road holding.
Optimized Reactive Power Compensation Using Fuzzy Logic Controller
NASA Astrophysics Data System (ADS)
George, S.; Mini, K. N.; Supriya, K.
2015-03-01
Reactive power flow in a long transmission line plays a vital role in power transfer capability and voltage stability in power system. Traditionally, shunt connected compensators are used to control reactive power in long transmission line. Thyristor controlled reactor is used to control reactive power under lightly loaded condition. By controlling firing angle of thyristor, it is possible to control reactive power in the transmission lines. However, thyristor controlled reactor will inject harmonic current into the system. An attempt to reduce reactive power injection will increase harmonic distortion in the line current and vice versa. Thus, there is a trade-off between reactive power injection and harmonics in current. By optimally controlling the reactive power injection, harmonics in current can be brought within the specified limit. In this paper, a Fuzzy Logic Controller is implemented to obtain optimal control of reactive power of the compensator to maintain voltage and harmonic in current within the limits. An algorithm which optimizes the firing angle in each fuzzy subset by calculating the rank of feasible firing angles is proposed for the construction of rules in Fuzzy Logic Controller. The novelty of the algorithm is that it uses a simple error formula for the calculation of the rank of the feasible firing angles in each fuzzy subset.
NASA Astrophysics Data System (ADS)
Reif, Konrad
Die adaptive Fahrgeschwindigkeitsregelung (ACC, Adaptive Cruise Control) ist eine Weiterentwicklung der konventionellen Fahrgeschwindigkeitsregelung, die eine konstante Fahrgeschwindigkeit einstellt. ACC überwacht mittels eines Radarsensors den Bereich vor dem Fahrzeug und passt die Geschwindigkeit den Gegebenheiten an. ACC reagiert auf langsamer vorausfahrende oder einscherende Fahrzeuge mit einer Reduzierung der Geschwindigkeit, sodass der vorgeschriebene Mindestabstand zum vorausfahrenden Fahrzeug nicht unterschritten wird. Hierzu greift ACC in Antrieb und Bremse ein. Sobald das vorausfahrende Fahrzeug beschleunigt oder die Spur verlässt, regelt ACC die Geschwindigkeit wieder auf die vorgegebene Sollgeschwindigkeit ein (Bild 1). ACC steht somit für eine Geschwindigkeitsregelung, die sich dem vorausfahrenden Verkehr anpasst.
Two-Stage Fuzzy Logic Controller Based on Adjustable Phase Sequence for Urban Traffic Intersection
Peng Xiaohong; Xiao Laisheng; Mo Zhi; Liu Guodong
2009-01-01
On the basis of research on fuzzy traffic control algorithm for a single intersection, we present a new adjustable multi-phase sequence fuzzy control algorithm. By this method, not only the traffic flow of every phase is considered, but also the green time and red time. Base on this algorithm, a two-stage fuzzy control system which consist of the green time
Architecture and Preliminary Design of a Fuzzy Logic-Based Microbattery Charge Controller
Singh, Pritpal
Architecture and Preliminary Design of a Fuzzy Logic-Based Microbattery Charge Controller. In this paper, we will describe the architecture and preliminary design of a fuzzy logic-based microbattery for an integrated micropower supply. This charge controller employs a fuzzy logic approach to controlling
DESIGN AND IMPLEMENTATION OF A FUZZY LOGIC-BASED VOLTAGE CONTROLLER FOR VOLTAGE REGULATION
LaMeres, Brock J.
1 DESIGN AND IMPLEMENTATION OF A FUZZY LOGIC-BASED VOLTAGE CONTROLLER FOR VOLTAGE REGULATION In this paper the design and implementation of a fuzzy logic-based controller is described for regulating the output voltage of a synchronous generator. An automated fuzzy logic-based control strategy is presented
Learning of Hybrid Fuzzy Controller for the Optical Data Storage Device
Leehter Yao; Po-Zhao Huang
2008-01-01
A hybrid track-seeking fuzzy controller for an optical disk drive (ODD) is proposed in this paper. The proposed hybrid fuzzy controller (HFC) smoothes the voltage applied to the sled motor and improves the track-seeking efficiency. The HFC consists of two subsystems including an intelligent time switch and a driving force controller. Both subsystems are designed based on fuzzy logic inferences.
A Reinforcement Learning Fuzzy Controller for the Ball and Plate system
Nima Mohajerin; Mohammad B. Menhaj; Ali Doustmohammadi
2010-01-01
In this paper, a new fuzzy logic controller, namely Reinforcement Learning Fuzzy Controller (RLFC), is proposed and implemented. Based on fuzzy logic, this newly proposed online-learning controller is capable of improving its behavior by learning from experiences it gains through interaction with the plant. RLFC is well established for hardware implementation with or without a priori knowledge about the plant.
Fuzzy control of a direct current motor system with the guaranteed stability
Euntai Kim; Heejin Lee; Mignon Park
1999-01-01
One of the most common ways of driving mechatronical systems is through the use of a DC motor system. In this paper, fuzzy control methodology for a DC motor system using a singleton-type fuzzy logic controller (FLC) is proposed. As opposed to the conventional works, the fuzzy control methodology proposed in this paper is guaranteed to be asymptotically stable in
NASA Technical Reports Server (NTRS)
Kreinovich, V.; Lea, R.; Fuentes, O.; Lokshin, A.
1992-01-01
Fuzzy control techniques are analyzed to explain why the fuzzy control that is based on the expert's knowledge is often smoother and more stable than the control performed manually by the same experts. A precise mathematical explanation of this phenomenon is presented. Results obtained make it possible to predict the quality of the fuzzy control.
Adaptive neuro fuzzy inference system for profiling of the atmosphere
NASA Astrophysics Data System (ADS)
Ramesh, K.; Kesarkar, A. P.; Bhate, J.; Venkat Ratnam, M.; Jayaraman, A.
2014-03-01
Retrieval of accurate profiles of temperature and water vapor is important for the study of atmospheric convection. However, it is challenging because of the uncertainties associated with direct measurement of atmospheric parameters during convection events using radiosonde and retrieval of remote-sensed observations from satellites. Recent developments in computational techniques motivated the use of adaptive techniques in the retrieval algorithms. In this work, we have used the Adaptive Neuro Fuzzy Inference System (ANFIS) to retrieve profiles of temperature and humidity over tropical station Gadanki (13.5° N, 79.2° E), India. The observations of brightness temperatures recorded by Radiometrics Multichannel Microwave Radiometer MP3000 for the period of June-September 2011 are used to model profiles of atmospheric parameters up to 10 km. The ultimate goal of this work is to use the ANFIS forecast model to retrieve atmospheric profiles accurately during the wet season of the Indian monsoon (JJAS) season and during heavy rainfall associated with tropical convections. The comparison analysis of the ANFIS model retrieval of temperature and relative humidity (RH) profiles with GPS-radiosonde observations and profiles retrieved using the Artificial Neural Network (ANN) algorithm indicates that errors in the ANFIS model are less even in the wet season, and retrievals using ANFIS are more reliable, making this technique the standard. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 99% for temperature profiles for both techniques and therefore both techniques are successful in the retrieval of temperature profiles. However, in the case of RH the retrieval using ANFIS is found to be better. The comparison of mean absolute error (MAE), root mean square error (RMSE) and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and RH profiles using ANN and ANFIS also indicates that profiles retrieved using ANFIS are significantly better compared to the ANN technique. The error analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the retrievals substantially; however, retrieval of RH by both techniques (ANN and ANFIS) has limited success.
Fuzzy learning control for anti-skid braking systems
J. R. Layne; K. M. Passino; Stephen Yurkovich
1992-01-01
Although antiskid braking systems (ABSs) are designed to optimize braking effectiveness while maintaining steerability, their performance often degrades for harsh road conditions (e.g., icy\\/snowy roads). The authors introduce the idea of using the fuzzy model reference learning control (FMRLC) technique for maintaining adequate performance even under such adverse road conditions. This controller utilizes a learning mechanism which observes the plant
Tuning Fuzzy Logic Controllers for Energy Efficiency Consumption in Buildings
Casillas Barranquero, Jorge
Tuning Fuzzy Logic Controllers for Energy Efficiency Consumption in Buildings R. Alcal´a DECSAI- tion in buildings represents about 40% of to- tal energy consumption and more than a half controllers, tuning techniques, multiobjective optimisation, en- ergy efficiency, buildings, BEMS, HVAC sys
Fuzzy Control of HVAC Systems Optimized by Genetic Algorithms
Rafael Alcalá; José Manuel Benítez; Jorge Casillas; Oscar Cordón; Raúl Pérez
2003-01-01
Abstract. This paper presents the use of genetic algorithms to develop smartly tuned fuzzy logic controllers dedicated to the control of heating, ventilating and air conditioning systems concerning energy performance and indoor comfort requirements. This problem has some specific restrictions that make it very particular and complex because of the large time requirements existing due to the need of considering
Cross-coupled fuzzy logic control for multiaxis machine tools
Y. S. Tarng; Y. S. Lin
1997-01-01
This paper presents a design and implementation case study that focuses on contour control of a biaxial CNC machine tools. Since, it is difficult to obtain an accurate nonlinear mathematical model of cross-coupled multiaxis machine tools, here we investigate an alternative to conventional approaches where we employ crosscoupled fuzzy logic controllers for improving the contouring accuracy of multiaxis CNC machine
Fuzzy self-learning control for magnetic servo system
NASA Technical Reports Server (NTRS)
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.
Reinforcement structure\\/parameter learning for neural-network-based fuzzy logic control systems
Chin-Teng Lin; C. S. G. Lee
1994-01-01
This paper proposes a reinforcement neural-network-based fuzzy logic control system (RNN-FLCS) for solving various reinforcement learning problems. The proposed RNN-FLCS is constructed by integrating two neural-network-based fuzzy logic controllers (NN-FLC's), each of which is a connectionist model with a feedforward multilayered network developed for the realization of a fuzzy logic controller. One NN-FLC performs as a fuzzy predictor, and the
Control of a flexible beam using fuzzy logic
NASA Technical Reports Server (NTRS)
Mccullough, Claire L.
1991-01-01
The goal of this project, funded under the NASA Summer Faculty Fellowship program, was to evaluate control methods utilizing fuzzy logic for applicability to control of flexible structures. This was done by applying these methods to control of the Control Structures Interaction Suitcase Demonstrator developed at Marshall Space Flight Center. The CSI Suitcase Demonstrator is a flexible beam, mounted at one end with springs and bearing, and with a single actuator capable of rotating the beam about a pin at the fixed end. The control objective is to return the tip of the free end to a zero error position (from a nonzero initial condition). It is neither completely controllable nor completely observable. Fuzzy logic control was demonstrated to successfully control the system and to exhibit desirable robustness properties compared to conventional control.
Research on fuzzy PID control to electronic speed regulator
NASA Astrophysics Data System (ADS)
Xu, Xiao-gang; Chen, Xue-hui; Zheng, Sheng-guo
2007-12-01
As an important part of diesel engine, the speed regulator plays an important role in stabilizing speed and improving engine's performance. Because there are so many model parameters of diesel-engine considered in traditional PID control and these parameters present non-linear characteristic.The method to adjust engine speed using traditional PID is not considered as a best way. Especially for the diesel-engine generator set. In this paper, the Fuzzy PID control strategy is proposed. Some problems about its utilization in electronic speed regulator are discussed. A mathematical model of electric control system for diesel-engine generator set is established and the way of the PID parameters in the model to affect the function of system is analyzed. And then it is proposed the differential coefficient must be applied in control design for reducing dynamic deviation of system and adjusting time. Based on the control theory, a study combined control with PID calculation together for turning fuzzy PID parameter is implemented. And also a simulation experiment about electronic speed regulator system was conducted using Matlab/Simulink and the Fuzzy-Toolbox. Compared with the traditional PID Algorithm, the simulated results presented obvious improvements in the instantaneous speed governing rate and steady state speed governing rate of diesel-engine generator set when the fuzzy logic control strategy used.
NASA Technical Reports Server (NTRS)
Ying, Hao
1993-01-01
The fuzzy controllers studied in this paper are the ones that employ N trapezoidal-shaped members for input fuzzy sets, Zadeh fuzzy logic and a centroid defuzzification algorithm for output fuzzy set. The author analytically proves that the structure of the fuzzy controllers is the sum of a global nonlinear controller and a local nonlinear proportional-integral-like controller. If N approaches infinity, the global controller becomes a nonlinear controller while the local controller disappears. If linear control rules are used, the global controller becomes a global two-dimensional multilevel relay which approaches a global linear proportional-integral (PI) controller as N approaches infinity.
MR images restoration with the use of fuzzy filter having adaptive membership parameters.
Güler, I; Toprak, A; Demirhan, A; Karaki?, R
2008-06-01
A new fuzzy adaptive median filter is presented for the noise reduction of magnetic resonance images corrupted with heavy impulse (salt and pepper) noise. In this paper, we have proposed a Fuzzy Adaptive Median Filter with Adaptive Membership Parameters (FAMFAMP) for removing highly corrupted salt and pepper noise, with preserving image edges and details. The FAMFAMP filter is an improved version of Adaptive Median Filter (AMF) and is presented in the aim of noise reduction of images corrupted with additive impulse noise. The proposed filter can preserve image details better than AMF while suppressing additive salt and pepper or impulse type noise. In this paper, we placed our preference on bell-shaped membership function with adaptive parameters instead of triangular membership function without variable coefficients in order to observe better results. PMID:18444360
NASA Astrophysics Data System (ADS)
Abdurrahim, Mahabuba; Abdullah Khan, M.; Edriss, Ali Ahmed
2011-12-01
This paper presents a design procedure for a Robust and Adaptive Fuzzy Logic based Power System Stabilizer (RAFLPSS) to improve the small signal stability of Power System. The parameters of RAFLPSS are tuned by adaptive neural network. This RAFLPSS uses ANFIS network (Adaptive Network based Fuzzy Inference System) which provides a natural framework of multi-layered feed forward adaptive network using fuzzy logic inference system. In this approach, the hybrid-learning algorithm tunes the fuzzy rules and the membership functions of the RAFLPSS. The dynamic performance of SMIB system with the proposed RAFLPSS under different operating conditions and change in system parameters has been investigated. The simulation results obtained from the conventional PSS (CPSS) and Fuzzy logic based PSS (FPSS) are compared with the proposed RAFLPSS. The simulation results demonstrate that the proposed RAFLPSS performs well in damping and quicker response when compared with the other two PSSs.
NASA Astrophysics Data System (ADS)
Abdurrahim, Mahabuba; Abdullah Khan, M.; Edriss, Ali Ahmed
2012-01-01
This paper presents a design procedure for a Robust and Adaptive Fuzzy Logic based Power System Stabilizer (RAFLPSS) to improve the small signal stability of Power System. The parameters of RAFLPSS are tuned by adaptive neural network. This RAFLPSS uses ANFIS network (Adaptive Network based Fuzzy Inference System) which provides a natural framework of multi-layered feed forward adaptive network using fuzzy logic inference system. In this approach, the hybrid-learning algorithm tunes the fuzzy rules and the membership functions of the RAFLPSS. The dynamic performance of SMIB system with the proposed RAFLPSS under different operating conditions and change in system parameters has been investigated. The simulation results obtained from the conventional PSS (CPSS) and Fuzzy logic based PSS (FPSS) are compared with the proposed RAFLPSS. The simulation results demonstrate that the proposed RAFLPSS performs well in damping and quicker response when compared with the other two PSSs.
Wing-Chi So; Chi K. Tse; Yim-Shu Lee
1996-01-01
The design of a fuzzy logic controller for DC\\/DC power converters is described in this paper. A brief review of fuzzy logic and its application to control is first given. Then, the derivation of a fuzzy control algorithm for regulating DC\\/DC power converters is described in detail. The proposed fuzzy control scheme is evaluated by computer simulations as well as
Neuro-Fuzzy Hardware and DSPs: a Promising Marriage for Control of Complex Systems
Reyneri, Leonardo
Neuro-Fuzzy Hardware and DSPs: a Promising Marriage for Control of Complex Systems B. Bona, S intelligent con- trol paradigms mixing Neuro-Fuzzy algorithms with nite state automata and or digital con of control problems. Neuro-Fuzzy systems, specially when com- bined with DSPs can solve e ciently both
CINTIA: A Neuro-Fuzzy Real Time Controller for Low Power Embedded Systems
Reyneri, Leonardo
CINTIA: A Neuro-Fuzzy Real Time Controller for Low Power Embedded Systems L.M. Reyneri, M@iet.unipi.it e.mail: chiaberge@polito.it Abstract 1 This paper describes CINTIA, a Neuro-Fuzzy real embedded systems. The pro- posed system mixes two di erent approaches, namely Neuro-Fuzzy Controllers
CINTIA: A Neuro-Fuzzy Real Time Controller for Low Power Embedded Systems
Reyneri, Leonardo
CINTIA: A Neuro-Fuzzy Real Time Controller for Low Power Embedded Systems L.M. Reyneri , M@iet.unipi.it e.mail: chiaberge@polito.it Abstract 1 This paper describes CINTIA, a Neuro-Fuzzy real embedded systems. The pro- posed system mixes two di erent approaches, namely Neuro-Fuzzy Controllers
NASA Technical Reports Server (NTRS)
Abihana, Osama A.; Gonzalez, Oscar R.
1993-01-01
The main objectives of our research are to present a self-contained overview of fuzzy sets and fuzzy logic, develop a methodology for control system design using fuzzy logic controllers, and to design and implement a fuzzy logic controller for a real system. We first present the fundamental concepts of fuzzy sets and fuzzy logic. Fuzzy sets and basic fuzzy operations are defined. In addition, for control systems, it is important to understand the concepts of linguistic values, term sets, fuzzy rule base, inference methods, and defuzzification methods. Second, we introduce a four-step fuzzy logic control system design procedure. The design procedure is illustrated via four examples, showing the capabilities and robustness of fuzzy logic control systems. This is followed by a tuning procedure that we developed from our design experience. Third, we present two Lyapunov based techniques for stability analysis. Finally, we present our design and implementation of a fuzzy logic controller for a linear actuator to be used to control the direction of the Free Flight Rotorcraft Research Vehicle at LaRC.
Verleysen, Michel
On Designing Mixed-Signal Programmable Fuzzy Logic Controllers as Embedded Subsystems in Standard analogue Fuzzy Logic Controller (FLC) is presented. Input and output signals are processed in the analog performances, namely: from 2.22 to 5.26 Mflips (Mega fuzzy logic inferences per second) at the pin terminals
Call Admission Control in Wideband CDMA Cellular Networks by Using Fuzzy Logic
Shen, Xuemin "Sherman"
Call Admission Control in Wideband CDMA Cellular Networks by Using Fuzzy Logic Jun Ye, Xuemin admission control (CAC) scheme using fuzzy logic is proposed for the reverse link transmission in wideband-division multiple access (CDMA), effective bandwidth, fuzzy logic, user mobility. æ 1 INTRODUCTION WIDEBAND code
Mei-Yung Chen; Yi-Cheng Chen; Shia-Chung Chen
The present study has successfully applied fuzzy control logic in controlling the weld line position for an injection-molded part. Although only a simple molded part was dealt with here, the incorporation of fuzzy control with CAE software in controlling the weld line position is a breakthrough in the concept of mold-design optimization. During the analysis, only four calculations of decision
A fuzzy behaviorist approach to sensor-based robot control
Pin, F.G.
1996-05-01
Sensor-based operation of autonomous robots in unstructured and/or outdoor environments has revealed to be an extremely challenging problem, mainly because of the difficulties encountered when attempting to represent the many uncertainties which are always present in the real world. These uncertainties are primarily due to sensor imprecisions and unpredictability of the environment, i.e., lack of full knowledge of the environment characteristics and dynamics. An approach. which we have named the {open_quotes}Fuzzy Behaviorist Approach{close_quotes} (FBA) is proposed in an attempt to remedy some of these difficulties. This approach is based on the representation of the system`s uncertainties using Fuzzy Set Theory-based approximations and on the representation of the reasoning and control schemes as sets of elemental behaviors. Using the FBA, a formalism for rule base development and an automated generator of fuzzy rules have been developed. This automated system can automatically construct the set of membership functions corresponding to fuzzy behaviors. Once these have been expressed in qualitative terms by the user. The system also checks for completeness of the rule base and for non-redundancy of the rules (which has traditionally been a major hurdle in rule base development). Two major conceptual features, the suppression and inhibition mechanisms which allow to express a dominance between behaviors are discussed in detail. Some experimental results obtained with the automated fuzzy, rule generator applied to the domain of sensor-based navigation in aprion unknown environments. using one of our autonomous test-bed robots as well as a real car in outdoor environments, are then reviewed and discussed to illustrate the feasibility of large-scale automatic fuzzy rule generation using the {open_quotes}Fuzzy Behaviorist{close_quotes} concepts.
Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions
Alex. B. McBratney; Inakwu O. A. Odeh
1997-01-01
Fuzzy systems, including fuzzy set theory and fuzzy logic, provide a rich and meaningful improvement, or extension of conventional logic. The mathematics generated by this theory is consistent, and fuzzy set theory may be seen as a generalisation of classic set theory. Applications in soil science, which may be generated from, or adapted to fuzzy set theory and fuzzy logic,
Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions
B. McBratney; Inakwu O. A. Odeh
Fuzzy systems, including fuzzy set theory and fuzzy logic, provide a rich and meaningful improvement, or extension of conventional logic. The mathematics generated by this theory is consistent, and fuzzy set theory may be seen as a generalisation of classic set theory. Applications in soil science, which may be generated from, or adapted to fuzzy set theory and fuzzy logic,
Comparison on fuzzy logic and PID controls for a DC motor position controller
Paul-l-Hai Lin; Sentai Hwang; John Chou
1994-01-01
Fuzzy logic and proportional-integral-derivative (PID) controllers are compared for use in a 486 PC-based DC motor positioning system. A simulation study of the PID position controller for the desired DC motor is performed. The parameters of a standard PID controller for the DC motor position control system under the investigation is tuned and fixed throughout the control. The fuzzy logic
Robust fuzzy control of a nonlinear magnetic bearing system with computing time delay
Kai Zheng; Heng Liu; Lie Yu
2008-01-01
This paper presents a robust fuzzy logic-base control scheme for a nonlinear magnetic bearing system with computing time delay. A proper Takagi-Sugeno fuzzy model is chosen to represent the nonlinear magnetic bearing. A fuzzy-model-based PDC controller is design in terms of a proposed delay-dependent stabilization criterion which guarantees the asymptotic stability of the fuzzy model. Simulation show the effectiveness and
Adaptive nonlinear flight control
NASA Astrophysics Data System (ADS)
Rysdyk, Rolf Theoduor
1998-08-01
Research under supervision of Dr. Calise and Dr. Prasad at the Georgia Institute of Technology, School of Aerospace Engineering. has demonstrated the applicability of an adaptive controller architecture. The architecture successfully combines model inversion control with adaptive neural network (NN) compensation to cancel the inversion error. The tiltrotor aircraft provides a specifically interesting control design challenge. The tiltrotor aircraft is capable of converting from stable responsive fixed wing flight to unstable sluggish hover in helicopter configuration. It is desirable to provide the pilot with consistency in handling qualities through a conversion from fixed wing flight to hover. The linear model inversion architecture was adapted by providing frequency separation in the command filter and the error-dynamics, while not exiting the actuator modes. This design of the architecture provides for a model following setup with guaranteed performance. This in turn allowed for convenient implementation of guaranteed handling qualities. A rigorous proof of boundedness is presented making use of compact sets and the LaSalle-Yoshizawa theorem. The analysis allows for the addition of the e-modification which guarantees boundedness of the NN weights in the absence of persistent excitation. The controller is demonstrated on the Generic Tiltrotor Simulator of Bell-Textron and NASA Ames R.C. The model inversion implementation is robustified with respect to unmodeled input dynamics, by adding dynamic nonlinear damping. A proof of boundedness of signals in the system is included. The effectiveness of the robustification is also demonstrated on the XV-15 tiltrotor. The SHL Perceptron NN provides a more powerful application, based on the universal approximation property of this type of NN. The SHL NN based architecture is also robustified with the dynamic nonlinear damping. A proof of boundedness extends the SHL NN augmentation with robustness to unmodeled actuator dynamics. The architecture is demonstrated on the XV-15 tiltrotor.
Jianfang Wang; Weihua Li
2009-01-01
To solve fuzzy and non-linear features of mechanical equipment. A new computational intelligence method was proposed by combing based on extended T-S fuzzy model of self-adaptive disturbed PSO and BP neural network algorithm. Firstly, the T-S fuzzy model is modified, and then uses the extended T-S model to adjust the PSO parameter. Secondly, the neural network is trained by the
Fuzzy adaptive agent for supply chain Yain-Whar Si a,*
Si, Yain Whar "Lawrence"
. In the past decade, agent technology has been ex- tensively used to model supply chain components. The generic activities. Specifically, a multi-agent approach to #12;modeling supply chain management has been extenFuzzy adaptive agent for supply chain management Yain-Whar Si a,* and Sio-Fan Lou b a,b Faculty
Modeling and Simulation of An Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning
ERIC Educational Resources Information Center
Al-Hmouz, A.; Shen, Jun; Al-Hmouz, R.; Yan, Jun
2012-01-01
With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy…
Genetic Programming of Full Knowledge Bases for Fuzzy Logic Controllers
Fernandez, Thomas
Propulsion Lab Robotic Vehicles Grp. 4800 Oak Grove Dr. Pasadena, CA 91109 tunstel@jpl.nasa.gov Gerry Dozier this objective. In addition, GP is employed to handle selection of fuzzy set intersection operators (t-norms). The new GP system is applied to design a mobile robot path tracking controller and performance is shown
Intelligent fuzzy logic controller for a solar charging system
Cong-Hui Huang; Chung-Chi Huang; Ting-Chia Ou; Kai-Hung Lu; Chih-Ming Hong
2009-01-01
This paper presents an intelligent solar charging system with fuzzy logic control method. With the scarce energy source and the worsening environmental pollution, how to create and use a clean and never exhausted energy is becoming very important day by day. This solar charging system is composed of a solar cell, a charger, batteries, a buck converter and a digital
Design of Hierarchical Fuzzy Logic Control for Mobile Robot System
Lon-Chen Hung; Hung-Yuan Chung
2006-01-01
This paper studies the problem of motion and control law design for a mobile robot that moves inside a partially unknown environment, under the assumption of parametric uncertainty in the model that describes the motion of the robot. This paper deals with a fuzzy-based intelligent mobile robotic system by hierarchical structure that requires various capabilities normally associated with intelligence. The
Mobile robot control using type-2 fuzzy logic system
Pisit Phokharatkul; Supachai Phaiboon
2004-01-01
This paper presents a type-2 fuzzy logic system that can be applied to a mobile robot which is a project associated with using infrared sensors as distance sensors, DC motor control system, knowledge of multiplex, RS-232 interface line, knowledge of microcontroller and knowledge of intelligent system. Infrared sensors are used to measure distance between a robot and obstacle. The type-2
Optimized fuzzy logic control strategy of hybrid vehicles using ADVISOR
Xia Meng; Nicolas LANGLOIS
2010-01-01
In order to increase fuel economy and decrease emitted pollution of Parallel Hybrid Vehicle (PHV), firstly a Fuzzy Logic Controller (FLC) is considered in this paper. Based on the torque desired for driving and the state of charge, its purpose is to select the optimum power split between the dual sources, which is one of the key points for PHV.
Fuzzy logic sliding mode control for command guidance law design.
Elhalwagy, Y Z; Tarbouchi, M
2004-04-01
Recently, the combination of sliding mode and fuzzy logic techniques has emerged as a promising methodology for dealing with nonlinear, uncertain, dynamical systems. In this paper, a sliding mode control algorithm combined with a fuzzy control scheme is developed for the trajectory control of a command guidance system. The acceleration command input is mathematically derived. The proposed controller is used to compensate for the influence of unmodeled dynamics and to alleviate chattering. Simulation results show that the proposed controller gives good system performance in the face of system parameters variation and external disturbances. In addition, they show the effectiveness of the proposed missile guidance law against different engagement scenarios where the results demonstrate better performance over the conventional sliding mode control. PMID:15098583
Implementation Of Fuzzy Automated Brake Controller Using TSK Algorithm
NASA Astrophysics Data System (ADS)
Mittal, Ruchi; Kaur, Magandeep
2010-11-01
In this paper an application of Fuzzy Logic for Automatic Braking system is proposed. Anti-blocking system (ABS) brake controllers pose unique challenges to the designer: a) For optimal performance, the controller must operate at an unstable equilibrium point, b) Depending on road conditions, the maximum braking torque may vary over a wide range, c) The tire slippage measurement signal, crucial for controller performance, is both highly uncertain and noisy. A digital controller design was chosen which combines a fuzzy logic element and a decision logic network. The controller identifies the current road condition and generates a command braking pressure signal Depending upon the speed and distance of train. This paper describes design criteria, and the decision and rule structure of the control system. The simulation results present the system's performance depending upon the varying speed and distance of the train.
General-purpose fuzzy controller for dc-dc converters
Mattavelli, P.; Rossetto, L.; Spiazzi, G.; Tenti, P. [Univ. of Padova (Italy)] [Univ. of Padova (Italy)
1997-01-01
In this paper, a general-purpose fuzzy controller for dc-dc converters is investigated. Based on a qualitative description of the system to be controlled, fuzzy controllers are capable of good performances, even for those systems where linear control techniques fail, e.g., when a mathematical description is not available or is in the presence of wide parameter variations. The presented approach is general and can be applied to any dc-dc converter topologies. Controller implementation is relatively simple and can guarantee a small-signal response as fast and stable as other standard regulators and an improved large-signal response. Simulation results of Buck-Boost and Sepic converters show control potentialities.
Combustion control of municipal incinerators by fuzzy neural network logic
Chang, N.B.; Chang, Y.H. [National Cheng-Kung Univ., Tainan (Taiwan, Province of China). Dept. of Environmental Engineering
1996-12-31
The successful operation of mass burn waterwall incinerators involves many uncertain factors. Not only the physical composition and chemical properties of the refuse but also the complexity of combustion mechanism would significantly influence the performance of waste treatment. Due to the rising concerns of dioxin/furan emissions from municipal incinerators, improved combustion control algorithms, such as fuzzy and its fusion control technologies, have gradually received attention in the scientific community. This paper describes a fuzzy and neural network control logic for the refuse combustion process in a mass burn waterwall incinerator. It is anticipated that this system can also be easily applied to several other types of municipal incinerators, such as modular, rotary kiln, RDF and fluidized bed incinerators, by slightly modified steps. Partial performance of this designed controller is tested by computer simulation using identified process model in this analysis. Process control could be sensitive especially for the control of toxic substance emissions, such as dioxin and furans.
NASA Technical Reports Server (NTRS)
Yen, John; Wang, Haojin; Daugherity, Walter C.
1992-01-01
Fuzzy logic controllers have some often-cited advantages over conventional techniques such as PID control, including easier implementation, accommodation to natural language, and the ability to cover a wider range of operating conditions. One major obstacle that hinders the broader application of fuzzy logic controllers is the lack of a systematic way to develop and modify their rules; as a result the creation and modification of fuzzy rules often depends on trial and error or pure experimentation. One of the proposed approaches to address this issue is a self-learning fuzzy logic controller (SFLC) that uses reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of its fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of a self-learning fuzzy controller is highly contingent on its design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for application to a petrochemical process are discussed, and its performance is compared with that of a PID and a self-tuning fuzzy logic controller.
Evolving fuzzy rules in a learning classifier system
NASA Technical Reports Server (NTRS)
Valenzuela-Rendon, Manuel
1993-01-01
The fuzzy classifier system (FCS) combines the ideas of fuzzy logic controllers (FLC's) and learning classifier systems (LCS's). It brings together the expressive powers of fuzzy logic as it has been applied in fuzzy controllers to express relations between continuous variables, and the ability of LCS's to evolve co-adapted sets of rules. The goal of the FCS is to develop a rule-based system capable of learning in a reinforcement regime, and that can potentially be used for process control.
P. J. Escamilla-Ambrosio; N. Mort
2002-01-01
In this work a novel multi-sensor data fusion (MSDF) architecture is presented. First, each measurement-vector coming from each sensor is fed to a fuzzy logic-based adaptive Kalman filter (FL-AKF); thus there are N sensors and N FL-AKFs working in parallel. The adaptation in each FL-AKF is, in the sense of dynamically tuning the measurement noise covariance matrix R, employing a
Rodrigo, M A; Seco, A; Ferrer, J; Penya-roja, J M; Valverde, J L
1999-01-01
In this paper, several tuning algorithms, specifically ITAE, IMC and Cohen and Coon, were applied in order to tune an activated sludge aeration PID controller. Performance results of these controllers were compared by simulation with those obtained by using a nonlinear fuzzy PID controller. In order to design this controller, a trial and error procedure was used to determine, as a function of error at current time and at a previous time, sets of parameters (including controller gain, integral time and derivative time) which achieve satisfactory response of a PID controller actuating over the aeration process. Once these sets of data were obtained, neural networks were used to obtain fuzzy membership functions and fuzzy rules of the fuzzy PID controller. PMID:10560141
Taylor, James H.
Fuzzy-Logic Controller Synthesis for Electro-mechanical Systems with Nonlinear Friction James H. Taylor for the synthesis of fuzzy-logic con- trollers for amplitude-sensitive nonlinear plants based on sinusoidal of fuzzy-logic controllers are, in func- tional terms, of the proportional-plus-derivative (pd
Taylor, James H.
Fuzzy-Logic Controller Synthesis Based on Sinusoidal-Input Describing Functions & Optimization Lan Sheng E3B 5A3 Internet: jtaylor@unb.ca Abstract We present a new method for the synthesis of fuzzy- logic. The resulting fuzzy-logic controller obtained in this paper includes derivative action in an inner-loop feedback
Fuzzy Feedback Scheduling of Resource-Constrained Embedded Control Systems
Xia, Feng; Tian, Yu-Chu; Tade, Moses; Dong, Jinxiang
2008-01-01
The quality of control (QoC) of a resource-constrained embedded control system may be jeopardized in dynamic environments with variable workload. This gives rise to the increasing demand of co-design of control and scheduling. To deal with uncertainties in resource availability, a fuzzy feedback scheduling (FFS) scheme is proposed in this paper. Within the framework of feedback scheduling, the sampling periods of control loops are dynamically adjusted using the fuzzy control technique. The feedback scheduler provides QoC guarantees in dynamic environments through maintaining the CPU utilization at a desired level. The framework and design methodology of the proposed FFS scheme are described in detail. A simplified mobile robot target tracking system is investigated as a case study to demonstrate the effectiveness of the proposed FFS scheme. The scheme is independent of task execution times, robust to measurement noises, and easy to implement, while incurring only a small overhead.
An automatic tuning method of a fuzzy logic controller for nuclear reactors
Ramaswamy, P.; Lee, K.Y. (Pennsylvania State Univ., University Park, PA (United States). Dept. of Electrical and Computer Engineering); Edwards, R.M. (Pennsylvania State Univ, University Park, PA (United States). Dept. of Nuclear Engineering)
1993-08-01
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.
Feedforward Tracking Control of Flat Recurrent Fuzzy Systems
NASA Astrophysics Data System (ADS)
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.
A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots
Hani A. Hagras
2004-01-01
Autonomous mobile robots navigating in changing and dynamic unstructured environments like the outdoor environments need to cope with large amounts of uncertainties that are inherent of natural environments. The traditional type-1 fuzzy logic controller (FLC) using precise type-1 fuzzy sets cannot fully handle such uncertainties. A type-2 FLC using type-2 fuzzy sets can handle such uncertainties to produce a better
Trends and Issues in Fuzzy Control and Neuro-Fuzzy Modeling
NASA Technical Reports Server (NTRS)
Chiu, Stephen
1996-01-01
Everyday experience in building and repairing things around the home have taught us the importance of using the right tool for the right job. Although we tend to think of a 'job' in broad terms, such as 'build a bookcase,' we understand well that the 'right job' associated with each 'right tool' is typically a narrowly bounded subtask, such as 'tighten the screws.' Unfortunately, we often lose sight of this principle when solving engineering problems; we treat a broadly defined problem, such as controlling or modeling a system, as a narrow one that has a single 'right tool' (e.g., linear analysis, fuzzy logic, neural network). We need to recognize that a typical real-world problem contains a number of different sub-problems, and that a truly optimal solution (the best combination of cost, performance and feature) is obtained by applying the right tool to the right sub-problem. Here I share some of my perspectives on what constitutes the 'right job' for fuzzy control and describe recent advances in neuro-fuzzy modeling to illustrate and to motivate the synergistic use of different tools.
Novel hybrid adaptive controller for manipulation in complex perturbation environments.
Smith, Alex M C; Yang, Chenguang; Ma, Hongbin; Culverhouse, Phil; Cangelosi, Angelo; Burdet, Etienne
2015-01-01
In this paper we present a hybrid control scheme, combining the advantages of task-space and joint-space control. The controller is based on a human-like adaptive design, which minimises both control effort and tracking error. Our novel hybrid adaptive controller has been tested in extensive simulations, in a scenario where a Baxter robot manipulator is affected by external disturbances in the form of interaction with the environment and tool-like end-effector perturbations. The results demonstrated improved performance in the hybrid controller over both of its component parts. In addition, we introduce a novel method for online adaptation of learning parameters, using the fuzzy control formalism to utilise expert knowledge from the experimenter. This mechanism of meta-learning induces further improvement in performance and avoids the need for tuning through trial testing. PMID:26029916
NASA Astrophysics Data System (ADS)
Lohani, A. K.; Kumar, Rakesh; Singh, R. D.
2012-06-01
SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.
Neuro-fuzzy systems for intelligent robot navigation and control under uncertainty
Wei Li
1995-01-01
This paper describes neuro-fuzzy systems for intelligent robot navigation and control under uncertainty. First, we present a new neuro-fuzzy system architecture for behavior navigation of a mobile robot in unknown environments. In this neuro-fuzzy system, a neural network is used to process range information for understanding distribution of obstacles in local regions; while fuzzy sets and a rule base are
A Study on Improved Fuzzy Neural Network Controller for Air-Condition with Frequency Change
Shuqing Wang; Zipeng Zhang; Zhihuai Xiao; Xiaohui Yuan
2009-01-01
The environment of room temperature is complicated and it is difficult to get precise mathematics model for the control of\\u000a air-condition with frequency change. It is difficult using conventional fuzzy control way to control air-condition to get\\u000a better control performance. Fuzzy neural network has strong fuzzy reasoning ability and learning ability, which can control\\u000a air-condition with frequency change to get
Intelligent digitally re-designed PAM fuzzy controller for nonlinear systems
Ho Jae Lee; Young Hoon Joo; Jin Bae Park; Leang-San Shieh
1999-01-01
We develop an intelligent digitally re-designed PAM fuzzy controller for nonlinear systems. Takagi-Sugeno (TS) fuzzy model is used to model the nonlinear systems and a continuous-time fuzzy-model-based controller is designed based on an extended parallel-distributed-compensation method. The digital controllers are determined from existing analogue controllers. The proposed method provides an accurate and effective method for digital control of continuous-time nonlinear
Motion Control of the Soccer Robot Based on Fuzzy Logic
NASA Astrophysics Data System (ADS)
Coman, Daniela; Ionescu, Adela
2009-08-01
Robot soccer is a challenging platform for multi-agent research, involving topics such as real-time image processing and control, robot path planning, obstacle avoidance and machine learning. The conventional robot control consists of methods for path generation and path following. When a robot moves away the estimated path, it must return immediately, and while doing so, the obstacle avoidance behavior and the effectiveness of such a path are not guaranteed. So, motion control is a difficult task, especially in real time and high speed control. This paper describes the use of fuzzy logic control for the low level motion of a soccer robot. Firstly, the modelling of the soccer robot is presented. The soccer robot based on MiroSoT Small Size league is a differential-drive mobile robot with non-slipping and pure-rolling. Then, the design of fuzzy controller is describes. Finally, the computer simulations in MATLAB Simulink show that proposed fuzzy logic controller works well.
Zheng Yunfeng; Bu Renxiang; Hong Biguang
2010-01-01
In this paper, we consider the tracking control problem of a class of SISO nonlinear systems transformable into uncertain nonlinear systems in output-feedback form using output feedback control. The unknown output functions are approximated by fuzzy logic systems. The unmeasured states are estimated by K-filters. Based on these filters, observer backstepping method is used to design the controller. Nonlinear damping
Sleep apnea detection using an adaptive fuzzy logic based screening system.
Morsy, Ahmed A; Al-Ashmouny, Khaled M
2005-01-01
We report an adaptive diagnostic system for the classification of breathing events for the purpose of detecting sleep apnea syndromes. The system employs two classification engines used in series. The first engine is fuzzy logic-based and generates one of three outcomes for each breathing event: normal, abnormal, and not-sure. The second classification engine is based on a center of gravity engine which is trained using the normal and abnormal events, generated by the first engine, and is specifically designed for sorting out the not-sure events. The fuzzy logic engine can be tuned very conservatively to reduce or eliminate the chance of error at the first stage. Since the second engine is trained adaptively using normal and abnormal data of the same patient, its accuracy is generally better than relying on multi-patient training approaches. The two-step, adaptive nature of the system allows for high accuracy and lends itself well for practical implementation. PMID:17281661
A fuzzy logic based approach to direct load control
Bhattacharyya, K.; Crow, M.L.
1996-05-01
Demand side management programs are strategies designed to alter the shape of the load curve. In order to successfully implement such a strategy, customer acceptance of the program is vital. It is thus desirable to design a model for direct load control which may accommodate customer preferences. This paper presents a methodology for optimizing both customer satisfaction and utility unit commitment savings, based on a fuzzy load model for the direct load control of appliances.
Adaptive Neuro-Fuzzy Modeling of UH-60A Pilot Vibration
NASA Technical Reports Server (NTRS)
Kottapalli, Sesi; Malki, Heidar A.; Langari, Reza
2003-01-01
Adaptive neuro-fuzzy relationships have been developed to model the UH-60A Black Hawk pilot floor vertical vibration. A 200 point database that approximates the entire UH-60A helicopter flight envelope is used for training and testing purposes. The NASA/Army Airloads Program flight test database was the source of the 200 point database. The present study is conducted in two parts. The first part involves level flight conditions and the second part involves the entire (200 point) database including maneuver conditions. The results show that a neuro-fuzzy model can successfully predict the pilot vibration. Also, it is found that the training phase of this neuro-fuzzy model takes only two or three iterations to converge for most cases. Thus, the proposed approach produces a potentially viable model for real-time implementation.
Adaptive fuzzy leader clustering of complex data sets in pattern recognition
NASA Technical Reports Server (NTRS)
Newton, Scott C.; Pemmaraju, Surya; Mitra, Sunanda
1992-01-01
A modular, unsupervised neural network architecture for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns on-line in a stable and efficient manner. The initial classification is performed in two stages: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from fuzzy C-means system equations for the centroids and the membership values. The AFLC algorithm is applied to the Anderson Iris data and laser-luminescent fingerprint image data. It is concluded that the AFLC algorithm successfully classifies features extracted from real data, discrete or continuous.
Simões, Marcelo Godoy
IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 12, NO. 1, JANUARY 1997 87 Fuzzy Logic Based a variable speed wind genera- tion system where fuzzy logic principles are used for efficiency optimization system. The generation system has fuzzy logic control with vector control in the inner loops. A fuzzy
ADAPTIVE ROBUST CONTROL FOR A CLASS OF NONLINEAR UNCERTAIN SYSTEM WITH UNKNOWN INPUT BACKLASH
Yao, Bin
][6] or fuzzy logic [7] has been used in feedback control system. For those compensations, neural networks to get the controller, which maybe unavailable. Backlash compensation using neural network [5ADAPTIVE ROBUST CONTROL FOR A CLASS OF NONLINEAR UNCERTAIN SYSTEM WITH UNKNOWN INPUT BACKLASH Jian
Adaptive Fuzzy Consensus Clustering Framework for Clustering Analysis of Cancer Data.
Zhiwen Yu; Hantao Chen; You, Jane; Jiming Liu; Hau-San Wong; Guoqiang Han; Le Li
2015-01-01
Performing clustering analysis is one of the important research topics in cancer discovery using gene expression profiles, which is crucial in facilitating the successful diagnosis and treatment of cancer. While there are quite a number of research works which perform tumor clustering, few of them considers how to incorporate fuzzy theory together with an optimization process into a consensus clustering framework to improve the performance of clustering analysis. In this paper, we first propose a random double clustering based cluster ensemble framework (RDCCE) to perform tumor clustering based on gene expression data. Specifically, RDCCE generates a set of representative features using a randomly selected clustering algorithm in the ensemble, and then assigns samples to their corresponding clusters based on the grouping results. In addition, we also introduce the random double clustering based fuzzy cluster ensemble framework (RDCFCE), which is designed to improve the performance of RDCCE by integrating the newly proposed fuzzy extension model into the ensemble framework. RDCFCE adopts the normalized cut algorithm as the consensus function to summarize the fuzzy matrices generated by the fuzzy extension models, partition the consensus matrix, and obtain the final result. Finally, adaptive RDCFCE (A-RDCFCE) is proposed to optimize RDCFCE and improve the performance of RDCFCE further by adopting a self-evolutionary process (SEPP) for the parameter set. Experiments on real cancer gene expression profiles indicate that RDCFCE and A-RDCFCE works well on these data sets, and outperform most of the state-of-the-art tumor clustering algorithms. PMID:26357330
Fuzzy-SMC-PI flux and speed control for induction motors
Haider A. F. Mohamed; En Lau Lau; Soo Siang Yang; Mahmoud Moghavvemi
2008-01-01
This paper presents the design and implementation of Fuzzy-SMC-PI methodology to control the flux and speed of an induction motor. The Fuzzy-SMC-PI is basically a combination of Sliding Mode Control (SMC) and PI control methodologies through fuzzy logic. In this strategy, SMC is responsive during transient state while PI control becomes fully active in the steady state area. This will
Control of a benchmark structure using GA-optimized fuzzy logic control
Shook, David Adam
2009-05-15
-elitist multi-objective genetic algorithm is utilized to optimize a set of fuzzy logic controllers with concurrent consideration to four structural response metrics. The genetic algorithm is able to identify optimal passive cases for MR damper operation...
Han, Honggui; Wu, Xiao-Long; Qiao, Jun-Fei
2014-04-01
In this paper, a self-organizing fuzzy-neural-network with adaptive computation algorithm (SOFNN-ACA) is proposed for modeling a class of nonlinear systems. This SOFNN-ACA is constructed online via simultaneous structure and parameter learning processes. In structure learning, a set of fuzzy rules can be self-designed using an information-theoretic methodology. The fuzzy rules with high spiking intensities (SI) are divided into new ones. And the fuzzy rules with a small relative mutual information (RMI) value will be pruned in order to simplify the FNN structure. In parameter learning, the consequent part parameters are learned through the use of an ACA that incorporates an adaptive learning rate strategy into the learning process to accelerate the convergence speed. Then, the convergence of SOFNN-ACA is analyzed. Finally, the proposed SOFNN-ACA is used to model nonlinear systems. The modeling results demonstrate that this proposed SOFNN-ACA can model nonlinear systems effectively. PMID:23782841
Wu Yihu; Song Dandan; Hou Zhixiang; Yuan Xiang
2007-01-01
In this paper, a fuzzy control method is proposed to improve vehicle yaw stability by integrated yaw moment control and active front steering. This control system is designed by actively controlling the front steering angle and the distribution of braking forces, using feed-forward regulation and feedback revision control strategy, and a fuzzy controller is designed to suppress the output error
Hardware implementation of fuzzy Petri net as a controller.
Gniewek, Les?aw; Kluska, Jacek
2004-06-01
The paper presents a new approach to fuzzy Petri net (FPN) and its hardware implementation. The authors' motivation is as follows. Complex industrial processes can be often decomposed into many parallelly working subprocesses, which can, in turn, be modeled using Petri nets. If all the process variables (or events) are assumed to be two-valued signals, then it is possible to obtain a hardware or software control device, which works according to the algorithm described by conventional Petri net. However, the values of real signals are contained in some bounded interval and can be interpreted as events which are not only true or false, but rather true in some degree from the interval [0, 1]. Such a natural interpretation from multivalued logic (fuzzy logic) point of view, concerns sensor outputs, control signals, time expiration, etc. It leads to the idea of FPN as a controller, which one can rather simply obtain, and which would be able to process both analog, and binary signals. In the paper both graphical, and algebraic representations of the proposed FPN are given. The conditions under which transitions can be fired are described. The algebraic description of the net and a theorem which enables computation of new marking in the net, based on current marking, are formulated. Hardware implementation of the FPN, which uses fuzzy JK flip-flops and fuzzy gates, are proposed. An example illustrating usefulness of the proposed FPN for control algorithm description and its synthesis as a controller device for the concrete production process are presented. PMID:15484905
NASA Astrophysics Data System (ADS)
Liu, Yanming; Gordaninejad, Faramarz; Evrensel, Cahit A.; Karakas, E. Sinan; Dogruer, Umit
2005-05-01
This work presents an experimental study of fuzzy logic control for a quarter-car-model of a high-mobility multi-purpose wheeled vehicle suspension system using a magneto-rheological fluid damper. Sprung mass displacement and velocity based fuzzy control, and acceleration based fuzzy control are proposed and compared to a skyhook control strategy. The displacement and acceleration of the sprung mass under rough road excitation are analyzed using power spectral density and root mean square methods. It is demonstrated that the displacement and acceleration based fuzzy control strategy performs well as compared to the other method considered.
On-line fuzzy logic control of tube bending
NASA Astrophysics Data System (ADS)
Lieh, Junghsen; Li, Wei Jie
2005-11-01
This paper describes the simulation and on-line fuzzy logic control of tube bending. By combining elasticity and plasticity theories, a conventional model was developed. The results from simulation were compared with those obtained from testing. The experimental data reveal that there exists certain level of uncertainty and nonlinearity in tube bending, and its variation could be significant. To overcome this, a on-line fuzzy logic controller with self-tuning capabilities was designed. The advantages of this on-line system are (1) its computational requirement is simple in comparison with more algorithmic-based controllers, and (2) the system does not need prior knowledge of material characteristics. The device includes an AC motor, a servo controller, a forming mechanism, a 3D optical sensor, and a microprocessor. This automated bending machine adopts primary and secondary errors between the actual response and desired output to conduct on-line rule reasoning. Results from testing show that the spring back angle can be effectively compensated by the self- tuning fuzzy system in a real-time fashion.
Fuzzy Controllers Based Multipath Routing Algorithm in MANET
NASA Astrophysics Data System (ADS)
Pi, Shangchao; Sun, Baolin
Mobile ad hoc networks (MANETs) consist of a collection of wireless mobile nodes which dynamically exchange data among themselves without the reliance on a fixed base station or a wired backbone network. Due to the limited transmission range of wireless network nodes, multiple hops are usually needed for a node to exchange information with any other node in the network. Multipath routing allows the establishment of multiple paths between a single source and single destination node. The multipath routing in mobile ad hoc networks is difficult because the network topology may change constantly, and the available alternative path is inherently unreliable. This paper introduces a fuzzy controllers based multipath routing algorithm in MANET (FMRM). The key idea of FMRM algorithm is to construct the fuzzy controllers with the help to reduce reconstructions in the ad hoc network. The simulation results show that the proposed approach is effective and efficient in applications to the MANETs. It is an available approach to multipath routing decision.
V. S. C. Raviraj; P. C. Sen
1997-01-01
This paper presents a comparative evaluation of the proportional-integral, sliding mode and fuzzy logic controllers for applications to power converters. The mismatch between the characteristics which lead to varying performance is outlined. This paper also demonstrates certain similarities of both the fuzzy logic controller and sliding mode controller. Sensitivity of these controllers to supply voltage disturbances and load disturbances is
A composite self tuning strategy for fuzzy control of dynamic systems
NASA Technical Reports Server (NTRS)
Shieh, C.-Y.; Nair, Satish S.
1992-01-01
The feature of self learning makes fuzzy logic controllers attractive in control applications. This paper proposes a strategy to tune the fuzzy logic controller on-line by tuning the data base as well as the rule base. The structure of the controller is outlined and preliminary results are presented using simulation studies.
Design and Implementation of a Hybrid Fuzzy Controller for a High-Performance Induction Motor
M. Zerikat; S. Chekroun
2007-01-01
This paper proposes an effective algorithm approach to hybrid control systems combining fuzzy logic and conventional control techniques of controlling the speed of induction motor assumed to operate in high-performance drives environment. The introducing of fuzzy logic in the control systems helps to achieve good dynamical response, disturbance rejection and low sensibility to parameter variations and external influences. Some fundamentals
Multiple-valued Logic Approach to Fuzzy Controllers Implementation
V. VARSHAVSKY; V. MARAKHOVSKY; I. LEVIN; H. SAITO
2007-01-01
This paper offers a new technique for designing fuzzy controllers as analog hardware devices on bases of CMOS implementation of multi-valued logical functions. This approach is based on using a summing amplifier with saturation as a building block that can be considered as a multi-threshold logical element. The functional completeness in an arbitrary-valued logic of a summing amplifier with saturation
A VLSI fuzzy logic controller with reconfigurable, cascadable architecture
HIROY UKI WATANABE; WAYNE D. DETTLOFF; KATHY E. YOUNT
1990-01-01
A general-purpose fuzzy logic inference engine for real-time control applications, designed and fabricated in a 1.1-?m, 3.3-V, double-level-metal CMOS technology, is discussed. Up to 102 rules are processed in parallel with a single 688 K transistor device. Features include a dynamically reconfigurable and cascadable architecture, TTL-compatible host interface, laser-programmable redundancy, a special mode for testability, RAM rule storage, and on-chip
Quadcopter see and avoid using a fuzzy controller
Miguel A. Olivares-Mendez; Luis Mejias; Pascual Campoy; Ignacio Mellado-Bataller
2012-01-01
Unmanned Aerial Vehicles (UAVs) industry is a fast growing sector. Nowadays, the market offers numerous possibilities for off-the-shelf UAVs such as quadrotors or fixed-wings. Until UAVs demonstrate advance capabilities such as autonomous collision avoidance they will be segregated and restricted to flight in controlled environments. This work presents a visual fuzzy servoing system for obstacle avoidance using UAVs. To accomplish
Predictive functional control based on fuzzy model for heat-exchanger pilot plant
I. Skrjanc; D. Matko
2000-01-01
In this paper, a new method of predictive control is presented. In this approach, a well-known method of predictive functional control is combined with fuzzy model of the process. The prediction is based on fuzzy model given in the form of Takagi-Sugeno type. The proposed fuzzy predictive control has been evaluated by implementation on heat-exchanger plant, which exhibits a strong
Multiple fuzzy model-based temperature predictive control for HVAC systems
Ming He; Wen-jian Cai; Shao-Yuan Li
2005-01-01
In this paper, a multiple model predictive control (MMPC) strategy based on Takagi–Sugeno (T–S) fuzzy models for temperature control of air-handling unit (AHU) in heating, ventilating, and air-conditioning (HVAC) systems is presented. The overall control system is constructed by a hierarchical two-level structure. The higher level is a fuzzy partition based on AHU operating range to schedule the fuzzy weights
Karami, Ali; Keiter, Steffen; Hollert, Henner; Courtenay, Simon C
2013-03-01
This study represents a first attempt at applying a fuzzy inference system (FIS) and an adaptive neuro-fuzzy inference system (ANFIS) to the field of aquatic biomonitoring for classification of the dosage and time of benzo[a]pyrene (BaP) injection through selected biomarkers in African catfish (Clarias gariepinus). Fish were injected either intramuscularly (i.m.) or intraperitoneally (i.p.) with BaP. Hepatic glutathione S-transferase (GST) activities, relative visceral fat weights (LSI), and four biliary fluorescent aromatic compounds (FACs) concentrations were used as the inputs in the modeling study. Contradictory rules in FIS and ANFIS models appeared after conversion of bioassay results into human language (rule-based system). A "data trimming" approach was proposed to eliminate the conflicts prior to fuzzification. However, the model produced was relevant only to relatively low exposures to BaP, especially through the i.m. route of exposure. Furthermore, sensitivity analysis was unable to raise the classification rate to an acceptable level. In conclusion, FIS and ANFIS models have limited applications in the field of fish biomarker studies. PMID:22752811
Adaptive Inverse Control based on Linear and Nonlinear Adaptive Filtering
Widrow, Bernard
Adaptive Inverse Control based on Linear and Nonlinear Adaptive Filtering Bernard Widrow and Gregory L. Plett Department of Electrical Engineering, Stanford University, Stanford, CA 94305. This paper proposes an alternative approach that uses adaptive filtering to achieve feedforward control
Adaptive neuro-fuzzy inference systems for automatic detection of breast cancer.
Ubeyli, Elif Derya
2009-10-01
This paper intends to an integrated view of implementing adaptive neuro-fuzzy inference system (ANFIS) for breast cancer detection. The Wisconsin breast cancer database contained records of patients with known diagnosis. The ANFIS classifiers learned how to differentiate a new case in the domain by given a training set of such records. The ANFIS classifier was used to detect the breast cancer when nine features defining breast cancer indications were used as inputs. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of breast cancer were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performances and classification accuracies and the results confirmed that the proposed ANFIS model has potential in detecting the breast cancer. PMID:19827261
Abstract-- Stable direct and indirect decentralized adaptive radial basis neural network controllers a universal approximator (such as a fuzzy system or neural network) to estimate unknown nonlinearities which Control of Nonlinear Systems Using Radial Basis Neural Networks Jeffrey T. Spooner and Kevin M. Passino
Clustering of noisy image data using an adaptive neuro-fuzzy system
NASA Technical Reports Server (NTRS)
Pemmaraju, Surya; Mitra, Sunanda
1992-01-01
Identification of outliers or noise in a real data set is often quite difficult. A recently developed adaptive fuzzy leader clustering (AFLC) algorithm has been modified to separate the outliers from real data sets while finding the clusters within the data sets. The capability of this modified AFLC algorithm to identify the outliers in a number of real data sets indicates the potential strength of this algorithm in correct classification of noisy real data.
A NOISE ADAPTIVE FUZZY EQUALIZATION METHOD FOR PROCESSING SOLAR EXTREME ULTRAVIOLET IMAGES
Druckmueller, M.
2013-08-15
A new image enhancement tool ideally suited for the visualization of fine structures in extreme ultraviolet images of the corona is presented in this paper. The Noise Adaptive Fuzzy Equalization method is particularly suited for the exceptionally high dynamic range images from the Atmospheric Imaging Assembly instrument on the Solar Dynamics Observatory. This method produces artifact-free images and gives significantly better results than methods based on convolution or Fourier transform which are often used for that purpose.
Fuzzy control for a nonlinear mimo-liquid level problem
Smith, R. E.; Mortensen, F. N.; Wantuck, P. J.; Parkinson, W. J. ,
2001-01-01
Nonlinear systems are very common in the chemical process industries. Control of these systems, particularly multivariable systems, is extremely difficult. In many chemical plants, because of this difficulty, control is seldom optimal. Quite often, the best control is obtained in the manual mode using experienced operators. Liquid level control is probably one of the most common control problems in a chemical plant. Liquid level is important in heat exchanger control where heat and mass transfer rates can be controlled by the amount of liquid covering the tubes. Distillation columns, mixing tanks, and surge tanks are other examples where liquid level control is very important. The problem discussed in this paper is based on the simultaneous level control of three tanks connected in series. Each tank holds slightly less than 0.01 m{sup 3} of liquid. All three tanks are connected, Liquid is pumped into the first and the third tanks to maintain their levels. The third tank in the series drains to the system exit. The levels in the first and third tank control the level in the middle tank. The level in the middle tank affects the levels in the two end tanks. Many other chemical plant systems can be controlled in a manner similar to this three-tank system. For example, in any distillation column liquid level control problems can be represented as a total condenser with liquid level control, a reboiler with liquid level control, with the interactive column in between. The solution to the three-tank-problem can provide insight into many of the nonlinear control problems in the chemical process industries. The system was tested using the fuzzy logic controller and a proportional-integral (PI) controller, in both the setpoint tracking mode and disturbance rejection mode. The experimental results are discussed and comparisons between fuzzy controller and the standard PI controller are made.
Neural-network-based fuzzy logic control system with applications on compliant robot control
NASA Astrophysics Data System (ADS)
Hor, MawKae; Lu, Hui L.
1994-10-01
In view of the success of neural network applications in inverted pendulum control, speech recognition, and other problem solving, we believe that one could inject the noise removing concepts and learning spirits into the algorithm in constructing the neural networks and apply it to the various tasks such as compliant coordinated motion using multiple robots. Based on the fuzzy logic, a fuzzy logical control system is a logical system which is much closer to human thinking than any other logical systems. During recent years, fuzzy logic control has emerged as a fruitful area in applications, especially the applications lacking quantitative data regarding the input-output relations. Whereas, the connectionist model injects the learning ability to the fuzzy logic system. This model, proposed by Lin and Lee, is a connected neural network that embedded the fuzzy rules in the architecture. Since this model is general enough and we expect the embedded fuzzy concepts can solve the problems caused by the defective training data, it is chosen as our base structure. Appropriate modifications have been made to this model to reflect the real situations encountered in the robot applications. Our goal is to control two different types of robots for coordinated motion using sensory feedback information.
Han, Seong-Ik; Lee, Jang-Myung
2014-01-01
This paper proposes a backstepping control system that uses a tracking error constraint and recurrent fuzzy neural networks (RFNNs) to achieve a prescribed tracking performance for a strict-feedback nonlinear dynamic system. A new constraint variable was defined to generate the virtual control that forces the tracking error to fall within prescribed boundaries. An adaptive RFNN was also used to obtain the required improvement on the approximation performances in order to avoid calculating the explosive number of terms generated by the recursive steps of traditional backstepping control. The boundedness and convergence of the closed-loop system was confirmed based on the Lyapunov stability theory. The prescribed performance of the proposed control scheme was validated by using it to control the prescribed error of a nonlinear system and a robot manipulator. PMID:24055100
Chang, H.C.; Wang, M.H. [National Taiwan Institute of Technology, Taipei (Taiwan, Province of China). Dept. of Electrical Engineering] [National Taiwan Institute of Technology, Taipei (Taiwan, Province of China). Dept. of Electrical Engineering
1995-06-01
An efficient self-organizing neural fuzzy controller (SONFC) is designed to improve the transient stability of multimachine power systems. First, an artificial neural network (ANN)-based model is introduced for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the ANN model. With the excellent learning capability inherent in the ANN, the traditional heuristic fuzzy control rules and input-output fuzzy membership functions can be optimally tuned from training examples by the back propagation learning algorithm. Considerable rule-matching times of the inference engine in the traditional fuzzy system can be saved. To illustrate the performance and usefulness of the SONFC, comparative studies with a bang-bang controller are performed on the 34-generator Taipower system with rather encouraging results.
A fuzzy inference engine in nonlinear analog mode and its application to a fuzzy logic control
Takeshi Yamakawa
1993-01-01
In this tutorial, the utility of a fuzzy system is demonstrated by providing a broad overview, emphasizing analog mode hardware, along with a discussion of the author's original work. First, the difference between deterministic words and fuzzy words is explained as well as fuzzy logic. The description of the system using mathematical equations, linguistic rules, or parameter distributions (e.g., neural
Remote fuzzy logic control of networked control system via Profibus-DP
Kyung Chang Lee; Suk Lee; Man Hyung Lee
2003-01-01
This paper focuses on the feasibility of fuzzy logic control for networked control systems (NCSs). In order to evaluate its feasibility, a networked control system for servo motor control is implemented on a Profibus-DP network. The NCS consists of several independent, but interacting, processes running on two separate stations. By using this NCS, the network-induced delay is analyzed to find
A Neuro-Fuzzy Systems for Control Applications F. Berardi, M. Chiaberge, E. Miranda and L.M. Reyneri
Reyneri, Leonardo
and computationally inten- sive task. Neural networks and fuzzy systems neuro-fuzzy systems, in general are raising - ITALY e.mail marcello@polimage.polito.it, fax: ++39 11 564 4099 Keywords Arti cial Neural Networks examples, the adaptation capability, etc. Furthermore the availability of dedicated neuro-fuzzy processors
Stability analysis of the fuzzy logic controller designed by the phase portrait assignment algorithm
Shehu S. Farinwata; George Vachtsevanos
1993-01-01
The authors analyze the stability of a fuzzy logic controller, designed using the phase portrait assignment algorithm from a topological point of view. The fuzzy logic controller was designed for a simplified automobile engine which is a two-state dynamic model. The controller was designed using the automatic rule generation technique reported by G.J. Vachtsevanos et al. (1992). The general form
World Automation Congress 2010 TSI Press. USING A FUZZY LOGIC CONTROL SYSTEM FOR AN XPILOT
Parker, Gary B.
example, they used EC applied to neural networks [4] to produce an effective controller, but this requiredWorld Automation Congress © 2010 TSI Press. USING A FUZZY LOGIC CONTROL SYSTEM FOR AN XPILOT COMBAT {ahubley, parker}@conncoll.edu ABSTRACT--Fuzzy logic controllers provide a means of reasoning in uncertain
Fuzzy-logic applied to yaw moment control for vehicle stability
B. L. Boada; M. J. L. Boada; V. Díaz
2005-01-01
In this paper, we propose a new yaw moment control based on fuzzy logic to improve vehicle handling and stability. The advantages of fuzzy methods are their simplicity and their good performance in controlling non-linear systems. The developed controller generates the suitable yaw moment which is obtained from the difference of the brake forces between the front wheels so that
Pitch-control for large-scale wind turbines based on feed forward fuzzy-PI
Feng Gao; Daping Xu; Yuegang Lv
2008-01-01
Wind turbine system is a complex nonlinear system involving some random disturbances. Based on analyzing characteristics of wind turbine and requisitions of pitch control, a compound approach was investigated by combining dynamic feed forward and fuzzy-PI to control pitch angle. Fuzzy control can not depend on mathematical model, so it solves the problem of modeling wind turbine system including feed
E. Saeidpour; V. S. Parizy; A. Mohammadi; M. Abedi; H. Rastegar
2008-01-01
The purpose of this paper is to implement a new method for designation of fuzzy logic controller for STATCOM to improve the voltage profile. Because of the importance of power quality in industrial bus bars when the loads are numerous, the classic methods of control (because of inherent time delay) can not been used. Therefore the fuzzy controller has been
Design and implementation of integrated fuzzy logic controller for a servomotor system
Ming-Yuan Shieh; Tzuu-Hseng S. Li
1998-01-01
In this paper, we present the design and implementation of an integrated fuzzy logic controller (IFLC) for a DC-servomotor system. The proposed strategy is intended to improve the performance of the original control system by use of a fuzzy logic controller (FLC)_as the motor load changes. Both computer simulation and experimental implementation demonstrate that IFLC is particularly effective in position
Juang, Chia-Feng; Lai, Min-Ge; Zeng, Wan-Ting
2015-09-01
This paper presents a method that allows two wheeled, mobile robots to navigate unknown environments while cooperatively carrying an object. In the navigation method, a leader robot and a follower robot cooperatively perform either obstacle boundary following (OBF) or target seeking (TS) to reach a destination. The two robots are controlled by fuzzy controllers (FC) whose rules are learned through an adaptive fusion of continuous ant colony optimization and particle swarm optimization (AF-CACPSO), which avoids the time-consuming task of manually designing the controllers. The AF-CACPSO-based evolutionary fuzzy control approach is first applied to the control of a single robot to perform OBF. The learning approach is then applied to achieve cooperative OBF with two robots, where an auxiliary FC designed with the AF-CACPSO is used to control the follower robot. For cooperative TS, a rule for coordination of the two robots is developed. To navigate cooperatively, a cooperative behavior supervisor is introduced to select between cooperative OBF and cooperative TS. The performance of the AF-CACPSO is verified through comparisons with various population-based optimization algorithms for the OBF learning problem. Simulations and experiments verify the effectiveness of the approach for cooperative navigation of two robots. PMID:25398185
Fast Hybrid PSO and Tabu Search Approach for Optimization of a Fuzzy Controller
Talbi, Nesrine
2011-01-01
In this paper, a fuzzy controller type Takagi_Sugeno zero order is optimized by the method of hybrid Particle Swarm Optimization (PSO) and Tabu Search (TS). The algorithm automatically adjusts the membership functions of fuzzy controller inputs and the conclusions of fuzzy rules. At each iteration of PSO, we calculate the best solution and we seek the best neighbor by Tabu search, this operation minimizes the number of iterations and computation time while maintaining accuracy and minimum response time. We apply this algorithm to optimize a fuzzy controller for a simple inverted pendulum with three rules.
Fuzzy logic based intelligent control of a variable speed cage machine wind generation system
Simoes, M.G. [Univ. of Sao Paulo (Brazil)] [Univ. of Sao Paulo (Brazil); Bose, B.K. [Univ. of Tennessee, Knoxville, TN (United States). Dept. of Electrical Engineering] [Univ. of Tennessee, Knoxville, TN (United States). Dept. of Electrical Engineering; Spiegel, R.J. [Environmental Protection Agency, Research Triangle Park, NC (United States), Air and Energy Engineering Research Lab.] [Environmental Protection Agency, Research Triangle Park, NC (United States), Air and Energy Engineering Research Lab.
1997-01-01
The paper describes a variable speed wind generation system where fuzzy logic principles are used for efficiency optimization and performance enhancement control. A squirrel cage induction generator feeds the power to a double-sided pulse width modulated converter system which pumps power to a utility grid or can supply to an autonomous system. The generation system has fuzzy logic control with vector control in the inner loops. A fuzzy controller tracks the generator speed with the wind velocity to extract the maximum power. A second fuzzy controller programs the machine flux for light load efficiency improvement, and a third fuzzy controller gives robust speed control against wind gust and turbine oscillatory torque. The complete control system has been developed, analyzed, and validated by simulation study. Performances have then been evaluated in detail.
Abnormal red blood cells detection using adaptive neuro-fuzzy system.
Babazadeh Khameneh, Nahid; Arabalibeik, Hossein; Salehian, Piruz; Setayeshi, Saeed
2012-01-01
Features like size, shape, and volume of red blood cells are important factors in diagnosing related blood disorders such as iron deficiency and anemia. This paper proposes a method to detect abnormality in red blood cells using cell microscopic images. Adaptive local thresholding and bounding box methods are used to extract inner and outer diameters of red cells. An adaptive network-based fuzzy inference system (ANFIS) is used to classify blood samples to normal and abnormal. Accuracy of the proposed method and area under ROC curve are 96.6% and 0.9950 respectively. PMID:22356952
PLC-based fuzzy logic controller for induction-motor drive with constant V\\/Hz ratio
M. Arrofiq; Nordin Saad
2007-01-01
This paper presents the design and implementation of a PLC-based fuzzy logic controller for an induction motor speed control at constant V\\/Hz ratio. The PLC has arithmetic and logic operations instructions set that was utilized in the implementation of the fuzzy control of induction motor speed control. Fuzzy logic algorithm applies rules obtained from human expert of a system. The
Fuzzy Logic Controller for Low Temperature Application
NASA Technical Reports Server (NTRS)
Hahn, Inseob; Gonzalez, A.; Barmatz, M.
1996-01-01
The most common temperature controller used in low temperature experiments is the proportional-integral-derivative (PID) controller due to its simplicity and robustness. However, the performance of temperature regulation using the PID controller depends on initial parameter setup, which often requires operator's expert knowledge on the system. In this paper, we present a computer-assisted temperature controller based on the well known.
PC based speed control of dc motor using fuzzy logic controller
Mandal, S.K.; Kanphade, R.D.; Lavekar, K.P.
1998-07-01
The dc motor is extensively used as constant speed drive in textile mills, paper mills, printing press, etc.. If the load and supply voltage are time varying, the speed will be changed. Since last few decades the conventional PID controllers are used to maintain the constant speed by controlling the duty ratio of Chopper. Generally, four quadrant chopper is used for regenerative braking and reverse motoring operation. Fuzzy Logic is newly introduced in control system. Fuzzy Control is based on Fuzzy Logic, a logical system which is too much closer in spirit to human thinking and natural language. The Fuzzy Logic Controller (FLC) provides a linguistic control strategy based on knowledge base of the system. Firstly, the machine is started very smoothly from zero to reference speed in the proposed scheme by increasing the duty ratio. Then change and rate of change of speed (dN, dN/dt), change and rate of change input voltage (dV, dV/dt) and load current are input to FLC. The new value of duty ratio is determined from the Fuzzy rule base and defuzzification method. The chopper will be 'ON' according to new duty ratio to maintain the constant speed. The dynamic and steady state performance of the proposed system is better than conventional control system. In this paper mathematical simulation and experimental implementation are carried out to investigate the drive performance.
Design and Construction of Intelligent Traffic Light Control System Using Fuzzy Logic
NASA Astrophysics Data System (ADS)
Lin, Htin; Aye, Khin Muyar; Tun, Hla Myo; Theingi, Naing, Zaw Min
2008-10-01
Vehicular travel is increasing throughout the world, particularly in large urban areas. Therefore the need arises for simulation and optimizing traffic control algorithms to better accommodate this increasing demand. This paper presents a microcontroller simulation of intelligent traffic light controller using fuzzy logic that is used to change the traffic signal cycles adaptively at a two-way intersection. This paper is an attempt to design an intelligent traffic light control systems using microcontrollers such as PIC 16F84A and PIC 16F877A. And then traffic signal can be controlled depending upon the densities of cars behind green and red lights of the two-way intersection by using sensors and detectors circuits.
Design, modelling, implementation, and intelligent fuzzy control of a hovercraft
NASA Astrophysics Data System (ADS)
El-khatib, M. M.; Hussein, W. M.
2011-05-01
A Hovercraft is an amphibious vehicle that hovers just above the ground or water by air cushion. The concept of air cushion vehicle can be traced back to 1719. However, the practical form of hovercraft nowadays is traced back to 1955. The objective of the paper is to design, simulate and implement an autonomous model of a small hovercraft equipped with a mine detector that can travel over any terrains. A real time layered fuzzy navigator for a hovercraft in a dynamic environment is proposed. The system consists of a Takagi-Sugenotype fuzzy motion planner and a modified proportional navigation based fuzzy controller. The system philosophy is inspired by human routing when moving between obstacles based on visual information including the right and left views from which he makes his next step towards the goal in the free space. It intelligently combines two behaviours to cope with obstacle avoidance as well as approaching a goal using a proportional navigation path accounting for hovercraft kinematics. MATLAB/Simulink software tool is used to design and verify the proposed algorithm.
Reducing the Impact of Uncertainties in Networked Control Systems Using Type-2 Fuzzy Logic
NASA Astrophysics Data System (ADS)
Michal, Blaho; J´n, Murgaš; Eugen, Viszus; Peter, Fodrek
2015-01-01
The networked control systems (NCS) have grown in popularity in recent years. Despite their advantages over the traditional control schemes, some of their drawbacks emerged as well (time delays, packet losses). There are several ways of dealing with the time delays and packet losses in NCS, but only a few authors have ever used type-2 fuzzy controllers for this purpose to our knowledge. This paper is aimed at dealing with the negative effects that occur in NCS, by using type-2 fuzzy control systems. It is presented that this approach can be successfully used to decrease the effects of time delays and packet losses. A type-2 fuzzy controller has been designed and compared to a type-1 fuzzy controller. The intervals of type-2 fuzzy controller were optimized via genetic algorithm.
Fuzzy chaos control for vehicle lateral dynamics based on active suspension system
NASA Astrophysics Data System (ADS)
Huang, Chen; Chen, Long; Jiang, Haobin; Yuan, Chaochun; Xia, Tian
2014-07-01
The existing research of the active suspension system (ASS) mainly focuses on the different evaluation indexes and control strategies. Among the different components, the nonlinear characteristics of practical systems and control are usually not considered for vehicle lateral dynamics. But the vehicle model has some shortages on tyre model with side-slip angle, road adhesion coefficient, vertical load and velocity. In this paper, the nonlinear dynamic model of lateral system is considered and also the adaptive neural network of tire is introduced. By nonlinear analysis methods, such as the bifurcation diagram and Lyapunov exponent, it has shown that the lateral dynamics exhibits complicated motions with the forward speed. Then, a fuzzy control method is applied to the lateral system aiming to convert chaos into periodic motion using the linear-state feedback of an available lateral force with changing tire load. Finally, the rapid control prototyping is built to conduct the real vehicle test. By comparison of time response diagram, phase portraits and Lyapunov exponents at different work conditions, the results on step input and S-shaped road indicate that the slip angle and yaw velocity of lateral dynamics enter into stable domain and the results of test are consistent to the simulation and verified the correctness of simulation. And the Lyapunov exponents of the closed-loop system are becoming from positive to negative. This research proposes a fuzzy control method which has sufficient suppress chaotic motions as an effective active suspension system.
Thermal comfort based fuzzy logic controller
M M Gouda; S Danaher; C P Underwood
2001-01-01
Most heating, ventilation and air conditioning (HVAC) control systems are considered as temperature control problems. In this work, the predicted mean vote (PMV) is used to control the indoor temperature of a space by setting it at a point where the PMV index becomes zero and the predicted percentage of persons dissatisfied (PPD) achieves a maximum threshold of 5%. This
Introduction to Fuzzy Control Marcelo Godoy Simoes
Simões, Marcelo Godoy
a human control activity. 1 Introduction Traditional control approach requires modeling of the physical-output reaction, one can design a controller. The disadvantages are several: the process equipment may differential equations and consequently no comprehensive analysis tools for nonlinear dynamic systems. Another
Hybrid Adaptive Flight Control with Model Inversion Adaptation
NASA Technical Reports Server (NTRS)
Nguyen, Nhan
2011-01-01
This study investigates a hybrid adaptive flight control method as a design possibility for a flight control system that can enable an effective adaptation strategy to deal with off-nominal flight conditions. The hybrid adaptive control blends both direct and indirect adaptive control in a model inversion flight control architecture. The blending of both direct and indirect adaptive control provides a much more flexible and effective adaptive flight control architecture than that with either direct or indirect adaptive control alone. The indirect adaptive control is used to update the model inversion controller by an on-line parameter estimation of uncertain plant dynamics based on two methods. The first parameter estimation method is an indirect adaptive law based on the Lyapunov theory, and the second method is a recursive least-squares indirect adaptive law. The model inversion controller is therefore made to adapt to changes in the plant dynamics due to uncertainty. As a result, the modeling error is reduced that directly leads to a decrease in the tracking error. In conjunction with the indirect adaptive control that updates the model inversion controller, a direct adaptive control is implemented as an augmented command to further reduce any residual tracking error that is not entirely eliminated by the indirect adaptive control.
NASA Astrophysics Data System (ADS)
Yi, J.; Choi, C.
2014-12-01
Rainfall observation and forecasting using remote sensing such as RADAR(Radio Detection and Ranging) and satellite images are widely used to delineate the increased damage by rapid weather changeslike regional storm and flash flood. The flood runoff was calculated by using adaptive neuro-fuzzy inference system, the data driven models and MAPLE(McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as the input variables.The result of flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated by comparing it with the actual data.The Adaptive Neuro Fuzzy method was applied to the Chungju Reservoir basin in Korea. The six rainfall events during the flood seasons in 2010 and 2011 were used for the input data.The reservoir inflow estimation results were comparedaccording to the rainfall data used for training, checking and testing data in the model setup process. The results of the 15 models with the combination of the input variables were compared and analyzed. Using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation in this study.The model using the MAPLE forecasted precipitation data showed better result for inflow estimation in the Chungju Reservoir.
Adaptive neuro-fuzzy prediction of modulation transfer function of optical lens system
NASA Astrophysics Data System (ADS)
Petkovi?, Dalibor; Shamshirband, Shahaboddin; Anuar, Nor Badrul; Md Nasir, Mohd Hairul Nizam; Pavlovi?, Nenad T.; Akib, Shatirah
2014-07-01
The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to predict MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using MATLAB/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.
Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology
NASA Astrophysics Data System (ADS)
Petkovi?, Dalibor; Shamshirband, Shahaboddin; Pavlovi?, Nenad T.; Anuar, Nor Badrul; Kiah, Miss Laiha Mat
2014-07-01
The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.
So, W.C.; Tse, C.K.; Lee, Y.S. [Hong Kong Polytechnic Univ. (Hong Kong). Dept. of Electronic Engineering
1996-01-01
The design of a fuzzy logic controller for dc/dc converters is described in this paper. A brief review of fuzzy logic and its application to control is first given. Then, the derivation of a fuzzy control algorithm for regulating dc/dc converters is described in detail. The proposed fuzzy control is evaluated by computer simulations as well as experimental measurements of the closed-loop performance of simple dc/dc converters in respect of load regulation and line regulation.
NASA Astrophysics Data System (ADS)
Liu, Yanming; Gordaninejad, Faramarz; Evrensel, Cahit; Karakas, E. Sinan; Dogruer, Umit
2004-07-01
Skyhook control is an effective control strategy for suppressing vehicle vibration. It is typically classified as on-off skyhook control and continuous skyhook control. In this study, a fuzzy skyhook control is proposed. It combines the fuzzy logic theory with the skyhook principle to improve control performance. In order to compare performance of each control strategy, an experimental study is prepared utilizing a quarter car model of high-mobility multi-purpose wheeled vehicle (HMMWV) with a controllable magneto-rheological fluid (MRF) damper. The experimental results under rough road excitation demonstrate that the fuzzy skyhook control offers more robust suspension performance over the continuous skyhook control while still out performing the on-off skyhook control. The system model-independent fuzzy skyhook control is simpler and provides some robust advantages for real application as compared to the system model-dependent continuous skyhook control.
Fuzzy model reference learning (FMRL) control applied to a boiler steam drum
Grudzinski, J.J.; Tarabishy, M.N. [Illinois Inst. of Tech., Chicago, IL (United States). Dept. of Mechanical, Materials, and Aerospace Engineering
1996-10-01
The use of fuzzy logic controllers (FLCs) has seen a surge in many applications. Although these are simple to synthesis once the fuzzy rules are known; it takes experience to produce these rules and takes a fair amount of time to tune them. For this reason, a fuzzy system that can learn these rules has significant advantage over one that doesn`t. In this paper, the authors examine one such method for constructing the fuzzy rules. The method is then applied to the problem of boiler steam drum control to demonstrate its feasibility.
Design of fuzzy logic controller for DC-DC converter fed traction motor drives
E. Ozdemir; A. Ural; N. Abut; E. Karakas; E. Olcer; B. Karagoz
1997-01-01
A control method of a separately excited DC traction motor using fuzzy logic is proposed. The design of a fuzzy logic controller for a DC-DC converter fed motor drive, that produces traction moment for a light metro vehicle, is illustrated and implemented. The drive system has been simulated and implemented using a 486 personal computer with a data acquisition card
Using fuzzy logic in ATM source traffic control: lessons and perspectives
V. Catania; G. Ficili; S. Palazzo; D. Panno
1996-01-01
Due to its capacity to capture human expertise and to formalize approximate reasoning processes, fuzzy logic can be a good answer to the many challenges of congestion control in ATM networks. The authors deal with the application of fuzzy logic to problems of usage parameter control and propose a simple mechanism which, avoiding complex mathematical calculations, guarantees low response times
On Genetic Programming of Fuzzy RuleBased Systems for Intelligent Control
Fernandez, Thomas
a humanderived solution exists? What is the potential of genetic programming for evolution of fuzzyOn Genetic Programming of Fuzzy RuleBased Systems for Intelligent Control Edward Tunstel \\Lambda and Mo Jamshidi NASA Center for Autonomous Control Engineering Department of Electrical and Computer
Hierarchical Fuzzy Cooperative Control and Path Following for a Team of Mobile Robots
Hasan Mehrjerdi; Maarouf Saad; Jawhar Ghommam
2011-01-01
In this paper, an intelligent cooperative control and path-following algorithm is proposed and tested for a group of mo- bile robots. The core of this algorithm uses a fuzzy model, which mimics human thought to control the robot's velocity, movement, and group behavior. The designed fuzzy model employs two be- haviors: path following and group cooperation. Hierarchical con- trollers have
Path planning for a mobile robot using fuzzy logic controller tuned by GA
Iraj Hassanzadeh; Sevil M Sadigh
2009-01-01
This paper presents a fuzzy logic controller tuned by genetic algorithm (GA) for path planning near the optimal time to avoid obstacles in unknown environments. A GA is applied to modify the input and output membership functions of the fuzzy controller. A Matlab application, Kiks II, is used to simulate a Khepera II robot. Also, this approach is implemented by
Design of fuzzy logic based mobile robot position controller using genetic algorithm
Bakir Lacevic; Jasmin Velagic; Nedim Osmic
2007-01-01
This paper develops a fuzzy logic position controller which membership functions are tuned by genetic algorithm. The main goals are to ensure both successfully velocity and position trajectories tracking between the mobile robot and the reference cart. The proposed fuzzy controller has two inputs and two outputs. The first input represents the distance between the mobile robot and the reference
Design of fuzzy based controller for modern elevator group with floor priority constraints
M. M. Rashid; Banna Kasemi; Alias Faruq; Ahm Zahirul Alam
2011-01-01
The elevator technologies are developed to serve the requested passenger's floors with high consideration of passengers' satisfaction and elevator optimal performance. Responding to the issue, this paper focuses on development of an elevator group controller based on fuzzy algorithm. By introducing a fuzzy controller in an elevator system, this project is developed to manage the required passenger traffic density keeping
Multiple Designs of Fuzzy Controllers for Car Parking Using Evolutionary Algorithm
Joon-Yong Lee; Ju-Jang Lee
2007-01-01
This paper proposes an automatic method to design a fuzzy logic multiple controllers for the automated car parking problem. To tackle the problem, design of a fuzzy logic controller is solved using multi-objective evolutionary optimization framework, which requires three factors: an encoding scheme, design of multi-objective evaluation criteria, and design of proper evolutionary operations. Along with the parameters of antecedent
Fuzzy-PI Control for BLDCM with the Greater Inertia Load
Lei Jinli; Dou Manfeng; Li Yansheng; Wang Guangwei
2010-01-01
The big inertia torque will be produced, and the speed-adjusting system performance will become bad, when the brushless direct current motor (BLDCM) starts, speeds up and down with the greater inertia load. In order to solve this kind of problem, a fuzzy-PI control scheme combined fuzzy logic strategy and traditional PI control method was presented in this paper, and the
The quarter car fuzzy controller design based on model from intelligent system identification
Dirman Hanafi
2009-01-01
In this paper, the design of the quarter car fuzzy logic control (FLC) for passive suspension model has been done. The quarter car model is identified using intelligent system identification and assumed has NARX model. The car input output data are collected in experiment by driving car on special road event. The result shows that the fuzzy logic controller is
Chou, Chien-Hsing (Ister)
to demonstrate its performance. Key-words: reinforcement learning, neural network, neuro-fuzzy system 1 the learning abilities of neural networks in the design of fuzzy systems has recently become a very active research area, e.g., fuzzy adaptive learning control network [1], back-propagation fuzzy systems [2
Fuzzy logic grasp control using tactile sensors
N. I Glossas; N. A Aspragathos
2001-01-01
This paper presents a method to control a two-fingers gripper for safe grasping of fragile and delicate objects like glass, fruits or vegetables. The proposed control algorithm adjusts the motion of the fingers of a gripper using tactile feedback, so that an object is grasped with the minimal required force without the measurement of this force. The rules set of
A neuro-fuzzy controller for axial power distribution an nuclear reactors
Man Gyun Na; B. R. Upadhyaya
1998-01-01
A neuro-fuzzy control algorithm is applied for the core power distribution in a pressurized water reactor. The inputs of the neural fuzzy system are composed of data from each region of the reactor core. Rule outputs consist of linear combinations of their inputs (first-order Sugeno-Takagi type). The consequent and antecedent parameters of the fuzzy rules are updated by the backpropagation
Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers
Dongrui Wu; Woei Wan Tan
2006-01-01
Type-2 fuzzy sets, which are characterized by membership functions (MFs) that are themselves fuzzy, have been attracting interest. This paper focuses on advancing the understanding of interval type-2 fuzzy logic controllers (FLCs). First, a type-2 FLC is evolved using Genetic Algorithms (GAs). The type-2 FLC is then compared with another three GA evolved type-1 FLCs that have different design parameters.
Controlling a drone: Comparison between a based model method and a fuzzy inference system
Kadda Meguenni Zemalache; Hichem Maaref
2009-01-01
The work describes an automatically on-line self-tunable fuzzy inference system (STFIS) of a new configuration of mini-flying called XSF (X4 Stationnary Flyer) drone. A fuzzy controller based on on-line optimization of a zero order Takagi–Sugeno fuzzy inference system (FIS) by a back propagation-like algorithm is successfully applied. It is used to minimize a cost function that is made up of
Treesatayapun, Chidentree
2008-10-01
This article introduces an adaptive controller for a class of nonlinear discrete-time systems, based on self adjustable networks called Multi-Input Fuzzy Rules Emulated Networks (MIFRENs), and its reinforcement learning algorithm. Because of the universal function approximation of MIFREN, the first MIFREN called MIFREN(c) is used to estimate a long-term cost function, which demonstrates as a performance index for the tuning procedure. Another network or MIFREN(a) is designed as a direct controller via the human knowledge through defined If-Then rules. The selection procedure for any system parameters, such as learning rates and some constant parameters, is represented by the proof of proposed theorems. The system's performance is demonstrated by computer simulations via selected nonlinear discrete-time systems, and comparison results with other controllers to validate theoretical development. PMID:18675416
A robust adaptive robot controller
Berghuis, H.; Ortega, R.; Nijmeijer, H. )
1993-12-01
A globally convergent adaptive control scheme for robot motion control with the following features is proposed. First, the adaptation law possess enhanced robustness with respect to noisy velocity measurements. Second, the controller does not require the inclusion of high gain loops that may excite the unmodeled dynamics and amplify the noise level. Third, the authors derive for the unknown parameter design a relationship between compensator gains and closed-loop convergence rates that is independent of the robot task. A simulation example of a two-DOF manipulator features some aspects of the control scheme.
Aircraft adaptive learning control
NASA Technical Reports Server (NTRS)
Lee, P. S. T.; Vanlandingham, H. F.
1979-01-01
The optimal control theory of stochastic linear systems is discussed in terms of the advantages of distributed-control systems, and the control of randomly-sampled systems. An optimal solution to longitudinal control is derived and applied to the F-8 DFBW aircraft. A randomly-sampled linear process model with additive process and noise is developed.
Fuzzy and Internal Model Control of an Active Suspension System for a 2-DOF Vehicle Model
NASA Astrophysics Data System (ADS)
Demir, Özgür; Karakurt, Derya; Alarçin, Fuat
2007-09-01
In this study, Fuzzy-Logic-Based (FL) controller and Internal Model Control (IMC) scheme are designed for active suspension system. An aim of active suspension systems for a vehicle model is to provide good road handling and high passenger comfort by shaping the output function. The simulated system was considered to be a two-degree-of-freedom (2-DOF) model. The effectiveness of this Fuzzy Control is verified by comparison with Internal Model Control simulation results. Simulation results show that the effectiveness of the fuzzy controller is better than Internal Model Control under the same conditions.
Virtual reality simulation of fuzzy-logic control during underwater dynamic positioning
NASA Astrophysics Data System (ADS)
Thekkedan, Midhin Das; Chin, Cheng Siong; Woo, Wai Lok
2015-03-01
In this paper, graphical-user-interface (GUI) software for simulation and fuzzy-logic control of a remotely operated vehicle (ROV) using MATLAB™ GUI Designing Environment is proposed. The proposed ROV's GUI platform allows the controller such as fuzzy-logic control systems design to be compared with other controllers such as proportional-integral-derivative (PID) and sliding-mode controller (SMC) systematically and interactively. External disturbance such as sea current can be added to improve the modelling in actual underwater environment. The simulated results showed the position responses of the fuzzy-logic control exhibit reasonable performance under the sea current disturbance.
ON AN ADAPTIVE CONTROL ALGORITHM FOR ADAPTIVE OPTICS APPLICATIONS
ON AN ADAPTIVE CONTROL ALGORITHM FOR ADAPTIVE OPTICS APPLICATIONS MOODY T. CHU Abstract imaging system. Adaptive optics refers to the process of removing unwanted wave front distortions with the use of a phase corrector before the image is formed. The basic idea in adaptive optics is to control
A genetic algorithms approach for altering the membership functions in fuzzy logic controllers
NASA Technical Reports Server (NTRS)
Shehadeh, Hana; Lea, Robert N.
1992-01-01
Through previous work, a fuzzy control system was developed to perform translational and rotational control of a space vehicle. This problem was then re-examined to determine the effectiveness of genetic algorithms on fine tuning the controller. This paper explains the problems associated with the design of this fuzzy controller and offers a technique for tuning fuzzy logic controllers. A fuzzy logic controller is a rule-based system that uses fuzzy linguistic variables to model human rule-of-thumb approaches to control actions within a given system. This 'fuzzy expert system' features rules that direct the decision process and membership functions that convert the linguistic variables into the precise numeric values used for system control. Defining the fuzzy membership functions is the most time consuming aspect of the controller design. One single change in the membership functions could significantly alter the performance of the controller. This membership function definition can be accomplished by using a trial and error technique to alter the membership functions creating a highly tuned controller. This approach can be time consuming and requires a great deal of knowledge from human experts. In order to shorten development time, an iterative procedure for altering the membership functions to create a tuned set that used a minimal amount of fuel for velocity vector approach and station-keep maneuvers was developed. Genetic algorithms, search techniques used for optimization, were utilized to solve this problem.
A fuzzy controlled three-phase centrifuge for waste separation
Parkinson, W.J.; Smith, R.E. [Los Alamos National Lab., NM (United States); Miller, N. [Centech, Inc., Casper, WY (United States)
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.
An active passive absorber by using hierarchical fuzzy methodology for vibration control
NASA Astrophysics Data System (ADS)
Lin, J.
2007-07-01
It has been shown that piezoelectric materials are highly promising as passive electromechanical vibration absorbers when shunted with electrical networks. However, these passive devices have limitations that restrict their practical applications. The main goal of this study is to develop an innovative approach for achieving a high performance adaptive piezoelectric absorber—an active-passive hybrid configuration. This investigation addresses the first application of the concept of hierarchy for controlling fuzzy systems in such an active-passive absorber. It attempts to demonstrate the general methodology by decomposing a large-scale system into smaller subsystems in a parallel structure so that the method developed here can be applied for studying complex systems. The design of the lower-level controllers takes into account each subsystem ignoring the interactions among them, while a higher-level controller handles subsystem interactions. One of the main advantages of using a hierarchical fuzzy system is to minimize the size of the rule base by eliminating "the curse of dimensionality". Therefore, the computational complexity in the process can be reduced as a consequence of the rule-base size reduction. Although the performance of the optimal passive absorber is already much better than the original system (no absorber), the intelligent active-passive absorber can still significantly outperform the passive system.
Fuzzy Logic Control Based QoS Management in Wireless Sensor/Actuator Networks
Xia, Feng; Sun, Youxian; Tian, Yu-Chu
2008-01-01
Wireless sensor/actuator networks (WSANs) are emerging rapidly as a new generation of sensor networks. Despite intensive research in wireless sensor networks (WSNs), limited work has been found in the open literature in the field of WSANs. In particular, quality-of-service (QoS) management in WSANs remains an important issue yet to be investigated. As an attempt in this direction, this paper develops a fuzzy logic control based QoS management (FLC-QM) scheme for WSANs with constrained resources and in dynamic and unpredictable environments. Taking advantage of the feedback control technology, this scheme deals with the impact of unpredictable changes in traffic load on the QoS of WSANs. It utilizes a fuzzy logic controller inside each source sensor node to adapt sampling period to the deadline miss ratio associated with data transmission from the sensor to the actuator. The deadline miss ratio is maintained at a pre-determined desired level so that the required QoS can be achieved. The FLC-QM has the advantag...
FUZZY LOGIC CONTROL FOR AN AUTONOMOUS ROBOT
Simon, Dan
ultrasonic sensors mounted. These inputs are sent to a Microchip PIC16F877 microcontroller onboard the robot axis of the corridor. In [2], two lateral cameras mounted on the robot are used, and the optical flow is computed to compare the apparent image velocity on both cameras in order to control robot motion. In [3, 4
Intelligent fuzzy supervisory control for distillation columns
Santhanam, Srinivasan
1993-01-01
(disturbance) and the response of the top tray temperature(controlled variable). This thesis will also outline a simulation software to characterize a benzene-toluene binary distillation column and an X-window based Graphical User Interface to run the simulation....
An Approach to Supervisory Control of an Energy Management Control System Using Fuzzy Logic
Langari, R.
1997-01-01
In this paper an approach to supervisory control of multi-stage industrial control systems is presented. This approach is based on the notion of an internal reference model, and further makes use of a fuzzy multi-objective optimization strategy...
Study of fuzzy controller to control vertical position of an air-cushion tracked vehicle
Altab Hossain; Ataur Rahman; A. K. M. Mohiuddin
2011-01-01
This paper presents the fuzzy logic control system of an air-cushion tracked vehicle (ACTV) operating on swamp peat terrain. Vehicle vertical position is maintained by using an inflated air-cushion system attached with the vehicle. It is desired that the vehicle vertical position be maintained at a desired position so that vehicle obtains sufficient traction control and to propel the driving
Fuzzy skyhook control of semi-active suspensions using a genetic algorithm
NASA Astrophysics Data System (ADS)
Jung, Taegeun; Cho, Jeongmok; Huh, Nam; Joung, Tae Whee; Kim, Sujin; Joh, Joongseon
2006-03-01
Recently, there exists an abundance of research on the semi-active suspension system. The skyhook control is commonly known to control semi-active suspension system because it has practicality. In this paper, the fuzzy logic control based on heuristic knowledge is combined with the skyhook control. And it simulated in a quarter car model. The acceleration value of the sprung mass was reflected in fuzzy inference to reduce the vertical acceleration RMS value of the sprung mass. Then scale factors and membership functions that determine performance efficiency of fuzzy skyhook controller are tuned by a genetic algorithm known as a kind of optimization method.
Robust, near time-optimal control of power system oscillations with fuzzy logic
Noroozian, M.; Andersson, G. [Royal Inst. of Tech., Stockholm (Sweden)] [Royal Inst. of Tech., Stockholm (Sweden); Tomsovic, K. [Washington State Univ., Pullman, WA (United States). Dept. of Electrical Engineering and Computer Science] [Washington State Univ., Pullman, WA (United States). Dept. of Electrical Engineering and Computer Science
1996-01-01
This paper presents a fuzzy logic controller for series reactance switching to damp power system electro mechanical oscillations. A set of control rules are constructed and inference is provided by fuzzy logic reasoning. The knowledge base for the controller is established from observation of the dynamical behavior of a simple power system and the general engineering knowledge about the system dynamics. The performance of the controller is shown to be robust and comparable to that of a minimum-time optimal controller.
Limited Authority Adaptive Flight Control
Johnson, Eric N.
), under the Advanced Guidance and Control for Reusable Launch Vehicles Project, grant number NAG 3 1.1 Adaptive Flight Control for Reusable Launch Vehicles .......................................1 1 significant feedback and support throughout, including Warren Adams, Kerry Funston, Charles Hall, John Hansen
Adaptive Control For Flexible Structures
NASA Technical Reports Server (NTRS)
Bayard, David S.; Ih, Che-Hang Charles; Wang, Shyh Jong
1988-01-01
Paper discusses ways to cope with measurement noise in adaptive control system for large, flexible structure in outer space. System generates control signals for torque and thrust actuators to turn all or parts of structure to desired orientations while suppressing torsional and other vibrations. Main result of paper is general theory for introduction of filters to suppress measurement noise while preserving stability.
Nozzle Fuzzy Controller of Agricultural Spraying Robot Aiming Toward Crop Rows
NASA Astrophysics Data System (ADS)
Ren, Jianqiang
A novel nozzle controller of spraying robot aiming toward crop-rows based on fuzzy control theory was studied in this paper to solve the shortcomings of existing nozzle control system, such as the long regulation time, the higher overshoot and so on. The new fuzzy controller mainly consists of fuzzification interface, defuzzification interface, rule-base and inference mechanism. Considering the actual application, the fuzzy controller was designed as a 2-inputs&1-output closed-loop system. The inputs are the distance from nozzle to crop row and its change rate, the output is the control signal to the execution unit. Based on the design project, we selected the FMC chip NLX230, the EMCU chip AT89S52 and the EEPROM chip AT93C57 to make the fuzzy controller. Experimental results show that the project is workable and efficient, it can solve the shortcomings of existing controller perfectly and the control efficiency can be improved greatly.
Petriu, Emil M.
--This paper describes a fuzzy error correction control system used to navigate a robot along an easily is processed by an analog-to-digital converter and the output signals are then inputted into a fuzzy logic. The fuzzy logic controller stores prior disk information to predict a path trajectory when no path
A 5.26 Mflips Programmable Analogue Fuzzy Logic Controller in a Standard CMOS 2.4 Technology
Verleysen, Michel
A 5.26 Mflips Programmable Analogue Fuzzy Logic Controller in a Standard CMOS 2.4µ Technology analogue Fuzzy Logic Controller (FLC) is presented. The design of some new functional blocks, namely: 5.26 Mflips (Mega fuzzy logic inferences per second) at the pin terminals (@CL=13pF), 933 µ
Vasquez, Juan Carlos
Aalborg Universitet Fuzzy-Logic-Based Gain-Scheduling Control for State-of-Charge Balance for published version (APA): Aldana, N. L. D., Dragicevic, T., Vasquez, J. C., & Guerrero, J. M. (2014). Fuzzy-Logic.aau.dk on: juli 07, 2015 #12;Fuzzy-Logic-Based Gain-Scheduling Control for State-of-Charge Balance
NASA Technical Reports Server (NTRS)
Kopasakis, George
1997-01-01
Performance Seeking Control (PSC) attempts to find and control the process at the operating condition that will generate maximum performance. In this paper a nonlinear multivariable PSC methodology will be developed, utilizing the Fuzzy Model Reference Learning Control (FMRLC) and the method of Steepest Descent or Gradient (SDG). This PSC control methodology employs the SDG method to find the operating condition that will generate maximum performance. This operating condition is in turn passed to the FMRLC controller as a set point for the control of the process. The conventional SDG algorithm is modified in this paper in order for convergence to occur monotonically. For the FMRLC control, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for effective tuning of the FMRLC controller.
Darvishi, Sam; Al-Ani, Ahmed
2007-01-01
The purpose of this paper is to analyze the electroencephalogram (EEG) signals of imaginary left and right hand movements, an application of Brain-Computer Interface (BCI). We propose here to use an Adaptive Neuron-Fuzzy Inference System (ANFIS) as the classification algorithm. ANFIS has an advantage over many classification algorithms in that it provides a set of parameters and linguistic rules that can be useful in interpreting the relationship between extracted features. The continuous wavelet transform will be used to extract highly representative features from selected scales. The performance of ANFIS will be compared with the well-known support vector machine classifier. PMID:18002681
Robust and fast learning for fuzzy cerebellar model articulation controllers.
Su, Shun-Feng; Lee, Zne-Jung; Wang, Yan-Ping
2006-02-01
In this paper, the online learning capability and the robust property for the learning algorithms of cerebellar model articulation controllers (CMAC) are discussed. Both the traditional CMAC and fuzzy CMAC are considered. In the study, we find a way of embeding the idea of M-estimators into the CMAC learning algorithms to provide the robust property against outliers existing in training data. An annealing schedule is also adopted for the learning constant to fulfill robust learning. In the study, we also extend our previous work of adopting the credit assignment idea into CMAC learning to provide fast learning for fuzzy CMAC. From demonstrated examples, it is clearly evident that the proposed algorithm indeed has faster and more robust learning. In our study, we then employ the proposed CMAC for an online learning control scheme used in the literature. In the implementation, we also propose to use a tuning parameter instead of a fixed constant to achieve both online learning and fine-tuning effects. The simulation results indeed show the effectiveness of the proposed approaches. PMID:16468579
Fuzzy Control Hardware for Segmented Mirror Phasing Algorithm
NASA Technical Reports Server (NTRS)
Roth, Elizabeth
1999-01-01
This paper presents a possible implementation of a control model developed to phase a system of segmented mirrors, with a PAMELA configuration, using analog fuzzy hardware. Presently, the model is designed for piston control only, but with the foresight that the parameters of tip and tilt will be integrated eventually. The proposed controller uses analog circuits to exhibit a voltage-mode singleton fuzzifier, a mixed-mode inference engine, and a current-mode defuzzifier. The inference engine exhibits multiplication circuits that perform the algebraic product composition through the use of operational transconductance amplifiers rather than the typical min-max circuits. Additionally, the knowledge base, containing exemplar data gained a priori through simulation, interacts via a digital interface.
Nonlinear and adaptive control
NASA Technical Reports Server (NTRS)
Athans, Michael
1989-01-01
The primary thrust of the research was to conduct fundamental research in the theories and methodologies for designing complex high-performance multivariable feedback control systems; and to conduct feasibiltiy studies in application areas of interest to NASA sponsors that point out advantages and shortcomings of available control system design methodologies.
ADAPTIVE NEURAL NETWORK CONTROL BY ADAPTIVE INTERACTION George Saikalis
Lin, Feng
discussed. Many books on neural network control have been published, including [20] [21] [22] [231 ADAPTIVE NEURAL NETWORK CONTROL BY ADAPTIVE INTERACTION George Saikalis Hitachi America, Ltd, Michigan 48202 Abstract In this paper, we propose an approach to adaptive neural network control by using
Norepinephrine weaning in septic shock patients by closed loop control based on fuzzy logic
Merouani, Mehdi; Guignard, Bruno; Vincent, François; Borron, Stephen W; Karoubi, Philippe; Fosse, Jean-Philippe; Cohen, Yves; Clec'h, Christophe; Vicaut, Eric; Marbeuf-Gueye, Carole; Lapostolle, Frederic; Adnet, Frederic
2008-01-01
Introduction The rate of weaning of vasopressors drugs is usually an empirical choice made by the treating in critically ill patients. We applied fuzzy logic principles to modify intravenous norepinephrine (noradrenaline) infusion rates during norepinephrine infusion in septic patients in order to reduce the duration of shock. Methods Septic patients were randomly assigned to norepinephrine infused either at the clinician's discretion (control group) or under closed-loop control based on fuzzy logic (fuzzy group). The infusion rate changed automatically after analysis of mean arterial pressure in the fuzzy group. The primary end-point was time to cessation of norepinephrine. The secondary end-points were 28-day survival, total amount of norepinephine infused and duration of mechanical ventilation. Results Nineteen patients were randomly assigned to fuzzy group and 20 to control group. Weaning of norepinephrine was achieved in 18 of the 20 control patients and in all 19 fuzzy group patients. Median (interquartile range) duration of shock was significantly shorter in the fuzzy group than in the control group (28.5 [20.5 to 42] hours versus 57.5 [43.7 to 117.5] hours; P < 0.0001). There was no significant difference in duration of mechanical ventilation or survival at 28 days between the two groups. The median (interquartile range) total amount of norepinephrine infused during shock was significantly lower in the fuzzy group than in the control group (0.6 [0.2 to 1.0] ?g/kg versus 1.4 [0.6 to 2.7] ?g/kg; P < 0.01). Conclusions Our study has shown a reduction in norepinephrine weaning duration in septic patients enrolled in the fuzzy group. We attribute this reduction to fuzzy control of norepinephrine infusion. Trial registration Trial registration: Clinicaltrials.gov NCT00763906. PMID:19068113
NASA Technical Reports Server (NTRS)
Richardson, Albert O.
1997-01-01
This research has investigated the use of fuzzy logic, via the Matlab Fuzzy Logic Tool Box, to design optimized controller systems. The engineering system for which the controller was designed and simulate was the container crane. The fuzzy logic algorithm that was investigated was the 'predictive control' algorithm. The plant dynamics of the container crane is representative of many important systems including robotic arm movements. The container crane that was investigated had a trolley motor and hoist motor. Total distance to be traveled by the trolley was 15 meters. The obstruction height was 5 meters. Crane height was 17.8 meters. Trolley mass was 7500 kilograms. Load mass was 6450 kilograms. Maximum trolley and rope velocities were 1.25 meters per sec. and 0.3 meters per sec., respectively. The fuzzy logic approach allowed the inclusion, in the controller model, of performance indices that are more effectively defined in linguistic terms. These include 'safety' and 'cargo swaying'. Two fuzzy inference systems were implemented using the Matlab simulation package, namely the Mamdani system (which relates fuzzy input variables to fuzzy output variables), and the Sugeno system (which relates fuzzy input variables to crisp output variable). It is found that the Sugeno FIS is better suited to including aspects of those plant dynamics whose mathematical relationships can be determined.
On Problems of Knowledge in Fuzzy Control: Extended Abstract Gal A. Kaminka
Kaminka, Gal A.
on the SAM additive fuzzy system model (Kosko 1997) was built to provide goaldirected navigation will motivate the discussion with a very small scale decision problem, which despite its size, captures some that will bridge the gap between fuzzy control techniques and the required technology. Motivating Examples We