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CONSTRUCTION OF FUZZY CONTROLLERS FOR ELECTROHYDRAULIC SERVO DRIVES OF AUTOMOBILES  [PDF]
O. Nikonov,V. Shulyakov
Аvtomob?lnyi Transport , 2012,
Abstract: The creation problem of controllers for electrohydraulic servo drives of automobiles with the use оf fuzzy logic, artificial neural networks and evolutionary simulation methods is considered.
Analytical calculation of the CNC machines servo drives position loop gain  [PDF]
Z. Pandilov,V. Dukovski
Journal of Achievements in Materials and Manufacturing Engineering , 2009,
Abstract: Purpose: One of the most important factors which influence on the dynamical behavior of the servo drives with rotary and linear motors for CNC machine tools is position loop gain or Kv factor.Design/methodology/approach: From the magnitude of the Kv-factor depends tracking or following error. In multi-axis contouring the following errors along the different axes may cause form deviations of the machined contours. Generally position loop gain Kv should be high for faster system response and higher accuracy, but the maximum gains allowable are limited due to undesirable oscillatory responses at high gains and low damping factor. Usually Kv factor is experimentally tuned on the already assembled machine tool.Findings: This paper presents a simple method for analytically calculation of the position loop gain Kv. A combined digital-analog models of the 6-th order (for rotary motors) and 4-th order (for linear motors) of the position loop are presented. In order to ease the calculation, the 6-th order system or 4-th order system is simplified with a second order model. With this approach it is very easy to calculate the Kv factor for necessary position loop damping. The difference of the replacement of the 6-th order system and 4-th order system with second order system is presented with the simulation program MATLAB. Analytically calculated Kv factor for the servo drives with rotary motors is function of the nominal angular frequency ω and damping D of the servo drive electrical parts (rotary motor and regulator), nominal angular frequency ωm and damping Dm of the mechanical transmission elements, as well as sampling period T. Kv factor for the servo drives with linear motors is calculated as function of the nominal angular frequency ωm and damping D of the linear motor servo drive electrical parts (motor and regulator) and sampling period T.Research limitations/implications: The influence of nonlinearities was taken with the correction factorOriginality/value: Our investigations have proven that experimentally tuned Kv factor differs from analytically calculated Kv factor less than 10%, which is completely acceptable.
High-performance adaptive intelligent Direct Torque Control schemes for induction motor drives  [PDF]
Vasudevan M.,Arumugam R.,Paramasivam S.
Serbian Journal of Electrical Engineering , 2005, DOI: 10.2298/sjee0501093v
Abstract: This paper presents a detailed comparison between viable adaptive intelligent torque control strategies of induction motor, emphasizing advantages and disadvantages. The scope of this paper is to choose an adaptive intelligent controller for induction motor drive proposed for high performance applications. Induction motors are characterized by complex, highly non-linear, time varying dynamics, inaccessibility of some states and output for measurements and hence can be considered as a challenging engineering problem. The advent of torque and flux control techniques have partially solved induction motor control problems, because they are sensitive to drive parameter variations and performance may deteriorate if conventional controllers are used. Intelligent controllers are considered as potential candidates for such an application. In this paper, the performance of the various sensor less intelligent Direct Torque Control (DTC) techniques of Induction motor such as neural network, fuzzy and genetic algorithm based torque controllers are evaluated. Adaptive intelligent techniques are applied to achieve high performance decoupled flux and torque control. This paper contributes: i) Development of Neural network algorithm for state selection in DTC; ii) Development of new algorithm for state selection using Genetic algorithm principle; and iii) Development of Fuzzy based DTC. Simulations have been performed using the trained state selector neural network instead of conventional DTC and Fuzzy controller instead of conventional DTC controller. The results show agreement with those of the conventional DTC.
Feedforward Compensation Based on Process Inverse Model under FOUNDATION Fieldbus
Ramon Ferreiro Garcia,Javier Perez Castelo
Journal of Engineering and Applied Sciences , 2012,
Abstract: This study deals with the problem of disturbance compensation by means of a novel feedforward control procedure. It is based in the association of a conventional feedback control action with the feedforward action consisting in the prediction of the steady state control effort necessary to keep the controlled plant under setpoint requirements. Such steady state control effort is achieved by means of a neural network based inverse model which actuates as a control effort predictor. Predictors are based in an inverse neural network steady state plant model. Implementation procedure is carried out with the facilities supplied by a FOUNDATION Fieldbus compliant tool which manage databases, neural network structures and back-propagation training algorithms.
Intelligent Circuit Breaker Monitoring System Based on DeviceNet
基于DeviceNet 智能断路器监控系统

ZHOU Qin,DAI Yu-Xing,LI Er-Qiang,WANG Xing-Xian,
周琴
,戴瑜兴,李二强,王兴仙

计算机系统应用 , 2011,
Abstract: This paper designed and implemented the intelligent circuit breaker monitoring system based on DeviceNet.DeviceNet FieldBus protocol converter of this monitoring system can pass parameters of intelligent circuit breaker like opening state,closing state,current value,etc to DeviceNet FieldBus,monitoring software received the data real-time by using SST_DN3_PCI_1,and processing the data accordingly.Test results prove that DeviceNet FieldBus protocol converter fully meets the DeviceNet specifications,and minit...
Performance of H1 Network in Wind Turbine Generator with Foundation Fieldbus  [PDF]
P SIVA SATYANARAYANA,Dr. M.SAILAJA
International Journal of Engineering Science and Technology , 2012,
Abstract: By using a Valve Delta Distributed control system (DCS) with a steam generator level controller (SGLC), the effect of foundation fieldbus H1 networks on dynamic performance of steam generator level controlloops are evaluated using simulation. A comparison between the performances of conventional communication channels and Valve Delta distributed control system channels are provided. And found that for a long FF H1 Macro cycle more serious performance degradation occurs. All these effects of networks are due to the network induced delays. Through this timing analysis of FF H1 network based loop, some suggestions were made to reduce the delays potentially and their impact. Further this technique can be applied to wind turbine generators and observe the response.
PID Based on a Single Artificial Neural Network Algorithm for Intelligent Sensors
J. Rivera-Mejía,A.G. Léon-Rubio,E. Arzabala-Contreras
Journal of applied research and technology , 2012,
Abstract: Today control is required in any field or application. Nowadays, classic control is the most used, but it is well-known that users need to know the system’s characteristics to reach optimal control. This paper is focused on designing a proportional integral derivative control, based on a single artificial neural network with the aim to improve its performance and its use with minimal control knowledge from the end user. The proposed control was assessed with simulated and practical physical systems of first and second order. In order to increase the confidence of the intelligent sensor control, the evaluation was made using the classical test of control response of a step as input. The proposed control was implemented on an intelligent sensor with a small microcontroller. Also, the performance was compared between the proposed control and a commercial control. Here, an intelligent sensor is presented with control capability for a wide variety of physical systems. The experiments performed demonstrated the capability of the proposed control, which can be easily used and save time at the initial control set up.
Fieldbus Device Drivers for Accelerator Control at DESY  [PDF]
H. G. Wu
Physics , 2001,
Abstract: In order to interface the DESY fieldbus adapter, SEDAC (SErial Data Acquisition and Control system), a full duplex device driver was developed for the Windows NT, Linux, VxWorks, and Solaris operating systems. Detailed driver development issues as well as a common user interface will be presented, along with a comparison of the device drivers among the different operating systems. In particular, we shall present benchmark results concerning general performance as well as ease of development.
Study and Comparison of Two Tpyes of Fieldbus Network with TPN
过程TPN模拟方法用于两种现场总线网络的性能比较研究

Zhao Hai,Wang Guangxing,
赵海
,王光兴

自动化学报 , 1996,
Abstract: Two efficient fieldbus protocol are studied, analyzeo and compared based on Timed Petri Net with procedures, in which transitions are described in high level progra Language. In the polling scheme, the producer/consumer communicationmodel is used. Polling queues are managed by the master node. The polling request message is broadcasted to all nodes and the poll response message is broadcasted by the producer. In the token-passing scheme, the circulated token and the delegated token allow very different communication requires to be met respectively. They are managed by the arbitrator. Operations of two types of the fieldbus network models are simulated in the paper. The performances of response time, throughput and jitters are discussed in details. The differences of performances and the threshold of the response time with the limited jitters are given. The result show that: when the response time is greater than the threshold, the polling protocol in the fieldbus has a intrinsicrhythm and is very useful for the communication problems in continuous Process control, the token passing protocol in the fieldbus just breaks the intrinsic rhythm and is better in discrete and hybrid process control.
Online Intelligent Controllers for an Enzyme Recovery Plant: Design Methodology and Performance  [PDF]
M. S. Leite,T. L. Fujiki,F. V. Silva,A. M. F. Fileti
Enzyme Research , 2010, DOI: 10.4061/2010/250843
Abstract: This paper focuses on the development of intelligent controllers for use in a process of enzyme recovery from pineapple rind. The proteolytic enzyme bromelain (EC 3.4.22.4) is precipitated with alcohol at low temperature in a fed-batch jacketed tank. Temperature control is crucial to avoid irreversible protein denaturation. Fuzzy or neural controllers offer a way of implementing solutions that cover dynamic and nonlinear processes. The design methodology and a comparative study on the performance of fuzzy-PI, neurofuzzy, and neural network intelligent controllers are presented. To tune the fuzzy PI Mamdani controller, various universes of discourse, rule bases, and membership function support sets were tested. A neurofuzzy inference system (ANFIS), based on Takagi-Sugeno rules, and a model predictive controller, based on neural modeling, were developed and tested as well. Using a Fieldbus network architecture, a coolant variable speed pump was driven by the controllers. The experimental results show the effectiveness of fuzzy controllers in comparison to the neural predictive control. The fuzzy PI controller exhibited a reduced error parameter (ITAE), lower power consumption, and better recovery of enzyme activity. 1. Introduction The present study is concerned with the design and experimental testing of intelligent control systems for temperature control in the precipitation plant of bromelain enzyme recovery. This biotechnological process may be considered the first step in the downstream processing of the protein. It is motivated by the high commercial value of this enzyme, the increasing demand for bromelain in pharmaceutical and industrial applications [1, 2], and the fact that bromelain can be recovered from kitchen waste (pineapple stem and rind). The aim of the precipitation process is to achieve separation of solutes by conversion to solids. Precipitants can be chosen which do not denature the biological product, and the precipitate is often more stable than the dissolved form. Although precipitation is a simple operation, in the recovery of bromelain from pineapple, temperature control is crucial to avoid irreversible protein denaturation and hence improve the precipitation yield and the enzyme activity of the product [3]. Despite that automation and process control can significantly influence the yield and final quality of bioproducts, there are few experimental studies on the application of automatic controllers in the bioprocesses. Most works focus on results obtained from computational simulations, which indeed do not represent these
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