基于模糊理论的异步电动机软起动器研究

针对传统软起动器的不足,提出将模糊控制理论应用于异步电动机软起动器设计。介绍了软起动原理和系统软、硬件设计过程。最后,通过计算机仿真实现了异步电动机的软起与软停控制策略,结果表明所提出的策略是正确和有效的。

煤矿胶带输送机的智能化PID控制运输功能的实现

胶带输送机运输功能的常规PID控制具有响应时间长、调节误差大等缺陷,为了使胶带输送机运输系统控制更为稳定,设计智能化PID控制及模糊控制理论对原有输入参数进行调节。经过仿真对比测试发现,智能化设计后的运输系统控制在抵御外界干扰能力上更强,恢复平稳运行所需时间也更短,设计效果较为理想。

Fuzzy variable structure delay control algorithms and their applications to AQM

This paper analyzes the fuzzy variable structure control algorithms for delay systems and describes the compensation mechanism of the integral factor to the effect of the delay. Based on the linearized model of the congestion-avoidance flow-control mode of transmission control protocol (TCP), we present delay control algorithms for active queue management (AQM) and discuss the parameter tuning of the algorithms. The NS (network simulator) simulation results show that the proposed control scheme for the nonlinear TCP/AQM model has good performance and robustness with respect to the uncertainties of the round-trip time (RTT) and the number of active TCP sessions. Compared to other similar schemes, our algorithms perform better in terms of packet loss ratio, throughput and butter fluctuation.

APPLICATION OF FUZZY INFERENCE IN IDENTIFICATION OF HELICOPTER MODEL

Helicopter mathematical model mainly depends on design helicopter control system, flight simulator, and real time control simulation system. But it is difficult to establish a helicopter flight dynamics mathematical model that has features such as rapidness, reliability and precision, because there is no unique and precise expression to some sophisticated phenomenon of helicopter. In this paper a fuzzy helicopter flight model is constructed based on the flight experimental data. The fuzzy model, which is identified by fuzzy inference, has characteristics of computed rapidness and high precision. In order to guarantee the precision of the identified fuzzy model, a new method is adopted to handle the conflict fuzzy rules. Additionally, using fuzzy clustering technology can effectively reduce the number of rules of fuzzy model, namely, the order of the fuzzy model. The simulation results indicate that the method of this paper is effective and feasible.

Tuning of fuzzy PID controller for Smith predictor

An analytical tuning method was proposed for fuzzy PID controller used in Smith predictor in order to extend its application and improve its robustness. The fuzzy PID controller was expressed as a sliding mode control. Based on Lyapunov theory, Smith predictor was analyzed in time domain. The parameters of the fuzzy PID controller can be obtained using traditional linear control theory and sliding mode control theory. The simulation experiments were implemented. The simulation results show that the control performance, robustness and stability of the fuzzy PID controller are better than those of the PID controller in Smith predictor.

Optimal fuzzy PID controller with adjustable factors based on flexible polyhedron search algorithm

A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an on line fuzzy inference system, and the other is a conventional PID controller. In the fuzzy inference system, three adjustable factors x p, x i , and x d are introduced. Their functions are to further modify and optimize the result of the fuzzy inference so as to make the controller have the optimal control effect on a given object. The optimal values of these adjustable factors are determined based on the ITAE criterion and the Nelder and Mead′s flexible polyhedron search algorithm. This optimal fuzzy PID controller has been used to control the executive motor of the intelligent artificial leg designed by the authors. The result of computer simulation indicates that this controller is very effective and can be widely used to control different kinds of objects and processes.

Development of single-chip fuzzy controller based on FFSI in binary

Length and concise structure of fuzzy logic reasoning program and its real-time reasoning characteris-tic have their effect on the performance of a digital single-chip fuzzy controller. The control effect of a digitalfuzzy controller based on looking up fuzzy control responding table is only relative to the table and not relative tothe fuzzy control rules in the practical control process. Aiming at above problem and having combined fuzzy log-ic reasoning with digital operational characteristics of a single-chip microcomputer, functioning-fuzzy-subset in-ference (FFSI) in binary, in which triangle membership functions of error and error-in-change are all represen-ted in binary and singleton membership functions of control variable is binary too, has been introduced. The cir-cuit principle plans of a single-chip fuzzy controller have been introduced for development of its hardware, andthe primary program structure, fuzzy logic reasoning subroutine, serial communication subroutine with PC andreliability design of the fuzzy controller are all discussed in detail. The control of indoor temperature by a fuzzycontroller has been conducted using a testing-room thermodynamic system. Research results show that the FFSIin binary can exercise a concise fuzzy control in a single-chip fuzzy controller, and the fuzzy controller is there-fore reliable and possesses a high performance-price ratio.

FUZZY COMPREHENSIVE EVALUATION MODEL OF ECOLOGICAL DEMONSTRATION AREA

Ecological demonstration area (EDA) is an authorized nomination, which should be assessed from several aspects, including ecological, social, environmental, economic ones and so on. It is difficult to advance an exact developing level index of EDA due to its indicator system’s complexity and disequilibrium. In this paper, a framework of indicators was set to evaluate, monitor and examine the comprehensive level of ecological demonstration area (EDA). Fuzzy logic method was used to develop the fuzzy comprehensive evaluation model (FCEM), which could quantitatively reveal the developing degree of EDA. Huiji District of Zhengzhou, Henan Province, one of the 9th group of national EDAs, was taken as a study case. The framework of FCEM for the integrated system included six subsystems, which were social, economic, ecological, rural, urban and accessorial description ones. The research would be valuable in the comprehensive quantitative evaluation of EDA and would work as a guide in the construction practices of Huiji ecological demonstration area.

Fuzzy robust sliding mode control of a class of uncertain systems

Aiming at a class of systems under parameter perturbations and unknown external disturbances, a method of fuzzy robust sliding mode control was proposed. Firstly, an integral sliding mode surface containing state feedback item was designed based on robust H∞ control theory. The robust state feedback control was utilized to substitute for the equivalent control of the traditional sliding mode control. Thus the robustness of systems sliding mode motion was improved even the initial states were unknown. Furthermore, when the upper bound of disturbance was unknown, the switching control logic was difficult to design, and the drawbacks of chattering in sliding mode control should also be considered simultaneously. To solve the above-mentioned problems, the fuzzy nonlinear method was applied to approximate the switching control term. Based on the Lyapunov stability theory, the parameter adaptive law which could guarantee the system stability was devised. The proposed control strategy could reduce the system chattering effectively. And the control input would not switch sharply, which improved the practicality of the sliding mode controller. Finally, simulation was conducted on system with parameter perturbations and unknown external disturbances. The result shows that the proposed method could enhance the approaching motion performance effectively. The chattering phenomenon is weakened, and the system possesses stronger robustness against parameter perturbations and external disturbances.

Effect of the computer fuzzy control on coal seam injection

To enhance the technology and efficiency of strata injection is a problem to be solved urgently. Because of effected by the rough and poor production conditions of mining and the changeable bearing condition of strata, the technological process of injection can not be controlled automatically.The fuzzy controlling technology of the coal strata injection is applied. This is the good way to ensure the effectiveness and efficiency of coal strata injection and to solve the current problems of production and safety in mining industry.

ISS-based robust adaptive fuzzy algorithm for maintaining a ship's track

This paper focuses on the problem of linear track keeping for marine surface vessels. The influence exerted by sea currents on the kinematic equation of ships is considered first. The input-to-state stability(ISS) theory used to verify the system is input-to-state stable. Combining the Nussbaum gain with backstepping techniques,a robust adaptive fuzzy algorithm is presented by employing fuzzy systems as an approximator for unknown nonlinearities in the system. It is proved that the proposed algorithm that guarantees all signals in the closed-loop system are ultimately bounded. Consequently,a ship's linear track-keeping control can be implemented. Simulation results using Dalian Maritime University's ocean-going training ship 'YULONG' are presented to validate the effectiveness of the proposed algorithm.

Fuzzy Sliding Mode Control for a Discrete Servo System

A discrete fuzzy sliding mode variable structure controller was studied and designed for a class of electro-hydraulic servo system by means of the combination of sliding mode control theory and fuzzy control theory. Designed based on the exponential reaching law, paratmter e of conventional sliding mode controller is the key factor of system chattering, and is ~onal to it as well. In view of this, fuzzy control theory was introduced into the design to ensure the real- time adjusting of parameter e. Sinmlation results show that the sys tem chattering is eliminated perfectly, and the dynamic performance of the control system is improved effectively.

Fuzzy variable structure algorithms for active queue management with delay compensation

Based on the linearized model of the TCP connections through the congested routers, this paper puts forward an active queue management algorithm (FVS-T). The algorithm utilizes the fuzzy variable structure control algorithm with delay factor to compensate time varying round-trip times (RTT) and uncertainties with respect to the number of active TCP sessions. By analyzing the robustness and performance of the control scheme for the nonlinear TCP/AQM model, we show that the proposed design has good performance and robustness, which are central to the notion of AQM. Implementation issues were discussed and ns simulations were provided to validate the design and compare its performance to other peer schemes in different scenarios. The results show that the proposed design significantly outperforms the other congestion schemes in terms of packet loss ratio, throughput and buffer fluctuation.

Intelligent handoff decision in radio heterogeneous network

Abstract： In order to improve the accuracy of vertical handoff decision for radio heterogeneous network, this paper proposes an intelligent adaptive multi-criteria vertical handoff （AMVHO） decision algorithm. This algo- rithm uses a fuzzy inference system （FIS） and a modified Elman neural network （MENN）. The FIS adopts the crucial criteria of vertical handoff as input variables and makes handoff decision based on the defined rule base. The MENN helps to predict the number of users of the after-handoff network, which is a pivotal variable of the FIS. Simulation results show that, compared with the conventional method, the AMVHO decision algorithm a- chieves better performance in guaranteeing the quality of service （QoS） of the after-handoff communication.

A Novel Robot Motion Planning Model based on Visual Navigation and Fuzzy Control

In this paper, we propose the novel robot motion planning model based on the visual navigation and fuzzy control. A robot operating system can be viewed as the mechanical energy converter from the joint space to the global operation space, and the fiexibility of the robot system refi ects the global transformation ability of the whole system. Fuzzy control technology is a kind of fuzzy science, artificial intelligence, knowledge engineering and other disciplines interdisciplinary fields, the theory of strong science and technology, to achieve this fuzzy control technology theory, known as the fuzzy control theory. Besides this, this paper integrates the visual navigation system to construct the better robust methodology which is meaningful.

Fuzzy Based Auto-coagulation Control Through Photometric Dispersion Analyzer

The main role of water treatment plants is to supply high-quality safe drinking water. Coagulation is one of the most important stages of surface water treatment. The photometric dispersion analyzer(PDA) is a new optical method for flocculation monitoring, and is feasible to realize coagulation feedback control. The on line modification of the coagulation control system' s set point( or optimum dosing coagulant) has influenced the application of this technology in water treatment plant for a long time. A fuzzy control system incorporating the photometric dispersion analyzer was utilized in this coagulation control system. Proposed is a fuzzy logic inference control system by using Takagi and Sugeno' s fuzzy if-then rule for the self-correction of set point on line. Programmed is the dosing rate fuzzy control system in SIEMENS small-scale programmable logic controller. A 400 L/min middle-scale water treatment plant was utilized to simulate the reaction. With the changes of raw water quality, the set point was modified correctly in time, as well as coagulant dosing rate, and residual turbility before filtration was eligible and stable. Results show that this fuzzy inference and control system performs well on the coagulation control system through PDA.

Trust Management Mechanism for Internet of Things

Trust management has been proven to be a useful technology for providing security service and as a consequence has been used in many applications such as P2P, Grid, ad hoc network and so on. However, few researches about trust mechanism for Internet of Things （IoT） could be found in the literature, though we argue that considerable necessity is held for applying trust mechanism to IoT. In this paper, we establish a formal trust management control mechanism based on architecture modeling of IoT. We decompose the IoT into three layers, which are sensor layer, core layer and application layer, from aspects of network composition of loT. Each layer is controlled by trust management for special purpose： self-organized, affective routing and multi-service respectively. And the final decision-making is performed by service requester according to the collected trust information as well as requester＇ policy. Finally, we use a formal semantics-based and fuzzy set theory to realize all above trust mechanism, the result of which provides a general framework for the development of trust models of IoT.

Analysis of Noise at Coal Face by Fully-Mechanized Coal Winning Technology

The noise level of coal face by full-mechanized coal winning technology was measured in a coal mine. And then it was analyzed and evaluated using environment science, ergonomics and fussy mathematics analysis. Basis of the statistics and analysis of the measured noise level some measures, such as applying the new materials and improving the construction of the equipment, were carried out. The resuts show that they can reduce the noise level, improve the working environment and enhance the work efficiency.

High-Performance of Power System Based upon ANFIS （Adaptive Neuro-Fuzzy Inference System） Controller

The proposed controller incorporates FL （fuzzy logic） algorithm with ANN （artificial neural network）. ANFIS replaces the conventional PI controller, tuning the fuzzy inference system with a hybrid learning algorithm. A tuning method is proposed for training of the neuro-fuzzy controller. The best rule base and the best training algorithm chosen produced high performance in the ANFIS controller. Simulation was done on Matlab Ver. 2010a. A case study was chopper-fed DC motor drive, in continuous and discrete modes. Satisfactory results show the ANFIS controller is able to control dynamic highly-nonlinear systems. Tuning it further improved the results.

A Neuro-Fuzzy Based Adaptive Set-Point Heat Exchanger Control Scheme in District Heating System

The control of heat exchange stations in district heating system is critical for the overall energy efficiency and can be very difficult due to high level of complexity. A conventional method is to control the equipment such that the temperature of hot water supply is maintained at a set-point that may be a fixed value or be compensated against the external temperature. This paper presents a novel scheme that can determine the optimal set-point of hot water supply that maximizes the energy efficiency whilst providing sufficient heating capacity to the load. This scheme is based on Adaptive Neuro-Fuzzy Inferential System. The aim of this study is to improve the overall performance of district heating systems.