永磁型无轴承电机的设计与控制研究

为了深入研究永磁型无轴承电机的设计方法及控制策略,提出了计及定、转子定位偏心的悬浮力解析模型及该解析模型中悬浮绕组参数的设计方法.该方法基于永磁型无轴承电机悬浮力产生机理和电磁场有限元分析,进行电机设计,得到永磁型无轴承样机的系统参数.在此样机系统基础上,讨论了转矩控制子系统的转子磁场定向矢量控制、悬浮控制子系统的模糊自调整比例-积分-微分(PID)控制策略,并进行了悬浮运行实验,结果表明该设计参数与实际样机参数基本吻合,验证了该设计方法的正确可行性,该控制策略有效地解决了无轴承电机模型复杂、参数时变等实际因素的影响,保证了其运行控制的鲁棒性.

Direct adaptive fuzzy control based on integral-type Lyapunov function

A new scheme of direct adaptive fuzzy controller for a class of nonlinear systems with unknown triangular control gain structure is proposed. The design is based on the principle of sliding mode control and the approximation capability of the first type fuzzy systems. By introducing integral-type Lyapunov function and adopting the adaptive compensation term of optimal approximation error, the closed-loop control system is proved to be globally stable, with tracking error converging to zero. Simulation results demonstrate the effectiveness of the approach.

INDUCTION MOTOR SPEED CONTROL SYSTEM BASED ON FUZZY NEURAL NETWORK

A fuzzy neural network controller with the teaching controller guidance and parameter regulations for vector-controlled induction motor is proposed. The design procedures of the fuzzy neural controller and the teaching controller are described. The parameters of the membership function are regulated by an on-line learning algorithm. The speed responses of the system under the condition, where the target functions are chosen as I qs and ω, are analyzed. The system responses with the variant of parameter moment of inertial J, viscous coefficients B and torque constant K tare also analyzed. Simulation results show that the control scheme and the controller have the advantages of rapid speed response and good robustness.

Performance analysis of air suspension system of heavy truck with semi-active fuzzy control

In order to analyze and evaluate the performance of the air suspension system of heavy trucks with semi-active fuzzy control, a three-dimensional nonlinear dynamical model of a typical heavy truck with 16-DOF（degree of freedom） is established based on Matlab/Simulink software. The weighted root-mean-square（RMS） acceleration responses of the vertical driver ＇s seat, the pitch and roll angle of the cab, and the dynamic load coefficient（DLC） are chosen as objective functions, and the air suspension system is optimized and analyzed by the semi-active fuzzy control algorithm when vehicles operate under different operation conditions. The results show that the influence of the roll angle of the cab on the heavy truck ride comfort is clear when vehicles move on the road surface conditions of the ISO level D and ISO level E at a velocity over 27.5 m/s. The weighted RMS acceleration responses of vertical driver＇ s seat, the pitch and roll angle of the cab are decreased by 24%, 30% and 25%, respectively,when vehicles move on the road surface condition of the ISO level B at a velocity of 20 m/s. The value of the DLC also significantly decreases when vehicles operate under different operation conditions. Particularly, the DLC value of the tractor driver axle is greatly reduced by 27.4% when the vehicle operates under a vehicle fully-loaded condition on the road surface condition of ISO level B at a velocity of 27.5 m/s.

Automobile active suspension system with fuzzy control

A quarter-automobile active suspension model was proposed. High speed on/off solenoid valves were used as control valves and fuzzy control was chosen as control method . Based on force analyses of system parts, a mathematical model of the active suspension system was established and simplified by linearization method. Simulation study was conducted with Matlab and three scale coefficients of fuzzy controller (ke, kec, ku) were acquired. And an experimental device was designed and produced. The results indicate that the active suspension system can achieve better vibration isolation performance than passive suspension system, the displacement amplitude of automobile body can be reduced to 55%. Fuzzy control is an effective control method for active suspension system.

Simulation and Design of Fuzzy Sliding-mode Controller for Ship Heading-tracking

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.

Fuzzy adaptive genetic algorithm based on auto-regulating fuzzy rules

There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms （SGA） as the values of crossover probability （Pc） and mutation probability （Pro） are fixed. To solve the problems, the fuzzy control method and the genetic algorithms were systematically integrated to create a kind of improved fuzzy adaptive genetic algorithm （FAGA） based on the auto-regulating fuzzy rules （ARFR-FAGA）. By using the fuzzy control method, the values of Pc and Pm were adjusted according to the evolutional process, and the fuzzy rules were optimized by another genetic algorithm. Experimental results in solving the function optimization problems demonstrate that the convergence rate and solution quality of ARFR-FAGA exceed those of SGA, AGA and fuzzy adaptive genetic algorithm based on expertise （EFAGA） obviously in the global search.

SERVICE-AWARE BASED FUZZY ADMISSION CONTROL SCHEME IN MULTI-SERVICE NETWORKS

Multi-service aggregated transmission is the direction of IP network. Providing different Quality of Service (QoS) assurance for different services has become a crucial problem in future network. Admission control is a vital function for multi-service IP network. This paper proposes a novel fuzzy admission control scheme based on coarse granularity service-aware technique. Different service has discriminative sensitivity to the same QoS characteristic parameter in general. The traffic class can be perceived by the service request parameter and the proposed QoS function. And requirements of dif- ferent applications can be met by maintaining the life parameter. From simulation results, the proposed scheme shows a better QoS provisioning than those traditional fuzzy logic based methods under the same admission probability.

Designing similarity measurement with distance measure and application on laterally directional mode flight test

Similarity measure construction has been proposed as fault detection of flight test method in order to obtain the primary control surface stuck and the combination stuck of primary control.Similarity measures were obtained through analyzing the certainty and uncertainty of fuzzy membership functions,which were designed based on well-known Hamming distance.It was applied to the fault detection of primary control surface stuck of uninhabited aerial vehicle(UAV).At post-failure control surface,if the UAV is controllable and trimmable using other control surfaces,the UAV is able to fly or return to the safety region through reconfiguration of flight control system.To detect the fault,similarity measure computations were carried out.This result could be applicable with the real-time parameter estimation method.By monitoring the value of coefficients due to the control surface deviation,it becomes aware that the control surface fault occurs or not.The control surface stuck position and value were separated by comparing the trim value with the reference value.This is the advantage of increasing in reliability without adding sensors or with additional low cost.

Fast generation method of fuzzy rules and its application to flux optimization in process of matter converting

A fast generation method of fuzzy rules for flux optimization decision-making was proposed in order to extract the linguistic knowledge from numerical data in the process of matter converting. The fuzzy if-then rules with consequent real number were extracted from numerical data, and a linguistic representation method for deriving linguistic rules from fuzzy if-then rules with consequent real numbers was developed. The linguistic representation consisted of The simulat two linguistic variables with the degree of certainty and the storage structure of rule base was described. on results show that the method involves neither the time-consuming iterative learning procedure nor the complicated rule generation mechanisms, and can approximate complex system. The method was applied to determine the flux amount of copper converting furnace in the process of matter converting. The real result shows that the mass fraction of Cu in slag is reduced by 0.5 %.

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.

Optimal arrangement for pressure measurement points in working chamber of earth pressure balance shield

In order to exactly provide scientific basis for pressure dynamic balance control of working chamber of earth pressure balance shield (EPBS),study on optimal arrangement of pressure measurement points in working chamber was conducted. Based on mathematical description of optimal arrangement for pressure measurement points,fuzzy clustering analysis and discriminant analysis were used to divide pressure regions of nodes on bulkhead. Finally,the selection method of optimal measurement points was proposed,and by selecting d6.28 m EPBS as study object,the case study was conducted. By contrast,based on optimal arrangement scheme of pressure measurement points,through adopting weighted algorithm,the absolute error mean of equivalent pressure of working chamber is the smallest. In addition,pressure curve of optimal arrangement points presents parabola,and it can show the state of pressure distribution on bulkhead truly. It is concluded that the optimal arrangement method of pressure measurement points in working chamber is effective and feasible,and the method can provide basis for realizing high precision pressure control of EPBS.

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.

Genetic-fuzzy HEV control strategy based on driving cycle recognition

A genetic-fuzzy HEV control strategy based on driving cycle recognition （DCR） was built. Six driving cycles were selected to represent different traffic conditions e.g. freeway, urban, suburb. A neural algorithm was used for traffic condition recognition based on ten parameters of each driving cycle. The DCR was utilized for optimization of the HEV control parameters using a genetic-fuzzy approach. A fuzzy logic controller （FLC） was designed to be intelligent to manage the engine to work in the vicinity of its optimal condition. The fuzzy membership function parameters were optimized using the genetic algorithm （GA） for each driving cycle. The result is that the DCR_ fuzzy controller can reduce the fuel consumption by 1. 9%, higher than only CYC _ HWFET optimized fuzzy （0.2%） or CYC _ WVUSUB optimized fuzzy （0.7%）. The DCR_ fuzzy method can get the better result than only optimizing one cycle on the complex real traffic conditions.

Data Flow Fuzzy Control of Communication Network with Finite Terminal

We consider the problem of data flow fuzzy control of discrete queuing systems with three different service-rate servers. The objective is to dynamically assign customers to idle severs based on the state of the system so as to minimize the mean sojourn time of customers. Simulation shows the validity of the fuzzy controller.

Fuzzy controller based on chaos optimal design and its application

In order to overcome difficulty of tuning parameters of fuzzy controller, a chaos optimal design method based on annealing strategy is proposed. First, apply the chaotic variables to search for parameters of fuzzy contro-(ller,) and transform the optimal variables into chaotic variables by carrier-wave method. Making use of the intrinsic stochastic property and ergodicity of chaos movement to escape from the local minimum and direct optimization searching within global range, an approximate global optimal solution is obtained. Then, the chaos local searching and optimization based on annealing strategy are cited, the parameters are optimized again within the limits of the approximate global optimal solution, the optimization is realized by means of combination of global and partial chaos searching, which can converge quickly to global optimal value. Finally, the third order system and discrete nonlinear system are simulated and compared with traditional method of fuzzy control. The results show that the new chaos optimal design method is superior to fuzzy control method, and that the control results are of high precision, with no overshoot and fast response.

Efficient Modeling of Photovoltaic Systems Using CMEX S-Function under MATLAB-Simulink Environment

This paper discusses an implementation based on CMEX S-functions to model and to check implementation feasibility of two most commonly used Maximum Power Point Tracking （MPPT） algorithms, namely Hill Climbing/Perturb ＆ Observe （P＆O） and Incremental Conductance. This study can also be generalized to encompass the whole digital techniques family, including artificial intelligence like Neural Network and Fuzzy Logic Control.

Accurate Reactive Power Controller and Power Factor Correction Using Fuzzy Logic

This paper presents the use of fuzzy logic technique to control the reactive power of load and hence improve the source power factor. A shunt compensator is proposed, which consists of a voltage controlled reactor by full-wave thyristor bridge in parallel with a capacitor. The proposed voltage control technique composed of two independent fuzzy controllers, primary and secondary. The PFC （primary fuzzy controller） is designed based on linearization method to introduce to the network the nearest value of reactive power （VAR） required to correct the power factor. The SFC （secondary fuzzy controller） is designed to achieve accurate compensation for the required VAR to achieve the pre-set power factor value. Simulations for 15 different practical study cases are presented to evaluate the performance of the controller, and the results show how the designed controller is fast and accurate. Harmonics analyses are carried out up to the 13th harmonic to determine the requirement of harmonics filter.

A maximum power point tracker for photovoltaic energy systems based on fuzzy neural networks

To extract the maximum power from a photovoltaic （PV） energy system, the real-time maximum power point （MPP） of the PV array must be tracked closely. The non-linear and time-variant characteristics of the PV array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP for traditional control strategies. We propose a fuzzy neural network controller （FNNC）, which combines the reasoning capability of fuzzy logical systems and the learning capability of neural networks, to track the MPP. With a derived learning algorithm, the parameters of the FNNC are updated adaptively. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the FNNC. Simulation results show that the proposed control algorithm provides much better tracking performance compared with the filzzy logic control algorithm.