This application note provides users with a general understanding of the SVF and XSVF fileformats as they apply to Xilinx devices. Some familiarity with IEEE STD 1149.1 (JTAG) isassumed. For information on using Serial Vector Format (SVF) and Xilinx Serial Vector Format(XSVF) files in embedded programming applications
The CoolRunner-II CPLD is a highly uniform family of fast, low-power devices. Theunderlying architecture is a traditional CPLD architecture, combining macrocells intofunction blocks interconnected with a global routing matrix, the Xilinx AdvancedInterconnect Matrix (AIM). The function blocks use a PLA configuration that allowsall product terms to be routed and shared among any of the macrocells of the functionblock.
The power of programmability gives industrial automation designers a highly efficient， cost-effective alternative to traditional motor control units （MCUs）。 The parallel-processing power， fast computational speeds， and connectivity versatility of Xilinx® FPGAs can accelerate the implementation of advanced motor control algorithms such as Field Oriented Control （FOC）。
Additionally， Xilinx devices lower costs with greater on-chip integration of system components and shorten latencies with high-performance digital signal processing （DSP） that can tackle compute-intensive functions such as PID Controller， Clark/Park transforms， and Space Vector PWM.
The Xilinx Spartan®-6 FPGA Motor Control Development Kit gives designers an ideal starting point for evaluating time-saving， proven， motor-control reference designs. The kit also shortens the process of developing custom control capabilities， with integrated peripheral functions （Ethernet， PowerLink， and PCI® Express）， a motor-control FPGA mezzanine card （FMC） with built-in Texas Instruments motor drivers and high-precision Delta-Sigma modulators， and prototyping support for evaluating alternative front-end circuitry.
為了提高直接轉(zhuǎn)矩控制（DTC）系統(tǒng)定子磁鏈估計(jì)精度，降低電流、電壓測(cè)量的隨機(jī)誤差，提出了一種基于擴(kuò)展卡爾曼濾波（EKF）實(shí)現(xiàn)異步電機(jī)轉(zhuǎn)子位置和速度估計(jì)的方法。擴(kuò)展卡爾曼濾波器是建立在基于旋轉(zhuǎn)坐標(biāo)系下由定子電流、電壓、轉(zhuǎn)子轉(zhuǎn)速和其它電機(jī)參量所構(gòu)成的電機(jī)模型上，將定子電流、定子磁鏈、轉(zhuǎn)速和轉(zhuǎn)子角位置作為狀態(tài)變量，定子電壓為輸入變量，定子電流為輸出變量，通過對(duì)磁鏈和轉(zhuǎn)速的閉環(huán)控制提高定子磁鏈的估計(jì)精度，實(shí)現(xiàn)了異步電機(jī)的無速度傳感器直接轉(zhuǎn)矩控制策略，仿真結(jié)果驗(yàn)證了該方法的可行性,提高了直接轉(zhuǎn)矩的控制性能。
Abstract:
In order to improve the Direct Torque Control（DTC） system of stator flux estimation accuracy and reduce the current， voltage measurement of random error， a novel method to estimate the speed and rotor position of asynchronous motor based on extended Kalman filter was introduced. EKF was based on d-p axis motor and other motor parameters （state vector: stator current， stator flux linkage， rotor angular speed and position； input: stator voltage； output: staror current). EKF was designed for stator flux and rotor speed estimation in close-loop control. It can improve the estimated accuracy of stator flux. It is possible to estimate the speed and rotor position and implement asynchronous motor drives without position and speed sensors. The simulation results show it is efficient and improves the control performance.
There are many manufacturers of dot matrix LCD modules. However, most of these displaysare similar. They all have on-board controllers and drivers capable of displaying alpha numericsand a wide variety of other symbols (including Japanese "Katakana" characters). The internaloperation of LCD controller devices is determined by signals sent from a central processing unit(in this case, a CoolRunner-II CPLD).
Rainbow is a C program that performs document classification usingone of several different methods, including naive Bayes, TFIDF/Rocchio,K-nearest neighbor, Maximum Entropy, Support Vector Machines, Fuhr sProbabilitistic Indexing, and a simple-minded form a shrinkage withnaive Bayes.
最新的支持向量機(jī)工具箱，有了它會(huì)很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, "Fast training of support vector machines using sequential minimal optimization", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, "Estimating the Generalization Performance of a SVM Efficiently", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999.
