卅広字備CHANGZHOU INSTITUTE OF TECHNOLOGY毕业设计说明书附录：英文资料翻译英文题目:Introduction to Modern ControlTheory中文题目:现代控制理论简介二级学院（直属学部）专业：自动化学生姓名：庞策指导教师姓名：关静评阅教师姓名：延陵学院班级： 07自Y学号： 07121217职称：讲师职称：常州工学院延陵学院2011年 6 月ARM-Cortex Microcontroller fuzzy positionControl on an automatic door test-bedAbstract This paper describes an application of a fuzzy logic 1 implementation on an ARM-Cortex microcontroller. The microcontroller with integrated fuzzy logic was tested on motor position and speed control application. Fuzzy logic is a subtype of multi-valued logic and can be used in combination with other controller types (PI, PID, neural networks, genetic algorithms, etc.).brains ”C,oomf pthlexddeevvicicee.sThe microcontroller is the core, or the include two or more microcontrollers that exchange data via various communication protocols. Each microcontroller has integrated software, which represents the “ mind”of the microcontroller. Without software, the microcontroller is just a useless electronic component. The software represents fuzzy logic, which controls the motor position in this application. The microcontroller softsware is often written in the C programming language. Expression often means that there are available more programming languages. Position control has a closed loop, meaning that the position of the motor is regulated to a reference position if the motor load is changing. The first goal of this application is to write a C language source code for a fuzzy logic inference engine for the ARM Cortex M3 microcontroller. The second goal is to test this fuzzy logic inference engine on an automatic door for position control with combination of PI speed controller. The last goal is to analyze the automatic door behavior with fuzzy logic controller by variable door wing weight.I. INTRODUCTIONIn recent years, fuzzy control has spread to various macaronis devices that require operation control. The use of fuzzy logic is also gaining ground in home appliances and industrial machinery. The main advantage of fuzzy control is the robustnessof various versions of mechanism functionality (changes in inertia, friction, etc.). A PI and PID controllers are based on mathematical equations; these controllers become instable when mechanism functionality changes.The controller PI or PID output is calculated mathematically with parameters and process measurements. The controller task is to reduce process error. Fuzzy logic control can be configured by the user, and the controller progress of an output can be defined by rule bases and membership functions regarding of process measurement. The first half of the mechanical device position speed has to be as fast as possible, for example. The second half of the mechanical device positionspeed has to be slower becauseof high inertia. The standard controller cannot provide this control method, while a fuzzy logic controller can. A similar problem occurs with the automatic door. The automatic translate doors have a similar problem because of various weight versions or variable bearings friction that increasesin time. A door PI controller has to be set up after every door assembly, or when bearing friction increases. The fuzzy logic controller doesn trequire a setup when the mechanism functionality changes. Automatic door movement can be more flexible with the fuzzy logic control.The fuzzy logic controller, work of Paul-l-Hay Lin et al. 6, has a resemblanceto this application. Their work describes a comparison between a standard PID controller and the fuzzy logic controller on DC motor position control. The controller is PC-based with analog I/O. The position of the DC motor rotor is defined with a position sensor via analog input. The DC motor power interface is controlled via PC analog output. The next work of Tips wan Y. and Mo-Yuen Chow 7 is fuzzy logic speed control of a DC motor. Their work is based on a 16-bit microcontroller with analog input control data from a tachometer, PWM output for motor control, and I/O for other controls (LCD, keyboard). A fuzzy logic speed controller simulation in MATLAB/Stimuli is the work of Montale O. et al. 8. Their work describes the difference between PID and fuzzy speed control in simulations. A direct-drive robot with adaptive fuzzy disturbance estimation is the work of Rajkot, A. and Jeering, K. 9. Their work describes fuzzy controllers for adaptive adjustment, which improves dynamic changes. The controller algorithms are PC-based and communicate with the robot controller. The fuzzy speed controller is another similar work of Isis A. et al. 10, which is implemented on a PIC microcontroller. There are various decisions on how to implement a fuzzy logic in a microcontroller. Some companies offer software with an integrated compiler for various microcontroller types, or independently write a fuzzy inference engine in common C programming language. The