自动化专业毕业论文外文文献翻译

目录

Part 1 PID type fuzzy controller and parameters

adaptive method (1)

Part 2 Application of self adaptation fuzzy-PID

control for main steam

temperature control system in power station错误!未定义书签Part 3 Neuro-fuzzy generalized predictive

control of boiler steam temperature .... (22)

Part 4 为Part3译文：锅炉蒸汽温度模糊神经网络的广义

预测控制37

Part 1 PID type fuzzy controller

and Parameters adaptive method

Wu zhi QIAO, Masaharu Mizumoto

Abstract: The authors of this paper try to analyze the dynamic behavior of the product-sum crisp type fuzzy controller, revealing that this type of fuzzy controller behaves approximately like a PD controller that may yield steady-state error for the control system. By relating to the conventional PID control theory, we propose a new fuzzy controller structure, namely PID type fuzzy controller which retains the characteristics similar to the conventional PID controller. In order to improve further the performance of the fuzzy controller, we work out a method to tune the parameters of the PID type fuzzy controller on line, producing a parameter adaptive fuzzy controller. Simulation experiments are made to demonstrate the fine performance of these novel fuzzy controller structures.

Keywords: Fuzzy controller; PID control; Adaptive

control

1. Introduction

Among various inference methods used in the fuzzy controller found in literatures , the most widely used ones in practice are the Mamdani method proposed by Mamdani and his associates who adopted the Min-max compositional rule of inference based on an interpretation of a control rule as a conjunction of the antecedent and consequent, and the product-sum method proposed by Mizumoto who suggested to introduce the product and arithmetic mean aggregation operators to replace the logical AND (minimum) and OR (maximum) calculations in the Min-max compositional rule of inference.

In the algorithm of a fuzzy controller, the fuzzy function calculation is also a complicated and time consuming task. Tagagi and Sugeno proposed a crisp type model in which the consequent parts of the fuzzy control rules are crisp functional representation or crisp real numbers in the simplified case instead of fuzzy sets . With

this model of crisp real number output, the fuzzy set of the inference consequence will be a discrete fuzzy set with

a finite number of points, this can greatly simplify the fuzzy function algorithm.

Both the Min-max method and the product-sum method are often applied with the crisp output model in a mixed manner. Especially the mixed product-sum crisp model has a fine performance and the simplest algorithm that is very easy to be implemented in hardware system and converted into a fuzzy neural network model. In this paper, we will take account of the product-sum crisp type fuzzy controller.

2. PID type fuzzy controller structure

As illustrated in previous sections, the PD function approximately behaves like a parameter time-varying PD controller. Since the mathematical models of most industrial process systems are of type, obviously there would exist an steady-state error if they are controlled by this kind of fuzzy controller. This characteristic has been stated in the brief review of the PID controller in

the previous section.

If we want to eliminate the steady-state error of the control system, we can imagine to substitute the input (the change rate of error or the derivative of error) of the fuzzy controller with the integration of error. This will result the fuzzy controller behaving like a parameter time-varying PI controller, thus the steady-state error is expelled by the integration action. However, a PI type fuzzy controller will have a slow rise time if the P parameters are chosen small, and have a large overshoot if the P or I parameters are chosen large. So there may be the time when one wants to introduce not only the integration control but the derivative control to the fuzzy control system, because the derivative control can reduce the overshoot of the system's response so as to improve the control performance. Of course this can be realized by designing a fuzzy controller with three inputs, error, the change rate of error and the integration of error. However, these methods will be hard to implement in practice because

of the difficulty in constructing fuzzy control rules. Usually fuzzy control rules are constructed by summarizing the manual control experience of an operator who has been controlling the industrial process skillfully and successfully. The operator intuitively regulates the executor to control the process by watching the error and the change rate of the error between the system's output and the set-point value. It is not the practice for the operator to observe the integration of error. Moreover, adding one input variable will greatly increase the number of control rules, the constructing of fuzzy control rules are even more difficult task and it needs more computation efforts. Hence we may want to design a fuzzy controller that possesses the fine characteristics of the PID controller by using only the error and the change rate of error as its inputs.

One way is to have an integrator serially connected to the output of the fuzzy controller as shown in Fig. 1. In Fig. 1,

K and2K are scaling factors for e and ~ 1

respectively, and fl is the integral constant. In the proceeding text, for convenience, we did not consider the scaling factors. Here in Fig. 2, when we look at the neighborhood of NODE point in the e - ~ plane, it follows from (1) that the control input to the plant can be approximated by

(1)

Hence the fuzzy controller becomes a parameter time-varying PI controller, its equivalent proportional control and integral control components are BK2D and ilK1 P respectively. We call this fuzzy controller as the PI type

fuzzy controller (PI fc). We can hope that in a PI type fuzzy control system, the steady-state error becomes zero. To verify the property of the PI type fuzzy controller, we carry out some simulation experiments. Before presenting the simulation, we give a description of the simulation model. In the fuzzy control system shown in Fig. 3, the plant model is a second-order and type system with the following transfer function: )1)(1()(21++=

s T s T K s G (2)

Where K = 16, 1T = 1, and 2T = 0.5. In our simulation

experiments, we use the discrete simulation method, the results would be slightly different from that of a continuous system, the sampling time of the system is set to be 0.1 s. For the fuzzy controller, the fuzzy subsets of e and d are defined as shown in Fig. 4. Their cores

The fuzzy control rules are represented as Table

1. Fig. 5 demonstrates the simulation result of step response of the fuzzy control system with a Pl fc. We can see that the steady-state error of the control system becomes zero, but when the integration factor fl is small, the system's response is slow, and when it is too large, there is a high overshoot and serious oscillation. Therefore, we may want to introduce the derivative control law into the fuzzy controller to overcome the overshoot and instability. We propose a controller structure that simply connects the PD type and the PI type fuzzy controller together in parallel. We have the equivalent structure of that by connecting a PI device with the basic fuzzy controller serially as shown in Fig.6. Where ~ is the weight on PD type fuzzy controller and fi is that on PI type fuzzy controller, the larger a/fi means more emphasis on the derivative control and less emphasis on the integration control, and vice versa. It follows from (7) that the output of the fuzzy controller is

(3)

3. The parameter adaptive method

Thus the fuzzy controller behaves like a time-varying PID controller, its equivalent proportional control, integral control and derivative control components are respectively. We call this new controller structure a PID type fuzzy controller (PID fc). Figs. 7 and 8 are the simulation results of the system's step response of such control system. The influence of ~ and fl to the system performance is illustrated. When ~ > 0 and/3 = 0, meaning that the fuzzy controller behaves like PD fc, there exist a steady-state error. When ~ = 0 and fl > 0, meaning that the fuzzy controller behaves like a PI fc, the steady-state error of the system is eliminated but there

is a large overshoot and serious oscillation.

When ~ > 0 and 13 > 0 the fuzzy controller becomes a PID fc, the overshoot is substantially reduced. It is possible to get a comparatively good performance by carefully choosing the value of αandβ.

4. Conclusions

We have studied the input-output behavior of the product-sum crisp type fuzzy controller, revealing that this type of fuzzy controller behaves approximately like a parameter time-varying PD controller. Therefore, the analysis and designing of a fuzzy control system can take advantage of the conventional PID control theory. According to the coventional PID control theory, we have

been able to propose some improvement methods for the crisp type fuzzy controller.

It has been illustrated that the PD type fuzzy controller yields a steady-state error for the type system, the PI type fuzzy controller can eliminate the steady-state error. We proposed a controller structure, that combines the features of both PD type and PI type fuzzy controller, obtaining a PID type fuzzy controller which allows the control system to have a fast rise and a small overshoot as well as a short settling time.

To improve further the performance of the proposed PID type fuzzy controller, the authors designed a parameter adaptive fuzzy controller. The PID type fuzzy controller can be decomposed into the equivalent proportional control, integral control and the derivative control components. The proposed parameter adaptive fuzzy controller decreases the equivalent integral control component of the fuzzy controller gradually with the system response process time, so as to increase the damping of the system when the system

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1、 外文原文 A: Fundamentals of Single-chip Microcomputer Th e si ng le -c hi p m ic ro co mp ut er i s t he c ul mi na ti on of both t h e de ve lo pm en t o f t he d ig it al co m pu te r an d th e i n te gr at ed c i rc ui t a rg ua bl y t h e to w m os t s ig ni f ic an t i nv en ti on s o f t he 20th c e nt ur y [1]. Th es e t ow ty pe s of ar ch it ec tu re a re fo un d i n s in g le -ch i p m i cr oc om pu te r. So m e em pl oy t he spl i t pr og ra m/da ta m e mo ry o f th e H a rv ar d ar ch it ect u re , sh ow n in Fi g.3-5A -1, o th ers fo ll ow t he p h il os op hy , wi del y a da pt ed f or ge n er al -p ur po se co m pu te rs a nd m i cr op ro ce ss o r s, o f ma ki ng n o log i ca l di st in ct ion be tw ee n p r og ra m an d d at a m e mo ry a s i n t he P r in ce to n ar ch ite c tu re , sh ow n i n F ig.3-5A-2. In g en er al te r ms a s in gl e -chi p m ic ro co mp ut er i s c h ar ac te ri ze d b y t h e i nc or po ra ti on o f a ll t he un it s of a co mp uter i n to a s in gl e d ev i ce , as s ho wn in Fi g3-5A -3. Fig.3-5A-1 A Harvard type Program memory Data memory CPU Input& Output unit memory CPU Input& Output unit

毕业论文 外文翻译#(精选.)

毕业论文（设计）外文翻译 题目：中国上市公司偏好股权融资：非制度性因素 系部名称：经济管理系专业班级:会计082班 学生姓名：任民学号： 200880444228 指导教师：冯银波教师职称:讲师 年月日

译文： 中国上市公司偏好股权融资：非制度性因素 国际商业管理杂志 2009.10 摘要：本文把重点集中于中国上市公司的融资活动，运用西方融资理论，从非制度性因素方面，如融资成本、企业资产类型和质量、盈利能力、行业因素、股权结构因素、财务管理水平和社会文化，分析了中国上市公司倾向于股权融资的原因，并得出结论，股权融资偏好是上市公司根据中国融资环境的一种合理的选择。最后，针对公司的股权融资偏好提出了一些简明的建议。 关键词：股权融资，非制度性因素，融资成本 一、前言 中国上市公司偏好于股权融资，根据中国证券报的数据显示，1997年上市公司在资本市场的融资金额为95.87亿美元，其中股票融资的比例是72.5％，，在1998年和1999年比例分别为72.6％和72.3％，另一方面，债券融资的比例分别是17.8％，24.9％和25.1％。在这三年，股票融资的比例，在比中国发达的资本市场中却在下跌。以美国为例，当美国企业需要的资金在资本市场上，于股权融资相比他们宁愿选择债券融资。统计数据显示，从1970年到1985年，美日企业债券融资占了境外融资的91.7％，比股权融资高很多。阎达五等发现，大约中国3/4的上市公司偏好于股权融资。许多研究的学者认为，上市公司按以下顺序进行外部融资：第一个是股票基金，第二个是可转换债券，三是短期债务，最后一个是长期负债。许多研究人员通常分析我国上市公司偏好股权是由于我们国家的经济改革所带来的制度性因素。他们认为，上市公司的融资活动违背了西方古典融资理论只是因为那些制度性原因。例如，优序融资理论认为，当企业需要资金时，他们首先应该转向内部资金（折旧和留存收益），然后再进行债权融资，最后的选择是股票融资。在这篇文章中，笔者认为，这是因为具体的金融环境激活了企业的这种偏好，并结合了非制度性因素和西方金融理论，尝试解释股权融资偏好的原因。

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Infrared Remote Control System Abstract Red outside data correspondence the technique be currently within the scope of world drive extensive usage of a kind of wireless conjunction technique,drive numerous hardware and software platform support. Red outside the transceiver product have cost low, small scaled turn, the baud rate be quick, point to point SSL, be free from electromagnetism thousand Raos etc.characteristics, can realization information at dissimilarity of the product fast, convenience, safely exchange and transmission, at short distance wireless deliver aspect to own very obvious of advantage.Along with red outside the data deliver a technique more and more mature, the cost descend, red outside the transceiver necessarily will get at the short distance communication realm more extensive of application. The purpose that design this system is transmit cu stomer’s operation information with infrared rays for transmit media, then demodulate original signal with receive circuit. It use coding chip to modulate signal and use decoding chip to demodulate signal. The coding chip is PT2262 and decoding chip is PT2272. Both chips are made in Taiwan. Main work principle is that we provide to input the information for the PT2262 with coding keyboard. The input information was coded by PT2262 and loading to high frequent load wave whose frequent is 38 kHz, then modulate infrared transmit dioxide and radiate space outside when it attian enough power. The receive circuit receive the signal and demodulate original information. The original signal was decoded by PT2272, so as to drive some circuit to accomplish