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1、外文文献原稿和译文原 稿Introduction In the modern industrial control field, along with the rapid development of computer technology, the emergence of a new trend of intelligent control, namely to machine simulation human thinking mode, using reasoning, deduce and induction, so the means, the production control
2、, this is artificial intelligence. One expert system, fuzzy logic and neural network is the artificial intelligence of several key research hot spot. Relative to the expert system, the fuzzy logic belongs to the category of computational mathematics and contain the genetic algorithm, the chaos theor
3、y and linear theory etc, it comprehensive of operators practice experience, has the design is simple and easy to use, strong anti-interference ability and reaction speed, easy to control and adaptive ability, etc. In recent years, in a process control, built to touch, estimation, identify, diagnosis
4、, the stock market forecast, agricultural production and military sciences to a wide range of applications. To carry out in-depth research and application of fuzzy control technology, the paper introduces the basic theory of fuzzy control technology and development, and to some in the application of
5、 the power electronics are introduced. Fuzzy Logic and Fuzzy Control 1, fuzzy logic and fuzzy control concept In 1965, the university of California, Berkeley, computer experts Lofty Zadeh put forward fuzzy logic concept, the root lies in the areas logic or clear logic distribution, used to define th
6、e confused, unable to quantify or the problem of precision, for in a mans von based on true-false reasoning mechanism, and thus create a electronic circuit and integrated circuit of the Boolean algorithm, fuzzy logic to fill the gaps in special things in sampling and analysis of blank. On the basis
7、of fuzzy logic fuzzy set theory, a particular things as the set of features membership, he can be in is and no within the scope of the take between any value. And fuzzy logic is reasonable quantitative mathematical theory, the mathematical basis for fundamental for is to deal with these the statisti
8、cal uncertain imprecise information. Fuzzy control based on fuzzy logic is a process of description of the control algorithm. For parameters precisely known mathematical model, we can use Berd graph or chart to analysts the Nyquist process to obtain the accurate design parameters. And for some compl
9、ex system, such as particle reaction, meteorological forecast equipment, establishing a reasonable and accurate mathematical model is very difficult, and for power transmission speed of vector control problems, although it can be measured by the model that, but for many variables and nonlinear varia
10、tion, the accurate control is very difficult. And fuzzy control technology only on the basis of the practical experience and the operator and intuitive inference, also relies on design personnel and research and development personnel of experience and knowledge accumulation, it does not need to esta
11、blish equipment model, so basically is adaptive, and have strong robustness. After many years development, there have been many successful application of the fuzzy control theory of the case, such as Rutherford, Carter and Ostergaard were applied and metallurgical furnace and heat exchangers control
12、 device. 2, the analysis method is discussed Industrial control stability of the system is discussed the premise of the problem, because of the nonlinear and not to the unity of the description, make a judgment, so the fuzzy control system analysis method of stability analysis has been a hot spot, c
13、omprehensive in recent years you of scholars paper published the system stability analysis has these several circumstances : 1）, LiPuYa panov method: direct method based on the discrete time (D-T) and continuous time fuzzy control stability analysis and design method, the stability condition of the
14、relative comparison conservative. 2）, sliding variable structure system analysis method 3）, round stability criterion methods: use sector bounded nonlinear concept, according to the stability criterion, led to the stability of the fuzzy control. 4）, POPOV criterion 5）, other methods such as relation
15、ship matrix analysis, exceed stable theory, phase-plane, matrix inequality or convex optimization method, fuzzy hole-hole mapping etc, detailed information and relevant literature many, in this one no longer etc. Set Design of Fuzzy Control The design of the fuzzy control is a very complicated proce
16、ss, in general, take the design steps and tools is more normative. The fuzzy controller general use of the special software and hardware, universal hardware chip in on the market at present is more, including main products are shown below. And special IC has developed very fast, it special IC and so
17、ftware controller integrates in together. In the process of design, the design of the general to take steps for: 1, considering whether the subject by fuzzy control system. That is considered the routine control mode of may. 2, from equipment operation personnel place to get as much information. 3 a
18、nd selecting the mathematical model could, if use the conventional method design, estimate the equipment performance characteristics. 4, determine the fuzzy logic control object. 5, determine the input and output variables. 6, determine the variables as determined the belonging of the range. 7, conf
19、irm the variables of the corresponding rules. 8, determine the scale coefficients. 9, if have a ready-made, mathematical model of fuzzy controller with already certain of system simulation, observation equipment performance, and constantly adjust rules and scale coefficients until reaching satisfact
20、ion performance. Or to design fuzzy controller. 10, real-time operation controller, constantly adjust to the best performance. Fuzzy Control Application and Prospect As artificial intelligence of a new research field, the fuzzy control absorb lessons from the traditional design method and other new
21、technologys essence, in many fields has made considerable progress. In the new type of power electronic and automatic control system, some experts in the linear adding the conditions of the power amplifier, the application of the fuzzy control based on the servo motor control, in the fuzzy control s
22、ystem with the PID and model reference adaptive control (MRAC) comparison proved the advantages of the method of fuzzy control. Fuzzy turn sent gain tuned controller views of the induction motor drive system vector control Fuzzy control as a is the development of new technology, now in most experts
23、also to focus on application system research, and make considerable achievement, but in the theory research and system analysis or relative backward, so much so that some scholars have questioned its theoretical basis and effective. In view of this can be clear that the fuzzy control the combination
24、 of theory and practice is still needs to be further explored. The development prospects are very attractive, and in recent years, its theoretical study also made significant progress. In the past forty years of the development process, the fuzzy control also has some limitations: 1) control precisi
25、on low, performance is not high, stability is poorer; 2) theory system is not complete. 3) the adaptive ability low. For these weaknesses, the fuzzy control and some other new technology, such as neural network (NN), genetic algorithm, and the combination of to a higher level of application developm
26、ent expand the huge space. Summary Fuzzy control as a comprehensive application example, in the global information the push of wave, in the next few decades, to the rapid development of economy will inject new vitality, the expert thinks, the next generation of industrial control is the basis of fuz
27、zy control and neural network, and chaos theory as the pillar of the artificial intelligence. With the fuzzy control theory research and further more perfect of, the scope of application of the growing and supporting the development and manufacture of IC, the fuzzy control will be open to the field
28、of industrial automation development of light application prospect, but also to the various areas of the researchers suggest more important task. 译 文引言在现代工业控制领域，伴随着计算机技术的突飞猛进，出现了智能控制的新趋势，即以机器模拟人类思维模式，采用推理、演绎和归纳等手段，进行生产控制，这就是人工智能。其中专家系统逻辑和神经网络是人工智能的几个重点研究热点。相对于专家系统，模糊逻辑属于计算数、模糊学的范畴，包含遗传算法，混沌理论及线性理论等内
29、容，它综合了操作人员的实践经验，具有设计简单，易于应用、抗干扰能力强、反应速度快、便于控制和自适应能力强等优点。近年来，在过程控制、建摸、估计、辩识、诊断、股市预测、农业生产和军事科学等领域得到了广泛应用。为深入开展模糊控制技术的研究应用，本文综合介绍了模糊控制技术的基本理论和发展状况，并对一些在电力电子领域的应用作了简单介绍。模糊逻辑与模糊控制1.模糊逻辑与模糊控制的概念1965年，加州大学伯克利分校的计算机专家Lofty Zadeh提出“模糊逻辑”的概念，其根本在于区分布尔逻辑或清晰逻辑，用来定义那些含混不清，无法量化或精确化的问题，对于冯诺依曼开创的基于“真假”推理机制，以及因此开创的电
30、子电路和集成电路的布尔算法，模糊逻辑填补了特殊事物在取样分析方面的空白。在模糊逻辑为基础的模糊集合理论中，某特定事物具有特色集的隶属度，他可以在“是”和“非”之间的范围内取任何值。而模糊逻辑是合理的量化数学理论，是以数学基础为为根本去处理这些非统计不确定的不精确信息。模糊控制是基于模糊逻辑描述的一个过程的控制算法。对于参数精确已知的数学模型，我们可以用Berd图或者Nyquist图来分析家其过程以获得精确的设计参数。而对一些复杂系统，如粒子反应，气象预报等设备，建立一个合理而精确的数学模型是非常困难的，对于电力传动中的变速矢量控制问题，尽管可以通过测量得知其模型，但对于多变量的且非线性变化，起
31、精确控制也是非常困难的。而模糊控制技术仅依据与操作者的实践经验和直观推断，也依靠设计人员和研发人员的经验和知识积累，它不需要建立设备模型，因此基本上是自适应的，具有很强的鲁棒性。历经多年发展，已有许多成功应用模糊控制理论的案例，如Rutherford，Carter 和Ostergaard分别应用与冶金炉和热交换器的控制装置。2.分析方法探讨工业控制系统的稳定性是探讨问题的前提，由于难以对非线性和不统一的描述，做出判断，因此模糊控制系统的分析方法的稳定性分析一直是一个热点，综合近年来各位学者的发表的论文,目前系统稳定性分析有以下集中:1), 李普亚诺夫法:基于直接法的离散时间(D-T)和连续时间
32、模糊控制的稳定性分析和设计方法,相对而言起稳定条件比价保守。2),滑动变结构系统分析法3),圆稳定性判据方法:利用扇区有界非线性概念,根据稳定判据可推导模糊控制的稳定性.4),POPOV判据待添加的隐藏文字内容15),其他方法如关系矩阵分析法，超稳定理论，相平面法，矩阵不等式或凸优化法，模糊穴穴映射等，详细资料及有关文献很多，在这里不再一一阐述。模糊控制的设置设计模糊控制的设计是一个非常复杂的过程，一般而言，采取的设计步骤和工具比较规范。其中模糊控制器一般采用专用软硬件，通用型的硬件芯片在目前市场上比较多，其中主流产品如下表所示。而专用IC发展也很迅速，它把专用IC和软件控制器集成在一起。设计
33、过程中，一般采取的设计步骤为：1，综合考虑该课题能否采用模糊控制系统。即考虑采用常规控制方式的可能。2,从设备操作人员处获取尽可能多的信息。3，选取可能的数学模型，如果用常规方法设计，估计设备的性能特点。4，确定模糊逻辑的控制对象。5，确定输入输出变量。6，确定所确定的各个变量的归属范围。7，确定各变量的对应规则。8，确定比例系数。9，如果有现成的数学模型，用已确定的模糊控制器对系统仿真，观测设备性能，并不断调整规则和比例系数直到达到满意性能。否则重新设计模糊控制器。10，实时运行控制器，不断调整以达到最佳性能。模糊控制应用与前景展望作为人工智能的一种新研究领域，模糊控制吸收借鉴了传统设计方法
34、和其他新技术的精华，在诸多领域取得了长足的进展。在新型的电力电子和自动控制系统中，有些专家在线性功放的加设条件下，把模糊控制应用于为基础的伺服电机控制中，在把模糊控制系统与PID及模型参考自适应控制（MRAC）进行比较后证明了模糊控制方法的优越性。模糊控制作为一项正在发展的新技术，目前在大多数专家还把主要精力放在应用系统研究上，并取得了相当的成果，但在理论研究和系统分析上还是相对落后的，以至于一些学者质疑其理论依据和有效性。鉴于此可以明确得知：模糊控制理论和实践的结合仍有待于进一步探索。其发展前景是十分诱人的，而且在近年来，其理论研究也取得了显著进展。在近四十年的发展进程中，模糊控制也有一些局限性：1、控制精度低，性能不高，稳定性较差；2、理论体系不完整；3、自适应能力低。对于这些弱点，模糊控制与一些其他新技术，比如神经网络（NN），遗传算法相结合，向更高层次的应用发展拓展了巨大的空间。总结模糊控制作为一门综合应用范例，在全球信息化浪潮的推动下，在未来的几十年中，必将对经济的迅猛发展注入新的活力，有专家认为，下一代工控的基础是模糊控制，神经网络，混沌理论为支柱的人工智能。随着模糊控制理论研究的日益完善和深入，应用范围的日益扩大和配套IC的研发制造，模糊控制将给工控领域的发展开辟光明的应用前景，同时也给各领域的研究人员提出了更重大的任务。
