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信息与电子工程系毕业设计（中英文资料） 毕业设计(论文)中英文资料 信 电 系 工业电气自动化 专业 04 级 1 班课题名称：MCS-51单片机智能温度控制系统设计毕业设计(论文)起止时间：2006年 2月10日6月8日(共17周)学生姓名： 林晋斌 学号： 10 指导教师： 黄云龙 廖东进 朱秋琴报告日期： 2007年3月 1日 摘要：本文根据模糊控制和PID控制的特点及其原理，把模糊控制和PID控制结合起来，形成模糊PID控制，有效的克服了它们的缺点而发挥了它们的优势。本文详细阐述了该系统中模糊PID控制器的实现方法、系统的各种控制、故障检测以及状态显示。 模糊PID控制器实际上跟传统的PID控制器有很大联系。区别在于传统的控制器的控制前提必须是熟悉控制对象的模型结构，而模糊控制器因为它的非线性特性，所以控制性能优于传统PID控制器。对于时变系统，如果能够很好地采用模糊控制器进行调节，其控制结果的稳定性和活力性都会有改善。但是，如果调节效果不好，执行器会因为周期振荡影响使用寿命，特别是调节器是阀门的场合，就必须考虑这个问题。为了解决这个问题，出现了很多模糊控制的分析方法。本文提出的方法采用一个固定的初始域，这样相当程度上简化了模糊控制的设定问题以及实现。文中分析了振荡的原因并分析如何抑制这种振荡的各种方法，最后，还给出一种方案，通过减少隶属函数的数量以及改善解模糊化的方法缩短控制信号计算时间，有效的改善了控制的实时性。模糊控制器的一个主要缺陷就是调整的参数太多。特别是参数设定的时候，因为没有相关的书参考，所以它的给定非常困难。众所周知，优化方法的收敛性跟它的初始化设定有很大关联，如果模糊控制器的初始域是固定的，那么它的控制就明显的简化了。而且我们要控制的参数大多有其实际的物理意义，所以模糊控制器完全可以利用PID算法的控制规律进行近似的调整。也就是说最简单的模糊PID控制器就是同时采用几种基本模糊控制算法（P+I+D或者PI+D），控制过程中它会根据控制要求，做出适当的选择，保证在处理跟踪以抗阶跃干扰问题上，其控制性能接近于任何一种PID控制。假设模糊集的初始域是对称的，两个调节器的参数采用Ziegler-Nichols方法。为了改善上述设计的模糊控制器，我们有必要考模糊控制器的参数问题，有两种方法可以采纳，一种采用手动的方法改变，另一种就是采用一些相关的优化算法。其中遗传算法就是一种。控制器采用的参数不同，其收敛的优化值也会不一样。这些参数包括模糊集的分布，模糊集的个数，映射规则，基本模糊控制器的参数和不同的算法组合等。要注意的是在优化前必须选定模糊推理及解模糊的方法。很明显，优化过程很耗时，更有甚者，有些优化方法要已知系统的精确模型，但是实际过程中难以得到系统的精确模型，所以在大多数情况下，这些优化算法不能直接应用在实际过程。也就是说模型不精确直接影响优化成败。模糊控制的主要思想就是针对那些传递函数未知的或者结构难以辨识的系统进行控制，这也是模糊控制的性能为什么优于传统方法的原因。同时，把模糊控制和传统的PID控制算法结合起来，更能体现这种算法的优点，因为它大大简化实际过程的调整。 参数集的启发式优化法也适用于模糊PI控制器，它采用固定的定义域，其参数的选取和传统的PI控制器都一样。我们采用的控制方法是结合模糊PI算法和PD算法并利用启发式优化法处理参数集，特别要注意这里的调节器出现了两个比例环节，所以它的控制可能不同于传统的PID算法。但是我们调整的参数它们本身具有实际的物理意义，值得一提的是前面所提到的控制可以通过改变采样时间而不改变定义域的范围实现调整。 关键词：单片机；热处理温度控制；模糊 PIDAbstract:This paper adopts fuzzy PID control algorithm which combines fuzzy control and PID control according individual characteristic and theory effectively gets over their disadvantage, at the same time, preserving their merits. The methods of the fuzzy-PID controller, system-controlling, failure-detecting，states-displaying are described in details. A fuzzy PID controllers are physically related to classical PID controller. The settings of classical controllers are based on deep common physical background. Fuzzy controller can embody better behavior comparing with classical linear PID controller because of its non linear characteristics. Well tuned fuzzy controller can be also more stable and more robust for the time varying systems. On the other hand, when the fuzzy controller is tuned badly it can exhibit limit cycle which can decrease lifetime of the actuator. This phenomenon is critical especially when the actuator is valve. Knowing about these problems, more analytical methods of tuning fuzzy controllers can be found. The method with unified universe considerably simplifies the setting and realization of fuzzy controllers. This paper tries to analyze causes of oscillations and it outlines the possibilities how to reduce them. The paper also shows solution how to reduce time needed for computation of control signal by decreasing the number of membership functions and by changing defuzzification method. 1. INTRODUCTIONOne of the main drawbacks of fuzzy controllers is big amount of parameters to be tuned. It is especially difficult to make initial approximate adjustment because there is no cookery book how to do it. Also it is very well known that good convergence of optimum method is strongly dependent on initial settings. The adjustment of fuzzy controllers is considerably simplified when fuzzy controller with a unified universe is used. The parameters to be tune then have their physical meaning and fuzzy controller can be approximately adjusted using known rules for classical controllers. Probably the easiest way how to implement fuzzy PID controller is to create it as a parallel combination of basic fuzzy controllers (P+I+D 4 or PI+D 5). Suitable choice of inference method can ensure behavior which is close to one of classical PID controller for both the tracking problem and the step disturbance rejection. The fuzzy sets are assumed to have initially symmetrical layout and the parameters of both regulators are tuned using for example by Ziegler- Nichols method.To improve behavior of such designed fuzzy controller it is necessary either to manually change the quantities of fuzzy controller or to use some optimum methods which do this operation. One which can be implied are genetic algorithms. Different quantities can be changed to reach the optimum values. These quantities are fuzzy set layout, number of fuzzy sets, rule base mapping, the parameters of basic fuzzy controllers and their various combinations. Note that all the optimum must be always performed according to the chosen inference and defuzzification method. It is apparent that process of optimum can take a lot of time. Moreover this method is contingent on existence of accurate mathematical model of the process because in vast majority of the cases it is not possible to perform any kind of optimum directly on real process. The model usually does not correspond to real system which limits the success of optimum methods. The prime idea of fuzzy control was to apply it at the place where there is no deep knowledge of transfer function of controlled system and where this knowledge can be hardly identified. These are often the cases where the fuzzy control leads to better performance comparing with classical approach. Also for this instance it seems to be advantageous to have physical connection between fuzzy controller and its classical counterpart because it can significantly simplify the adjustment of regulator for real process.The heuristic optimum of parameters settings is also suitable for fuzzy PI controller with unified universe where the parameters are the same as the ones of class