1、 目 录Part 1 PID type fuzzy controller and parameters adaptive method1Part 2 Application of self adaptation fuzzy-PID control for main steam temperature control system in power station7Part 3 Neuro-fuzzy generalized predictive control of boiler steam temperature13Part 4 为Part3译文：锅炉蒸汽温度模糊神经网络的广义预测控制21P
2、art 1 PID type fuzzy controller and Parameters adaptive method 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 e
3、rror 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 contro
4、ller, 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; Adapti
5、ve 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 c
6、ontrol 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 infe
7、rence. 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 simplif
8、ied 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 a
9、pplied 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 prod
10、uct-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 exis
11、t 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 ch
12、ange 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 wil
13、l 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 con
14、trol can reduce the overshoot of the systems 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
15、 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 cont
16、rol the process by watching the error and the change rate of the error between the systems 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 construct
17、ing 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 integr
18、ator serially connected to the output of the fuzzy controller as shown in Fig. 1. In Fig. 1,andare scaling factors for e and 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 neighborho
19、od 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 ca
20、ll 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 des
21、cription 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: (2)Where K = 16, = 1, and= 0.5. In our simulation experiments, we use the discrete simulation method, the results would be slightly d
22、ifferent 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 resp
23、onse 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 systems response is slow, and when it is too large, there is a high overshoot and serious oscillation. Therefore, we may want to
24、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
25、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
26、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
27、 controller (PID fc). Figs. 7 and 8 are the simulation results of the systems 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
28、 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 comparativel
29、y good performance by carefully choosing the value of and. 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
30、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 yie
31、lds 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
32、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, inte
33、gral 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 is about to se
34、ttle down, meanwhile keeps the proportional control component unchanged so as to guarantee quick reaction against the systems error. With the parameter adaptive fuzzy controller, the oscillation of the system is strongly restrained and the settling time is shortened considerably. We have presented t
35、he simulation results to demonstrate the fine performance of the proposed PID type fuzzy controller and the parameter adaptive fuzzy controller structure. Part 2 Application of self adaptation fuzzy-PID control for main steam temperature control system in power stationAbstract: In light of the large
36、 delay, strong inertia, and uncertainty characteristics of main steam temperature process, a self adaptation fuzzy-PID serial control system is presented, which not only contains the anti-disturbance performance of serial control, but also combines the good dynamic performance of fuzzy control. The
37、simulation results show that this control system has more quickly response, better precision and stronger anti-disturbance abilityKeywords：Main steam temperature；Self adaptation；Fuzzy control；Serial control1. IntroductionThe boiler superheaters of modem thermal power station run under the condition
38、of high temperature and high pressure, and the superheaters temperature is highest in the steam channelsso it has important effect to the running of the whole thermal power stationIf the temperature is too high, it will be probably burnt out. If the temperature is too low ,the efficiency will be red
39、uced So the main steam temperature mast be strictly controlled near the given valueFig l shows the boiler main steam temperature system structure.Fig.1 boiler main steam temperature systemIt can be concluded from Fig l that a good main steam temperature control system not only has adequately quickly
40、 response to flue disturbance and load fluctuation, but also has strong control ability to desuperheating water disturbance. The general control scheme is serial PID control or double loop control system with derivative. But when the work condition and external disturbance change large, the performa
41、nce will become instable. This paper presents a self adaptation fuzzy-PID serial control system. which not only contains the anti-disturbance performance of serial control, but also combines the good dynamic character and quickly response of fuzzy control1. Design of Control System The general regul
42、ation adopts serial PID control system with load feed forwardwhich assures that the main steam temperature is near the given value 540in most conditionIf parameter of PID control changeless and the work condition and external disturbance change large, the performance will become in stableThe fuzzy c
43、ontrol is fit for controlling non-linear and uncertain process. The general fuzzy controller takes error E and error change ratio EC as input variablesactually it is a non-linear PD controller, so it has the good dynamic performanceBut the steady error is still in existence. In linear system theory,
44、 integral can eliminate the steady error. So if fuzzy control is combined with PI control, not only contains the anti-disturbance performance of serial control, but also has the good dynamic performance and quickly response.In order to improve fuzzy control self adaptation ability, ProfLong Sheng-Zh
45、ao and Wang Pei-zhuang take the located in bringing forward a new idea which can modify the control regulation onlineThis regulation is: This control regulation depends on only one parameter.Onceis fixedthe weight of E and EC will be fixed and the self adaptation ability will be very smallIt was imp
46、roved by Prof. Li Dong-hui and the new regulation is as follow; Because it is very difficult to find a self of optimum parameter, a new method is presented by ProfZhou Xian-Lan, the regulation is as follow: But this algorithm still can not eliminate the steady errorThis paper combines this algorithm
47、 with PI control，the performance is improved2. Simulation of Control System3.1 Dynamic character of controlled object Papers should be limited to 6 pages Papers longer than 6 pages will be subject to extra fees based on their lengthFig .2 main steam temperature control system structureFig 2 shows th
48、e main steam temperature control system structure，are main controller and auxiliary controller,are characters of the leading and inertia sections,are measure unit.3.2 Simulation of the general serial PID control systemThe simulation of the general serial PID control system is operated by MATLAB, the simulation modal is as Fig.3.Setp1 and Setp2 are the given value disturbance and superheating water disturb & rice .PID Controller1 and PID Controller2 are main controller and auxiliary controllerThe parameter value which co
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