电气自动化专业毕业论文中英文资料外文翻译文献
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g PD controller. Since the mathematical models of mostindustrial process systems are of type obviously there would exist an steady-stateerror if they are controlled by this kind of fuzzy controller. This characteristic hasbeen 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 canimagine to substitute the input the change rate of error or the derivative of error ofthe fuzzy controller with the integration of error. This will result the fuzzy controllerbehaving like a parameter time-varying PI controller thus the steady-state error isexpelled by the integration action. However a PI type fuzzy controller will have aslow rise time if the P parameters are chosen small and have a large overshoot if the Por I parameters are chosen large. So there may be the time when one wants tointroduce not only the integration control but the derivative control to the fuzzycontrol system because the derivative control can reduce the overshoot of thesystems response so as to improve the control performance. Of course this can berealized by designing a fuzzy controller with three inputs error the change rate oferror and the integration of error. However these methods will be hard to implementin practice because of the difficulty in constructing fuzzy control rules. Usually fuzzycontrol rules are constructed by summarizing the manual control experience of anoperator who has been controlling the industrial process skillfully and operator intuitively regulates the executor to control the process by watching the 2error and the change rate of the error between the systems output and the set-pointvalue. It is not the practice for the operator to observe the integration of er adding one input variable will greatly increase the number of control rulesthe constructing of fuzzy control rules are even more difficult task and it needs morecomputation efforts. Hence we may want to design a fuzzy controller that possessesthe fine characteristics of the PID controller by using only the error and the changerate of error as its inputs. One way is to have an integrator serially connected to the output of the fuzzycontroller as shown in Fig. 1. In Fig. 1 K1 and K 2 are scaling factors for e and respectively and fl is the integral constant. In the proceeding text for conveniencewe did not consider the scaling factors. Here in Fig. 2 when we look at theneighborhood of NODE point in the e - plane it follows from 1 that the controlinput to the plant can be approximated by 1 Hence the fuzzy controller becomes a parameter time-varying PI controller itsequivalent proportional control and integral control components are BK2D and ilK1 Prespectively. We call this fuzzy controller as the PI type fuzzy controller PI fc. Wecan hope that in a PI type fuzzy control system the steady-state error becomes zero. 3To verify the property of the PI type fuzzy controller we carry out som
e simulationexperiments. Before presenting the simulation we give a description of the simulationmodel. In the fuzzy control system shown in Fig. 3 the plant model is a second-orderand type system with the following transfer function: K Gs 2 T1s 1T2 s 1 Where K 16 T1 1 and T2 0.5. In our simulation experiments we use thediscrete simulation method the results would be slightly different from that of acontinuous system the sampling time of the system is set to be 0.1 s. For the fuzzycontroller 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 thesimulation result of step response of the fuzzy control system with a Pl fc. We can seethat the steady-state error of the control system becomes zero but when theintegration factor fl is small the systems response is slow and when it is too largethere is a high overshoot and serious oscillation. Therefore we may want to introducethe derivative control law into the fuzzy controller to overcome the overshoot andinstability. We propose a controller structure that simply connects the PD type and thePI type fuzzy controller together in parallel. We have the equivalent structure of thatby connecting a PI device with the basic fuzzy controller serially as shown in is the weight on PD type fuzzy controller and fi is that on PI type fuzzycontroller the larger a/fi means more emphasis on the derivative control and lessemphasis on the integration control and vice versa. It follows from 7 that the outputof the fuzzy controller is 3 43. The parameter adaptive method Thus the fuzzy controller behaves like a time-varying PID controller itsequivalent proportional control integral control and derivative control componentsare respectively. We call this new controller structure a PID type fuzzy controller PIDfc. Figs. 7 and 8 are the simulation results of the systems step response of suchcontrol system. The influence of and fl to the system performance is gt 0 and/3 0 meaning that the fuzzy controller behaves like PD fc thereexist a steady-state error. When 0 and fl gt 0 meaning that the fuzzy controllerbehaves like a PI fc the steady-state error of the system is eliminated but there is alarge overshoot and serious gt 0 and 13 gt 0 the fuzzy controller becomes a PID fc the overshoot issubstantially reduced. It is possible to get a comparatively good performance bycarefully choosing the value of and .4. Conclusions 5 We have studied the input-output behavior of the product-sum crisp type fuzzycontroller revealing that this type of fuzzy controller behaves approximately like aparameter time-varying PD controller. Therefore the analysis and designing of afuzzy control system can take advantage of the conventional PID control ing to the coventional PID control theory we have been able to propose someimprovement methods for the crisp type fuzzy controller. It has been illustrated
that the PD type fuzzy controller yields a steady-state errorfor the type system the PI type fuzzy controller can eliminate the steady-state proposed a controller structure that combines the features of both PD type and PItype fuzzy controller obtaining a PID type fuzzy controller which allows the controlsystem 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 controllerthe authors designed a parameter adaptive fuzzy controller. The PID type fuzzycontroller can be decomposed into the equivalent proportional control integral controland the derivative control components. The proposed parameter adaptive fuzzycontroller decreases the equivalent integral control component of the fuzzy controllergradually with the system response process time so as to increase the damping of thesystem when the system is about to settle down meanwhile keeps the proportionalcontrol component unchanged so as to guarantee quick reaction against the systemserror. With the parameter adaptive fuzzy controller the oscillation of the system isstrongly restrained and the settling time is shortened considerably. We have presented the simulation results to demonstrate the fine performance ofthe proposed PID type fuzzy controller and the parameter adaptive fuzzy controllerstructure. 6Part 2 Application of self adaptation fuzzy-PIDcontrol for main steam temperature control system in power stationAbstract: In light of the large delay strong inertia and uncertainty characteristics ofmain steam temperature process a self adaptation fuzzy-PID serial control system ispresented which not only contains the anti-disturbance performance of serial controlbut also combines the good dynamic performance of fuzzy control. The simulationresults show that this control system has more quickly response better precision andstronger anti-disturbance ability．Keywords ： Main steam temperature ； Self adaptation ； Fuzzy control ； Serial control1. Introduction The boiler superheaters of modem thermal power station run under the conditionof high temperature and high pressure and the superheater’s temperature is highest inthe steam channels．so it has important effect to the running of the whole thermalpower station．If the temperature is too high it will be probably burnt out. If thetemperature is too low the efficiency will be reduced So the main steam temperaturemast be strictly controlled near the given value．Fig l shows the boiler main steamtemperature system structure. Fig.1 boiler main steam temperature system It can be concluded from Fig l that a good main steam temperature control system not only has adequately quickly response to flue disturbance and load 7 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 disturba
nce change large the performance 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 control．1. Design of Control System The general regulation adopts serial PID control system with load feedforward．which assures that the main steam temperature is near the given value540℃in most condition ．If parameter of PID control changeless and the workcondition and external disturbance change large the performance will become instable．The fuzzy control is fit for controlling non-linear and uncertain process. Thegeneral fuzzy controller takes error E and error change ratio EC as inputvariables．actually it is a non-linear PD controller so it has the good dynamic Butperformance． the steady error is still in existence. In linear system theory integralcan eliminate the steady error. So if fuzzy control is combined with PI control notonly contains the anti-disturbance performance of serial control but also has the gooddynamic performance and quickly response. In order to improve fuzzy control self adaptation ability Prof．Long Sheng-Zhaoand Wang Pei-zhuang take the located in bringing forward a new idea which canmodify the control regulation online．This regulation is: U E 1 EC 01 This control regulation depends on only one parameter .Once is fixed．theweight of E and EC will be fixed and the self adaptation ability will be very small．Itwas improved by Prof. Li Dong-hui and the new regulation is as follow 8 0 E 1 0 EC E 0 E 1 1 EC E 1 U 1 2 E 1 2 EC E 2 3 E 1 3 EC E 3 0 1 2 3 01 Because it is very difficult to find a self of optimum parameter a new method ispresented by Prof．Zhou Xian-Lan the regulation is as follow: 1 expke 2 k 0 But this algorithm still can not eliminate the steady error．This paper combinesthis algorithm with PI control，the performance is improved．2. 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 toextra fees based on their length． Fig .2 main steam temperature control system structure Fig 2 shows the main steam temperature control system structure ，W 1 s W 2 s are main controller and auxiliary controller Wo1 s Wo 2 s are charactersof the leading and inertia sections WH 1 s WH 2 s are measure unit.3.2 Simulation of the general serial PID control system 9 The simulation of the general serial PID control system is operated by MATLABthe simulation modal is as 1 and Setp2 are the given value disturb.