基于s7 - 300的plc模糊pid控制器调节因子 英语论文及其翻译.doc

A Fuzzy-PID Controller with adjustable factor based on S7-300 PLCAbstract: A Fuzzy-PID controller with adjustable factor is designed in this paper. Scale factor’s self-adjust will come true. Fuzzy control algorithm is finished in STEP7 software, and then downloaded in S7-300 PLC. WinCC software will be used to control the change-trend in real time. Data communication between S7-300 PLC and WinCC is achieved by MPI. The research shows that this Fuzzy-PID controller has better robust capability and stability. It’s an effective method in controlling complex long time-varying delay systems.Keywords: fuzzy-PID, adjustable factor, temperature control, MPI.1 INTRODUCTIONTemperature control is very important in industrial production. The most common temperature control objects in modern industry are boiler, electric furnace, the control system of steam plant and distillation column (F. G. Hinskey, 2004). Temperature control system generally has the characteristic of large inertia and delay, so it’s difficult to establish mathematical model exactly. In industrial production process, some control methods have been employed, such as PID control (Bolat, E.D., Erkan, K., Postalcioglu, S., 2005), Smith predictive control (He, S.- Z.,Xu, F.-L., Tan, S., 1992), Model predictive control, Fuzzy control (Chia-Feng Juang, Jung-Shing Chen, Hao- Jung Huang , 2004), Robust control (Ingram, J.E., Hodel, A.S., Kirkici, H., 1997) Neural network (Khalid, M., Omatu, S. etc 1992). PID controller is still widely used in process control field for its many advantages. But for the time- varying process with large time-delay, traditional PID algorithm has many shortcomings: the control accuracy is low, the structure is difficult to stabilize and the algorithm is more sensitive in the match degree of the models. Therefore, industrial process control which has large time-delay is still a recognized difficult problem at present. And for large lag, time-varying process whose object parameters changed as working condition and environment changed, it is more difficult to control it. And for large lag, time-varying process whose object parameters change as working condition and environment change, it is more difficult to control it. Fuzzy control has the characteristic that doesn’t charged with the object model and with strong robust, but conventional fuzzy control can not overcome negative effects caused by large-lag very well. In this page we’ll give a design of a hybrid fuzzy controller.The project is aided by the key Scientific and Technological Project (Industry Part) of Jiangsu Province (BE2006090), the Science and Technology Innovation Fund of Jiangsu University (1293000240) and the Natural Fund for Colleges and Universities in Jiangsu Province (05KDJ470048).2 THE SELECT AND IMPLEMENT OF CONTROL METHODCommonly used two-dimensional fuzzy control system always takes systematic error e and the error rate ec as input variables. This kind of control system can be divided into two categories: Fuzzy PD control and Fuzzy PI control. Fuzzy PD control takes u as output while Fuzzy PI control takes Δu as output (Han-Xiong Li, Gatland. H.B., 1996). In this page, we choose fuzzy PI controller as shown in Figure 1 （Chu Jing etc., 1999). EtetKeΔUtFuzzyDeduc-tionutKuect+1-Z-1KcECtZ-1u(t-1) Figure 1 Fuzzy PI Control’s block diagramIn this fuzzy controller, ut is control variable, is controlled variable, SV is reference input, the input of fuzzy controller is error Et and error difference ECt, the output isΔut. Ke and Kc are the quantify factors of error and error rate respective. Ku is the proportion factor of fuzzy PI controller. The fuzzy control algorithm has been brought into effect in Step7 (Liao Changchu , 2005) and downloaded in S7-300PLC, the monitor picture and tendency chart have been established by monitor software WinCC (Kun Zhe, 2004) and used to monitor the change trends of controlled plant, the data communication between S7-300 PLC and WinCC is built by MPI net. In this page we choose AE2000A process control equipment’s boiler temperature as controlled plant.The fuzzy control algorithm was realized by inquiring a two-dimensional table on-line. The process can be divided into the following three steps:Step 1: Calculate system's error and error rate according to the sampling signal and the given value in the control circuit. Then fuzzed the error and error rate according to these two equations: Ke = n/emax and Kc=m/cmaxStep 2: Inquire the two-dimensional table according to the fuzzified error and error rate. In Step7, there’s no special instruction for inquiring two-dimensional table. As we known that the data structure in microprocessor is linear, so we written a two-dimensional polling routine based on this characteristic. 。