【導(dǎo)讀】本篇論文提出一種系統(tǒng)方法來實(shí)現(xiàn)補(bǔ)償多回路協(xié)調(diào)控制策略。該方法使用模糊系統(tǒng)，使火電廠單元機(jī)組可以承擔(dān)大范圍負(fù)荷跟蹤。它可以被描述為是用兩個(gè)步驟來改進(jìn)常規(guī)協(xié)。第一步，用類PID模糊控制器替代常規(guī)PID控制器.這種調(diào)節(jié)器被證實(shí)。對(duì)于電廠參數(shù)的變化具有魯棒性，這使得改進(jìn)后的調(diào)節(jié)器擁有更大的調(diào)節(jié)范圍。用一個(gè)根據(jù)機(jī)組輸入輸出數(shù)據(jù)設(shè)計(jì)的MIMO模糊補(bǔ)償器。來處理由動(dòng)態(tài)工藝系統(tǒng)中的非線性和耦合作用而造成的控制回路之間存在的相互作用。真試驗(yàn)顯示了提出的方法的可行性和表現(xiàn)。在于燃燒燃料，產(chǎn)生蒸汽，并推動(dòng)汽輪機(jī)轉(zhuǎn)動(dòng)產(chǎn)生電力，然后蒸汽被冷凝。構(gòu)成一個(gè)大的熱力循環(huán)，并且這些轉(zhuǎn)換相互關(guān)系緊密。組內(nèi)部能量轉(zhuǎn)換過程的平衡。協(xié)調(diào)控制策略是綜合前兩種控制策略優(yōu)點(diǎn)的控制策略。伸考慮到響應(yīng)緩慢的鍋爐和響應(yīng)快速的汽輪機(jī)。據(jù)文獻(xiàn)3稱，該方案可以設(shè)定最優(yōu)設(shè)定點(diǎn)的響應(yīng)，并過濾已。出的模糊系統(tǒng)，其中其中n表示每個(gè)輸入的值。

　　

【正文】 m (ANFIS) [8,9] was used here. This technique allows the implementation of multiinputsingleoutput (MISO) fistorder Sugenotype FIS with weighted average defuzzification. This approach provides offline learning capability to the control system, where learning is understood as the process of tuning the input membership functions, building the fuzzy inference rules of the knowledge base, and identifying the consequent parameters of the feedforward FIS. The learning process is of the training type and could take place prior to the operation of the control system, or upon demand during operation. Only the former case is considered here. The learning process develops through the following stages. First, a set of inputoutput data, to be used as training data, needs to be generated or obtained from the optional data set can be used as checking data after training. Second, initial FIS structures need to be each input, the range of operation, number of membership functions, as well as their shape, must be defined. Finally, the learning process is carried out using the training data to adjust the membership functions, to create the inference rules, and to determine their consequent parameters. The resultant FIS is verified using the checking data set. IV. COMMENTS AND CONCLUSIONS In this project a conventional CC configuration was updated to attain better response during wide range operation. Performed in two steps, the first step incorporates the operator?s control knowledge in a very simple and general way. In addition, the second step incorporated process knowledge learned offline from inputoutput data in a straightforward fashion. Results showed that the pensated multiloop configuration achieves its objective, Effectively reduces the interaction effects among the control loops, facilitating the control action to the controllers. Current research activities include performance evaluation and parison to other control the main disadvantages of this approach are the need to open the control loops to insert the pensator, and the need to retune the fuzzy controllers after the introduction of the pensator. Nevertheless, tuning the fuzzyPID controllers is easier with the pensator in the loop. V. ACKNOWLEDGMENTS This work was supported in part by NSF under grants INT9605028 and ECS9705 105, The Pennsylvania State University, The Electrical Research Institute, and the FulbrightConacyt Commission. VI. REERENCES 1) ICs. Ray and . Majumder, “Fuzzy Logic Control of a Nonlinear Multivariable Steam Generating Unit Using Decoupling Theory,” IEEE trans. on Systems, Man, and Cyberics, vol. SMC15, no. 4, JulyJAugust 1985, pp. 539558. 2) . Rossiter, B. Kouvaritakis and . Dunt, “Application of Generalized Predictive Control to a BoilerTurbine Unit for Electricity Generation:?IEE ProceedingsD, vol. 138, no. 1, January 1991, pp. 59 67. 3) S. Lu and . Hogg, “Predictive Coordinated Control for Power Plant Steam Pressure and Power Output,” Proceedings IFAC Symposium on Control of Power Plants and Power Systems, vol. 2, December 1995, pp. 245250. 4) R. Dimeo and ICY. Lee, “BoilerTurbine Control System Design Using a Geic Algorithm,” IEEE trans. on Energy Conversion, vol. 10, no. 4, December 1995, pp. 752759. 5) . Weng and A. Ray, “Robust WideRange Control of SteamElectric Power Plants,” IEEE trans. on Control Systems Technology, vol. 5, no. 1, January 1997, pp. 7488. 6) Babcock amp。 Wilcox, Steam 401 ie Generation and Use, Chapter 41. Controls for Fossil FuelFired Steam Generating Plants. 1978 7) . Kwok, P. Tam, Li and P. Wang, “Linguistic PID Controllers,” Proceedings IFAC Illh Triennial World Congress, 1990, pp. 205210. 8) . Jang, “ANFIS: adaptiveworkbased fuzzy inference system,” IEEE Trans. On systems, man, and cyberics, vol. 23, no. 3, May June 1993, pp. 665685 9) . Jang and N. Gulley, Fuzzy logic toolbox. Natick, MS, Mathworks, 1995. 10) . Bell and . Astrom, Dynamic models for boilerturbine alternator unirr: Data logs and parameter estimation for a 160 MW unit, Report: TFRT3192, Lund Institute of Technology, Sweden, 1987 11) R. GardunoRamirez and ICY. Lee, “Fuzzy supelvisor for power plant cyclic operation,” Proceedings of Intelligent System Applications to Power Systems ISAP ?97, July 1997, pp. 431435.