【正文】 put of the fuzzy controller as shown in Fig. 1. In Fig. 1, 1K and 2K are 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 neighborhood 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 bees a parameter timevarying PI controller, its equivalent proportional control and integral control ponents are BK2D and ilK1 P respectively. We call this fuzzy controller as the PI type fuzzy controller (PI fc). We can hope that in a PI type fuzzy control system, the steadystate error bees zero. 4 To verify the property of the PI type fuzzy controller, we carry out some simulation experiments. Before presenting the simulation, we give a description of the simulation model. In the fuzzy control system shown in Fig. 3, the plant model is a secondorder and type system with the following transfer function: )1)(1()( 21 ??? sTsT KsG (2) Where K = 16, 1T = 1, and 2T = . In our simulation experiments, we use the discrete simulation method, the results would be slightly different from that of a continuous system, the sampling time of the system is set to be 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 response of the fuzzy control system with a Pl fc. We can see that the steadystate error of the control system bees zero, but when the integration factor fl is small, the system39。s 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 steadystate error. When ~ = 0 and fl 0, meaning that the fuzzy controller behaves like a PI fc, the steadystate error of the system is eliminated but there is a large overshoot and serious oscillation. When ~ 0 and 13 0 the fuzzy controller bees a PID fc, the overshoot is substantially reduced. It is possible to get a paratively good performance by carefully choosing the value of ? and? . 4. Conclusions 6 We have studied the inputoutput behavior of the productsum crisp type fuzzy controller, revealing that this type of fuzzy controller behaves approximately like a parameter timevarying PD controller. Therefore, the analysis 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 yields a steadystate error for the type system, the PI type fuzzy controller can eliminate the steadystate error. We proposed a controller structure, that bines the features of both PD type and PI type fuzzy controller, obtaining a PID type fuzzy controller which allows the control system 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 controller, the authors designed a parameter adaptive fuzzy controller. The PID type fuzzy controller can be deposed into the equivalent proportional control, integral control and the derivative control ponents. The proposed parameter adaptive fuzzy controller decreases the equivalent integral control ponent 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 settle down, meanwhile keeps the proportional control ponent unchanged so as to guarantee quick reaction against the system39。 9 ]1,0[,3,)1(2,)1(1,)1(0,)1({321033221100?????????????????????????????EECEEECEEECEEECEU Because it is very difficult to find a self of optimum parameter, a new method is presented by Prof． Zhou XianLan, the regulation is as follow: )0(),e x p (1 2 ???? kke? But this algorithm still can not eliminate the steady error． This paper bines this algorithm with PI control， the performance is improved． 2. Simulation of Control System 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 length． Fig .2 main steam temperature control system structure Fig 2 shows the main steam temperature control system structure ，)(),( 21 sWsW ?? are main controller and auxiliary controller, )(),( 21 sWsW oo are characters of the leading and inertia sections, )(),( 21 sWsW HH are measure unit. Simulation of the general serial PID control system 10 The simulation of the general serial PID control system is operated by MATLAB, the simulation modal is as and Setp2 are the given value disturbance and superheating water disturb amp。 Generalized predictive control。39。 tentete e (1) where f[.]is a smooth nonlinear function such that a Taylor series expansion exists, e(t)is a zero mean white noise andΔ is the differencing operator, 39。39。現(xiàn)代電廠的運行中，為確保電廠的高效率和高負荷的能力，準確的 控制過熱蒸汽溫度是必要的。所提出的 非線性控制器適用于控制一臺 200 MW 電廠 的 過熱蒸汽溫度。 關(guān)鍵詞 ：模糊神 經(jīng)網(wǎng)絡(luò) ； 廣義預測控制 ； 過熱蒸汽溫度 1. 引言 電廠過熱汽溫控制 系統(tǒng)的特點是非線性 、 不確定性和負載擾動。 圖 1 鍋爐過熱器和蒸汽生成過程 從圖 1可以看出， 產(chǎn)生的蒸汽從鍋爐汽包通過低溫過熱器 后 進入輻射型屏。適當?shù)目刂齐姀S過熱蒸汽溫度是極其重要的， 可以 確保整體效率和安全性。減少溫度波動也是 非常 重要的，因為它有助于減少在單位內(nèi)機械應(yīng)力造成的微裂紋，延長單位秩序壽命，并減少維修成本。 22 多變量多步自適應(yīng)調(diào)節(jié)已適用于控制過熱蒸汽溫度在 150 ht/ 的 鍋爐 ，提出了 廣義預測控制以控制蒸汽溫度 ， 基于神經(jīng)網(wǎng)絡(luò)發(fā)展 的 非線性預測控制器是以控制主蒸汽溫度和壓力 。 模糊邏輯是把人類的經(jīng)驗透過模糊規(guī)則表現(xiàn)出來。在此相反，神經(jīng)網(wǎng)絡(luò)不僅有近似的非線性職能與任意精度，他們也可以有 經(jīng)過試驗 的實驗