【正文】 code of fuzzy control Comparison of the simulation Given the same given value disturbance and the superheating water disturbance，we pare the response of fuzzyPID control system with PID serial control system. The simulation results are as . From Fig67,we can conclude that the self adaptation fuzzyPID control system has the more quickly response, smaller excess and stronger antidisturbance． 4. Conclusion (1)Because it bines the advantage of PID controller and fuzzy controller, the 12 self adaptation fuzzyPID control system has better performance than the general PID serial control system. (2)The parameter can self adjust according to the error E value. so this kind of controller can harmonize quickly response with system stability． 13 Part 3 Neurofuzzy generalized predictive control of boiler steam temperature Xiangjie LIU, Jizhen LIU, Ping GUAN Abstract: Power plants are nonlinear and uncertain plex systems. Reliable control of superheated steam temperature is necessary to ensure high efficiency and high loadfollowing capability in the operation of modern power plant. A nonlinear generalized predictive controller based on neurofuzzy work (NFGPC) is proposed in this paper. The proposed nonlinear controller is applied to control the superheated steam temperature of a 200MW power plant. From the experiments on the plant and the simulation of the plant, much better performance than the traditional controller is obtained. Keywords: Neurofuzzy works。s 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 the simulation results to demonstrate the fine performance of the proposed PID type fuzzy controller and the parameter adaptive fuzzy controller structure. 7 Part 2 Application of self adaptation fuzzyPID control for main steam temperature control system in power station Abstract: In light of the large delay, strong inertia, and uncertainty characteristics of main steam temperature process, a self adaptation fuzzyPID serial control system is presented, which not only contains the antidisturbance performance of serial control, but also bines the good dynamic performance of fuzzy control. The simulation results show that this control system has more quickly response, better precision and stronger antidisturbance ability． Keywords： Main steam temperature ； Self adaptation； Fuzzy control ； Serial control 1. Introduction The boiler superheaters of modem thermal power station run under the condition of high temperature and high pressure, and the superheater’s temperature is highest in the steam channels． so it has important effect to the running of the whole thermal power station． If the temperature is too high, it will be probably burnt out. If the temperature is too low ,the efficiency will be reduced So the main steam temperature mast be strictly controlled near the given value． Fig l shows the boiler main steam temperature system structure. 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 8 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 performance will bee instable. This paper presents a self adaptation fuzzyPID serial control system. which not only contains the antidisturbance performance of serial control, but also bines 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 feed forward． which assures that the main steam temperature is near the given value 540℃ in most condition． If parameter of PID control changeless and the work condition and external disturbance change large, the performance will bee in stable． The fuzzy control is fit for controlling nonlinear and uncertain process. The general fuzzy controller takes error E and error change ratio EC as input variables． actually it is a nonlinear PD controller, so it has the good dynamic performance． But the steady error is still in existence. In linear system theory, integral can eliminate the steady error. So if fuzzy control is bined with PI control, not only contains the antidisturbance performance of serial control, but also has the good dynamic performance and quickly response. In order to improve fuzzy control self adaptation ability, Prof． Long ShengZhao and Wang Peizhuang take the located in bringing forward a new idea which can modify the control regulation online． This regulation is: ]1,0[,)1( ???? ??? ECEU This control regulation depends on only one parameter? .Once? is fixed． the weight of E and EC will be fixed and the self adaptation ability will be very small． It was improved by Prof. Li Donghui and the new regulation is as follow。s response is slow, and when it is too large, there is a high overshoot and serious oscillation. Therefore, we may want to introduce the derivative control law into the fuzzy controller to overe 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 the basic fuzzy controller serially as shown in . Where ~ is the weight on PD type fuzzy controller and fi is that on PI type fuzzy controller, the larger a