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The simulation of the general serial PID control system is operated by MATLAB, the simulation modal is as Fig.3.Setp1 and Setp2 are the given value disturbance and superheating water disturb & rice .PID Controller1 and PID Controller2 are main controller and auxiliary controller．
The parameter value which comes from references is as follow：
W?2(s)?kp2?251W?1(s)?kp1?kI1?kD1s
skp1?3.33,kI1?0.074,kD1?37.667
Fig.3. the general PID control system simulation modal
3.3 Simulation of self adaptation fuzzy-PID control system Spacing
The simulation modal is as Fig 4.Auxiliary controller is:W?2(s)?kp2?25.Main controller is Fuzzy-PI structure, and the PI controller is:
W?1(s)?kp1?kI11s
kp1?3.33,kI1?0.074Fuzzy controller is realized by S-function, and the code is as fig.5.
Fig.4. the fuzzy PID control system simulation modal
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Fig 5 the S-function code of fuzzy control
3.4 Comparison of the simulation
Given the same given value disturbance and the superheating water disturbance，we compare the response of fuzzy-PID control system with PID serial control system. The simulation results are as fig.6-7.
From Fig6-7,we can conclude that the self adaptation fuzzy-PID control system has the more quickly response, smaller excess and stronger anti-disturbance．
4. Conclusion
(1)Because it combines the advantage of PID controller and fuzzy controller, the
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self adaptation fuzzy-PID 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．
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Part 3 Neuro-fuzzy generalized predictive control of boiler steam temperature
Xiangjie LIU, Jizhen LIU, Ping GUAN
Abstract: Power plants are nonlinear and uncertain complex systems. Reliable control of superheated steam temperature is necessary to ensure high efficiency and high load-following capability in the operation of modern power plant. A nonlinear generalized predictive controller based on neuro-fuzzy network (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: Neuro-fuzzy networks; Generalized predictive control; Superheated
steam temperature
1. Introduction
Continuous process in power plant and power station are complex systems characterized by nonlinearity, uncertainty and load disturbance. The superheater is an important part of the steam generation process in the boiler-turbine system, where steam is superheated before entering the turbine that drives the generator. Controlling superheated steam temperature is not only technically challenging, but also economically important.
From Fig.1,the steam generated from the boiler drum passes through the low-temperature superheater before it enters the radiant-type platen superheater. Water is sprayed onto the steam to control the superheated steam temperature in both the low and high temperature superheaters. Proper control of the superheated steam temperature is extremely important to ensure the overall efficiency and safety of the power plant. It is undesirable that the steam temperature is too high, as it can damage the superheater and the high pressure turbine, or too low, as it will lower the efficiency of the power plant. It is also important to reduce the temperature
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fluctuations inside the superheater, as it helps to minimize mechanical stress that causes micro-cracks in the unit, in order to prolong the life of the unit and to reduce maintenance costs. As the GPC is derived by minimizing these fluctuations, it is amongst the controllers that are most suitable for achieving this goal.
The multivariable multi-step adaptive regulator has been applied to control the superheated steam temperature in a 150 t/h boiler, and generalized predictive control was proposed to control the steam temperature. A nonlinear long-range predictive controller based on neural networks is developed into control the main steam temperature and pressure, and the reheated steam temperature at several operating levels. The control of the main steam pressure and temperature based on a nonlinear model that consists of nonlinear static constants and linear dynamics is presented in that.
Fig.1 The boiler and superheater steam generation process
Fuzzy logic is capable of incorporating human experiences via the fuzzy rules. Nevertheless, the design of fuzzy logic controllers is somehow time consuming, as the fuzzy rules are often obtained by trials and errors. In contrast, neural networks not only have the ability to approximate non-linear functions with arbitrary accuracy, they can also be trained from experimental data. The neuro-fuzzy networks developed recently have the advantages of model transparency of fuzzy logic and learning capability of neural networks. The NFN is have been used to develop self-tuning control, and is therefore a useful tool for developing nonlinear predictive control. Since NFN is can be considered as a network that consists of several local re-gions, each of which contains a local linear model, nonlinear predictive control based on
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