自动化专业外文翻译---锅炉蒸汽温度模糊神经网络的广义预测控制

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1、PDF外文：http:/ 中文2527字1   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 st

2、eam 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

3、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.

4、 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 en

5、tering 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 ra

6、diant-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

7、 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  2 fluctuations inside the superheater, as it helps to minimize

8、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 mult

9、i-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 stea

10、m 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 s

11、team 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 t

12、he 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 devel

13、op 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  3 NFN can be devised with

14、the network incorporating all the local generalized predictive controllers (GPC) designed using the respective local linear models. Following this approach, the nonlinear generalized predictive controllers based on the NFN, or simply, the neuro-fuzzy generalized predictive controllers (NFG-PCs)are d

15、erived here. The proposed controller is then applied to control the superheated steam temperature of the 200MW power unit. Experimental data obtained from the plant are used to train the NFN model, and from which local GPC that form part of the NFGPC is then designed. The proposed controller is test

16、ed first on the simulation of the process, before applying it to control the power plant. 2. Neuro-fuzzy network modelling Consider the following general single-input single-output nonlinear dynamic system:         ),1() ,. .,(),() ,. .,1()( '' uy ndtudtuntytyfty   &

17、nbsp;           /)()() ,. .,1( ' 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 and is the differencing operator,

18、 ''' ,euy nnnand d are respectively the known orders and time delay of the system. Let the local linear model of the nonlinear system (1) at the operating point )(to be given by the following Controlled Auto-Regressive Integrated Moving Average (CARIMA) model:        

19、          )()()()()()( 111 tezCtuzBztyzA d               (2) Where )()(),()( 1111 za n d CzBzAzA are polynomials in 1z , the backward shift operator. Note that the coefficients of these polynomials are a function of the operating point )(to

20、 .The nonlinear system (1) is partitioned into several operating regions, such that each region can be approximated by a local linear model. Since NFN is a class of associative memory networks with knowledge stored locally, they can be applied to model this class of nonlinear systems. A schematic diagram of the NFN is shown in Fig.2.B-spline functions are used as the membership functions in the