自动化专业毕业论文外文文献翻译

目录
Part 1 PID type fuzzy controller and parameters adaptive
method ........ 1 Part 2 Application of self adaptation fuzzy-PID
control for main steam
temperature control system in power station错误～未定义书签。

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Part 3 Neuro-fuzzy generalized predictive control of boiler steam temperature ........................................................ .......... (13)
Part 4 为Part3译文:锅炉蒸汽温度模糊神经网络的广义预测控制21
Part 1 PID type fuzzy controller and Parameters
adaptive method
Wu zhi QIAO, Masaharu Mizumoto
Abstract: The authors of this paper try to analyze the dynamic behavior of the product-sum crisp type fuzzy controller, revealing that this type of fuzzy controller behaves approximately like a PD controller that may yield steady-state error for the control system. By relating to the conventional PID control theory, we propose a new fuzzy controller structure, namely PID type fuzzy controller which retains the characteristics similar to the conventional PID controller. In order to improve further the performance of the fuzzy controller, we work out a method to tune the parameters of the PID type fuzzy controller on line, producing a parameter adaptive fuzzy controller. Simulation experiments are made to demonstrate the fine performance of these novel fuzzy controller structures.
Keywords: Fuzzy controller; PID control; Adaptive control
1. Introduction
Among various inference methods used in the fuzzy controller found
in literatures , the most widely used ones in practice are the Mamdani method proposed by Mamdani and his associates who adopted the Min-max compositional rule of inference based on an interpretation of a control rule as a conjunction of the antecedent and consequent, and the product-sum method proposed by Mizumoto who suggested to introduce the product and arithmetic mean aggregation operators to replace the logical AND (minimum) and OR (maximum) calculations in the Min-max compositional rule of inference.
In the algorithm of a fuzzy controller, the fuzzy function calculation is also a complicated and time consuming task. Tagagi and Sugeno proposed a crisp type model in which the consequent parts of the fuzzy control rules are crisp functional representation or crisp real numbers in the simplified case instead of fuzzy sets . With this model of crisp real number output, the fuzzy set of the inference consequence will
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be a discrete fuzzy set with a finite number of points, this can greatly simplify the fuzzy function algorithm.
Both the Min-max method and the product-sum method are often applied with the crisp output model in a mixed manner. Especially the mixed product-sum crisp model has a fine performance and the simplest
algorithm that is very easy to be implemented in hardware system and converted into a fuzzy neural network model. In this paper, we will take account of the product-sum crisp type fuzzy controller. 2. PID type
fuzzy controller structure
As illustrated in previous sections, the PD function approximately behaves like a parameter time-varying PD controller. Since the mathematical models of most industrial process systems are of type, obviously there would exist an steady-state error if they are controlled by this kind of fuzzy controller. This characteristic has been stated in the brief review of the PID controller in the previous section.
If we want to eliminate the steady-state error of the control system, we can imagine to substitute the input (the change rate of error or the derivative of error) of the fuzzy controller with the integration of error. This will result the fuzzy controller behaving like a parameter time-varying PI controller, thus the steady-state error is expelled by the integration action. However, a PI type fuzzy controller will have a slow rise time if the P parameters are chosen small, and have a large overshoot if the P or I parameters are chosen large. So there may be the time when one wants to introduce not only the integration control but
the derivative control to the fuzzy control system, because the
derivative control can reduce the overshoot of the system's response so as to improve the control performance. Of course this can be realized by designing a fuzzy controller with three inputs, error, the change rate
of error and the integration of error. However, these methods will be
hard to implement in practice because of the difficulty in constructing fuzzy control rules. Usually fuzzy control rules are constructed by summarizing the manual control experience of an operator who has been controlling the industrial process skillfully and successfully. The operator intuitively regulates the executor to control the process by watching the
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error and the change rate of the error between the system's output and the set-point value. It is not the practice for the operator to observe the integration of error. Moreover, adding one input variable will greatly increase the number of control rules, the constructing of fuzzy control rules are even more difficult task and it needs more computation efforts. Hence we may want to design a fuzzy controller that possesses the fine characteristics of the PID controller by using only the error and the change rate of error as its inputs.
One way is to have an integrator serially connected to the output of the fuzzy controller as shown in Fig. 1. In Fig. 1,andare scaling
factors for e and ~ KK12
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 becomes a parameter time-varying PI controller, its equivalent proportional control and integral control components 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 steady-state error becomes zero.
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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 second-order and type system with the following transfer function:
K (2) G(s),(Ts，1)(Ts，1)12
Where K = 16, = 1, and= 0.5. In our simulation experiments, we use the TT12
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 0.1 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 steady-state error of the
control system becomes zero, but when the integration factor fl is small, the system'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 overcome 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 Fig.6. Where ~ is the weight on PD type fuzzy controller and fi is that on PI type fuzzy controller, the larger a/fi means more emphasis on the derivative control and less emphasis on the integration control, and vice versa. It follows from (7) that the output of the fuzzy controller is
(3)
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3. The parameter adaptive method
Thus the fuzzy controller behaves like a time-varying PID controller, its equivalent proportional control, integral control and derivative control components are respectively. We call this new controller
structure a PID type fuzzy controller (PID fc). Figs. 7 and 8 are the simulation results of the system'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 steady-state error. When ~ = 0 and fl > 0, meaning that
the fuzzy controller behaves like a PI fc, the steady-state error of the system is eliminated but there is a large overshoot and serious oscillation.
When ~ > 0 and 13 > 0 the fuzzy controller becomes a PID fc, the overshoot is substantially reduced. It is possible to get a
comparatively good performance by carefully choosing the value of ,and. ,
4. Conclusions
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We have studied the input-output behavior of the product-sum crisp type fuzzy controller, revealing that this type of fuzzy controller behaves approximately like a parameter time-varying 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 steady-state error for the type system, the PI type fuzzy controller can eliminate the steady-state error. We proposed a controller structure,
that combines 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 decomposed into the equivalent proportional control, integral control and the derivative control components. The proposed parameter adaptive fuzzy controller decreases the equivalent integral control component 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 component unchanged so as to guarantee quick reaction against the system'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.
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Part 2 Application of self adaptation fuzzy-PID control for main steam temperature control system in
power station
ZHI-BIN LI
Abstract: In light of the large delay, strong inertia, and
uncertainty characteristics of main steam temperature process, a self adaptation fuzzy-PID serial control system is presented, which not only contains the anti-disturbance performance of serial control, but also combines the good dynamic performance of fuzzy control. The simulation results show that this control system has more quickly response, better precision and stronger anti-disturbance 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.
Fig.1 boiler main steam temperature system
It can be concluded from Fig l that a good main steam temperature control
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system not only has adequately quickly response to flue disturbance and load
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 become instable. This paper presents a
self adaptation
fuzzy-PID serial control system. which not only contains the anti-disturbance
performance of serial control, but also combines 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 become in stable(The fuzzy control is fit for controlling non-
linear and uncertain process. The general fuzzy controller takes error E and error change ratio EC as input variables(actually it is a non-linear 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 combined with PI control, not only contains the anti-disturbance 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 Sheng-Zhao
and Wang Pei-zhuang take the located in bringing forward a new idea which can modify the control regulation online(This regulation is: U,,E，(1,,)EC,,,[0,1]
This control regulation depends on only one parameter.Onceis
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 Dong-hui and the new regulation is as follow;
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,,，(1,),,0EECE00
,,，(1,),,,1EECE11,{U,,，(1,),,,2EECE 22
,,E，(1,)EC,E,,333
,,,,,,,,[0,1]0123
Because it is very difficult to find a self of optimum parameter, a new method is
presented by Prof(Zhou Xian-Lan, the regulation is as follow:
2,,1,exp(,ke),(k,0)
But this algorithm still can not eliminate the steady error(This paper combines
this algorithm with PI control，the performance is improved(
2. Simulation of Control System
3.1 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，W(s),W(s)W(s),W(s)are main controller and auxiliary controller,are characters ,1,2o1o2
W(s),W(s)of the leading and inertia sections,are measure unit. H1H2
3.2 Simulation of the general serial PID control system
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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(s),k,25,2p2
1W(s),k，k，ks 1p1I1D1,s
k,3.33,k,0.074,k,37.667p1I1D1
Fig.3. the general PID control system simulation modal 3.3
Simulation of self adaptation fuzzy-PID control system Spacing W(s),k,25The simulation modal is as Fig 4.Auxiliary controller
is:.Main ,2p2
controller is Fuzzy-PI structure, and the PI controller is:
1W(s),k，k,1p1I1s
k,3.33,k,0.074p1I1
Fuzzy 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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NFN can be devised with 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 derived 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 tested 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:
''y(t),f[y(t,1),...,y(t,n),u(t,d),...,u(t,d,n，1), yu
'e(t,1),...,e(t,n)]，e(t)/, (1) e
where f[.]is a smooth nonlinear function such that a Taylor series expansion exists,
'''n,n,ne(t)is a zero mean whi te noise andΔis the differencing operator,and d are yue
respectively the known orders and time delay of the system. Let the local linear model of the nonlinear system (1) at the operating pointbe given by the following o(t)
Controlled Auto-Regressive Integrated Moving Average (CARIMA) model: ,1,d,1,1A(z)y(t),zB(z),u(t)，C(z)e(t) (2)
,1,1,1,1,1A(z),,A(z),B(z)andC(z)Whereare polynomials in, the backward shift z
operator. Note that the coefficients of these polynomials are a function of the operating pointo(t).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
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NFN for the following reasons. First, B-spline functions can be readily specified by the order of the basis function and the number of inner knots. Second, they are defined on a bounded support, and the output of the basis function is always positive,
jji.e.,and.Third, the basis functions
form ,(x),0,x,[,,,],(x),0,x,[,,,]kj,kjkj,kj
a partition of unity, i.e.,
j (3) ,(x),1,x,[xx].,kmammin,j
And fourth, the output of the basis functions can be obtained by a recurrence equation.
Fig. 2 neuro-fuzzy network
The membership functions of the fuzzy variables derived from the fuzzy rules can be obtained by the tensor product of the univariate basis functions. As an example, consider the NFN shown in Fig.2, which consists of the following fuzzy rules:
xx IF operating condition i (is positive small, ... , andis negative large), n1
THEN the output is given by the local CARIMA model i:
ˆˆˆy(t),ay(t,1)，...，ay(t,n)，b,u(t,d)，... ii1iiniai0ia
，b,u(t,d,n)，e(t)，...，ce(t,n) (4) inibiinicbc
,,d,,111ˆA(z)y(t),z,B(z)u(t)，C(z)e(t)or (5) iiiiii
,1,1,1A(z),B(z)andC(z)Whereare polynomials in the backward shift iii ,1u(t)e(t)operatorz, and d is the dead time of the plant,is the control, and is a ii
2zero mean independent random variable with a variance of . The multivariate basis ,
a(x)functionis obtained by the tensor products of the univariate basis functions, ik
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nia,,A(x),i,1,2,...,p (6) ,ikk,1k
where n is the dimension of the input vector x, and p, the total number of weights
in the NFN, is given by,
n
p,(R，k) (7) ,ii,1k
kRWhere and are the order of the basis function and the number of inner ii
knots respectively. The properties of the univariate B-spline basis functions described previously also apply to the multivariate basis function, which is defined on the hyper-rectangles. The output of the NFN is,
p
ˆya,iip,1iˆˆy,,ya (8) ,iip,1ia,i,1i
3. Neuro-fuzzy modelling and predictive control of superheated
steam temperature
,Letbe the superheated steam temperature, and, the flow of spray water to the ,,
high temperature superheater. The response ofcan be approximated by a second ,
order model:
K,p,,sG(s),,e (9) ,()(，1)(，1)sTsTs12,
The linear models, however, only a local model for the selected operating point. Since load is the unique antecedent variable, it is used to select the division between the local regions in the NFN. Based on this approach, the load is divided into five regions as shown in Fig.3,using also the experience of the operators, who regard a load of 200MW as high,180MW as medium high,160MW as medium,140MW as medium low and 120MW as low. For a sampling interval of 30s, the estimated linear ,1A(z)local models used in the NFN are shown in Table 1.
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Fig. 3 Membership function for local models
Table 1 Local CARIMA models in neuro-fuzzy model
Cascade control scheme is widely used to control the superheated steam temperature. Feed forward control, with the steam flow and the gas temperature as inputs, can be applied to provide a faster response to large variations in these two variables. In practice, the feed forward paths are activated only when there are significant changes in these variables. The control scheme also prevents the faster dynamics of the plant, i.e., the spray water valve and the water/steam mixing, from affecting the slower dynamics of the plant, i.e., the high temperature superheater. With the global nonlinear NFN model in Table 1, the proposed NFGPC scheme is shown in Fig.4.
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Fig. 4 NFGPC control of superheated steam temperature with feed-for-ward control.
As a further illustration, the power plant is simulated using the NFN model given in Table 1,and is controlled respectively by the NFGPC, the conventional linear GPC controller, and the cascaded PI controller while the load changes from 160MW to 200MW.The conventional linear GPC controller is the local controller designed for the“medium”operating region. The results are shown in Fig.5,showing that, as expected, the best performance is obtained from the NFGPC as it is designed based on a
more accurate process model. This is followed by the conventional linear GPC controller. The performance of the conventional cascade PI
controller is the worst, indicating that it is unable to control satisfactory the superheated steam temperature under large load changes. This may be the reason for controlling the power plant manually when there are large load changes.
Fig.5 comparison of the NFGPC, conventional linear GPC, and cascade PI controller. 4. Conclusions
The modeling and control of a 200 MW power plant using the neuro-fuzzy approach is presented in this paper. The NFN consists of five
local CARIMA models.
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The out-put of the network is the interpolation of the local models using memberships given by the B-spline basis functions. The proposed NFGPC is similarly constructed, which is designed from the CARIMA models in the NFN. The NFGPC is most suitable for processes with smooth nonlinearity, such that its full operating range can be partitioned into several local linear operating regions. The proposed NFGPC therefore provides a useful alternative for controlling this class of nonlinear
power plants, which are formerly difficult to be controlled using traditional methods.
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Part 4 为Part3译文:
锅炉蒸汽温度模糊神经网络的广义预测控制
Xiangjie LIU, Jizhen LIU, Ping GUAN
摘要:发电厂是非线性和不确定性的复杂系统。

现代电厂的运行中，为确保电厂的高效率和高负荷的能力，准确的控制过热蒸汽温度是必要的。

本文提出了一类在非线性广义预测控制器的基础上的模糊神经网络。

所提出的非线性控制器适用于控制一台200 MW电厂的过热蒸汽温度。

从实验方案的仿真结果中可以看出，此方案的控制品质优于传统的控制方案。

关键词:模糊神经网络;广义预测控制;过热蒸汽温度
1. 引言
电厂过热汽温控制系统的特点是非线性、不确定性和负载扰动。

蒸汽发电的过程中锅炉-汽轮机温度过热是一个重要的问题，蒸汽加热后，进入涡轮驱动发电机，控制过热蒸汽温度不仅是在技术上具有挑战性，在经济上的意义也是十分重要的。

图1 锅炉过热器和蒸汽生成过程。