CN104750131A

CN104750131A - Fluidized bed temperature control method based on parameter identification

Info

Publication number: CN104750131A
Application number: CN201510172775.1A
Authority: CN
Inventors: 申涛; 魏孝吉; 任万杰; 代桃桃
Original assignee: University of Jinan
Current assignee: University of Jinan
Priority date: 2015-04-14
Filing date: 2015-04-14
Publication date: 2015-07-01
Anticipated expiration: 2035-04-14
Also published as: CN104750131B

Abstract

The invention discloses a temperature control method of a fluidized bed based on parameter identification, which uses the coal feed amount and the coal amount feedback value to establish a parameter identification function with a stable point, and obtains the difference between the constant parameter and the expected value of the coal feed amount; The coal feed amount and fluidized bed temperature are identified by the least squares method, and the increment of coal feed amount is obtained by adaptive PID control; through the difference and increment calculated in the first two steps, the fuel utilization can be maximized; through the minimum The square method identifies a constant parameter, which can well compensate the deviation caused by the actuator. The present invention can make the coal feeding amount and the temperature of the fluidized bed reach a stable state through the self-tuning PID control without manual changes by the operator; the precision is high, and the fluidized bed boiler is easy to use; the waste of energy is reduced, and the pollution is reduced emissions; increased the service life of the fluidized bed boiler.

Description

A Method of Temperature Control in Fluidized Bed Based on Parameter Identification

技术领域technical field

本发明属于流化床温度控制技术领域，尤其涉及一种基于参数辨识的流化床温度控制方法。The invention belongs to the technical field of fluidized bed temperature control, in particular to a method for controlling fluidized bed temperature based on parameter identification.

背景技术Background technique

近年来，我国工业发展速度已经大大超过了能源增长速度，能源相对紧缺。我国在生产和使用锅炉方面是世界上做多的国家之一；锅炉在热能生产方面是重要的动力设备，同时又是以原煤为燃料的耗能较大的设备；然而现阶段的锅炉效率不高，能源方面浪费较严重；同时锅炉燃烧排放氮氧化物、二氧化硫、烟尘等污染物，严重污染大气，已成为我国大气主要污染源之一；随着人们生活水平的改善，环境保护问题逐渐被人们重视，尤其是2012年末至今发生在全国众多大城市的雾霾现象给我国的环境保护敲响了警钟。In recent years, the speed of my country's industrial development has greatly exceeded the growth rate of energy, and energy is relatively scarce. my country is one of the countries that do more in the production and use of boilers in the world; boilers are important power equipment in terms of thermal energy production, and at the same time are energy-consuming equipment that uses raw coal as fuel; however, the efficiency of boilers at this stage is not high. At the same time, boiler combustion emits nitrogen oxides, sulfur dioxide, smoke and other pollutants, seriously polluting the atmosphere, which has become one of the main sources of air pollution in my country; with the improvement of people's living standards, environmental protection issues are gradually being recognized by people. Pay attention, especially the smog phenomenon that has occurred in many large cities across the country since the end of 2012 has sounded the alarm for my country's environmental protection.

发达国家在锅炉运行效率方面平均比要高十个百分点，然而我国锅炉热效率低的差距原因主要在于：我国锅炉普遍存在运行负荷低，燃烧热量损失大，过量空气系数大等问题，所以研究锅炉燃烧控制技术，提高锅炉的控制品质和锅炉的效率，不仅能够带来较大的经济效益，而且还可以减少烟气中的烟尘量，减少空气污染，在锅炉燃烧方面进行控制的主要好处是风险小、效果明显，并且还可以达到提高运行效率，降低污染物排放的目的。The average ratio of boiler operating efficiency in developed countries is ten percentage points higher than that of developed countries. However, the reason for the low thermal efficiency of boilers in my country is mainly due to: low operating load, large combustion heat loss, and large excess air coefficient are common problems in boilers in my country. Therefore, the research on boiler combustion Control technology, improving the control quality of the boiler and the efficiency of the boiler can not only bring greater economic benefits, but also reduce the amount of smoke and dust in the flue gas and reduce air pollution. The main benefit of controlling boiler combustion is that the risk is small , The effect is obvious, and it can also achieve the purpose of improving operating efficiency and reducing pollutant emissions.

在国内很多都是通过煤粉转子秤是实现对系统的喂煤，煤粉转子秤是由转子、安装框架、传动装置、测重系统及软接头等组成，转子和密封板一起安装在一个防爆壳体内，整个转子秤壳体及转子的传动装置作为一个整体悬吊在一个框架里，框架上有两个固定的滚动轴承座悬挂支承转子秤，而第三个悬挂装置则与带有荷重传感器的称重装置相联，转子秤的进料管、出料管及压缩空气管均有软接头与相应部件联接。煤粉通过滑动闸门直接从煤粉仓排出，经过入口软接头进入转子部分，煤粉被转子的隔仓带走，旋转225°，到达卸料区域，由底部压缩空气管的空气把煤粉吹进出料口，送至燃烧器，虽然给煤量是稳定的，但是在喂煤过程中机械设备存在很多的外在因素及环境因素，会对转子称的给煤量发生变化，从而影响流化床温度的改变。In China, coal feeding to the system is realized through the pulverized coal rotor scale. The pulverized coal rotor scale is composed of a rotor, an installation frame, a transmission device, a weighing system and a soft joint. The rotor and the sealing plate are installed together in an explosion-proof In the casing, the entire rotor scale casing and the rotor transmission device are suspended in a frame as a whole. There are two fixed rolling bearing seats on the frame to suspend and support the rotor scale, and the third suspension device is connected with the load cell. The weighing device is connected, and the feed pipe, discharge pipe and compressed air pipe of the rotor scale have soft joints to connect with the corresponding parts. The pulverized coal is directly discharged from the pulverized coal bin through the sliding gate, and enters the rotor part through the inlet soft joint. Inlet and outlet, sent to the burner, although the amount of coal fed is stable, but there are many external factors and environmental factors in the mechanical equipment during the coal feeding process, which will change the amount of coal fed to the rotor, thus affecting the fluidization Changes in bed temperature.

目前在煤称对流化床温度方面存在以下不足：At present, there are the following deficiencies in the coal scale convective fluidized bed temperature:

1、人工进行检测效率低、检测精度方面达不到理想要求；1. The efficiency of manual detection is low, and the detection accuracy cannot meet the ideal requirements;

2、如果给煤量太少会使流化床温度达不到理想要求，给煤量太多会造成燃料的燃烧不充分，造成不必要的浪费；2. If the amount of coal fed is too small, the temperature of the fluidized bed will not meet the ideal requirements, and if the amount of coal fed is too large, the combustion of fuel will be insufficient, resulting in unnecessary waste;

3、在实际的工艺中，执行机构对工艺还会造成一定的影响，使实际给煤量超过或低于设定的给煤量。3. In the actual process, the executive agency will also have a certain impact on the process, making the actual coal supply exceed or lower than the set coal supply.

发明内容Contents of the invention

本发明的目的在于提供一种基于参数辨识的流化床温度控制方法，旨在解决煤称对流化床温度方面存在的人工进行检测效率低、检测精度方面达不到理想要求，给煤量太多会造成燃料的燃烧不充分，造成不必要的浪费，实际给煤量超过或低于设定给煤量的问题。The purpose of the present invention is to provide a fluidized bed temperature control method based on parameter identification, aiming to solve the problem of low efficiency of manual detection of fluidized bed temperature by coal scales, detection accuracy that does not meet ideal requirements, and coal supply Too much will cause insufficient combustion of fuel, resulting in unnecessary waste, and the actual coal supply exceeds or falls below the set coal supply.

本发明是这样实现的，一种将最小二乘法辨识常参数与通过自适应PID控制得到的增量相结合的方法对流化床温度进行控制，该基于参数辨识的流化床温度控制方法利用喂煤量与煤量反馈值，建立带有稳定点的参数辨识函数，得到常参数与喂煤量期望值的差值；利用喂煤量与流化床温度通过最小二乘法辨识出模型，再利用自适应PID控制得到喂煤量的增量；通过前两步计算的差值与增量，即最大化的利用燃料。The present invention is achieved in this way, a method of combining the constant parameter identification by the least squares method with the increment obtained through adaptive PID control controls the temperature of the fluidized bed, and the fluidized bed temperature control method based on parameter identification utilizes Coal feed amount and coal amount feedback value, establish a parameter identification function with a stable point, and obtain the difference between the constant parameter and the expected value of coal feed amount; use the coal feed amount and fluidized bed temperature to identify the model through the least square method, and then use Adaptive PID control obtains the increment of coal feeding; through the difference and increment calculated in the first two steps, that is, to maximize the utilization of fuel.

进一步，该基于参数辨识的流化床温度控制方法的具体步骤如下：Further, the specific steps of the fluidized bed temperature control method based on parameter identification are as follows:

步骤一，将实时检测到的喂煤量作为输入，检测到的煤的反馈量作为输出；Step 1, taking the real-time detected coal feeding amount as input, and the detected coal feedback amount as output;

步骤二，选用步骤一中采集到的喂煤量和反馈量作为参数辨识的输入输出，通过最小二乘参数辨识方法辨识出一个分量ξ；Step 2: Select the coal feeding amount and feedback amount collected in step 1 as the input and output of parameter identification, and identify a component ξ by least squares parameter identification method;

步骤三，所求出来的分量ξ与设定的喂煤量的期望值取差值β，即为对喂煤量的第一个影响因素；Step 3, take the difference β between the obtained component ξ and the expected value of the set coal feeding amount, which is the first influencing factor on the coal feeding amount;

步骤四，将实时检测到的流化床温度值为u，喂煤量的实时值记为y；Step 4, record the real-time detected fluidized bed temperature value as u, and the real-time value of coal feeding amount as y;

步骤五，选用步骤四中得到的实时流化床温度值与实时喂煤量值作为参数辨识的输入输出，通过最小二乘参数辨识方法辨识出一个分量α₀，并得出此时辨识出的模型；Step 5: Select the real-time fluidized bed temperature value and real-time coal feed value obtained in step 4 as the input and output of parameter identification, identify a component α ₀ through the least squares parameter identification method, and obtain the identified Model;

步骤六，通过辨识出的模型，利用自整定PID控制方法，对系统的煤称量进行控制；Step 6, through the identified model, using the self-tuning PID control method to control the coal weighing of the system;

步骤七，将步骤三计算得到的差值β与步骤六求出的给煤量修正值相加，并反馈给给煤量。Step 7: Add the difference β calculated in step 3 to the corrected value of the coal feeding amount calculated in step 6, and feed back to the coal feeding amount.

进一步，步骤二中给煤量与煤的反馈值作为最小二乘辨识的输入输出，通过最小二乘辨识方法辨识出常参数ξ，具体步骤包括：Further, in step 2, the coal feed rate and coal feedback value are used as the input and output of the least squares identification, and the constant parameter ξ is identified through the least squares identification method. The specific steps include:

第一步，给出单输入单输出线性、定常、随机系统的数学模型：In the first step, the mathematical model of a single-input single-output linear, steady, stochastic system is given:

$y the y ((k k)) + + {Σ Σ}_{i i = = 11}^{n no} {a a}_{i i} y the y ((k k - - i i)) = = {Σ Σ}_{i i = = 11}^{n no} {b b}_{i i} u u ((k k - - i i)) + + e e ((k k)) - - - - - - ((11))$

则最终输出为：Then the final output is:

$y the y ((k k)) = = - - {Σ Σ}_{i i = = 11}^{n no} {a a}_{i i} y the y ((k k - - i i)) + + {Σ Σ}_{i i = = 11}^{n no} {b b}_{i i} u u ((k k - - i i)) + + e e ((k k)) - - - - - - ((22))$

u(k)与y(k)为喂煤量和煤的反馈值数据序列{u(k)}，{y(k)}，e为模型误差，其中k＝1，2，…，n，n为自然数，(2)式中阶数n的取值为10，其中i＝1，2，…，n，a_i，b_i都为常数，通过a_i和b_i的值能够最终求得稳定点ξ；u(k) and y(k) are the coal feed amount and coal feedback value data sequence {u(k)}, {y(k)}, e is the model error, where k=1, 2,..., n, n is a natural number, and the value of order n in (2) formula is 10, wherein i=1, 2,..., n, a _i , b _i are all constants, and can finally be obtained by the values of a _i and b _i stable point ξ;

第二步，将模型具体化，从而得到目标函数：In the second step, the model is concretized to obtain the objective function:

令θ＝[a₁，a₂，…，a_n，b₁，b₂，…，b_n]^T；Let θ=[a ₁ , a ₂ ,..., a _n , b ₁ , b ₂ ,..., b _n ] ^T ;

则有：Then there are:

模型拟合残差ε(k)为：The model fitting residual ε(k) is:

对于n组数据，从(3)式得到：For n groups of data, it can be obtained from formula (3):

$ϵ ϵ ((n no)) = = y the y ((n no)) - - X x ((n no)) \overset{^^}{θ θ} - - - - - - ((44))$

ε(n)为对n组数据的模型拟合残差，y(n)为煤的反馈值的n组数据；ε(n) is the residual error of model fitting for n groups of data, and y(n) is the n groups of data of coal feedback value;

第三步，得到最小二乘估计：最小二乘是寻找一个θ的估计值使得各次的测量值与由估计确定的量测估计只差的平方和最小，从最小二乘准则推导正则方程，根据求极值原理可知，最小二乘估计满足：The third step is to get the least square estimate: the least square is to find an estimate of θ so that the measured values of each time are the same as those estimated by The determined measurement estimate only has the minimum sum of squares of the difference, and the regular equation is derived from the least squares criterion. According to the principle of seeking extreme values, the least squares estimate satisfy:

得最小二乘估计 get the least squares estimate

${\overset{^^}{θ θ}}_{LS LS} = = {(({X x}^{T T} X x))}^{- - 11} {X x}^{T T} y the y - - - - - - ((66))$

第四步，推导辨识出常参数：辨识出常参数ξ的过程为：选取脉冲响应模型作为系统的模型，模型如(7)式所示：The fourth step is to deduce and identify the constant parameters: the process of identifying the constant parameter ξ is: select the impulse response model as the model of the system, and the model is shown in formula (7):

$y the y ((k k)) = = {Σ Σ}_{i i = = 11}^{n no} h h ((i i)) u u ((k k - - i i)) - - - - - - ((77))$

u(k-i)与y(k)为喂煤量和煤的反馈值数据序列，h(i)为常数；u(k-i) and y(k) are the data sequence of coal feeding amount and coal feedback value, h(i) is a constant;

在模型中加入一个常参数ξ，ξ为能够准确反映煤称量的一个参数指标，模型成为：A constant parameter ξ is added to the model, ξ is a parameter index that can accurately reflect coal weighing, and the model becomes:

$y the y ((k k)) = = {Σ Σ}_{i i = = 11}^{n no} h h ((i i)) u u ((k k - - i i)) + + ξ ξ - - - - - - ((88))$

式中ξ为常数项，通过现场的实验，参数ξ与实际系统联系紧密，将ξ与对喂煤量的期望值之差β作为系统控制问题中的第一个控制因素；In the formula, ξ is a constant item. Through field experiments, the parameter ξ is closely related to the actual system, and the difference β between ξ and the expected value of coal feed is taken as the first control factor in the system control problem;

将(8)写成向量的形式：Write (8) in vector form:

Y(k)＝U(k)H (9)Y(k)＝U(k)H (9)

其中：in:

$Y Y ((k k)) = = [\begin{matrix} y the y ((k k)) \\ y the y ((k k + + 11)) \\ \cdot &Center Dot; \\ \cdot \cdot \\ \cdot &Center Dot; \\ y the y ((k k + + N N)) \end{matrix}],, H h = = [\begin{matrix} {h h}_{11} \\ {h h}_{22} \\ \cdot \cdot \\ \cdot &Center Dot; \\ \cdot \cdot \\ {h h}_{M m} \\ ξ ξ \end{matrix}];;$

$U u ((k k)) = = [\begin{matrix} u u ((k k - - 11)) & u u ((k k - - 22)) & \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot; & u u ((k k - - M m)) & 11 \\ u u ((k k)) & u u ((k k - - 11)) & \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot; & u u ((k k - - M m + + 11)) & 11 \\ \cdot \cdot & \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; \\ \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; & \cdot \cdot \\ \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; \\ u u ((k k + + N N - - 11)) & u u ((k k + + N N - - 22)) & \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot; & u u ((k k + + N N - - M m)) & 11 \end{matrix}];;$

H为通过常数h(i)与辨识出的常参数ξ组成的矩阵，U(k)、Y(k)为给煤量与反馈量组成的矩阵；H is a matrix composed of constant h(i) and identified constant parameter ξ, U(k), Y(k) are matrices composed of coal supply and feedback;

输入输出数据与最小二乘辨识的输入输出一致，求得当控制步长M＝10的时候得到最佳值，通过式(8)求得h₁，h₂...h_M，ξ；The input and output data are consistent with the input and output of the least squares identification, and the optimal value is obtained when the control step size M=10, and h ₁ , h ₂ ... h _M , ξ are obtained through formula (8);

求出常参数ξ，并记录下来，并与喂煤量的期望值取差值β。Find the constant parameter ξ, record it, and take the difference β with the expected value of coal feeding amount.

进一步，步骤四中流化床温度与给煤量作为最小二乘辨识的输入输出，通过最小二乘辨识方法辨识出常参数α₀，具体步骤与步骤二中求取常参数的方法大致相同，只是将输入输出进行了变换，具体步骤为：Further, in step four, the fluidized bed temperature and coal feed rate are used as the input and output of the least squares identification, and the constant parameter α ₀ is identified through the least squares identification method. The specific steps are roughly the same as the method for obtaining the constant parameters in step two. Just transform the input and output, the specific steps are:

$y the y ((k k)) + + {Σ Σ}_{i i = = 11}^{n no} {a a}_{i i} y the y ((k k - - i i)) = = {Σ Σ}_{i i = = 11}^{n no} {b b}_{i i} u u ((k k - - i i)) + + e e ((k k)) - - - - - - ((1010))$

则最终输出为：Then the final output is:

$y the y ((k k)) = = - - {Σ Σ}_{i i = = 11}^{n no} {a a}_{i i} y the y ((k k - - i i)) + + {Σ Σ}_{i i = = 11}^{n no} {b b}_{i i} u u ((k k - - i i)) + + e e ((k k)) - - - - - - ((1111))$

u(k)与y(k)为将实时检测到的流化床温度值和喂煤量的实时值数据序列{u(k)}，{y(k)}，e为模型误差，其中k＝1，2，…，n，n为自然数，(11)式中阶数n的取值为10，其中i＝1，2，…，n，a_i，b_i都为常数，通过a_i和b_i的值能够最终求得稳定点ξ；u(k) and y(k) are the real-time value data sequence {u(k)}, {y(k)} of the real-time detected fluidized bed temperature value and coal feeding amount, e is the model error, where k =1, 2, ..., n, n is a natural number, and the value of order number n in (11) formula is 10, and wherein i=1, 2, ..., n, a _i , b _i are all constants, by a _i and the value of b _i can finally obtain the stable point ξ;

则有：Then there are:

模型拟合残差ε(k)为：The model fitting residual ε(k) is:

$ϵ ϵ ((n no)) = = y the y ((n no)) - - X x ((n no)) \overset{^^}{θ θ} - - - - - - ((1313))$

ε(n)为对n组数据的模型拟合残差，y(n)为煤的反馈值的n组数据；ε(n) is the residual error of model fitting for n groups of data, and y(n) is n groups of data of coal feedback value;

第三步，得到最小二乘估计：最小二乘是寻找一个θ的估计值使得各次的测量值与由估计确定的量测估计只差的平方和最小，从最小二乘准则推导正则方程，根据求极值原理知，最小二乘估计满足：The third step is to get the least square estimate: the least square is to find an estimate of θ so that the measured values of each time are the same as those estimated by The determined measurement estimate only has the minimum sum of squares of the difference, and the regular equation is derived from the least squares criterion. According to the principle of extremum, the least squares estimate satisfy:

通过(10)式到(14)式，计算出此时的最小二乘估计 Through formula (10) to formula (14), calculate the least square estimation at this time

${\overset{^^}{θ θ}}_{LS LS} = = {(({X x}^{T T} X x))}^{- - 11} {X x}^{T T} y the y - - - - - - ((1515))$

选取脉冲响应模型作为系统的模型，模型如式(7)所示，在此基础上增加一个常参数α₀，α₀为能够准确反映煤称量的一个参数指标，模型成为：The impulse response model is selected as the model of the system. The model is shown in formula (7). On this basis, a constant parameter α ₀ is added. α ₀ is a parameter index that can accurately reflect coal weighing. The model becomes:

$y the y ((k k)) = = {Σ Σ}_{i i = = 11}^{n no} h h ((i i)) u u ((k k - - i i)) + + {α α}_{00} - - - - - - ((1616))$

u(k-i)与y(k)为流化床温度与给煤量的数据序列，h(i)、α₀为常数项。u(ki) and y(k) are data sequences of fluidized bed temperature and coal feed rate, h(i) and α ₀ are constant items.

进一步，则计算常参数α₀的方法与步骤二中计算参数的方法一致，具体步骤为：Further, the method for calculating the constant parameter α ₀ is consistent with the method for calculating parameters in step 2, and the specific steps are:

u(k-i)与y(k)为流化床温度值和喂煤量的实时值数据序列，h(i)为常数；u(k-i) and y(k) are the real-time value data series of fluidized bed temperature and coal feeding amount, h(i) is a constant;

在模型中加入一个常参数α₀，α₀为能够准确反映煤称量的一个参数指标，模型成为：A constant parameter α ₀ is added to the model, and α ₀ is a parameter index that can accurately reflect coal weighing, and the model becomes:

$y the y ((k k)) = = {Σ Σ}_{i i = = 11}^{n no} h h ((i i)) u u ((k k - - i i)) + + {α α}_{00} - - - - - - ((1717))$

式中α₀为常数项；where α ₀ is a constant term;

将(8)写成向量的形式：Write (8) in vector form:

Y(k)＝U(k)H (18)Y(k)＝U(k)H (18)

其中：in:

$Y Y ((k k)) = = [\begin{matrix} y the y ((k k)) \\ y the y ((k k + + 11)) \\ \cdot \cdot \\ \cdot \cdot \\ \cdot \cdot \\ y the y ((k k + + N N)) \end{matrix}],, H h = = [\begin{matrix} {h h}_{11} \\ {h h}_{22} \\ \cdot \cdot \\ \cdot \cdot \\ \cdot \cdot \\ {h h}_{M m} \\ ξ ξ \end{matrix}];;$

$U u ((k k)) = = [\begin{matrix} u u ((k k - - 11)) & u u ((k k - - 22)) & \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot; & u u ((k k - - M m)) & 11 \\ u u ((k k)) & u u ((k k - - 11)) & \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot; & u u ((k k - - M m + + 11)) & 11 \\ \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; & \cdot \cdot & \cdot &Center Dot; \\ \cdot \cdot & \cdot \cdot & \cdot \cdot & \cdot &Center Dot; & \cdot &Center Dot; \\ \cdot \cdot & \cdot &Center Dot; & \cdot \cdot & \cdot \cdot & \cdot \cdot \\ u u ((k k + + N N - - 11)) & u u ((k k + + N N - - 22)) & \cdot &Center Dot; \cdot &Center Dot; \cdot \cdot & u u ((k k + + N N - - M m)) & 11 \end{matrix}];;$

H为通过常数h(i)与辨识出的常参数α₀组成的矩阵，U(k)、Y(k)为给煤量与反馈量组成的矩阵；H is a matrix composed of the constant h(i) and the identified constant parameter α ₀ , and U(k) and Y(k) are the matrix composed of the coal feeding amount and the feedback amount;

输入输出数据与最小二乘辨识的输入输出一致，求得当控制步长M＝10的时候得到最佳值，通过式(17)求得h₁，h₂...h_M，α₀；The input and output data are consistent with the input and output of the least squares identification, and the optimal value is obtained when the control step size M=10, and h ₁ , h ₂ ... h _M , α ₀ are obtained through formula (17);

计算出此时的常参数α₀，并建立出以流化床温度为输入，给定量为输出的模型，为步骤六的自整定PID控制提供控制模型，。Calculate the constant parameter α ₀ at this time, and establish a model with the fluidized bed temperature as the input and the given quantity as the output, providing a control model for the self-tuning PID control in step six.

进一步，步骤六中的控制模型为步骤四中辨识出来的模型，具体步骤包括：Further, the control model in step six is the model identified in step four, and the specific steps include:

第一步，传统PID控制算法由控制器和被控对象所组成，而皮带控制器由比例、积分、微分三个环节组成，数学描述为：In the first step, the traditional PID control algorithm is composed of the controller and the controlled object, while the belt controller is composed of three links: proportional, integral and differential. The mathematical description is:

u(k)＝K_px(1)+K_dx(2)+K_ix(3) (19)u(k)=K _p x(1)+K _d x(2)+K _i x(3) (19)

式中，K_p为比例系数；K_i为积分时间常数；K_d为微分时间常数；u(k)为通过PID控制器后得到的煤称量的增加减少值x(1)为比例的校正值；x(2)为微分的校正值；x(3)为积分的校正值；In the formula, K _p is the proportional coefficient; K _i is the integral time constant; K _d is the differential time constant; u(k) is the increase and decrease value of coal weighing obtained after passing through the PID controller. x(1) is the proportional correction value; x(2) is the correction value of differential; x(3) is the correction value of integral;

第二步，通过温度输入量的测量值与温度的期望值的误差及采样时间求出第一步中的x(1)、x(2)、x(3)，计算公式为：In the second step, x(1), x(2), and x(3) in the first step are calculated by the error between the measured value of the temperature input and the expected value of the temperature and the sampling time, and the calculation formula is:

x(1)＝error(k)；x(1)=error(k);

x(2)＝[error(k)-error_1]/t_s；x(2)=[error(k)-error_1]/t _s ;

x(3)＝x(3)+error(k)*t_s；x(3)=x(3)+error(k)*t _s ;

式中，error(k)为在k时刻通过测量值与期望值计算出的误差；t_s为采样时间；In the formula, error(k) is the error calculated by the measured value and the expected value at time k; t _s is the sampling time;

第三步，将上两个步骤进行编程后，输出的值u(k)即为给煤量的修正值，并记录下来。In the third step, after programming the previous two steps, the output value u(k) is the correction value of the coal feeding amount, and it is recorded.

进一步，步骤七中将步骤三的分量ξ与步骤六求出的给煤量修正值u(k)相加，计算公式为：Further, in step 7, the component ξ of step 3 is added to the corrected value u(k) of coal feed amount obtained in step 6, and the calculation formula is:

η(k)＝u(k)+β (20)η(k)=u(k)+β (20)

式中，u(k)为通过步骤六的控制方法计算出来的给煤量修正值，β为通过步骤二通过最小二乘法辨识出来的常参数与喂煤量期望值的差值，η(k)为对系统给煤量的最终修正值。In the formula, u(k) is the corrected value of coal feed amount calculated by the control method in step 6, β is the difference between the constant parameter identified by the least square method in step 2 and the expected value of coal feed amount, η(k) is the final correction value for the coal supply to the system.

本发明提供的基于参数辨识的流化床温度控制方法，首先，利用喂煤量与煤量反馈值，建立带有稳定点的参数辨识函数，得到常参数与喂煤量期望值的差值；其次，利用喂煤量与流化床温度通过最小二乘法辨识出模型，再利用自适应PID控制得到喂煤量的增量；最后，通过前两步计算的差值与增量，即可以最大化的利用燃料。The fluidized bed temperature control method based on parameter identification provided by the present invention, firstly, uses the coal feed amount and the coal amount feedback value to establish a parameter identification function with a stable point, and obtains the difference between the constant parameter and the expected value of the coal feed amount; secondly , using the coal feed amount and fluidized bed temperature to identify the model through the least squares method, and then using the adaptive PID control to obtain the increment of the coal feed amount; finally, through the difference and increment calculated in the first two steps, the maximum use of fuel.

本发明的有益效果为：The beneficial effects of the present invention are:

1、在一般流化床锅炉温度控制中，只会通过观察锅炉温度的高低来手动的调节给煤量，来达到流化床温度的稳定，在本发明中，通过自整定PID控制使操作人员无需再通过手动控制就可以使温度达到稳定；1. In general fluidized bed boiler temperature control, only by observing the temperature of the boiler to manually adjust the amount of coal to achieve the stability of the fluidized bed temperature, in the present invention, through the self-tuning PID control to make the operator The temperature can be stabilized without manual control;

2、在一些流化床锅炉温度控制中，只会通过一般的控制方法对流化床锅炉温度进行控制，而在本发明中，不但采用了自整定PID控制，还通过最小二乘法辨识出一个常参数，通过常参数与期望值之间的差值，对给煤量进行改变，使温度达到稳定；2. In the temperature control of some fluidized bed boilers, the temperature of the fluidized bed boiler is only controlled by the general control method, but in the present invention, not only the self-tuning PID control is adopted, but also a least square method is used to identify a Constant parameter, through the difference between the constant parameter and the expected value, the coal supply is changed to stabilize the temperature;

3、将两种方法相结合后再对给煤量进行控制，比单独使用控制方法或手动操作更能使给煤量精确；3. Combining the two methods and then controlling the amount of coal feeding can make the amount of coal feeding more accurate than using the control method alone or manual operation;

4、精度高，易于流化床锅炉的使用；4. High precision, easy to use in fluidized bed boilers;

5、减少了能源的浪费，降低了污染物的排放；5. Reduce the waste of energy and reduce the discharge of pollutants;

6、增加了流化床锅炉的使用年限。6. The service life of the fluidized bed boiler has been increased.

附图说明Description of drawings

图1是本发明实施例提供的基于参数辨识的流化床温度控制方法流程图；Fig. 1 is a flowchart of a fluidized bed temperature control method based on parameter identification provided by an embodiment of the present invention;

图2是本发明实施例提供的实施例1的流程图；Fig. 2 is the flow chart of embodiment 1 provided by the embodiment of the present invention;

图3是本发明实施例提供的PID控制系统结构图；Fig. 3 is the structural diagram of the PID control system provided by the embodiment of the present invention;

图4是本发明实施例提供的实际的给煤量数据图；Fig. 4 is the actual coal feed data figure that the embodiment of the present invention provides;

图5是本发明实施例提供的通过最小二乘辨识后的常参数与喂煤量期望值的差值图；Fig. 5 is a difference diagram between the constant parameter identified by least squares and the expected value of coal feeding amount provided by the embodiment of the present invention;

图6是本发明实施例提供的通过自适应PID控制后得到的增量图；FIG. 6 is an incremental diagram obtained after adaptive PID control provided by an embodiment of the present invention;

图7是本发明实施例提供的给煤量的原始值与通过参数和PID控制增量控制后的图。Fig. 7 is a diagram of the original value of the coal feed amount provided by the embodiment of the present invention and the incremental control through parameters and PID control.

具体实施方式Detailed ways

为了使本发明的目的、技术方案及优点更加清楚明白，以下结合实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

下面结合附图及具体实施例对本发明的应用原理作进一步描述。The application principle of the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

如图1所示，本发明实施例的基于参数辨识的流化床温度控制方法包括以下步骤：As shown in Figure 1, the fluidized bed temperature control method based on parameter identification in the embodiment of the present invention includes the following steps:

S101：利用喂煤量与煤量反馈值，建立带有稳定点的参数辨识函数，得到常参数与喂煤量期望值的差值；S101: Using the coal feed amount and the coal amount feedback value, establish a parameter identification function with a stable point, and obtain the difference between the constant parameter and the expected value of the coal feed amount;

S102：利用喂煤量与流化床温度通过最小二乘法辨识出模型，再利用自适应PID控制得到喂煤量的增量；S102: Use the coal feed amount and the fluidized bed temperature to identify the model through the least square method, and then use the adaptive PID control to obtain the increment of the coal feed amount;

S103：通过前两步计算的差值与增量，即可以最大化的利用燃料。S103: By using the difference and increment calculated in the first two steps, the fuel can be utilized to the maximum.

本发明实施例的具体步骤如下：The concrete steps of the embodiment of the present invention are as follows:

步骤七，将步骤三计算得到的差值β与步骤六求出的给煤量修正值相加，并反馈给给煤量，即可使系统的煤称量在执行机构偏差等问题的影响下达到稳定，从而使流化床温度达到稳定。Step 7: Add the difference β calculated in step 3 to the corrected value of the coal supply amount calculated in step 6, and feed back to the coal supply amount, so that the coal weighing of the system can be under the influence of problems such as the deviation of the actuator To achieve stability, so that the temperature of the fluidized bed is stabilized.

所述步骤二中给煤量与煤的反馈值作为最小二乘辨识的输入输出，通过最小二乘辨识方法辨识出常参数ξ，其具体步骤包括：In the second step, the coal feed amount and the coal feedback value are used as the input and output of the least squares identification, and the constant parameter ξ is identified by the least squares identification method, and the specific steps include:

则最终输出为：Then the final output is:

u(k)与y(k)为喂煤量和煤的反馈值数据序列{u(k)}，{y(k)}，e为模型误差，其中k＝1，2，…，n，n为自然数，(2)式中通过对比实验得出模型的阶数n的取值为10，其中i＝1，2，…，n，a_i，b_i都为常数，通过a_i和b_i的值能够最终求得稳定点ξ；u(k) and y(k) are the coal feed amount and coal feedback value data sequence {u(k)}, {y(k)}, e is the model error, where k=1, 2,..., n, n is a natural number, and the value of the order n of the model obtained through comparative experiments in (2) formula is 10, wherein i=1, 2,..., n, a _i , b _i are all constants, through a _i and b The value of _i can finally obtain the stable point ξ;

令θ＝[a_i，a₂，…，a_n，b₁，b₂，…，b_n]^T；Let θ=[a _i , a ₂ ,..., a _n , b ₁ , b ₂ ,..., b _n ] ^T ;

则有：Then there are:

模型拟合残差ε(k)为：The model fitting residual ε(k) is:

对于n组数据，从(3)式可得到：For n groups of data, it can be obtained from formula (3):

第三步，得到最小二乘估计：最小二乘的思想就是寻找一个θ的估计值使得各次的测量值与由估计确定的量测估计只差的平方和最小，从最小二乘准则推导正则方程，根据求极值原理可知，最小二乘估计满足：The third step is to get the least squares estimate: the idea of least squares is to find an estimated value of θ so that the measured values of each time are the same as those estimated by The determined measurement estimate only has the minimum sum of squares of the difference, and the regular equation is derived from the least squares criterion. According to the principle of seeking extreme values, the least squares estimate satisfy:

可得最小二乘估计 available least squares estimate

式中ξ为常数项，通过现场的实验，参数ξ与实际系统联系紧密，并可将ξ与对喂煤量的期望值之差β作为系统控制问题中的第一个控制因素；In the formula, ξ is a constant item. Through field experiments, the parameter ξ is closely related to the actual system, and the difference β between ξ and the expected value of coal feed can be used as the first control factor in the system control problem;

将(8)写成向量的形式：Write (8) in vector form:

Y(k)＝U(k)H (9)Y(k)＝U(k)H (9)

其中：in:

$Y Y ((k k)) = = [\begin{matrix} y the y ((k k)) \\ y the y ((k k + + 11)) \\ \cdot &Center Dot; \\ \cdot &Center Dot; \\ \cdot &Center Dot; \\ y the y ((k k + + N N)) \end{matrix}],, H h = = [\begin{matrix} {h h}_{11} \\ {h h}_{22} \\ \cdot \cdot \\ \cdot &Center Dot; \\ \cdot &Center Dot; \\ {h h}_{M m} \\ ξ ξ \end{matrix}];;$

$U u ((k k)) = = [\begin{matrix} u u ((k k - - 11)) & u u ((k k - - 22)) & \cdot &Center Dot; \cdot &Center Dot; \cdot \cdot & u u ((k k - - M m)) & 11 \\ u u ((k k)) & u u ((k k - - 11)) & \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot; & u u ((k k - - M m + + 11)) & 11 \\ \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; \\ \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; \\ \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; \\ u u ((k k + + N N - - 11)) & u u ((k k + + N N - - 22)) & \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot; & u u ((k k + + N N - - M m)) & 11 \end{matrix}];;$

输入输出数据与最小二乘辨识的输入输出一致，通过实验求得当控制步长M＝10的时候可以得到最佳值，通过式(8)求得h₁，h₂...h_M，ξ；The input and output data are consistent with the input and output of the least squares identification. Through experiments, the optimal value can be obtained when the control step size M=10, and h ₁ , h ₂ ... h _M , ξ can be obtained through formula (8). ;

在步骤二中通过上述方法求出常参数ξ，并记录下来，并与喂煤量的期望值取差值β。In the second step, the constant parameter ξ is obtained by the above method, and recorded, and the difference β is taken from the expected value of coal feeding amount.

所述步骤四中流化床温度与给煤量作为最小二乘辨识的输入输出，通过最小二乘辨识方法辨识出常参数α₀，其具体步骤与步骤二中求取常参数的方法大致相同，只是将输入输出进行了变换，具体步骤为：The temperature of the fluidized bed and the coal feed rate in the step four are used as the input and output of the least squares identification, and the constant parameter α ₀ is identified through the least squares identification method. The specific steps are roughly the same as the method for obtaining the constant parameters in the second step , just transform the input and output, the specific steps are:

则最终输出为：Then the final output is:

则有：Then there are:

模型拟合残差ε(k)为：The model fitting residual ε(k) is:

通过(10)式到(14)式，可以计算出此时的最小二乘估计 Through (10) to (14), the least squares estimate at this time can be calculated

则计算常参数α₀的方法与步骤二中计算参数的方法一致，具体步骤为：Then the method of calculating the constant parameter α ₀ is consistent with the method of calculating parameters in step 2, and the specific steps are:

式中α₀为常数项；where α ₀ is a constant term;

将(8)写成向量的形式：Write (8) in vector form:

Y(k)＝U(k)H (18)Y(k)＝U(k)H (18)

其中：in:

$Y Y ((k k)) = = [\begin{matrix} y the y ((k k)) \\ y the y ((k k + + 11)) \\ \cdot \cdot \\ \cdot \cdot \\ \cdot \cdot \\ y the y ((k k + + N N)) \end{matrix}],, H h = = [\begin{matrix} {h h}_{11} \\ {h h}_{22} \\ \cdot &Center Dot; \\ \cdot \cdot \\ \cdot &Center Dot; \\ {h h}_{M m} \\ ξ ξ \end{matrix}];;$

$U u ((k k)) = = [\begin{matrix} u u ((k k - - 11)) & u u ((k k - - 22)) & \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot; & u u ((k k - - M m)) & 11 \\ u u ((k k)) & u u ((k k - - 11)) & \cdot &Center Dot; \cdot \cdot \cdot \cdot & u u ((k k - - M m + + 11)) & 11 \\ \cdot \cdot & \cdot &Center Dot; & \cdot \cdot & \cdot \cdot & \cdot &Center Dot; \\ \cdot \cdot & \cdot \cdot & \cdot \cdot & \cdot &Center Dot; & \cdot \cdot \\ \cdot \cdot & \cdot \cdot & \cdot \cdot & \cdot \cdot & \cdot \cdot \\ u u ((k k + + N N - - 11)) & u u ((k k + + N N - - 22)) & \cdot \cdot \cdot &Center Dot; \cdot &Center Dot; & u u ((k k + + N N - - M m)) & 11 \end{matrix}];;$

计算出此时的常参数α₀，并建立出以流化床温度为输入，给定量为输出的模型，为步骤六的自整定PID控制提供控制模型；Calculate the constant parameter α ₀ at this time, and establish a model with the fluidized bed temperature as the input and the given quantity as the output, providing a control model for the self-tuning PID control in step 6;

所述步骤六中的控制模型为步骤四中已经辨识出来的模型，其具体步骤包括：The control model in step six is the model identified in step four, and its specific steps include:

第一步，传统PID控制算法是一种经典的控制方法，它主要由控制器和被控对象所组成，而皮带控制器由比例、积分、微分三个环节组成，它的数学描述为：The first step, the traditional PID control algorithm is a classic control method, which is mainly composed of the controller and the controlled object, while the belt controller is composed of three links: proportional, integral, and differential. Its mathematical description is:

u(k)＝K_px(1)+K_dx(2)+K_ix(3) (19)u(k)=K _p x(1)+K _d x(2)+K _i x(3) (19)

第二步，通过温度输入量的测量值与温度的期望值的误差及采样时间可以求出第一步中的x(1)、x(2)、x(3)，其计算公式为：In the second step, x(1), x(2), and x(3) in the first step can be obtained through the error between the measured value of the temperature input and the expected value of the temperature and the sampling time, and the calculation formula is:

x(1)＝error(k)；x(1)=error(k);

x(2)＝[error(k)-error_1]/t_s；x(2)=[error(k)-error_1]/t _s ;

x(3)＝x(3)+error(k)*t_s；x(3)=x(3)+error(k)*t _s ;

第三步，将上两个步骤进行编程后，输出的值u(k)即为给煤量的修正值，并记录下来；In the third step, after programming the previous two steps, the output value u(k) is the correction value of the coal feeding amount, and record it;

所述步骤七中将步骤三的分量ξ与步骤六求出的给煤量修正值u(k)相加，其计算公式为：In the step seven, the component ξ of the step three is added to the correction value u(k) of the coal feed amount obtained in the step six, and the calculation formula is:

η(k)＝u(k)+β (20)η(k)=u(k)+β (20)

本发明的具体实施例：Specific embodiments of the present invention:

实施例1Example 1

如图2-图4所示，本发明实施例的一种基于参数辨识的流化床温度控制方法，具体包括以下步骤：As shown in Figure 2-Figure 4, a fluidized bed temperature control method based on parameter identification in the embodiment of the present invention specifically includes the following steps:

步骤一，将现场的流化床温度、喂煤量及煤量的反馈值分别选取同一时间段的10000组数据，时间间隔为1s，将10000组数据分为100组，并计算出它的平均值。每100个喂煤量与煤量反馈值辨识出一个参数数据ξ，并计算出辨识出的参数与喂煤量的期望值做差，记录下此时的差值作为执行机构对喂煤量影响的主要因素。通过步骤一就可以通过辨识得到影响喂煤量的第一个数据。Step 1: Select 10,000 sets of data in the same period of time for the on-site fluidized bed temperature, coal feed rate, and feedback value of coal amount. The time interval is 1s. Divide the 10,000 sets of data into 100 sets, and calculate its average value. Identify a parameter data ξ for every 100 coal feed and coal feed feedback values, and calculate the difference between the identified parameter and the expected value of coal feed, and record the difference at this time as the influence of the actuator on the coal feed major factor. Through the first step, the first data that affects the coal feeding amount can be obtained through identification.

步骤二，选用步骤一中采集到的喂煤量与煤量反馈值作为参数辨识的输入输出，比如其中的一组喂煤量数据为＝[25.038，27.895，25.306，26.788，...，26.79，25.573，27.804]，煤量的反馈值数据为＝[25.923，26.386，27.489，25.9，...，27.642，27.44，27.29]，设置出模型的阶数设置为10，通过最小二乘法对给定的带有参数的模型进行辨识，通过辨识后得到的常参数ξ＝26.252，并计算出与期望值的差值β。图5为通过100次计算得到的100个差值。此常参数及差值就是本发明的核心。Step 2: Select the coal feed amount and coal amount feedback value collected in step 1 as the input and output of parameter identification. For example, a set of coal feed amount data is =[25.038, 27.895, 25.306, 26.788,..., 26.79 , 25.573, 27.804], the feedback value data of coal quantity is =[25.923, 26.386, 27.489, 25.9, ..., 27.642, 27.44, 27.29], set the order of the model to 10, and use the least squares method to give Identify the model with certain parameters, get the constant parameter ξ=26.252 after identification, and calculate the difference β with the expected value. Figure 5 shows 100 differences obtained through 100 calculations. This constant parameter and difference are the core of the present invention.

步骤三，选用步骤一中采集到的喂煤量与流化床温度作为辨识的输入输出，选取的喂煤量数据为＝[25.038，27.895，25.306，26.788，...，26.79，25.573，27.804]，流化床温度数据为＝[919.72，917，71，908.1，912.54，907.71，...，918.97，913.82，912.31，917.9]，设置模型阶数为10，通过最小二乘法建立模型，为下一步的自适应PID做准备。Step 3: Select the coal feed amount and fluidized bed temperature collected in step 1 as the input and output of identification, and the selected coal feed amount data is = [25.038, 27.895, 25.306, 26.788, ..., 26.79, 25.573, 27.804 ], the fluidized bed temperature data is = [919.72, 917, 71, 908.1, 912.54, 907.71, ..., 918.97, 913.82, 912.31, 917.9], the model order is set to 10, and the model is established by the least square method, as Prepare for the next step of adaptive PID.

步骤四，在步骤三中已经建立了模型，利用自适应PID对系统进行控制，计算此时的u(k)＝1.912，图6为通过自适应PID控制后得到的系统增量值。将计算出来的增量与步骤二中计算出来的差值β相加，得到最终的调节增量η(k)＝u(k)+β＝2.16。此时求出来的调节增量也是本发明的核心。图7为喂煤量与经过控制及辨识参数进行调整后的喂煤量比较图。Step 4, the model has been established in step 3, and the adaptive PID is used to control the system, and u(k)=1.912 is calculated at this time. Figure 6 shows the incremental value of the system obtained after the adaptive PID control. Add the calculated increment to the difference β calculated in step 2 to obtain the final adjustment increment η(k)=u(k)+β=2.16. The adjustment increment obtained at this time is also the core of the present invention. Fig. 7 is a comparison chart of the coal feeding amount and the adjusted coal feeding amount after control and identification parameters.

以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention should be included in the protection of the present invention. within range.

Claims

1. the fluidized-bed temperature control method based on parameter identification, it is characterized in that, should utilize based on the fluidized-bed temperature control method of parameter identification and feed coal amount and coal amount value of feedback, and set up the parameter identification function with stable point, obtain normal parameter and the difference of feeding coal amount expectation value; Utilize and feed coal amount and fluidized-bed temperature and go out by least squares identification the increment that model and Adaptive PID Control obtain hello coal amount; By the difference that calculates and increment, namely maximizedly utilize fuel.

2., as claimed in claim 1 based on the fluidized-bed temperature control method of parameter identification, it is characterized in that, should be as follows based on the concrete steps of the fluidized-bed temperature control method of parameter identification:

Step one, using detect in real time feed coal amount as input, the feedback quantity of the coal detected is as output;

Step 2, the coal amount of feeding collected in optional step one and feedback quantity, as the input and output of parameter identification, pick out one-component ξ by least-squares parameter discrimination method;

Step 3, the expectation value of feeding coal amount of required component ξ out and setting gets difference β, is first influence factor to feeding coal amount;

The fluidized-bed temperature value detected in real time is u by step 4, and the instantaneous value of feeding coal amount is designated as y;

Step 5, the Real-time streaming bed temperature angle value obtained in optional step four and real-time coal value of feeding, as the input and output of parameter identification, pick out one-component α by least-squares parameter discrimination method ₀, and draw the model now picked out;

Step 6, by the model picked out, utilizes self-regulated PID control method, weighs control the coal of system;

Step 7, the coal-supplying amount modified value that difference β step 3 calculated and step 6 are obtained is added, and feeds back to coal-supplying amount.

3. as claimed in claim 2 based on the fluidized-bed temperature control method of parameter identification, it is characterized in that, in step 2, the value of feedback of coal-supplying amount and coal is as the input and output of linear least squares method, and pick out normal parameter ξ by linear least squares method method, concrete steps comprise:

The first step, provides that single-input single-output is linear, permanent, the mathematical model of stochastic system:

y (k) + Σ_{i = 1}^{n} a_{i} y (k - i) = Σ_{i = 1}^{n} b_{i} u (k - i) + e (k) - - - (1)

Then final output is:

y (k) = - Σ_{i = 1}^{n} a_{i} y (k - i) + Σ_{i = 1}^{n} b_{i} u (k - i) + e (k) - - - (2)

U (k) and y (k) they are the value of feedback data sequence { u (k) } of feeding coal amount and coal, { y (k) }, and e is model error, wherein k=1,2,, n, n are natural number, and in (2) formula, the value of exponent number n is 10, wherein i=1,2 ..., n, a _i, b _ibe all constant, pass through a _iand b _ivalue can finally try to achieve stable point ξ;

Second step, specializes model, thus obtains objective function:

Make θ=[a ₁, a ₂..., a _n, b ₁, b ₂..., b _n] ^t;

Then have:

Models fitting residual epsilon (k) is:

For n group data, obtain from (3) formula:

ϵ (n) = y (n) - X (n) \hat{θ} - - - (4)

ε (n) is the models fitting residual error to n group data, the n group data of the value of feedback that y (n) is coal;

3rd step, obtains least-squares estimation: least square is the estimated value of a searching θ make the measured value of each time with by estimating the measurement determined estimates that only poor quadratic sum is minimum, from criterion of least squares derivation canonical equation, according to asking extremum principle to know, and least-squares estimation meet:

Obtain least-squares estimation

{\hat{θ}}_{LS} = {(X^{T} X)}^{- 1} X^{T} y - - - (6)

4th step, derive and pick out normal parameter: the process picking out normal parameter ξ is: choose the model of impulse response model as system, and model is as shown in (7) formula:

y (k) = Σ_{i = 1}^{n} h (i) u (k - i) - - - (7)

U (k-i) and y (k) are the value of feedback data sequence of feeding coal amount and coal, and h (i) is constant;

Adding normal parameter ξ, a ξ is in a model a parameter index that accurately can reflect that coal weighs, and model becomes:

y (k) = Σ_{i = 1}^{n} h (i) u (k - i) + ξ - - - (8)

In formula, ξ is constant term, and by the experiment at scene, parameter ξ and real system contact closely, using ξ with to feeding the difference β of expectation value of coal amount as the controlling factor of first in Systematical control problem;

(8) are write as the form of vector:

Y(k)＝U(k)H (9)

Wherein:

Y (k) = [\begin{matrix} y (k) \\ y (k + 1) \\ . \\ . \\ . \\ y (k + N) \end{matrix}], H = \begin{matrix} [\begin{matrix} h_{1} \\ h_{2} \\ . \\ . \\ . \\ h_{M} \\ ξ \end{matrix}] \end{matrix};

U (k) = [\begin{matrix} u (k - 1) & u (k - 2) & . . . & u (k - M) & 1 \\ u (k) & u (k - 1) & . . . & u (k - M + 1) & 1 \\ . & . & . & . & . \\ . & . & . & . & . \\ . & . & . & . & . \\ u (k + N - 1) & u (k + N - 2) & . . . & u (k + N - M) & 1 \end{matrix}];

The matrix of H for being formed with the normal parameter ξ picked out by constant h (i), the matrix that U (k), Y (k) form for coal-supplying amount and feedback quantity;

Inputoutput data is consistent with the input and output of linear least squares method, tries to achieve and obtain optimum value when step size M=10 time, and through type (8) tries to achieve h ₁, h ₂... h _m, ξ;

Obtain normal parameter ξ, and record, and get difference β with the expectation value of feeding coal amount.

4., as claimed in claim 2 based on the fluidized-bed temperature control method of parameter identification, it is characterized in that, in step 4, fluidized-bed temperature and coal-supplying amount are as the input and output of linear least squares method, pick out normal parameter alpha by linear least squares method method ₀, concrete steps are roughly the same with the method asking for normal parameter in step 2, and just input and output converted, concrete steps are:

y (k) + Σ_{i = 1}^{n} a_{i} y (k - i) = Σ_{i = 1}^{n} b_{i} u (k - i) + e (k) - - - (10)

Then final output is:

y (k) = - Σ_{i = 1}^{n} a_{i} y (k - i) + Σ_{i = 1}^{n} b_{i} u (k - i) + e (k) - - - (11)

U (k) and y (k) are by the fluidized-bed temperature value detected in real time and the instantaneous value data sequence { u (k) } of feeding coal amount, { y (k) }, and e is model error, wherein k=1,2 ... .., n, n is natural number, and in (11) formula, the value of exponent number n is 10, wherein i=1,2,, n, a _i, b _ibe all constant, pass through a _iand b _ivalue can finally try to achieve stable point ξ;

Second step, specializes model, thus obtains objective function:

Make θ=[a ₁, a ₂..., a _n, b ₁, b ₂..., b _n] ^t;

Then have:

Models fitting residual epsilon (k) is:

For n group data, obtain from (3) formula:

ϵ (n) = y (n) - X (n) \hat{θ} - - - (13)

By (10) formula to (14) formula, calculate least-squares estimation now

{\hat{θ}}_{LS} = {(X^{T} X)}^{- 1} X^{T} y - - - (15)

Choose the model of impulse response model as system, model, such as formula shown in (7), increases a normal parameter alpha on this basis ₀, α ₀for accurately reflecting the parameter index that coal weighs, model becomes:

y (k) = Σ_{i = 1}^{n} h (i) u (k - i) + α_{0} - - - (16)

The data sequence that u (k-i) and y (k) are fluidized-bed temperature and coal-supplying amount, h (i), α ₀for constant term.

5., as claimed in claim 4 based on the fluidized-bed temperature control method of parameter identification, it is characterized in that, then calculate normal parameter alpha ₀methods and steps two in the method for calculating parameter consistent, concrete steps are:

U (k-i) is fluidized-bed temperature value and the instantaneous value data sequence of feeding coal amount with y (k), and h (i) is constant;

Add a normal parameter alpha in a model ₀, α ₀for accurately reflecting the parameter index that coal weighs, model becomes:

y (k) = Σ_{i = 1}^{n} h (i) u (k - i) + α_{0} - - - (17)

α in formula ₀for constant term;

(8) are write as the form of vector:

Y(k)＝U(k)H (18)

Wherein:

Y (k) = [\begin{matrix} y (k) \\ y (k + 1) \\ . \\ . \\ . \\ y (k + N) \end{matrix}], H = \begin{matrix} [\begin{matrix} h_{1} \\ h_{2} \\ . \\ . \\ . \\ h_{M} \\ ξ \end{matrix}] \end{matrix};

U (k) = [\begin{matrix} u (k - 1) & u (k - 2) & . . . & u (k - M) & 1 \\ u (k) & u (k - 1) & . . . & u (k - M + 1) & 1 \\ . & . & . & . & . \\ . & . & . & . & . \\ . & . & . & . & . \\ u (k + N - 1) & u (k + N - 2) & . . . & u (k + N - M) & 1 \end{matrix}];

H is by constant h (i) and the normal parameter alpha picked out ₀the matrix of composition, the matrix that U (k), Y (k) form for coal-supplying amount and feedback quantity;

Inputoutput data is consistent with the input and output of linear least squares method, tries to achieve and obtain optimum value when step size M=10 time, and through type (17) tries to achieve h ₁, h ₂... h _m, α ₀;

Calculate normal parameter alpha now ₀, and set up out with fluidized-bed temperature be input, specified rate be export model, for the self-regulated PID control of step 6 provides Controlling model.

6., as claimed in claim 2 based on the fluidized-bed temperature control method of parameter identification, it is characterized in that, the Controlling model in step 6 is identification model out in step 4, and concrete steps comprise:

The first step, traditional PID control algorithm is made up of controller and controlled device, and belt controller is made up of ratio, integration, differential three links, and mathematical description is:

u(k)＝K _px(1)+K _dx(2)+K _ix(3) (19)

In formula, K _pfor scale-up factor; K _ifor integration time constant; K _dfor derivative time constant; The corrected value that the increase reduced value x (1) that u (k) weighs for the coal by obtaining after PID controller is ratio; The corrected value that x (2) is differential; The corrected value that x (3) is integration;

Second step, obtained x (1), x (2), the x (3) in the first step by the error of the measured value of temperature input quantity and the expectation value of temperature and sampling time, computing formula is:

x(1)＝error(k)；

x(2)＝[error(k)-error_1]/t _s；

x(3)＝x(3)+error(k)*t _s；

In formula, error (k) is the error calculated by measured value and expectation value in the k moment; t _sfor the sampling time;

3rd step, after upper two steps being programmed, the value u (k) of output is the modified value of coal-supplying amount, and records.

7., as claimed in claim 2 based on the fluidized-bed temperature control method of parameter identification, it is characterized in that, be added with coal-supplying amount modified value u (k) that step 6 is obtained by the component ξ of step 3 in step 7, computing formula is:

η(k)＝u(k)+β (20)

In formula, the coal-supplying amount modified value of u (k) for being calculated by the control method of step 6, β is that η (k) is the final modified value to system coal-supplying amount by step 2 by least squares identification normal parameter out and the difference of hello coal amount expectation value.