[go: up one dir, main page]

CN103064294A - Chemical process decoupling non-minimal realization expansion state space quadric form control method - Google Patents

Chemical process decoupling non-minimal realization expansion state space quadric form control method Download PDF

Info

Publication number
CN103064294A
CN103064294A CN2013100181093A CN201310018109A CN103064294A CN 103064294 A CN103064294 A CN 103064294A CN 2013100181093 A CN2013100181093 A CN 2013100181093A CN 201310018109 A CN201310018109 A CN 201310018109A CN 103064294 A CN103064294 A CN 103064294A
Authority
CN
China
Prior art keywords
matrix
output
input
model
state space
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN2013100181093A
Other languages
Chinese (zh)
Inventor
张日东
吴锋
张乐
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hangzhou Dianzi University
Original Assignee
Hangzhou Dianzi University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hangzhou Dianzi University filed Critical Hangzhou Dianzi University
Priority to CN2013100181093A priority Critical patent/CN103064294A/en
Publication of CN103064294A publication Critical patent/CN103064294A/en
Pending legal-status Critical Current

Links

Landscapes

  • Feedback Control In General (AREA)

Abstract

The invention relates to a chemical process decoupling non-minimal realization expansion state space quadric form control method. According to an existing traditional and simple control method, control parameters are fully depended on experience of technical workers, and therefore control effect is unsatisfying. According to the method, a decoupling state space model is established based on a chemical process model so as to obtain basic process characteristics; a chemical process decoupling non-minimal realization expansion state space quadric form control loop is established based on the decoupling state space model; and parameters of a chemical process decoupling non-minimal realization expansion state space quadric form controller are computed to carry out decoupling non-minimal realization expansion state space quadric form control on the whole of process objects. By means of data acquisition, process processing, mechanism forecasting, data drive, optimization and the like, the chemical process decoupling non-minimal realization expansion state space quadric form control method is achieved, and the chemical process decoupling non-minimal realization expansion state space quadric form control method can effectively improve control accuracy and stability.

Description

化工过程解耦非最小实现扩展状态空间二次型控制方法Decoupling non-minimum realization of extended state space quadratic control method for chemical process

技术领域 technical field

本发明属于自动化技术领域，涉及一种化工过程解耦非最小实现扩展状态空间二次型控制方法。 The invention belongs to the technical field of automation, and relates to a chemical process decoupling non-minimum realization expansion state space quadratic control method.

背景技术 Background technique

化工过程是我国流程工业过程的重要组成部分，其要求是供给合格的工业产品，以满足我国工业发展的需要。作为工业生产的一个重要主体，流程工业生产过程水平的提高对整个工业经济效益的提高起着至关重要的作用。为此，生产过程的各个主要工艺参数必须严格控制。随着工业的发展以及对产品的质量、能源消耗和环境保护的要求越来越高，对工业过程的控制精度要求也越来越严格，传统的控制方法虽满足了一定的要求，但难以进一步提升控制水平，加上工艺过程变得更加复杂。简单的单回路过程控制已经无法满足控制精度和平稳性的要求，产品合格率低，装置效率低下。而目前实际工业中控制基本上采用传统的简单的控制手段，控制参数完全依赖技术人员经验，使生产成本增加，控制效果很不理想。我国化工过程控制与优化技术比较落后，能耗居高不下，控制性能差，自动化程度低，很难适应节能减排以及间接环境保护的需求，这其中直接的影响因素之一便是系统的控制方案问题。 The chemical process is an important part of my country's process industry process, and its requirement is to supply qualified industrial products to meet the needs of my country's industrial development. As an important subject of industrial production, the improvement of the production process level of the process industry plays a vital role in the improvement of the economic benefits of the entire industry. For this reason, each main process parameter of the production process must be strictly controlled. With the development of industry and the higher and higher requirements for product quality, energy consumption and environmental protection, the requirements for control precision of industrial processes are becoming more and more stringent. Although traditional control methods meet certain requirements, it is difficult to further improve Increased levels of control coupled with more complex processes. The simple single-loop process control can no longer meet the requirements of control accuracy and stability, the product qualification rate is low, and the device efficiency is low. At present, the control in the actual industry basically adopts traditional simple control methods, and the control parameters are completely dependent on the experience of technicians, which increases the production cost and the control effect is not ideal. my country's chemical process control and optimization technology is relatively backward, with high energy consumption, poor control performance, and low degree of automation. It is difficult to meet the needs of energy conservation, emission reduction, and indirect environmental protection. One of the direct influencing factors is the control of the system. program problem.

发明内容 Contents of the invention

本发明的目标是针对现有的化工过程系统控制技术的不足之处，提供一种化工过程解耦非最小实现扩展状态空间二次型控制方法。该方法弥补了传统控制方式的不足，保证控制具有较高的精度和稳定性的同时，也保证形式简单并满足实际工业过程的需要。 The object of the present invention is to provide a chemical process decoupling non-minimum realization extended state space quadratic control method for the deficiencies of the existing chemical process system control technology. This method makes up for the deficiency of the traditional control method, ensures high precision and stability of the control, and at the same time ensures the simplicity of the form and meets the needs of the actual industrial process.

本发明方法首先基于化工过程模型建立解耦状态空间模型，挖掘出基本的过程特性；然后基于该解耦状态空间模型建立化工过程解耦非最小实现扩展状态空间二次型控制回路；最后通过计算化工过程解耦非最小实现扩展状态空间二次型控制器的参数，将过程对象整体实施解耦非最小实现扩展状态空间二次型控制。 The method of the present invention first establishes a decoupling state space model based on the chemical process model, and digs out the basic process characteristics; then establishes a chemical process decoupling non-minimum realization of the extended state space quadratic control loop based on the decoupling state space model; finally, through calculation Chemical process decoupling non-minimum realizes the parameters of the extended state space quadratic controller, and decouples the process object as a whole to realize the extended state space quadratic control.

本发明的技术方案是通过数据采集、过程处理、预测机理、数据驱动、优化等手段，确立了一种化工过程解耦非最小实现扩展状态空间二次型控制方法，利用该方法可有效提高控制的精度，提高控制平稳度。 The technical solution of the present invention is to establish a chemical process decoupling non-minimum realization expansion state space quadratic control method through data collection, process processing, prediction mechanism, data drive, optimization and other means, which can effectively improve the control Accuracy, improve control smoothness.

本发明方法的步骤包括： The steps of the inventive method comprise:

(1)利用化工过程模型建立解耦状态空间模型，具体方法是： (1) Use the chemical process model to establish a decoupled state space model, the specific method is:

首先采集化工过程的输入输出数据，利用该数据建立输入输出模型如下： First collect the input and output data of the chemical process, and use the data to establish the input and output model as follows:

Figure 2013100181093100002DEST_PATH_IMAGE002
Figure 2013100181093100002DEST_PATH_IMAGE002

其中

Figure 2013100181093100002DEST_PATH_IMAGE004
、
Figure 2013100181093100002DEST_PATH_IMAGE006
、
Figure 2013100181093100002DEST_PATH_IMAGE008
分别为输出向量
Figure 2013100181093100002DEST_PATH_IMAGE010
变换、传递函数矩阵、输入向量
Figure 459535DEST_PATH_IMAGE010
变换；  in
Figure 2013100181093100002DEST_PATH_IMAGE004
,
Figure 2013100181093100002DEST_PATH_IMAGE006
,
Figure 2013100181093100002DEST_PATH_IMAGE008
are the output vectors
Figure 2013100181093100002DEST_PATH_IMAGE010
Transformation, transfer function matrix, input vector
Figure 459535DEST_PATH_IMAGE010
transform;

Figure 2013100181093100002DEST_PATH_IMAGE012
Figure 2013100181093100002DEST_PATH_IMAGE012

Figure 2013100181093100002DEST_PATH_IMAGE014
,
Figure 2013100181093100002DEST_PATH_IMAGE016
,
Figure 2013100181093100002DEST_PATH_IMAGE018
,
Figure 2013100181093100002DEST_PATH_IMAGE020
表示过程的各回路传递函数，
Figure 2013100181093100002DEST_PATH_IMAGE022
和分别为第
Figure 2013100181093100002DEST_PATH_IMAGE026
个输入和输出变量的
Figure 335962DEST_PATH_IMAGE010
变换，
Figure 2013100181093100002DEST_PATH_IMAGE028
，
Figure 812073DEST_PATH_IMAGE010
为计算机控制系统的离散变换算子，
Figure 2013100181093100002DEST_PATH_IMAGE030
为
Figure 213099DEST_PATH_IMAGE010
的倒数，
Figure 2013100181093100002DEST_PATH_IMAGE032
为过程的输入输出变量个数，所述的输入输出数据为数据采集器中存储的数据；
Figure 2013100181093100002DEST_PATH_IMAGE014
,
Figure 2013100181093100002DEST_PATH_IMAGE016
,
Figure 2013100181093100002DEST_PATH_IMAGE018
,
Figure 2013100181093100002DEST_PATH_IMAGE020
Represents the transfer function of each loop of the process,
Figure 2013100181093100002DEST_PATH_IMAGE022
and respectively
Figure 2013100181093100002DEST_PATH_IMAGE026
of input and output variables
Figure 335962DEST_PATH_IMAGE010
transform,
Figure 2013100181093100002DEST_PATH_IMAGE028
,
Figure 812073DEST_PATH_IMAGE010
is the discrete transform operator of the computer control system,
Figure 2013100181093100002DEST_PATH_IMAGE030
for
Figure 213099DEST_PATH_IMAGE010
the reciprocal of
Figure 2013100181093100002DEST_PATH_IMAGE032
is the number of input and output variables of the process, and the input and output data is the data stored in the data collector;

进一步对上述方程选取伴随矩阵解耦阵为： Further select the adjoint matrix decoupling matrix for the above equation as:

其中，

Figure 2013100181093100002DEST_PATH_IMAGE036
是伴随矩阵解耦阵，
Figure 2013100181093100002DEST_PATH_IMAGE038
为
Figure 242978DEST_PATH_IMAGE006
的伴随矩阵。 in,
Figure 2013100181093100002DEST_PATH_IMAGE036
is the adjoint matrix decoupling matrix,
Figure 2013100181093100002DEST_PATH_IMAGE038
for
Figure 242978DEST_PATH_IMAGE006
The adjoint matrix of .

将上述伴随矩阵解耦阵与过程输入输出模型合并得到： Combining the above adjoint matrix decoupling matrix with the process input and output model, we get:

其中，

Figure 2013100181093100002DEST_PATH_IMAGE042
是得到的解耦过程模型，
Figure 2013100181093100002DEST_PATH_IMAGE044
为
Figure 554005DEST_PATH_IMAGE006
的行列式，
Figure 2013100181093100002DEST_PATH_IMAGE046
为以
Figure 496291DEST_PATH_IMAGE006
的行列式为元素的对角矩阵。 in,
Figure 2013100181093100002DEST_PATH_IMAGE042
is the resulting decoupled process model,
Figure 2013100181093100002DEST_PATH_IMAGE044
for
Figure 554005DEST_PATH_IMAGE006
determinant of
Figure 2013100181093100002DEST_PATH_IMAGE046
for
Figure 496291DEST_PATH_IMAGE006
The determinant of is a diagonal matrix of elements.

 

Figure 2013100181093100002DEST_PATH_IMAGE048
 
Figure 2013100181093100002DEST_PATH_IMAGE048

将上述解耦过程模型处理成

Figure 587875DEST_PATH_IMAGE032
个单变量过程的离散表示方式： The above decoupled process model is processed into
Figure 587875DEST_PATH_IMAGE032
Discrete representation of a univariate process:

Figure 2013100181093100002DEST_PATH_IMAGE050
Figure 2013100181093100002DEST_PATH_IMAGE050

其中

Figure 2013100181093100002DEST_PATH_IMAGE052
和
Figure 2013100181093100002DEST_PATH_IMAGE054
分别是第
Figure 781965DEST_PATH_IMAGE026
个过程的输出和输入变量，
Figure 2013100181093100002DEST_PATH_IMAGE056
，
Figure 2013100181093100002DEST_PATH_IMAGE058
和
Figure 2013100181093100002DEST_PATH_IMAGE060
分别是
Figure 383716DEST_PATH_IMAGE052
和
Figure 2013100181093100002DEST_PATH_IMAGE062
的系数矩阵多项式；  in
Figure 2013100181093100002DEST_PATH_IMAGE052
and
Figure 2013100181093100002DEST_PATH_IMAGE054
respectively
Figure 781965DEST_PATH_IMAGE026
The output and input variables of a process,
Figure 2013100181093100002DEST_PATH_IMAGE056
,
Figure 2013100181093100002DEST_PATH_IMAGE058
and
Figure 2013100181093100002DEST_PATH_IMAGE060
respectively
Figure 383716DEST_PATH_IMAGE052
and
Figure 2013100181093100002DEST_PATH_IMAGE062
The coefficient matrix polynomial of ;

                           

Figure 2013100181093100002DEST_PATH_IMAGE064
                           
Figure 2013100181093100002DEST_PATH_IMAGE064

其中

Figure 2013100181093100002DEST_PATH_IMAGE066
是相应的系数，
Figure 2013100181093100002DEST_PATH_IMAGE068
为后移步算子，
Figure 2013100181093100002DEST_PATH_IMAGE072
是得到的模型阶次； in
Figure 2013100181093100002DEST_PATH_IMAGE066
is the corresponding coefficient,
Figure 2013100181093100002DEST_PATH_IMAGE068
to move back step operator,
Figure 2013100181093100002DEST_PATH_IMAGE072
is the obtained model order;

将过程模型通过后移算子

Figure 2013100181093100002DEST_PATH_IMAGE074
处理成过程的状态空间表示方式： Pass the process model through the backward shift operator
Figure 2013100181093100002DEST_PATH_IMAGE074
Processed into a state-space representation of a process:

Figure 2013100181093100002DEST_PATH_IMAGE076
Figure 2013100181093100002DEST_PATH_IMAGE076

Figure 2013100181093100002DEST_PATH_IMAGE078
；
Figure 2013100181093100002DEST_PATH_IMAGE078
;

其中， 

Figure 2013100181093100002DEST_PATH_IMAGE080
、
Figure 2013100181093100002DEST_PATH_IMAGE082
分别是第
Figure 2013100181093100002DEST_PATH_IMAGE084
时刻的变量值，
Figure 2013100181093100002DEST_PATH_IMAGE086
为第
Figure 2013100181093100002DEST_PATH_IMAGE088
时刻的输入增量变量值，
Figure 2013100181093100002DEST_PATH_IMAGE090
、
Figure 2013100181093100002DEST_PATH_IMAGE092
分别为第
Figure 2013100181093100002DEST_PATH_IMAGE094
时刻的输出变量增量和输入变量增量值，
Figure 2013100181093100002DEST_PATH_IMAGE096
、
Figure 2013100181093100002DEST_PATH_IMAGE098
、
Figure 2013100181093100002DEST_PATH_IMAGE100
分别为对应的状态矩阵、输入矩阵和输出矩阵，
Figure 2013100181093100002DEST_PATH_IMAGE102
为取转置符号。 in,
Figure 2013100181093100002DEST_PATH_IMAGE080
,
Figure 2013100181093100002DEST_PATH_IMAGE082
respectively
Figure 2013100181093100002DEST_PATH_IMAGE084
the value of the variable at time,
Figure 2013100181093100002DEST_PATH_IMAGE086
for the first
Figure 2013100181093100002DEST_PATH_IMAGE088
The input increment variable value at time,
Figure 2013100181093100002DEST_PATH_IMAGE090
,
Figure 2013100181093100002DEST_PATH_IMAGE092
respectively
Figure 2013100181093100002DEST_PATH_IMAGE094
The output variable increment and input variable increment value at time,
Figure 2013100181093100002DEST_PATH_IMAGE096
,
Figure 2013100181093100002DEST_PATH_IMAGE098
,
Figure 2013100181093100002DEST_PATH_IMAGE100
are the corresponding state matrix, input matrix and output matrix respectively,
Figure 2013100181093100002DEST_PATH_IMAGE102
to take the transpose sign.

Figure 2013100181093100002DEST_PATH_IMAGE106
Figure 2013100181093100002DEST_PATH_IMAGE106

Figure 2013100181093100002DEST_PATH_IMAGE108
Figure 2013100181093100002DEST_PATH_IMAGE108

定义一过程期望输出为

Figure 2013100181093100002DEST_PATH_IMAGE110
，并且输出误差
Figure 2013100181093100002DEST_PATH_IMAGE112
为： Define the expected output of a process as
Figure 2013100181093100002DEST_PATH_IMAGE110
, and the output error
Figure 2013100181093100002DEST_PATH_IMAGE112
for:

Figure 2013100181093100002DEST_PATH_IMAGE114
Figure 2013100181093100002DEST_PATH_IMAGE114

 进一步得到第

Figure 642045DEST_PATH_IMAGE084
时刻的输出误差
Figure 2013100181093100002DEST_PATH_IMAGE116
为： further get the
Figure 642045DEST_PATH_IMAGE084
time output error
Figure 2013100181093100002DEST_PATH_IMAGE116
for:

其中，

Figure 2013100181093100002DEST_PATH_IMAGE120
为第
Figure 893029DEST_PATH_IMAGE084
时刻的过程期望输出增量。 in,
Figure 2013100181093100002DEST_PATH_IMAGE120
for the first
Figure 893029DEST_PATH_IMAGE084
The process expects output increments at moments.

     定义一个新的复合状态变量： Defining a new composite state variable:

  将上述处理过程综合为一个解耦状态空间模型： Synthesize the above process into a decoupled state-space model:

Figure 2013100181093100002DEST_PATH_IMAGE124
Figure 2013100181093100002DEST_PATH_IMAGE124

其中，为第

Figure 890810DEST_PATH_IMAGE084
时刻的复合状态变量，
Figure 2013100181093100002DEST_PATH_IMAGE128
、、
Figure 2013100181093100002DEST_PATH_IMAGE132
分别为对应复合状态变量的状态矩阵、输入矩阵和输出矩阵，具体是: in, for the first
Figure 890810DEST_PATH_IMAGE084
Composite state variable at time,
Figure 2013100181093100002DEST_PATH_IMAGE128
, ,
Figure 2013100181093100002DEST_PATH_IMAGE132
are the state matrix, input matrix and output matrix corresponding to the composite state variable, specifically:

Figure 2013100181093100002DEST_PATH_IMAGE134
，
Figure 2013100181093100002DEST_PATH_IMAGE136
，
Figure 2013100181093100002DEST_PATH_IMAGE138
Figure 2013100181093100002DEST_PATH_IMAGE134
,
Figure 2013100181093100002DEST_PATH_IMAGE136
,
Figure 2013100181093100002DEST_PATH_IMAGE138

(2)基于该解耦状态空间模型设计解耦非最小实现扩展状态空间二次型控制器，具体方法是： (2) Based on the decoupled state space model, design a decoupled non-minimum quadratic controller with extended state space, the specific method is:

a.定义该解耦非最小实现扩展状态空间二次型控制器的目标函数为： a. Define the objective function of the decoupled non-minimum realization extended state space quadratic controller as:

Figure 2013100181093100002DEST_PATH_IMAGE140
Figure 2013100181093100002DEST_PATH_IMAGE140

其中，

Figure 2013100181093100002DEST_PATH_IMAGE142
为目标函数，
Figure 2013100181093100002DEST_PATH_IMAGE144
和
Figure 2013100181093100002DEST_PATH_IMAGE146
分别为状态变量和输出变量的加权矩阵。 in,
Figure 2013100181093100002DEST_PATH_IMAGE142
is the objective function,
Figure 2013100181093100002DEST_PATH_IMAGE144
and
Figure 2013100181093100002DEST_PATH_IMAGE146
are the weighting matrices of state variables and output variables, respectively.

b.计算该解耦非最小实现扩展状态空间二次型控制器的参数，具体是： b. Calculate the parameters of the decoupled non-minimum realization extended state space quadratic controller, specifically:

其中

Figure 2013100181093100002DEST_PATH_IMAGE150
为控制器反馈系数向量。 in
Figure 2013100181093100002DEST_PATH_IMAGE150
is the controller feedback coefficient vector.

本发明提出的一种化工过程解耦非最小实现扩展状态空间二次型控制方法弥补了传统控制的不足，并有效地方便了控制器的设计，保证控制性能的提升，同时满足给定的生产性能指标。 The invention proposes a chemical process decoupling non-minimum realization expansion state space quadratic control method to make up for the shortcomings of traditional control, and effectively facilitate the design of the controller, ensure the improvement of control performance, and meet the given production requirements at the same time Performance.

本发明提出的控制技术可以有效减少理想工艺参数与实际工艺参数之间的误差，进一步弥补了传统控制器的不足，同时保证控制装置操作在最佳状态，使生产过程的工艺参数达到严格控制。 The control technology proposed by the invention can effectively reduce the error between ideal process parameters and actual process parameters, further make up for the shortcomings of traditional controllers, and at the same time ensure that the control device operates in the best state, so that the process parameters of the production process can be strictly controlled.

具体实施方式 Detailed ways

以焦化加热炉辐射出口温度过程控制为例： Take the process control of the radiation outlet temperature of coking furnace as an example:

这里以焦化加热炉辐射出口温度过程控制作为例子加以描述。该过程是一个多变量耦合的过程，出口温度不仅受到燃料量流量的影响，同时也受炉膛压力，进风流量的影响。调节手段采用燃料量流量，其余的影响作为不确定因素。 Here, the coking furnace radiation outlet temperature process control is taken as an example to describe. This process is a multi-variable coupling process, and the outlet temperature is not only affected by the fuel flow rate, but also by the furnace pressure and the inlet air flow rate. The adjustment method adopts the fuel flow rate, and the rest of the effects are regarded as uncertain factors.

(1)建立解耦状态空间模型，具体方法是： (1) Establish a decoupled state space model, the specific method is:

首先利用数据采集器采集化工过程输入数据（燃料流量）和输出数据（加热炉辐射出口温度），建立输入输出模型如下： First, the data collector is used to collect the input data (fuel flow) and output data (radiation outlet temperature of the heating furnace) of the chemical process, and the input and output model is established as follows:

Figure 2013100181093100002DEST_PATH_IMAGE152
Figure 2013100181093100002DEST_PATH_IMAGE152

其中，

Figure DEST_PATH_IMAGE154
,
Figure DEST_PATH_IMAGE156
,
Figure DEST_PATH_IMAGE158
,表示加热炉出口温度过程的传递函数方程， 
Figure DEST_PATH_IMAGE162
分别为燃料流量、加热炉出口温度数据
Figure DEST_PATH_IMAGE164
变换； in,
Figure DEST_PATH_IMAGE154
,
Figure DEST_PATH_IMAGE156
,
Figure DEST_PATH_IMAGE158
, The transfer function equation representing the temperature process at the outlet of the heating furnace,
Figure DEST_PATH_IMAGE162
Respectively, the data of fuel flow rate and heating furnace outlet temperature
Figure DEST_PATH_IMAGE164
transform;

然后定义三个变量、

Figure DEST_PATH_IMAGE168
、
Figure DEST_PATH_IMAGE170
如下： Then define three variables ,
Figure DEST_PATH_IMAGE168
,
Figure DEST_PATH_IMAGE170
as follows:

Figure DEST_PATH_IMAGE172
Figure DEST_PATH_IMAGE172

将以上过程的输入数据和输出数据表示为： The input data and output data of the above process are expressed as:

Figure 281821DEST_PATH_IMAGE002
Figure 281821DEST_PATH_IMAGE002

进一步对上述方程选取伴随矩阵解耦阵为: Further select the adjoint matrix decoupling matrix for the above equation as:

Figure 831489DEST_PATH_IMAGE034
Figure 831489DEST_PATH_IMAGE034

其中，是伴随矩阵解耦阵，

Figure 918711DEST_PATH_IMAGE038
为的伴随矩阵。 in, is the adjoint matrix decoupling matrix,
Figure 918711DEST_PATH_IMAGE038
for The adjoint matrix of .

将上述过程模型展开得到： Expand the above process model to get:

Figure 405504DEST_PATH_IMAGE040
Figure 405504DEST_PATH_IMAGE040

其中，

Figure 988670DEST_PATH_IMAGE042
是得到的解耦过程模型，
Figure 954352DEST_PATH_IMAGE044
为
Figure 73617DEST_PATH_IMAGE006
的行列式，
Figure 466553DEST_PATH_IMAGE046
为以
Figure 802594DEST_PATH_IMAGE006
的行列式为元素的对角矩阵。 in,
Figure 988670DEST_PATH_IMAGE042
is the resulting decoupled process model,
Figure 954352DEST_PATH_IMAGE044
for
Figure 73617DEST_PATH_IMAGE006
determinant of
Figure 466553DEST_PATH_IMAGE046
for
Figure 802594DEST_PATH_IMAGE006
The determinant of is a diagonal matrix of elements.

 

Figure 306387DEST_PATH_IMAGE048
 
Figure 306387DEST_PATH_IMAGE048

将上述解耦过程模型处理成个单变量过程的离散表示方式： The above decoupled process model is processed into Discrete representation of a univariate process:

Figure 906313DEST_PATH_IMAGE050
Figure 906313DEST_PATH_IMAGE050

其中，、

Figure DEST_PATH_IMAGE174
分别是第
Figure 709239DEST_PATH_IMAGE026
个过程的输出、输入变量，
Figure 803097DEST_PATH_IMAGE058
、
Figure 600151DEST_PATH_IMAGE060
分别是
Figure 146670DEST_PATH_IMAGE052
、
Figure 490802DEST_PATH_IMAGE174
的系数矩阵多项式，
Figure 439166DEST_PATH_IMAGE072
是得到的模型阶次，
Figure 344805DEST_PATH_IMAGE066
是相应的系数，
Figure 440937DEST_PATH_IMAGE068
为后移
Figure 90225DEST_PATH_IMAGE070
步算子。 in, ,
Figure DEST_PATH_IMAGE174
respectively
Figure 709239DEST_PATH_IMAGE026
The output and input variables of a process,
Figure 803097DEST_PATH_IMAGE058
,
Figure 600151DEST_PATH_IMAGE060
respectively
Figure 146670DEST_PATH_IMAGE052
,
Figure 490802DEST_PATH_IMAGE174
The coefficient matrix polynomial of ,
Figure 439166DEST_PATH_IMAGE072
is the obtained model order,
Figure 344805DEST_PATH_IMAGE066
is the corresponding coefficient,
Figure 440937DEST_PATH_IMAGE068
to move back
Figure 90225DEST_PATH_IMAGE070
step operator.

                           

Figure 680647DEST_PATH_IMAGE064
                           
Figure 680647DEST_PATH_IMAGE064

将过程模型通过后移算子

Figure 757188DEST_PATH_IMAGE074
处理成过程的状态空间表示方式： Pass the process model through the backward shift operator
Figure 757188DEST_PATH_IMAGE074
Processed into a state-space representation of a process:

Figure 12720DEST_PATH_IMAGE076
Figure 12720DEST_PATH_IMAGE076

其中， 

Figure 528015DEST_PATH_IMAGE080
、
Figure 450971DEST_PATH_IMAGE082
分别是第
Figure 196948DEST_PATH_IMAGE084
时刻的变量值，
Figure 939776DEST_PATH_IMAGE086
为第
Figure 993183DEST_PATH_IMAGE088
时刻的输入增量变量值，、
Figure 188989DEST_PATH_IMAGE092
分别为第
Figure 183228DEST_PATH_IMAGE094
时刻的输出变量增量和输入变量增量值，、
Figure 609978DEST_PATH_IMAGE098
、
Figure 261539DEST_PATH_IMAGE100
分别为对应的状态矩阵、输入矩阵和输出矩阵，
Figure 978959DEST_PATH_IMAGE102
为取转置符号。 in,
Figure 528015DEST_PATH_IMAGE080
,
Figure 450971DEST_PATH_IMAGE082
respectively
Figure 196948DEST_PATH_IMAGE084
the value of the variable at time,
Figure 939776DEST_PATH_IMAGE086
for the first
Figure 993183DEST_PATH_IMAGE088
The input increment variable value at time, ,
Figure 188989DEST_PATH_IMAGE092
respectively
Figure 183228DEST_PATH_IMAGE094
The output variable increment and input variable increment value at time, ,
Figure 609978DEST_PATH_IMAGE098
,
Figure 261539DEST_PATH_IMAGE100
are the corresponding state matrix, input matrix and output matrix respectively,
Figure 978959DEST_PATH_IMAGE102
to take the transpose sign.

Figure 748070DEST_PATH_IMAGE078
。
Figure 748070DEST_PATH_IMAGE078
.

Figure 500125DEST_PATH_IMAGE104
Figure 500125DEST_PATH_IMAGE104

Figure 322588DEST_PATH_IMAGE106
Figure 322588DEST_PATH_IMAGE106

Figure 527304DEST_PATH_IMAGE108
Figure 527304DEST_PATH_IMAGE108

定义一过程期望输出为

Figure 162423DEST_PATH_IMAGE110
，并且输出误差为： Define the expected output of a process as
Figure 162423DEST_PATH_IMAGE110
, and the output error for:

Figure 762348DEST_PATH_IMAGE114
Figure 762348DEST_PATH_IMAGE114

 进一步得到第

Figure 454361DEST_PATH_IMAGE084
时刻的输出误差
Figure 394635DEST_PATH_IMAGE116
为： further get the
Figure 454361DEST_PATH_IMAGE084
time output error
Figure 394635DEST_PATH_IMAGE116
for:

Figure 354238DEST_PATH_IMAGE118
Figure 354238DEST_PATH_IMAGE118

其中，

Figure 456187DEST_PATH_IMAGE120
为第
Figure 697812DEST_PATH_IMAGE084
时刻的过程期望输出增量。 in,
Figure 456187DEST_PATH_IMAGE120
for the first
Figure 697812DEST_PATH_IMAGE084
The process expects output increments at moments.

     最后定义一个新的复合状态变量： Finally define a new composite state variable:

Figure 176198DEST_PATH_IMAGE122
Figure 176198DEST_PATH_IMAGE122

  将上述处理过程综合为一个解耦的过程模型： Synthesize the above processing into a decoupled process model:

其中，为第

Figure 929762DEST_PATH_IMAGE084
时刻的复合状态变量，
Figure 211839DEST_PATH_IMAGE128
、
Figure 381920DEST_PATH_IMAGE130
、
Figure 153567DEST_PATH_IMAGE132
分别为对应复合状态变量的状态矩阵、输入矩阵和输出矩阵，具体是: in, for the first
Figure 929762DEST_PATH_IMAGE084
Composite state variable at time,
Figure 211839DEST_PATH_IMAGE128
,
Figure 381920DEST_PATH_IMAGE130
,
Figure 153567DEST_PATH_IMAGE132
are the state matrix, input matrix and output matrix corresponding to the composite state variable, specifically:

(2)设计出口温度解耦非最小实现扩展状态空间二次型控制器，具体方法是： (2) Design the outlet temperature decoupling non-minimum to realize the extended state space quadratic controller, the specific method is:

第一步：定义该二次型控制器的目标函数为： Step 1: Define the objective function of the quadratic controller as:

其中，为目标函数，和

Figure 967229DEST_PATH_IMAGE146
分别为状态变量和输出变量的加权矩阵。 in, is the objective function, and
Figure 967229DEST_PATH_IMAGE146
are the weighting matrices of state variables and output variables, respectively.

b.计算二次型控制器的参数，具体是： b. Calculate the parameters of the quadratic controller, specifically:

其中

Figure 966726DEST_PATH_IMAGE150
为控制器反馈系数向量。 in
Figure 966726DEST_PATH_IMAGE150
is the controller feedback coefficient vector.

Claims (1)

1. 化工过程解耦非最小实现扩展状态空间二次型控制方法，其特征在于该方法的具体步骤是： 1. The chemical process decoupling non-minimum realization extended state space quadratic control method is characterized in that the specific steps of the method are: Ⅰ.利用化工过程模型建立解耦状态空间模型，具体方法是： Ⅰ. Using the chemical process model to establish a decoupled state space model, the specific method is: 首先采集化工过程的输入输出数据，利用该数据建立输入输出模型如下： First collect the input and output data of the chemical process, and use the data to establish the input and output model as follows:
Figure 2013100181093100001DEST_PATH_IMAGE002
Figure 2013100181093100001DEST_PATH_IMAGE002
 其中、
Figure 2013100181093100001DEST_PATH_IMAGE006
、
Figure 2013100181093100001DEST_PATH_IMAGE008
分别为输出向量变换、传递函数矩阵、输入向量
Figure 901256DEST_PATH_IMAGE010
变换；  in ,
Figure 2013100181093100001DEST_PATH_IMAGE006
,
Figure 2013100181093100001DEST_PATH_IMAGE008
are the output vectors Transformation, transfer function matrix, input vector
Figure 901256DEST_PATH_IMAGE010
transform;
Figure 2013100181093100001DEST_PATH_IMAGE012
Figure 2013100181093100001DEST_PATH_IMAGE012
 ,,
Figure DEST_PATH_IMAGE018
,表示过程的各回路传递函数，
Figure DEST_PATH_IMAGE022
和
Figure DEST_PATH_IMAGE024
分别为第
Figure DEST_PATH_IMAGE026
个输入和输出变量的
Figure 575557DEST_PATH_IMAGE010
变换，，为计算机控制系统的离散变换算子，
Figure DEST_PATH_IMAGE030
为
Figure 974363DEST_PATH_IMAGE010
的倒数，
Figure DEST_PATH_IMAGE032
为过程的输入输出变量个数，所述的输入输出数据为数据采集器中存储的数据； , ,
Figure DEST_PATH_IMAGE018
, Represents the transfer function of each loop of the process,
Figure DEST_PATH_IMAGE022
and
Figure DEST_PATH_IMAGE024
respectively
Figure DEST_PATH_IMAGE026
of input and output variables
Figure 575557DEST_PATH_IMAGE010
transform, , is the discrete transform operator of the computer control system,
Figure DEST_PATH_IMAGE030
for
Figure 974363DEST_PATH_IMAGE010
the reciprocal of
Figure DEST_PATH_IMAGE032
is the number of input and output variables of the process, and the input and output data is the data stored in the data collector; 进一步对上述方程选取伴随矩阵解耦阵为： Further select the adjoint matrix decoupling matrix for the above equation as:
Figure DEST_PATH_IMAGE034
Figure DEST_PATH_IMAGE034
 其中，
Figure DEST_PATH_IMAGE036
是伴随矩阵解耦阵，
Figure DEST_PATH_IMAGE038
为
Figure 176806DEST_PATH_IMAGE006
的伴随矩阵； in,
Figure DEST_PATH_IMAGE036
is the adjoint matrix decoupling matrix,
Figure DEST_PATH_IMAGE038
for
Figure 176806DEST_PATH_IMAGE006
The adjoint matrix; 将上述伴随矩阵解耦阵与过程输入输出模型合并得到： Combining the above adjoint matrix decoupling matrix with the process input and output model, we get: 其中，
Figure DEST_PATH_IMAGE042
是得到的解耦过程模型，
Figure DEST_PATH_IMAGE044
为
Figure 530599DEST_PATH_IMAGE006
的行列式，为以
Figure 317027DEST_PATH_IMAGE006
的行列式为元素的对角矩阵； in,
Figure DEST_PATH_IMAGE042
is the resulting decoupled process model,
Figure DEST_PATH_IMAGE044
for
Figure 530599DEST_PATH_IMAGE006
determinant of for
Figure 317027DEST_PATH_IMAGE006
The determinant of is a diagonal matrix of elements;     将上述解耦过程模型处理成个单变量过程的离散表示方式： The above decoupled process model is processed into Discrete representation of a univariate process: 其中
Figure DEST_PATH_IMAGE052
和
Figure DEST_PATH_IMAGE054
分别是第个过程的输出和输入变量，
Figure DEST_PATH_IMAGE056
，和分别是
Figure 480396DEST_PATH_IMAGE052
和
Figure DEST_PATH_IMAGE062
的系数矩阵多项式；  in
Figure DEST_PATH_IMAGE052
and
Figure DEST_PATH_IMAGE054
respectively The output and input variables of a process,
Figure DEST_PATH_IMAGE056
, and respectively
Figure 480396DEST_PATH_IMAGE052
and
Figure DEST_PATH_IMAGE062
The coefficient matrix polynomial of ;                            
Figure DEST_PATH_IMAGE064
                           
Figure DEST_PATH_IMAGE064
 其中
Figure DEST_PATH_IMAGE066
是相应的系数，
Figure DEST_PATH_IMAGE068
为后移
Figure DEST_PATH_IMAGE070
步算子，
Figure DEST_PATH_IMAGE072
是得到的模型阶次； in
Figure DEST_PATH_IMAGE066
is the corresponding coefficient,
Figure DEST_PATH_IMAGE068
to move back
Figure DEST_PATH_IMAGE070
step operator,
Figure DEST_PATH_IMAGE072
is the obtained model order; 将过程模型通过后移算子
Figure DEST_PATH_IMAGE074
处理成过程的状态空间表示方式： Pass the process model through the backward shift operator
Figure DEST_PATH_IMAGE074
Processed into a state-space representation of a process:
Figure DEST_PATH_IMAGE076
Figure DEST_PATH_IMAGE076
 ； ; 其中， 
Figure DEST_PATH_IMAGE080
、
Figure DEST_PATH_IMAGE082
分别是第
Figure DEST_PATH_IMAGE084
时刻的变量值，
Figure DEST_PATH_IMAGE086
为第
Figure DEST_PATH_IMAGE088
时刻的输入增量变量值，
Figure DEST_PATH_IMAGE090
、分别为第时刻的输出变量增量和输入变量增量值，
Figure DEST_PATH_IMAGE096
、、
Figure DEST_PATH_IMAGE100
分别为对应的状态矩阵、输入矩阵和输出矩阵，为取转置符号； in,
Figure DEST_PATH_IMAGE080
,
Figure DEST_PATH_IMAGE082
respectively
Figure DEST_PATH_IMAGE084
the value of the variable at time,
Figure DEST_PATH_IMAGE086
for the first
Figure DEST_PATH_IMAGE088
The input increment variable value at time,
Figure DEST_PATH_IMAGE090
, respectively The output variable increment and input variable increment value at time,
Figure DEST_PATH_IMAGE096
, ,
Figure DEST_PATH_IMAGE100
are the corresponding state matrix, input matrix and output matrix respectively, to take the transpose sign;
Figure DEST_PATH_IMAGE104
Figure DEST_PATH_IMAGE104
Figure DEST_PATH_IMAGE106
Figure DEST_PATH_IMAGE106
Figure DEST_PATH_IMAGE108
Figure DEST_PATH_IMAGE108
 定义一过程期望输出为
Figure DEST_PATH_IMAGE110
，并且输出误差
Figure DEST_PATH_IMAGE112
为： Define the expected output of a process as
Figure DEST_PATH_IMAGE110
, and the output error
Figure DEST_PATH_IMAGE112
for:
Figure DEST_PATH_IMAGE114
Figure DEST_PATH_IMAGE114
  进一步得到第
Figure 298661DEST_PATH_IMAGE084
时刻的输出误差
Figure DEST_PATH_IMAGE116
为： further get the
Figure 298661DEST_PATH_IMAGE084
time output error
Figure DEST_PATH_IMAGE116
for:
Figure DEST_PATH_IMAGE118
Figure DEST_PATH_IMAGE118
 其中，
Figure DEST_PATH_IMAGE120
为第
Figure 740138DEST_PATH_IMAGE084
时刻的过程期望输出增量； in,
Figure DEST_PATH_IMAGE120
for the first
Figure 740138DEST_PATH_IMAGE084
The expected output increment of the process at each moment;      定义一个新的复合状态变量
Figure DEST_PATH_IMAGE122
： Define a new composite state variable
Figure DEST_PATH_IMAGE122
:
Figure DEST_PATH_IMAGE124
Figure DEST_PATH_IMAGE124
   将上述处理过程综合为一个解耦状态空间模型： Synthesize the above process into a decoupled state-space model: 其中，
Figure DEST_PATH_IMAGE128
为第时刻的复合状态变量，
Figure DEST_PATH_IMAGE130
、
Figure DEST_PATH_IMAGE132
、
Figure DEST_PATH_IMAGE134
分别为对应复合状态变量的状态矩阵、输入矩阵和输出矩阵，具体是: in,
Figure DEST_PATH_IMAGE128
for the first Composite state variable at time,
Figure DEST_PATH_IMAGE130
,
Figure DEST_PATH_IMAGE132
,
Figure DEST_PATH_IMAGE134
are the state matrix, input matrix and output matrix corresponding to the composite state variable, specifically:
Figure DEST_PATH_IMAGE136
，，
Figure DEST_PATH_IMAGE140
Figure DEST_PATH_IMAGE136
, ,
Figure DEST_PATH_IMAGE140
 Ⅱ.基于该解耦状态空间模型设计解耦非最小实现扩展状态空间二次型控制器，具体方法是： Ⅱ. Based on the decoupled state-space model, design a decoupled non-minimum realization extended state-space quadratic controller. The specific method is: a.定义该解耦非最小实现扩展状态空间二次型控制器的目标函数为： a. Define the objective function of the decoupled non-minimum realization extended state space quadratic controller as:
Figure DEST_PATH_IMAGE142
Figure DEST_PATH_IMAGE142
 其中，
Figure DEST_PATH_IMAGE144
为目标函数，和
Figure DEST_PATH_IMAGE148
分别为状态变量和输出变量的加权矩阵； in,
Figure DEST_PATH_IMAGE144
is the objective function, and
Figure DEST_PATH_IMAGE148
are weighted matrices of state variables and output variables, respectively; b.计算该解耦非最小实现扩展状态空间二次型控制器的参数，
Figure DEST_PATH_IMAGE150
，其中
Figure DEST_PATH_IMAGE152
为控制器反馈系数向量。 b. Calculate the parameters of the decoupled non-minimum realization extended state space quadratic controller,
Figure DEST_PATH_IMAGE150
,in
Figure DEST_PATH_IMAGE152
is the controller feedback coefficient vector.
CN2013100181093A 2013-01-18 2013-01-18 Chemical process decoupling non-minimal realization expansion state space quadric form control method Pending CN103064294A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN2013100181093A CN103064294A (en) 2013-01-18 2013-01-18 Chemical process decoupling non-minimal realization expansion state space quadric form control method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN2013100181093A CN103064294A (en) 2013-01-18 2013-01-18 Chemical process decoupling non-minimal realization expansion state space quadric form control method

Publications (1)

Publication Number Publication Date
CN103064294A true CN103064294A (en) 2013-04-24

Family

ID=48106967

Family Applications (1)

Application Number Title Priority Date Filing Date
CN2013100181093A Pending CN103064294A (en) 2013-01-18 2013-01-18 Chemical process decoupling non-minimal realization expansion state space quadric form control method

Country Status (1)

Country Link
CN (1) CN103064294A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104317321A (en) * 2014-09-23 2015-01-28 杭州电子科技大学 Coking furnace hearth pressure control method based on state-space predictive functional control optimization
CN104317194A (en) * 2014-09-23 2015-01-28 杭州电子科技大学 Temperature control method for non-minimal state space model predictive control optimization

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102520617A (en) * 2011-12-30 2012-06-27 杭州电子科技大学 Prediction control method for unminimized partial decoupling model in oil refining industrial process
CN102880046A (en) * 2012-09-24 2013-01-16 杭州电子科技大学 Chemical multi-variable process decoupling prediction function control method

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102520617A (en) * 2011-12-30 2012-06-27 杭州电子科技大学 Prediction control method for unminimized partial decoupling model in oil refining industrial process
CN102880046A (en) * 2012-09-24 2013-01-16 杭州电子科技大学 Chemical multi-variable process decoupling prediction function control method

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
张日东等: "《非线性预测控制与工业应用》", 31 December 2012, article "扩展状态空间预测函数控制", pages: 79-80 *
张日东等: "基于支持向量机的非线性系统预测控制", 《自动化学报》, vol. 33, no. 10, 31 October 2007 (2007-10-31), pages 1066 - 1073 *
毕玉春: "Matlab在线性二次型最优控制器设计中的应用", 《克山师专学报》, 31 December 2004 (2004-12-31), pages 125 - 127 *
王国玉等: "预测函数控制及其应用研究", 《系统仿真学报》, vol. 14, no. 8, 31 August 2002 (2002-08-31) *
王晓哲等: "一种新的加权目标函数下的神经网络训练方法", 《东北大学学报（自然科学版）》, vol. 22, no. 6, 31 December 2001 (2001-12-31), pages 591 - 593 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104317321A (en) * 2014-09-23 2015-01-28 杭州电子科技大学 Coking furnace hearth pressure control method based on state-space predictive functional control optimization
CN104317194A (en) * 2014-09-23 2015-01-28 杭州电子科技大学 Temperature control method for non-minimal state space model predictive control optimization

Similar Documents

Publication Publication Date Title
CN102880046A (en) Chemical multi-variable process decoupling prediction function control method
CN102520616B (en) Partial decoupling unminimized model prediction function control method in oil refining industrial process
CN102520617B (en) Prediction control method for unminimized partial decoupling model in oil refining industrial process
CN104503502B (en) A kind of modified Smith estimates main-stream control structure
CN102902201A (en) Decoupling state spatial prediction control method of chemical multivariate processes
CN102053562A (en) Cracking furnace exit temperature hybrid control method
CN108579929B (en) A control system and control method for double-inlet and double-outlet ball mill based on RBF neural network predictive control
CN103116283A (en) Method for controlling dynamic matrix of non-self-balance object
CN203396032U (en) Room temperature control device based on fuzzy self-adaption PID (proportion integration differentiation)
CN101709863B (en) Hybrid control method for furnace pressure system of coal-fired boiler
CN113110046A (en) Desulfurization system control method based on big data self-learning prediction control
CN104101105A (en) Method and device used for compound control of crude oil heater temperature
CN105334751A (en) Design method for stability controller of batched injection molding process
CN106527125A (en) Model-free control method in intelligent control
CN104950670A (en) Integrated multi-model method for controlling CSTRs (continuous stirred tank reactors)
CN103064293A (en) Chemical process decoupling non-minimal realization state space linear quadric form control method
CN103900398A (en) Indirect dry type air cooling unit circulating water scheduling optimization method based on water temperature feedback
CN102998971A (en) Greenhouse mechanical ventilation PID (proportion integration differentiation) parameter tuning method and greenhouse mechanical ventilation control method and system
CN103064294A (en) Chemical process decoupling non-minimal realization expansion state space quadric form control method
CN104749956A (en) Structure optimization method of industrial robot based on harmony search algorithm
CN102520618A (en) Coking heating furnace radiation outlet temperature control method under error tolerance mechanism
CN102410138A (en) Method for obtaining optimal control input of wind generating set
CN104235820A (en) Boiler steam temperature control method based on improved single neuron adaptive PID (proportion integration differentiation) control strategy
CN102880047B (en) Adjoint matrix decoupling prediction control method for oil refining industrial heating furnace temperature process
CN104214772A (en) AGC load command response control method for circulating fluidized bed set

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
C12 Rejection of a patent application after its publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20130424