CN101221437B - Industrial production full process optimizing and controlling method in network information interchange mode - Google Patents

Abstract

An industrial production overall-process optimization control method under network information exchange mode belongs to the industrial optimization control technical field. The invention comprises the following steps that: (1) initialization of the industrial production overall-process optimization control method is completed; moreover, industrial production overall process is divided into a plurality of subsystems and the neighborhood of each subsystem is determined; (2) the optimized performance index of the local controller of each subsystem is determined; (3) each local optimization controller is solved by means of network information exchange, thereby ensuring the optimization of industrial production overall process. The method not only improves the overall performance of a system, but also takes the complexity of online calculation of the system into consideration, thereby having strong practicality.

Description

Industrial production full process optimizing and controlling method under the network information switch mode

Technical field

The present invention relates to the control method in a kind of telecommunication technology field, specifically is the industrial production full process optimizing and controlling method under a kind of network information switch mode.

Background technology

Along with the development that modern industry is produced, the structure of control system becomes increasingly complex, and the controlled device of many reality is made up of a plurality of subsystems, and each subsystem can be regarded independently partial control system as, but exists complicated association between the adjacent subsystem.The controlled target of control problem is many, not only requires local subsystem is carried out local Control and Optimization, and requires the global optimization of final realization system.Along with the raising of network technology and the distributed optimal control that develops into industrial process of industrial field bus provide condition.How utilizing network information exchange, make co-ordination mutually between each subsystem, industrial overall process overall goals is optimized, is a problem demanding prompt solution.

Find by prior art documents, Du X.N etc. are in " Proceedings of the AmericanControl Conference " (U.S. control meeting) (calendar year 2001, the 3142-3143 page or leaf), on " Distributed model predictive control for large-scale systems " (big system Distributed Predictive Control) of delivering a kind of Distributed Predictive Control has been proposed, but the realization of method is not specifically studied, as following the example of of iterative initial value etc., and the local control law that obtains not is a globally optimal solution.Chen Qing etc. were " Shanghai Communications University's journal " (2005, the 3rd phase, the 349-352 page or leaf), on " based on the production overall process Distributed Predictive Control of global optimum " delivered a kind of Distributed Predictive Control method based on global optimum has been proposed, but the local performance index of each subsystem still adopt the overall performance index, online calculation of complex.

Summary of the invention

The present invention is directed to above-mentioned the deficiencies in the prior art, industrial production full process optimizing and controlling method under a kind of network information switch mode has been proposed, the present invention has taken into account the online complexity of calculation of system in the overall performance that improves system, in each optimizing process, constantly and its neighborhood subsystem exchange message, and in optimizing process, considered the information of the neighborhood subsystem that it receives to have improved the overall performance of industrial processes.

The present invention is achieved through the following technical solutions, comprises following concrete steps:

Step 1 is carried out the initialization of industrial production full process optimizing and controlling method;

The initialization of described industrial production full process optimizing and controlling method, be that industrial process is divided into a plurality of subsystems, and determine the neighborhood of each subsystem according to the coupled relation between each system, concern that with i sub-system, coupled stronger subsystem is the neighborhood subsystem of i subsystem, the set of all these neighborhood subsystems is called the neighborhood of i subsystem, the transfer function model form of each subsystem that identification obtains:

Wherein, m is the subsystem number, y _iAnd u _iBe respectively the output of i subsystem and handle vector, G _Ij(s) be the input of j subsystem and the transport function between i subsystem, _iThe neighborhood that is called i subsystem.

Step 2 is determined the optimization performance index of the local control of each subsystem;

The optimization performance index of the local control of described each subsystem, be meant the output valve and the goal-setting value deviation of interior this subsystem of a period of time and neighborhood subsystem thereof, and be not only the output of this subsystem itself and the deviation of desired value, improved the performance of industrial process integral body, and reduce with respect to the global optimization computation amount, it is as follows that neighborhood is optimized the performance index formula:

Wherein,

The expression performance index, r _jThe export target reference locus of j subsystem of () expression, Be illustrated in the predicted value of k the moment, Q to k+s output constantly _j, R _jBe weight, P is the prediction time domain, and M is the control time domain, $U_{i,M}\left(k\right)={\left[\begin{array}{ccc}{u}_i^T\left(k\right|k)& \·\·\·& u_i^T(k+M-1|k)\end{array}\right]}^T$ Be the control sequence of i subsystem, for performance index

,

The smaller the better, because

Be worth less, illustrate on the one hand production run steadily, because the measured value of controlled variable approaches desired value, illustrated that also product quality is higher on the other hand.

Step 3 utilizes network information exchange to find the solution each local optimum controller, makes industrial overall process be optimized.

Described each local optimum controller of finding the solution, in an optimal control cycle, specific as follows:

The first step, exchange by the network information, each subsystem at first obtains the discreet value of each neighborhood subsystem output and control law, thinking that each neighborhood subsystem controls rule is to find the solution the optimizing control rule of current subsystem under the prerequisite of discreet value, the optimizing control rule of being tried to achieve is an optimization aim with neighborhood performance index minimum;

Second step, if optimizing control rule that each subsystem is newly tried to achieve and former control law discreet value are inconsistent, pass through network, the optimizing control rule of current subsystem is sent to of the discreet value of the neighborhood subsystem of current subsystem as current subsystem controls rule, like this, each subsystem has all obtained the discreet value of the new control law of neighborhood subsystem;

The 3rd step, all receive at each subsystem under the prerequisite of new optimizing control rule discreet value of its neighborhood subsystem by network, with neighborhood performance index minimum is the control law that optimization aim is found the solution current subsystem once more, if optimizing control rule that each subsystem is newly tried to achieve and former control law discreet value are also inconsistent, repeated exchanged information, solving-optimizing control law between each subsystem, the optimizing control rule of newly trying to achieve until each subsystem is consistent with the control law discreet value;

In the 4th step, the optimizing control rule that each subsystem is newly tried to achieve is consistent with the control law discreet value, illustrates that the optimizing control rule of being tried to achieve is for separating based on the optimal balance of neighborhood optimization performance index.

Compared with prior art, the present invention has following beneficial effect: the controller of method of the present invention is distributed in each local subsystem, is a control method that is suitable for distributed system; Adopt the local optimum strategy when improving industrial process global optimization performance, to take into account arithmetic speed; The present invention has provided detailed method how to utilize network information exchange solving-optimizing control method, makes practicality of the present invention improve greatly; The prediction time domain of each subsystem is controlled time domain and weighting coefficient matrix etc. and can be designed separately and adjust simultaneously, is convenient to the analysis and the application of system.

Description of drawings

Fig. 1 is the walking beam furnace structural representation of the embodiment of the invention;

Fig. 2 is a fundamental diagram of the present invention;

Fig. 3 is each Heating Zone Temperature curve map of heating furnace in the embodiment of the invention;

Fig. 4 is each heating zone fuel flow curve map of heating furnace in the embodiment of the invention.

Embodiment

Below in conjunction with accompanying drawing embodiments of the invention are elaborated: present embodiment is being to implement under the prerequisite with the technical solution of the present invention, provided detailed embodiment and concrete operating process, but protection scope of the present invention is not limited to following embodiment.

As shown in Figure 1, present embodiment illustrates the implementation process of this method as an example in conjunction with walking beam furnace, and walking beam furnace is one of explained hereafter equipment important in the steel rolling industry, and its effect is to be rolled being sent to milling train after the steel billet heating.

Walking beam furnace is divided into preheating zone (13398mm), heating zone (11500mm), soaking zone (up and down, 4450mm).Steel billet is delivered to the heating furnace porch, evenly moves heating in stove, is heated to the temperature of setting at the furnace outlet place.The linear model of heating furnace is described as:

$\left\{\begin{array}{c}{y}_1\left(s\right)=\frac{0.002757}{s+0.00572}{u}_1\left(s\right)\\ y_2\left(s\right)=\frac{0.0003983}{s+0.001891}{e}^{-32.8s}{u}_2\left(s\right)+\frac{0.001009}{s+0.001907}{e}^{-51.5s}{y}_1\left(s\right)\\ y_3\left(s\right)=\frac{0.001088}{S+0.003857}{e}^{-33.2s}{u}_3\left(s\right)+\frac{0.003224}{s+0.004634}{y}_2\left(s\right)\end{array}\right.$

Wherein, y ₁, y ₂And y ₃Be respectively the soaking zone, the furnace temperature of heating zone and preheating zone, u ₁, u ₂And u ₃Be respectively the fuel flow of these three heating zone.

Present embodiment comprises the steps:

Step 1, the initialization of overall process dynamic optimization: heating furnace process is resolved into three subsystems, is respectively soaking zone (subsystem 1), heating zone (subsystem 2) and preheating zone (subsystem 3), the model of each subsystem is as follows:

Subsystem 1: $y_1\left(s\right)=\frac{0.002757}{s+0.00572}{u}_1\left(s\right)$

Subsystem 2: $y_2\left(s\right)=\frac{0.0003983}{s+0.001891}{e}^{-32.8s}{u}_2\left(s\right)+\frac{0.001009}{s+0.001907}\·\frac{0.002757}{s+0.00572}{e}^{-51.5s}{u}_1\left(s\right)$

Subsystem 3:

$y_3\left(s\right)=\frac{0.001088}{s+0.003857}{e}^{-33.2s}{u}_3\left(s\right)+\frac{0.003224}{s+0.004634}\·\frac{0.0003983}{s+0.001891}{e}^{-32.8s}{u}_2\left(s\right)$

Then the industrial production full process optimal control schematic diagram of heating furnace as shown in Figure 2, m=3 wherein: the neighborhood of subsystem 1 is subsystem 1 and subsystem 2, the neighborhood of subsystem 2 is subsystem 1, subsystem 2 and subsystem 3, the neighborhood of subsystem 3 is subsystem 2 and subsystem 3, each subsystem local control adopts Model Predictive Control (MPC) method, and each controller is by the mutual co-ordination of network service.

Step 2, the setting parameter of each subsystem is: prediction time domain P=30, control time domain M=10, weight Q _i=1, weight R _i=0.2 (i=1,2,3), sampling instant is 1 minute, then adopts neighborhood optimization, each subsystem adopts following performance index:

${\stackrel{\&OverBar;}{J}}_1\left(k\right)=\underset{j\&Element;\left(\mathrm{1,2}\right)}{\mathrm{\&Sigma;}}[\underset{s=1}{\overset{30}{\mathrm{\&Sigma;}}}{\left|\right|r_j(k+s)-{\hat{y}}_j(k+s|k)\left|\right|}_{{\mathrm{<figure-callout\; id="2"\; label="Q\; j"\; filenames="S2008100329954E00011.png"\; state="\{\{state\}\}">Q</figure-callout>}}_j}^2+\underset{h=1}{\overset{10}{\mathrm{\&Sigma;}}}{\left|\right|u_j(k+h-1|k)\left|\right|}_{R_{\text{j}}}^2]$

${\stackrel{\&OverBar;}{J}}_2\left(k\right)=\underset{j\&Element;\left(\mathrm{1,2,3}\right)}{\mathrm{\&Sigma;}}[\underset{s=1}{\overset{30}{\mathrm{\&Sigma;}}}{\left|\right|r_j(k+s)-{\hat{y}}_j(k+s|k)\left|\right|}_{{\mathrm{<figure-callout\; id="2"\; label="Q\; j"\; filenames="S2008100329954E00011.png"\; state="\{\{state\}\}">Q</figure-callout>}}_j}^2+\underset{h=1}{\overset{10}{\mathrm{\&Sigma;}}}{\left|\right|u_j(k+h-1|k)\left|\right|}_{R_{\text{j}}}^2]$

${\stackrel{\&OverBar;}{J}}_3\left(k\right)=\underset{j\&Element;\left(\mathrm{2,3}\right)}{\mathrm{\&Sigma;}}[\underset{s=1}{\overset{30}{\mathrm{\&Sigma;}}}{\left|\right|r_j(k+s)-{\hat{y}}_j(k+s|k)\left|\right|}_{{\mathrm{<figure-callout\; id="2"\; label="Q\; j"\; filenames="S2008100329954E00011.png"\; state="\{\{state\}\}">Q</figure-callout>}}_j}^2+\underset{h=1}{\overset{10}{\mathrm{\&Sigma;}}}{\left|\right|u_j(k+h-1|k)\left|\right|}_{R_{\text{j}}}^2]$

Step 3 is found the solution each local control, and the detailed solution procedure of each local control is specific as follows:

The 1st step, initialization with communicate by letter, at k constantly, each subsystem at first exchanges the output and the control law estimated value of current time.Under the prerequisite that fixedly the neighborhood subsystem controls is restrained, estimate local optimum control law separately, $U_{i,M}^{\left(l\right)}\left(k\right)={\hat{U}}_{i,M}\left(k\right)(i=\mathrm{1,2,3}),$ And send it to its adjacent subsystems, and receive the estimated value of the local optimum control law of its adjacent subsystems, make iterations l=0;

The 2nd step, find the solution the local optimum problem, all subsystems are found the solution the local optimum problem simultaneously, obtain the local control law U of this iteration _{I, M} ^(l+1)(k);

In the 3rd step, check and renewal each subsystem checks whether stopping criterion for iteration satisfies, promptly for given precision ε _i, whether exist $\left|\right|U_{i,M}^{(l+1)}\left(k\right)-U_{i,M}^{\left(l\right)}\left(k\right)\left|\right|\&le;{\mathrm{\&epsiv;}}_i,$ If all stopping criterion for iteration are at l ^*All set up during inferior iteration, then stop iteration, the local optimum control law of each subsystem $U_{i,M}^{*}\left(k\right)=U_{i,M}^{\left(l^{*}\right)}\left(k\right),$ Changeed for the 4th step; Otherwise, make l=l+1, the local control law U that each subsystem exchange is newly tried to achieve _{I, M} ^(l)(k), returned for the 2nd step;

In the 4th step, U is got in assignment and realization _{I, M} ^(l)(k) first in as the constantly real-time control law u of k _i ^*(k), and with it affact in each subsystem;

In the 5th step, initialize again is provided with the estimated value of the local optimum control law of next each subsystem of sampling instant ${\hat{U}}_{i,M}(k+1)=U_{i,M}^{*}\left(k\right);$

In the 6th step, barrel shift is to next moment, and promptly k+1 → k turned back to for the 1st step, repeated above process.

Shown in Fig. 3,4, for moving the furnace temperature and the fuel flow curve of each heating zone of 4 hours, as seen from the figure, adopt the present embodiment method, under distributed frame, the furnace temperature of three heating zone can both be followed the tracks of its setting value curve (shown in the figure dotted line) well.When set point change, furnace temperature can be adjusted to new stationary value in 15 minutes, and deviation is between ± 13 ℃.Control effect and adopt centralized Model Predictive Control effect close; And the design parameter of each subsystem for example predicts that time domain, control time domain and weighting coefficient matrix etc. can design separately and adjust, and are convenient to the analysis and the application of system.

Claims (1)

1. the industrial production full process optimizing and controlling method under the network information switch mode is characterized in that, comprises the steps:

Step 1 is carried out the initialization of industrial production full process optimizing and controlling method, industrial production full process is divided into the neighborhood of a plurality of subsystems and definite each subsystem;

Step 2 is determined the optimization performance index of the local control of each subsystem;

Step 3 utilizes network information exchange to find the solution each local control, makes industrial overall process be optimized;

The initialization of described industrial production full process optimizing and controlling method, be that industrial process is divided into a plurality of subsystems, and determine the neighborhood of each subsystem according to the coupled relation between each system, concern that with i sub-system, coupled stronger subsystem is the neighborhood subsystem of i subsystem, the set of all these neighborhood subsystems is called the neighborhood of i subsystem, the transfer function model form of each subsystem that identification obtains:

Wherein, m is the subsystem number, y _iAnd u _iBe respectively the output of i subsystem and handle vector, G _Ij(s) be the input of j subsystem and the transport function between i subsystem, _iThe neighborhood that is called i subsystem;

The optimization performance index of the local control of described each subsystem are meant output valve and the goal-setting value deviation of this subsystem in a period of time and neighborhood subsystem thereof, and neighborhood optimization performance index formula is as follows:

Wherein, J _i() expression performance index, r _jThe export target reference locus of j subsystem of () expression,

Be illustrated in the predicted value of k the moment, Q to k+s output constantly _j, R _jBe weight, P is the prediction time domain, and M is the control time domain,

Be the control sequence of i subsystem, for performance index J _i(), J _i() is the smaller the better;

Described each local control of finding the solution, in an optimal control cycle, specific as follows:

The first step, message exchange, each subsystem at first obtains the discreet value of each neighborhood subsystem output and control law, thinking that each neighborhood subsystem controls rule is to find the solution the optimizing control rule of current subsystem under the prerequisite of discreet value, the optimizing control rule of being tried to achieve is an optimization aim with neighborhood performance index minimum;

Second step, if optimizing control rule that each subsystem is newly tried to achieve and former control law discreet value are inconsistent, pass through network, the optimizing control rule of current subsystem is sent to of the discreet value of the neighborhood subsystem of current subsystem as current subsystem controls rule, like this, each subsystem has all obtained the discreet value of the new control law of neighborhood subsystem;

The 3rd step, all receive at each subsystem under the prerequisite of new optimizing control rule discreet value of its neighborhood subsystem by network, with neighborhood performance index minimum is the control law that optimization aim is found the solution current subsystem once more, if optimizing control rule that each subsystem is newly tried to achieve and former control law discreet value are also inconsistent, repeated exchanged information, solving-optimizing control law between each subsystem, the optimizing control rule of newly trying to achieve until each subsystem is consistent with the control law discreet value;

In the 4th step, the optimizing control rule that each subsystem is newly tried to achieve is consistent with the control law discreet value, and the optimizing control rule of being tried to achieve is separated for the optimal balance of optimizing performance index based on neighborhood.

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