CN110045611A - A kind of Robust Iterative Learning model predictive control method applied to batch stirred tank reactor - Google Patents
A kind of Robust Iterative Learning model predictive control method applied to batch stirred tank reactor Download PDFInfo
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Abstract
The present invention relates to a kind of Robust Iterative Learning model predictive control methods, and can be applied to batch stirred tank reactor control, this method solves the problems, such as reaction temperature high precision tracking under batch stirred tank reactor variation reference locus.This method is specifically included that according to batch stirred tank reactor Nonlinear Mechanism model, is constructed linear ginseng and is become (LPV) prediction model；The control experience for drawing batch in the past is controlled by iterative learning, improves tracking accuracy；Real-time interference is effectively treated using Model Predictive Control, guarantees system time-domain stability and robustness；The variation of reference locus between batch stirred tank reactor batch is considered as iteration axis BOUNDED DISTURBANCES, construction H infinity constraint guarantees the control effect become under reference locus.Robust Iterative Learning model predictive control method of the invention becomes the more adaptable of reference locus compared with conventional iterative learning model forecast Control Algorithm, to batch stirred tank reactor, and tracking performance is more preferable.
Description
Technical field
The present invention relates to chemical production field more particularly to a kind of robust iteration applied to batch stirred tank reactor
Learning model forecast Control Algorithm.
Background technique
Batch reactor refers to the device that interval is chemically reacted.It is special for batch production in chemical production process
It is not different size and the high product of the output value, often uses batch reactor.Batch reactor has flexible operation, production can
Become, invest the features such as low, upper detective, therefore is widely used in medicine, pesticide, dyestuff and the industry of various fine chemistry industries.Interval is anti-
Answer the product quality of device very unstable, equipment capacity also differs greatly, and therefore, inscribes just between the automatic control of batch reactor
Seem increasingly important, the important directions of development are exactly that advanced control theory is applied to realize optimum control.
Iterative learning Model Predictive Control is a kind of Advanced Control Strategies answering batch process demand and proposing, it can lead to
The iterative learning along production batch is crossed, realizes the raising of tracking accuracy, eliminates the interference of system repeatability and in each production batch
Real-time control performance is guaranteed by Model Predictive Control in secondary.In recent years, iterative learning Model Predictive Control is in batch process
Field achieves successful application, but in chemical engineering industry, product quality and reaction temperature the track following essence of batch reactor
Degree connection is close, therefore still needs to the effective ways for proposing to improve iterative learning Model Predictive Control tracking accuracy.Secondly, according to not
With the production requirement of product, reaction temperature reference locus in practice is adjusted in time, this is pre- to iterative learning model
The change reference locus adaptability of observing and controlling is a biggish challenge, so need to study can adapt in order to improve productivity effect
Become the batch reactor iterative learning Model Predictive Control of reaction temperature reference locus.
Summary of the invention
In view of the problems of the existing technology, the present invention provides a kind of robusts applied to batch stirred tank reactor
Iterative learning model predictive control method becomes reference locus tracking problem to solve batch stirred tank reactor.
The present invention provides a kind of Robust Iterative Learning Model Predictive Control sides applied to batch stirred tank reactor
Method, comprising:
Step 1, a batch stirred tank reactor is set, and batch stirred tank reactor can repeat reactant A
The chemical reaction of target product B is generated, control target is the reaction temperature reference locus in the tracking variation of each production batch；
Step 2, the linear ginseng varying model of setting, to describe the dynamic of the batch stirred tank reactor nonlinear model
Characteristic；
Step 3, two-dimensional iteration tracking error prediction model is set, and quantity of state includes variation of the tracking error along iteration axis
Amount, system mode and consider the reference locus variable quantity between batch along the variable quantity of iteration axis and time shaft；
Step 4, the variation of the reference locus by the batch stirred tank reactor in the case where different control requires is considered as
Bounded Perturbations on iteration axis, setting H infinity constrains, to limit influence of the Bounded Perturbations to tracking performance；
Step 5, setting quadratic form tracking performance objective function and control input constraint, are established under H infinity constraint
Min-max optimization problem；
Step 6, converting the min-max optimization problem to can be asked by the Mathematical Planning that linear matrix inequality solves
The control input that optimization obtains is applied to control system by topic.
Further, the workflow of the batch stirred tank reactor are as follows: reactant and catalyst are packed into reaction
Device is reacted；Controller inside the batch stirred tank reactor tracks given reaction temperature track；It is heated to
Operation temperature terminates reaction, takes out product.
Further, the batch stirred tank reactor needs to control reaction feed amount to guarantee to have foot in reaction vessel
Enough reaction compartments, to prevent reactor superpressure；
At least one interlock is set on the batch stirred tank reactor, reaches reaction in material to forbid
Reactant is just added before temperature condition.
Further, the batch stirred tank reactor is controlled from two dimensions, including iterative learning control
And Model Predictive Control；
The iterative learning control action concludes the control experience of batch in the past by study, to correct in iteration axis
Control input；
The Model Predictive Control acts on time shaft, interferes in real time to processing production process, guarantees that system closed loop is steady
It is qualitative.
Further, it is described linear for converting batch stirred tank reactor primary nonlinear model by function Shift Method
Join varying model；
The stirred-tank reactor nonlinear model are as follows:
Wherein, reaction temperature T (K) and reactant concentration CAFor quantity of state, coolant temperature TcIt (K) is control input, other
For known response constant；
It is replaced by function, the linear ginseng varying model derived are as follows:
Wherein, θ is a certain parameter changed with operating condition, x=[CA,T]T, y=T, u=Tc。
Further, the two-dimensional iteration error prediction model is
Wherein, k represents production batch, δ Δ uk(t) variable quantity along iteration axis and time shaft is inputted for control,For comprising anti-
The two dimensional model state variable of temperature tracking error variable quantity and system state change amount is answered,To be accordingly
System matrix and control matrix.
Further, Control performance standard is set
Further, the H infinity is constrained to
Further, the quadratic form tracking performance objective function is
The control input constraint isWherein, Δ uk(t)、δuk(t) control input edge is respectively referred to
The variable quantity of iteration axis and time shaft.
Further, the condition for deriving time domain robust stability, when realizing batch stirred tank reactor control
The closed loop stability and robustness in domain；The convergent condition in iteration domain is derived, and is incorporated into controller design, to guarantee
The validity of algorithm.
Compared with prior art, the beneficial effects of the present invention are, become prediction model by introducing linear ginseng between batch,
Two-dimensional iteration tracking error prediction model is constructed to reference locus of the batch stirred tank reactor in the case where different control requires,
In conjunction with iterative learning control and feedback control, the adaptation that batch stirred tank reactor becomes reference locus tracking is finally improved
Property and accuracy.
Further, setting linear ginseng varying model covers the batch stirred tank reactor Nonlinear Dynamic, can be with
Model accuracy is improved, to improve reaction temperature tracking accuracy.
In particular, being capable of handling real-time interference, and Guarantee control system time-domain stability in each production batch.
In particular, derive time domain Robust Stability, realize batch stirred tank reactor control time domain closed loop stability and
Robustness.
Further, two-dimensional iteration tracking error prediction model is set, adapt to it can in time between each production batch
Reference locus variation, and repeated interference is eliminated, convergence of the Guarantee control system along iteration axis.
In particular, deriving the iteration domain condition of convergence, and it is incorporated into controller design, guarantees algorithm validity, maintenance
Production safety.
Further, setting H infinity constrains, and realizes and change reference locus is rapidly adapted to and being tracked.
Detailed description of the invention
Fig. 1 is chemical process of embodiment of the present invention batch stirred tank reactor schematic diagram；
Fig. 2 is the application Robust Iterative Learning model predictive control method flow chart of the embodiment of the present invention.
Specific embodiment
With reference to the accompanying drawings come describe invention preferred embodiment.It will be apparent to a skilled person that these
Embodiment is used only for explaining the technical principle of invention, not in the protection scope of limitation invention.
It should be noted that in the description of invention, the instructions such as term " on ", "lower", "left", "right", "inner", "outside"
The term of direction or positional relationship is direction based on the figure or positional relationship, this is intended merely to facilitate description, without
It is that indication or suggestion described device or element must have a particular orientation, be constructed and operated in a specific orientation, therefore cannot
It is interpreted as the limitation to invention.
In addition it is also necessary to explanation, in the description of invention unless specifically defined or limited otherwise, term " peace
Dress ", " connected ", " connection " shall be understood in a broad sense, for example, it may be being fixedly connected, may be a detachable connection, or integrally
Connection；It can be mechanical connection, be also possible to be electrically connected；Can be directly connected, can also indirectly connected through an intermediary,
It can be the connection inside two elements.To those skilled in the art, it can understand that above-mentioned term exists as the case may be
Concrete meaning in invention.
As shown in fig.1, it is the interval provided in an embodiment of the present invention using Robust Iterative Learning Model Predictive Control
The model schematic of stirred-tank reactor, comprising: blender 1, tank body 2, collet 3, agitating shaft 4, blow-off pipe 5, support 6, people
Hole 7, axle envelope 8, transmission device 9.
With continued reference to shown in Fig. 1, the main body of kettle provides enough volumes, it is ensured that the time needed for reaching regulation conversion ratio；
Transmission device 9 is arranged outside tank body 2, is connected with agitating shaft 4, to transmit kinetic energy to agitating shaft 4 to strengthen liquid flow
It is dynamic；Blender 1 is connected with agitating shaft 4, is located in tank body 2, be uniformly mixed reactant, enhancing mass and heat transfer；Axle envelope 8
Outside tank body 2, the inlet that agitating shaft 4 enters tank body 2 is set, to prevent letting out between tank body 2 and agitating shaft 4
Leakage；Manhole 7 is arranged above tank body 2, to install, overhaul and safety inspection；Blow-off pipe 5 is to by material input reactor
In, material enters blow-off pipe 5 from the material inlet being located at outside tank body 2, by being arranged on 2 inner wall of tank body and extending to
The blow-off pipe 5 of 2 bottom of tank body is discharged into tank body 2；Heat transfer unit (HTU) is wrapped in 2 outside of tank body comprising collet 3 and coiled pipe are used to
Input removes heat, to keep suitable reaction temperature.
As shown in fig.2, it is the application Robust Iterative Learning model predictive control method flow chart of the embodiment of the present invention,
The present embodiment method and step includes:
Step 1, a chemical process batch stirred tank reactor is set, generates target to repeat reactant A
The chemical reaction of product B, control target are the reaction temperature reference locus in the tracking variation of each production batch.
Batch stirred tank reactor needs to control reaction feed amount to guarantee to have enough reaction compartments in reaction vessel,
To prevent reactor superpressure；At least one interlock is set on batch stirred tank reactor, to forbid material to reach
Reactant is just added before to reaction temperature condition；The workflow of batch stirred tank reactor are as follows: by reactant and catalysis
Agent is packed into reactor and is reacted；Given reaction temperature track is tracked by controller；It is heated to operation temperature, terminates reaction,
Take out product.
Step 2, the linear ginseng varying model of setting converts batch stirred tank reactor primary nonlinear by function Shift Method
Model is linear ginseng varying model, it is made to cover the batch stirred tank reactor Nonlinear Dynamic；
The stirred-tank reactor nonlinear model are as follows:
Wherein, reaction temperature T (K) and reactant concentration CAFor quantity of state, coolant temperature TcIt (K) is control input, other
For known response constant；
It is replaced by function, the linear ginseng varying model derived are as follows:
Wherein, θ is a certain parameter changed with operating condition, x=[CA,T]T, y=T, u=Tc。
Step 3, two-dimensional iteration tracking error prediction model is set, and in view of reaction caused by being required due to different controls
Temperature reference trail change Rk(t+1)；
Its model isWherein, k represents production batch, δ Δ uk
(t) variable quantity along iteration axis and time shaft is inputted for control,Comprising reaction temperature tracking error variable quantity and to be
The two dimensional model state variable for amount of state variation of uniting,For corresponding sytem matrix and control matrix.
Step 4, the variation of the reference locus by the batch stirred tank reactor in the case where different control requires is considered as
Bounded Perturbations on iteration axis, setting H infinity constrains, to limit influence of the Bounded Perturbations to tracking performance；The H infinity is about
Shu Wei
Step 5, setting quadratic form tracking performance objective function and control input constraint, are established under H infinity constraint
Min-max optimization problem；
Control performance standard is set
The quadratic form tracking performance objective function is
The control input constraint isWherein, Δ uk(t)、δuk(t) control input edge is respectively referred to
The variable quantity of iteration axis and time shaft.
Step 6, converting the min-max optimization problem to can be asked by the Mathematical Planning that linear matrix inequality solves
The control input that optimization obtains is applied to control system by topic.
Embodiment 1
The experimental setup discrete sampling time is 0.03min, and batch length is 12min.In batch 1 to batch 5 using industry
Popular response temperature reference track；In batch 6, using fast starting reaction temperature reference locus；Batch 7 is using slow turn-on reaction temperature
Spend reference locus；Since batch 8, reaction temperature reference locus becomes conventional track again.Robust is run in matlab to change
For the program of learning model forecast Control Algorithm, and compared with traditional iterative learning model prediction method, verifies it and improving
Become the effect under reference locus in tracking accuracy and adaptability.
Embodiment 2
Discrete sampling time and batch length apply step in every a batch of 2min to 8min and disturb with embodiment 1
It is dynamic, the anti-repeated interference performance of verifying Robust Iterative Learning model predictive control method；Start to apply in the 3min of the 5th batch
Add step disturbance, verifies in the batch of Robust Iterative Learning model predictive control method and resist real-time interference performance.
Claims (10)
1. a kind of Robust Iterative Learning model predictive control method applied to batch stirred tank reactor, which is characterized in that
Include:
Step 1, a batch stirred tank reactor is set, and batch stirred tank reactor can repeat reactant A generation
The chemical reaction of target product B, control target are the reaction temperature reference locus in the tracking variation of each production batch；
Step 2, the linear ginseng varying model of setting, the dynamic to describe the batch stirred tank reactor nonlinear model are special
Property；
Step 3, two-dimensional iteration tracking error prediction model is set, quantity of state include tracking error along iteration axis variable quantity,
System mode and considers the reference locus variable quantity between batch along the variable quantity of iteration axis and time shaft；
Step 4, the variation of the reference locus by the batch stirred tank reactor in the case where different control requires is considered as iteration
Bounded Perturbations on axis, setting H infinity constrains, to limit influence of the Bounded Perturbations to tracking performance；
Step 5, setting quadratic form tracking performance objective function and control input constraint, are established under H infinity constraint
Min-max optimization problem；
Step 6, the min-max optimization problem is converted to the mathematical programming problem that can be solved by linear matrix inequality,
The control input that optimization obtains is applied to control system.
2. the Robust Iterative Learning Model Predictive Control side according to claim 1 applied to batch stirred tank reactor
Method, which is characterized in that the workflow of the batch stirred tank reactor are as follows: by reactant and catalyst be packed into reactor into
Row reaction；Controller inside the batch stirred tank reactor tracks given reaction temperature track；It is heated to operate
Temperature terminates reaction, takes out product.
3. the Robust Iterative Learning Model Predictive Control side according to claim 1 applied to batch stirred tank reactor
Method, which is characterized in that the batch stirred tank reactor needs to control reaction feed amount to guarantee to have in reaction vessel enough
Reaction compartment, to prevent reactor superpressure；
At least one interlock is set on the batch stirred tank reactor, reaches reaction temperature in material to forbid
Reactant is just added before condition.
4. the Robust Iterative Learning Model Predictive Control side according to claim 1 applied to batch stirred tank reactor
Method, which is characterized in that the batch stirred tank reactor is controlled from two dimensions, including iterative learning control and mould
Type PREDICTIVE CONTROL；
The iterative learning control action concludes the control experience of batch in the past by study, to Correction and Control in iteration axis
Input；
The Model Predictive Control acts on time shaft, interferes in real time to processing production process, guarantees system closed loop stability.
5. the Robust Iterative Learning Model Predictive Control side according to claim 1 applied to batch stirred tank reactor
Method, which is characterized in that converting batch stirred tank reactor primary nonlinear model by function Shift Method is the linear ginseng
Varying model；
The stirred-tank reactor nonlinear model are as follows:
Wherein, reaction temperature T (K) and reactant concentration CAFor quantity of state, coolant temperature TcIt (K) is control input, other is
Principal reaction constant；
It is replaced by function, the linear ginseng varying model derived are as follows:
Wherein, θ is a certain parameter changed with operating condition, x=[CA,T]T, y=T, u=Tc。
6. the Robust Iterative Learning Model Predictive Control side according to claim 1 applied to batch stirred tank reactor
Method, which is characterized in that the two-dimensional iteration error prediction model is
Wherein, k represents production batch, δ Δ uk(t) variable quantity along iteration axis and time shaft is inputted for control,For comprising anti-
The two dimensional model state variable of temperature tracking error variable quantity and system state change amount is answered,To be accordingly
System matrix and control matrix.
7. the Robust Iterative Learning Model Predictive Control side according to claim 1 applied to batch stirred tank reactor
Method, which is characterized in that setting Control performance standard
8. the Robust Iterative Learning model prediction control according to claim 1 or claim 7 applied to batch stirred tank reactor
Method processed, which is characterized in that the H infinity is constrained to
9. the Robust Iterative Learning Model Predictive Control side according to claim 1 applied to batch stirred tank reactor
Method, which is characterized in that
The quadratic form tracking performance objective function is
The control input constraint isWherein, Δ uk(t)、δuk(t) control input is respectively referred to along iteration
The variable quantity of axis and time shaft.
10. the Robust Iterative Learning Model Predictive Control according to claim 1 applied to batch stirred tank reactor
Method, which is characterized in that the condition for deriving time domain robust stability, to realize the batch stirred tank reactor control time domain
Closed loop stability and robustness；The convergent condition in iteration domain is derived, and is incorporated into controller design, is calculated to guarantee
The validity of method.
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Cited By (2)
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