CN110045611B - Robust iterative learning model prediction control method applied to intermittent stirred tank reactor - Google Patents
Robust iterative learning model prediction control method applied to intermittent stirred tank reactor Download PDFInfo
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Abstract
The invention relates to a robust iterative learning model prediction control method which can be applied to control of an intermittent stirred tank reactor, and solves the problem of high-precision tracking of reaction temperature under the condition that the intermittent stirred tank reactor changes a reference track. The method mainly comprises the following steps: constructing a Linear Parametric Variation (LPV) prediction model according to the nonlinear mechanism model of the batch stirred tank reactor; the control experience of the past batch is absorbed through iterative learning control, and the tracking precision is improved; the model prediction control is adopted to effectively process real-time interference, so that the time domain stability and robustness of the system are ensured; and (3) regarding the change of the reference track between batches of the batch stirred tank reactor as iteration axis bounded interference, and constructing H infinite constraint to ensure the control effect under the condition of changing the reference track. Compared with the traditional iterative learning model predictive control method, the robust iterative learning model predictive control method has stronger adaptability to the variable reference track of the intermittent stirred tank reactor and better tracking performance.
Description
Technical Field
The invention relates to the field of chemical production, in particular to a robust iterative learning model prediction control method applied to an intermittent stirred tank reactor.
Background
The batch reactor refers to an apparatus for intermittently performing a chemical reaction. In the chemical production process, batch reactors are often adopted for batch production, particularly for products with different specifications and high output values. The batch reactor has the characteristics of flexible operation, variable production, low investment, fast production and the like, thereby being widely applied to medicines, pesticides, dyes and various fine chemical industries. The product quality of the batch reactor is unstable, and the equipment production capacity is greatly different, so the automatic control problem of the batch reactor is extremely important, and the development direction of the batch reactor is to realize the optimal control by applying an advanced control theory.
Iterative learning model predictive control is an advanced control strategy provided in response to intermittent process requirements, and can improve tracking accuracy, eliminate system repetitive interference and ensure real-time control performance in each production batch through model predictive control by iterative learning along the production batch. In recent years, iterative learning model predictive control has been successfully applied in the field of batch processes, but in the chemical industry, the product quality of a batch reactor is closely related to the tracking precision of a reaction temperature trajectory, so an effective method for improving the tracking precision of iterative learning model predictive control is still needed. Secondly, according to the production requirements of different products, the actual reaction temperature reference trajectory needs to be adjusted in time, which is a great challenge to the adaptability of the variable reference trajectory of the iterative learning model predictive control, so that in order to improve the production benefit, the intermittent reactor iterative learning model predictive control which can adapt to the variable reaction temperature reference trajectory needs to be researched.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a robust iterative learning model prediction control method applied to an intermittent stirred tank reactor, which is used for solving the problem of variable reference trajectory tracking of the intermittent stirred tank reactor.
The invention provides a robust iterative learning model prediction control method applied to an intermittent stirred tank reactor, which comprises the following steps:
step 1, arranging an intermittent stirred tank reactor, wherein the intermittent stirred tank reactor can repeatedly perform chemical reaction of a reactant A to generate a target product B, and the control target of the intermittent stirred tank reactor is a reaction temperature reference track which tracks and changes in each production batch;
and 6, converting the min-max optimization problem into a mathematical programming problem which can be solved through a linear matrix inequality, and applying the optimized control input to a control system.
Further, the work flow of the batch stirred tank reactor is as follows: loading reactants and a catalyst into a reactor for reaction; a controller positioned in the intermittent stirred tank reactor tracks a given reaction temperature track; the reaction was terminated by heating to the operating temperature, and the product was taken out.
Further, the batch stirred tank reactor requires control of the reaction feed rate to ensure sufficient reaction space in the reaction vessel to prevent overpressure in the reactor;
at least one interlock is provided on the batch stirred tank reactor to inhibit addition of reactants until the material reaches reaction temperature conditions.
Further, controlling the batch stirred tank reactor from two dimensions, including iterative learning control and model predictive control;
the iterative learning control acts on an iterative axis, and the control experience of past batches is induced through learning so as to correct the control input;
the model predictive control acts on a time axis and is used for processing real-time interference in the production process and ensuring the closed loop stability of the system.
Further, converting the original nonlinear model of the batch stirred tank reactor into the linear parametric variation model by a function substitution method;
the nonlinear model of the stirred tank reactor is as follows:
wherein the reaction temperature T (K) and the reactant concentration CAAs a quantity of state, coolant temperature Tc(K) For control inputs, others are known reaction constants;
through function replacement, the derived linear parametric model is as follows:
where θ is a parameter varying with the operating condition, and x ═ CA,T]T，y＝T，u＝Tc。
Further, the two-dimensional iterative error prediction model is
Wherein k represents a production lot,. DELTA.uk(t) is the amount of change in the control input along the iteration axis and the time axis,
is a two-dimensional model state variable containing the variation of the tracking error of the reaction temperature and the variation of the system state,
are the corresponding system matrix and control matrix.
Further, a control performance index is set
Further, the H infinity constraint is
Further, the quadratic tracking performance objective function is
The control input is constrained to
Wherein, Δ uk(t)、uk(t) indicates the amount of change in the control input along the iteration axis and the time axis, respectively.
Further, deriving a time domain robust stability condition to realize the closed loop stability and robustness of the intermittent stirred tank reactor control time domain; the conditions for iterative domain convergence are derived and incorporated into the controller design to ensure the effectiveness of the algorithm.
Compared with the prior art, the method has the advantages that a linear parametric variation prediction model is introduced among batches, a two-dimensional iterative tracking error prediction model is constructed for reference tracks of the batch stirred tank reactor under different control requirements, iterative learning control and feedback control are combined, and finally adaptability and accuracy of variable reference track tracking of the batch stirred tank reactor are improved.
Further, a linear parametric model is arranged to cover the nonlinear dynamics of the batch stirred tank reactor, so that the model accuracy can be improved, and the reaction temperature tracking precision is improved.
In particular, within each production batch, real-time interference can be handled and the control system time domain stability is guaranteed.
Particularly, a time domain robust stable condition is deduced, and the time domain closed loop stability and robustness of the intermittent stirred tank reactor are controlled.
Furthermore, a two-dimensional iterative tracking error prediction model is arranged, so that the model can adapt to the change of the reference track in time among various production batches, the repetitive interference is eliminated, and the convergence of the control system along the iterative axis is ensured.
Particularly, iterative domain convergence conditions are deduced and combined into the controller design, algorithm effectiveness is guaranteed, and production safety is maintained.
Furthermore, H infinite constraint is set, and quick adaptation and tracking of the variable reference track are achieved.
Drawings
FIG. 1 is a schematic view of an intermittent stirred tank reactor in a chemical process according to an embodiment of the present invention;
fig. 2 is a flowchart of a prediction control method using a robust iterative learning model according to an embodiment of the present invention.
Detailed Description
Preferred embodiments of the invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the invention, and do not limit the scope of the invention.
It should be noted that in the description of the present invention, the terms of direction or positional relationship indicated by the terms "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, which are only for convenience of description, and do not indicate or imply that the device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Referring to fig. 1, a schematic model diagram of an intermittent stirred tank reactor using a robust iterative learning model predictive control according to an embodiment of the present invention is shown, including: the device comprises a stirrer 1, a tank body 2, a jacket 3, a stirring shaft 4, an extrusion pipe 5, a support 6, a manhole 7, a shaft seal 8 and a transmission device 9.
With continued reference to FIG. 1, the body of the tank provides sufficient volume to ensure the time required to achieve a specified conversion; the transmission device 9 is arranged outside the tank body 2, is connected with the stirring shaft 4 and is used for transmitting kinetic energy to the stirring shaft 4 so as to strengthen the liquid flow; the stirrer 1 is connected with a stirring shaft 4 and is positioned in the tank body 2 to uniformly mix reactants and strengthen mass and heat transfer; the shaft seal 8 is positioned outside the tank body 2 and is arranged at an inlet of the stirring shaft 4 entering the tank body 2 so as to prevent leakage between the tank body 2 and the stirring shaft 4; the manhole 7 is arranged above the tank body 2 so as to facilitate installation, overhaul and safety inspection; the press-out pipe 5 is used for inputting materials into the reactor, the materials enter the press-out pipe 5 from a material inlet positioned outside the tank body 2 and are discharged into the tank body 2 through the press-out pipe 5 which is arranged on the inner wall of the tank body 2 and extends to the bottom of the tank body 2; the heat transfer means, which is wrapped around the tank 2, includes a jacket 3 and a coil for inputting or removing heat to maintain a suitable reaction temperature.
Referring to fig. 2, which is a flowchart of a prediction control method using a robust iterative learning model according to an embodiment of the present invention, the method of the present embodiment includes the steps of:
step 1, setting a chemical process intermittent stirred tank reactor for repeatedly carrying out chemical reaction of a reactant A to generate a target product B, wherein the control target is a reaction temperature reference track which is tracked and changed in each production batch.
The batch stirred tank reactor needs to control the reaction feeding amount to ensure that enough reaction space is reserved in the reaction vessel to prevent the overpressure of the reactor; arranging at least one interlocking device on the intermittent stirred tank reactor to prohibit adding reactants before the materials reach the reaction temperature condition; the working flow of the batch stirred tank reactor is as follows: loading reactants and a catalyst into a reactor for reaction; tracking a given reaction temperature trajectory by a controller; the reaction was terminated by heating to the operating temperature, and the product was taken out.
the nonlinear model of the stirred tank reactor is as follows:
wherein the reaction temperature T (K) and the reactant concentration CAAs a quantity of state, coolant temperature Tc(K) For control inputs, others are known reaction constants;
through function replacement, the derived linear parametric model is as follows:
where θ is a parameter varying with the operating condition, and x ═ CA,T]T，y＝T，u＝Tc。
The model is
Wherein k represents a production lot,. DELTA.uk(t) is the amount of change in the control input along the iteration axis and the time axis,
is a two-dimensional model state variable containing the variation of the tracking error of the reaction temperature and the variation of the system state,
are the corresponding system matrix and control matrix.
setting control performance index
The quadratic form tracking performance objective function is
The control input is constrained to
Wherein, Δ uk(t)、uk(t) indicates the amount of change in the control input along the iteration axis and the time axis, respectively.
And 6, converting the min-max optimization problem into a mathematical programming problem which can be solved through a linear matrix inequality, and applying the optimized control input to a control system.
Example 1
The experiment set discrete sampling time to be 0.03min, and the batch length to be 12 min. Employing an industry-conventional reaction temperature reference trajectory from batch 1 to batch 5; in batch 6, a fast start reaction temperature reference trajectory was used; batch 7 used a slow start reaction temperature reference trajectory; from batch 8, the reaction temperature reference trace was changed back to the conventional trace. And running a program of the robust iterative learning model prediction control method in matlab, comparing the program with the traditional iterative learning model prediction method, and verifying the functions of the robust iterative learning model prediction control method in improving the tracking accuracy and the adaptability under the variable reference track.
Example 2
The discrete sampling time and the batch length are the same as those in the embodiment 1, step disturbance is applied from the 2 nd min to the 8 th min of each batch, and the repeated interference resistance of the robust iterative learning model predictive control method is verified; step disturbance is applied at the 3 rd min of the 5 th batch, and the batch internal anti-real-time interference capability of the robust iterative learning model predictive control method is verified.
Claims (5)
1. A robust iterative learning model predictive control method applied to an intermittent stirred tank reactor is characterized by comprising the following steps:
step 1, arranging an intermittent stirred tank reactor, wherein the intermittent stirred tank reactor can repeatedly perform chemical reaction of a reactant A to generate a target product B, and the control target of the intermittent stirred tank reactor is a reaction temperature reference track which tracks and changes in each production batch;
step 2, setting a linear parametric variation model for describing the dynamic characteristics of the nonlinear model of the batch stirred tank reactor, and converting the original nonlinear model of the batch stirred tank reactor into the linear parametric variation model by a function substitution method so as to cover the nonlinear dynamic of the batch stirred tank reactor, wherein the nonlinear model of the batch stirred tank reactor is as follows:
wherein the reaction temperature T (K) and the reactant concentration CAAs a quantity of state, coolant temperature TC(K) For control inputs, others are known reaction constants;
through function replacement, the derived linear parametric model is as follows:
wherein q is a parameter varying with the working condition, and x ═ CA，T]T，y＝T，u＝Tc；
Step 3, setting a two-dimensional iterative tracking error prediction model, wherein the state quantity of the model comprises the variation of the tracking error along the iteration axis and the variation of the system state along the iteration axis and the time axis, and the reference track variation among batches is considered, wherein the two-dimensional iterative tracking error prediction model is as follows:
wherein k represents a production lot,. DELTA.uk(t) is the amount of change in the control input along the iteration axis and the time axis,
is a two-dimensional model state variable containing the variation of the tracking error of the reaction temperature and the variation of the system state,
is a corresponding system matrix and control matrix;
step 4, regarding the change of the reference trajectory of the batch stirred tank reactor under different control requirements as bounded disturbance on an iteration axis, and setting H infinite constraint for limiting the influence of the bounded disturbance on the tracking performance, wherein the H infinite constraint is as follows:
step 5, setting a quadratic tracking performance objective function and control input constraint, and establishing a min-max optimization problem under the H infinite constraint, wherein,
setting control performance index
The quadratic form tracking performance objective function is
The control input constraint
Wherein, Δ uk(t)、uk(t) respectively indicates the amount of variation of the control input along the iteration axis and the time axis,
and 6, converting the min-max optimization problem into a mathematical programming problem which can be solved through a linear matrix inequality, and applying the optimized control input to a control system.
2. The robust iterative learning model predictive control method applied to an intermittent stirred tank reactor according to claim 1, wherein the work flow of the intermittent stirred tank reactor is as follows: loading reactants and a catalyst into a reactor for reaction; a controller positioned in the intermittent stirred tank reactor tracks a given reaction temperature track; the reaction was terminated by heating to the operating temperature, and the product was taken out.
3. The robust iterative learning model predictive control method for an intermittent stirred tank reactor as claimed in claim 1, wherein the intermittent stirred tank reactor requires control of reaction feed rates to ensure sufficient reaction space in the reaction vessel to prevent reactor overpressure;
at least one interlock is provided on the batch stirred tank reactor to inhibit addition of reactants until the material reaches reaction temperature conditions.
4. The robust iterative learning model predictive control method for an intermittent stirred tank reactor as claimed in claim 1, wherein the intermittent stirred tank reactor is controlled from two dimensions, including iterative learning control and model predictive control;
the iterative learning control acts on an iterative axis, and the control experience of past batches is induced through learning so as to correct the control input;
the model predictive control acts on a time axis and is used for processing real-time interference in the production process and ensuring the closed loop stability of the system.
5. The robust iterative learning model predictive control method applied to an intermittent stirred tank reactor as recited in claim 1, wherein a time domain robust stability condition is derived for achieving closed loop stability and robustness of the intermittent stirred tank reactor control time domain; the conditions for iterative domain convergence are derived and incorporated into the controller design to ensure the effectiveness of the algorithm.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008073259A1 (en) * | 2006-12-11 | 2008-06-19 | Fakhruddin T Attarwala | Dynamic model predictive control |
| CN104460319A (en) * | 2014-10-20 | 2015-03-25 | 南京工业大学 | Two-dimensional multi-model prediction iterative learning control method for multi-batch intermittent reaction process |
| CN105893654A (en) * | 2016-03-11 | 2016-08-24 | 中南大学 | Robust predictive control method for first-order continuous stirred tank reactor (CSTR) |
| CN105911868A (en) * | 2016-06-15 | 2016-08-31 | 南京工业大学 | Multi-batch intermittent reactor two-dimensional iterative learning feedback control method |
| EP3172629A1 (en) * | 2014-07-23 | 2017-05-31 | Honeywell International Inc. | Robust control design approach for chemical processing industries and other industries |
| CN106933107A (en) * | 2017-05-15 | 2017-07-07 | 中南大学 | A kind of output tracking Robust Predictive Control method based on the design of multifreedom controlling amount |
| CN107966902A (en) * | 2017-11-27 | 2018-04-27 | 辽宁石油化工大学 | A kind of constraint 2D tracking and controlling methods of uncertainty batch process |
| CN107976942A (en) * | 2017-11-22 | 2018-05-01 | 海南师范大学 | The batch process 2D constraint fault tolerant control methods of Infinite horizon optimization |
| CN108227494A (en) * | 2018-01-05 | 2018-06-29 | 海南师范大学 | The fuzzy fault tolerant control method of the non-linear optimal constraints of batch process 2D |
| CN108521143A (en) * | 2018-04-24 | 2018-09-11 | 华北电力大学（保定） | A kind of meter and probabilistic alternating current-direct current mix micro-grid connection robust control method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8200346B2 (en) * | 2009-02-02 | 2012-06-12 | Fisher-Rosemount Systems, Inc. | Model predictive controller with tunable integral component to compensate for model mismatch |
-
2019
- 2019-04-24 CN CN201910331986.3A patent/CN110045611B/en not_active Expired - Fee Related
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008073259A1 (en) * | 2006-12-11 | 2008-06-19 | Fakhruddin T Attarwala | Dynamic model predictive control |
| EP3172629A1 (en) * | 2014-07-23 | 2017-05-31 | Honeywell International Inc. | Robust control design approach for chemical processing industries and other industries |
| CN104460319A (en) * | 2014-10-20 | 2015-03-25 | 南京工业大学 | Two-dimensional multi-model prediction iterative learning control method for multi-batch intermittent reaction process |
| CN105893654A (en) * | 2016-03-11 | 2016-08-24 | 中南大学 | Robust predictive control method for first-order continuous stirred tank reactor (CSTR) |
| CN105911868A (en) * | 2016-06-15 | 2016-08-31 | 南京工业大学 | Multi-batch intermittent reactor two-dimensional iterative learning feedback control method |
| CN106933107A (en) * | 2017-05-15 | 2017-07-07 | 中南大学 | A kind of output tracking Robust Predictive Control method based on the design of multifreedom controlling amount |
| CN107976942A (en) * | 2017-11-22 | 2018-05-01 | 海南师范大学 | The batch process 2D constraint fault tolerant control methods of Infinite horizon optimization |
| CN107966902A (en) * | 2017-11-27 | 2018-04-27 | 辽宁石油化工大学 | A kind of constraint 2D tracking and controlling methods of uncertainty batch process |
| CN108227494A (en) * | 2018-01-05 | 2018-06-29 | 海南师范大学 | The fuzzy fault tolerant control method of the non-linear optimal constraints of batch process 2D |
| CN108521143A (en) * | 2018-04-24 | 2018-09-11 | 华北电力大学（保定） | A kind of meter and probabilistic alternating current-direct current mix micro-grid connection robust control method |
Non-Patent Citations (5)
| Title |
|---|
| A New Robust MPC for Uncertain Linear Systems;xiangjie Liu etal;《Proceedings of the 8th World Congress on Intelligent Control and Automation》;20131231;第1408-1413页 * |
| Improved design of constrained model predictive tracking control for batch processes against unknown uncertainties;Wu S, Jin Q, Zhang R, Zhang J, Gao F;《ISA Transactions》;20171231;第273-280页 * |
| 基于输入输出线性化的连续系统非线性模型预测控制;孔小兵等;《控制理论与应用》;20120228;第217-244页 * |
| 间歇过程最优迭代学习控制的发展:从基于模型到数据驱动;池荣虎等;《自动化学报》;20170630;第917-932页 * |
| 鲁棒预测迭代学习控制在间歇过程中的运用;孙海乔等;《信息与控制》;20151231;第129-134页 * |
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