CN107991874A - A kind of Multiple Model Control Method for multistage interval industrial process - Google Patents
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Abstract
The invention discloses a kind of Multiple Model Control Method for multistage interval industrial process.Repeatability and multistage characteristic of the invention according to interval industrial process, new system model is established to describe the dynamic change of interval industrial process, then in order to realize trajectory error stablize, introduce state error, establish the dynamic model of extension, then in conjunction with relevant conversion condition, the exponential stability of control system and the minimum run time that subsystem is run under stable condition ensure that.There is the deficiency that can only be applied in single phase present invention improves over traditional controller, effectively improve the overall performance of controller, improve control effect.
Description
Technical Field
The invention belongs to the technical field of automation, and relates to a multi-model control method for a multi-stage intermittent industrial process.
Background
With the development and research of a large number of new products in the fields of fine chemicals, biopharmaceuticals, metal processing, etc., the intermittent process industry receives more and more attention. In order to optimize the control performance of the intermittent industrial process control system, some scholars propose a control method of iterative learning, but the method has poor anti-interference performance and is inapplicable in some cases. Other scholars have proposed robust learning control methods, and still others have proposed fault tolerant control methods. These methods all play a role in improving the control performance of the batch industrial process. However, these processes are directed to single stage batch processes. However, in practical industrial processes, most batch processes are multistage. At present, a control method aiming at a single stage is mature, the performance can be optimized at each stage, but the optimal control of the whole process cannot be ensured at the same time. It is therefore highly desirable to provide a control method which can be used in a multi-stage batch industrial process.
Disclosure of Invention
The invention aims to provide a novel control strategy aiming at a multi-stage intermittent industrial process aiming at the condition that the existing control method of the intermittent industrial process is controlled in a single stage, and the control performance of a multi-stage intermittent industrial process controller is improved.
According to the repeatability and the multi-stage characteristics of the intermittent industrial process, a novel system model is established to describe the dynamic change of the intermittent industrial process, then in order to realize the stability of the track error, a state error is introduced, an expanded dynamic model is established, and then the index stability of a control system and the minimum running time of a subsystem running under the stable condition are ensured by combining related conversion conditions.
The technical scheme of the invention is that a novel model control strategy aiming at the multistage intermittent industrial process is established by means of data acquisition, model establishment, mechanism prediction, periodic learning, optimization and the like, and the overall control performance of the system is improved.
The method comprises the following steps:
designing a novel control system model of the intermittent industrial process. The specific method comprises the following steps:
1.1 considering the system disturbance in the intermittent industrial process, establishing a novel system model to describe the dynamic interference of a control system:
wherein t and k are respectively time step and period index, and x (t, k) belongs to R n ，u(t,k)∈R 1 And y (t, k) e R 1 Respectively represent the state of the system at the moment t of the kth period,input and output. σ (t +1, k) represents the transformed signal of the system at the time of the kth cycle t +1, σ (t, k) represents the transformed signal of the system at the time of the kth cycle t,C σ(t,k) a constant matrix representing the corresponding dimension. w is a σ(t,k) And (t, k) represents the unknown disturbance of the system at the moment of the kth period t. x is a radical of a fluorine atom 0.k Indicating the initial conditions of the system. Ω (σ (t, k), x (t, k)) represents a model transfer function.
1.2 determine the specific expression of the transfer function as:
wherein, G σ(t,k)+1 (x(t,k))<, 0, σ (t, k) +1= {1,2, \8230;, p } represents the conversion condition for the σ (t, k) +1 stage. σ (0, k) ≡ 1 denotes that the processing procedure starts from the first stage every cycle, and p denotes the number of stages contained in every cycle.
1.3 introduction of switching time:
wherein, the first and the second end of the pipe are connected with each other,indicating the transition time of the phase i of the k-th cycle,indicating the transition time of the k-th cycle i-1 phase,represents the initial transition time of the system, G i (x(t,k))&And lt, 0 represents the switching condition of the phase i at the moment t of the kth period.
1.4 consider the influence of disturbance, the system model is simplified as follows:
wherein x is i (t +1, k) represents the state vector at the moment of t +1 in the i stage of the kth period of the chemical intermittent process, x i (t, k) represents the state vector at the moment t of the i stage of the kth period of the chemical intermittent process, u i (t, k) represents the system control input at the time of i stage t in the k period of the chemical intermittent process, y i And (t, k) represents the system output at the moment t in the kth period i of the chemical intermittent process. w is a i (t, k) represents unknown disturbance of the system at the moment t in the phase i of the kth period of the chemical intermittent process,C i a matrix of constants representing the corresponding dimension.
1.5 in practice, the states at different stages may be the same, and in order to monitor this index, a state matrix is introduced:
in the formula (I), the compound is shown in the specification,is a state matrix, if the states of different stages are the same, J i I, = I, I denotes an identity matrix of the corresponding dimension.Represents the k period i +1 stage T of the chemical intermittent process k i The state of the system at the time of day,represents the kth period i stage T of the chemical intermittent process k i The system state at the moment.
And step two, designing a novel controller according to the novel control system model aiming at the intermittent industrial process in the step one.
2.1 according to the system model in the step 1.4, introducing a control system control law:
u i (t,k)＝u i (t,k-1)+r i (t,k)(for u i (t,0)＝0,t＝0,1,2,…,T i i = {1,2 }) wherein u i (t, k-1) represents the system control input at the i stage t moment of the k-1 th cycle of the chemical intermittent process, r i And (t, k) represents a system updating law at the moment t of the i stage of the kth period of the chemical intermittent process.
2.2 introducing State errors in the control System
Wherein, the first and the second end of the pipe are connected with each other,represents the state error, x, of the kth period i, the stage t in the chemical intermittent process i (t, k) represents the system state at the moment of i stage t of the kth period of the chemical intermittent process, x i And (t, k-1) represents the system state at the time of t in the phase i of the k-1 th period of the chemical intermittent process.
2.3 step 1.4 combining steps 2.1 and 2.2, one can obtain
Wherein, the first and the second end of the pipe are connected with each other, showing the t moment of the kth period i stage of the chemical intermittent processThe state error of (a) is detected,and the state error at the t +1 moment of the phase i of the kth period in the chemical intermittent process is shown. e.g. of the type i (t +1, k) represents the tracking error at the moment of t +1 in the i stage of the kth period of the chemical intermittent process, e i (t +1, k-1) represents the tracking error at the t +1 moment of the i stage in the k-1 th period of the chemical intermittent process, r i And (t, k) represents the updating law of the k period i phase t time in the chemical intermittent process. x (t, k-1) represents the system state at the time of t in the k-1 th period of the chemical intermittent process,represents the dynamic disturbance at the time t of the kth cycle, delta (Delta A) i )，δ(△B i ) Represents the corresponding perturbation coefficient, δ (w) i (t, k)) represents an unknown disturbance.
2.4 to achieve stable tracking error, the model extension of step 2.3 is:
in the formula
H i ，A matrix of constants representing the corresponding dimension is shown,and the state estimation vector of the system at the moment of the phase t +1 of the kth period i in the chemical intermittent process is shown.Representing the state estimation vector of the system at the moment t of the phase i of the kth period of the chemical intermittent process,and the state estimation vector of the system at the moment t +1 of the i stage in the k-1 th period of the chemical intermittent process is shown. C i ，D i ，Each representing a matrix of coefficients of the corresponding dimension.F i ，To simplify the coefficient matrix.
2.5 introduction of the update law of the control system:
wherein, the first and the second end of the pipe are connected with each other,
wherein the content of the first and second substances,a gain matrix representing the i-th stage,the coefficient matrix representing the i-th stage.
2.6 the update law in step 2.6 is used in step 2.1, and step 2.1 to step 2.6 are repeated until the optimal control law is obtained.
The invention has the beneficial effects that: the invention establishes a novel input/output model by collecting intermittent industrial process data, designs a novel intermittent industrial process controller, overcomes the defect that the traditional controller can only be applied in a single stage, effectively improves the overall performance of the controller and improves the control effect.
Detailed Description
Taking a batch injection molding process as an example: the batch injection molding process is a typical multi-stage process, and a novel control strategy is established according to the repeatability characteristic in the batch injection molding process, so that the efficiency and the product quality in the batch injection molding process are improved.
Designing a novel control system model of a batch injection molding process. The method comprises the following steps:
1.1 considering the system disturbance in the batch injection molding process, establishing a novel system model to describe the dynamic interference of a control system:
wherein, the first and the second end of the pipe are connected with each other,
△A i (t,k)＝D i F i (t,k)E i ；
F i (t,k)F i T (t,k)≤I i ；
wherein t and k are respectively time step and period index, and x (t, k) belongs to R n ，u(t,k)∈R 1 And y (t, k) e R 1 Respectively representing the system state, input and output at the moment t of the kth period. σ (t +1, k) represents the transformed signal of the system at the time of the kth cycle t +1, σ (t, k) represents the transformed signal of the system at the time of the kth cycle t,C σ(t,k) a constant matrix representing the corresponding dimension. w is a σ(t,k) And (t, k) represents the unknown disturbance of the system at the moment of the kth period t. x is the number of 0.k Indicating the initial conditions of the system. Ω (σ (t, k), x (t, k)) represents a model transfer function.
1.2 determining concrete expressions for transfer function
Wherein G is σ(t,k)+1 (x(t,k))<, 0, σ (t, k) +1= {1,2, \8230;, p } represents the conversion condition for the σ (t, k) +1 stage. σ (0, k) ≡ 1 denotes that the processing procedure starts from the first stage every cycle, and p denotes the number of stages contained in every cycle.
1.3 introduction of switching time:
wherein the content of the first and second substances,indicating the transition time of the phase i of the k-th cycle,indicating the transition time of the k-th cycle i-1 phase,represents the initial transition time of the system, G i (x(t,k))&And lt, 0 represents the conversion condition of the k-th period t at the i stage.
1.4 considering the effects of disturbances, the system model can be simplified as:
wherein x is i (t +1, k) represents the state vector at the moment t +1 of the kth cycle i phase of the batch injection process, x i (t, k) represents the state vector at the time t of the i phase of the kth cycle of the batch injection molding process, u i (t, k) represents the system control input at time t of phase i of the kth cycle of the batch injection molding process, y i (t, k) represents the system output at time t of phase i of the kth cycle of the batch injection molding process. w is a i (t, k) represents the unknown disturbance of the system at the moment t of the phase i of the kth cycle of the batch injection molding process,C i a matrix of constants representing the corresponding dimension.
1.5 in practice, the states at different stages may be the same, and in order to monitor this index, a state matrix is introduced:
in the formula (I), the compound is shown in the specification,is a state matrix, if the states of different stages are the same, J i ＝I。Represents the k cycle i +1 stage T of the batch injection process k i The state of the system at the time of day,represents the kth cycle i phase T of a batch injection molding process k i The system state at the moment.
And step two, designing a novel controller according to the novel control system model aiming at the batch injection molding process in the step one.
2.1 introducing a control system control law according to the system model in the step 1.4:
u i (t,k)＝u i (t,k-1)+r i (t,k)(for u i (t,0)＝0,t＝0,1,2,…,T i ,i＝{1,2})
wherein u is i (t, k-1) represents the system control input at the time of phase t in the k-1 cycle i of the batch injection molding process, r i And (t, k) represents a system updating law at the moment of the phase t of the kth cycle i of the batch injection molding process.
2.2 introducing State errors in the control System
Wherein, the first and the second end of the pipe are connected with each other,representing the state error, x, at the moment t of the kth cycle i of a batch injection molding process i (t, k) represents the system state at the time of stage t of the kth cycle i of the batch injection molding process, x i (t, k-1) represents the system state at the time of phase t of the k-1 th cycle i of the batch injection molding process.
2.3 step 1.4, combining steps 2.1 and 2.2, one can obtain
Wherein the content of the first and second substances,
representing the state error at the moment of phase t of the kth cycle i of the batch injection molding process,representing the state error at the moment of phase t +1 of the kth cycle i of the batch injection molding process.
e i (t +1, k) represents the tracking error at the moment t +1 of the phase i of the kth cycle of the batch injection molding process, e i (t +1, k-1) represents the tracking error at the moment of t +1 in the i phase t +1 of the k-1 th cycle of the batch injection molding process, r i And (t, k) represents the updating law of the k cycle i, the stage t in the batch injection molding process.
x (t, k-1) represents the system state at the moment of t in the k-1 th cycle of the batch injection molding process,represents the dynamic interference, delta (Delta A), at the time t of the kth cycle i )，δ(△B i ) Represents the corresponding perturbation coefficient, δ (w) i (t, k)) represents an unknown disturbance.
2.4 to achieve stable tracking errors, the model of step 2.3 is extended to:
in the formula
H i ，A matrix of constants representing the corresponding dimension is shown,represents the state estimation vector of the system at the moment of phase t +1 of the kth cycle i of the batch injection molding process.Represents the state estimation vector of the system at the moment t of the kth period i of the batch injection molding process,represents the state estimation vector of the system at the moment of the i phase t +1 of the k-1 th cycle of the batch injection molding process. C i ，D i ，Each representing a matrix of coefficients of the corresponding dimension.F i ，To simplify the coefficient matrix.
2.5 introducing the update law of the control system:
wherein the content of the first and second substances,
whereinA gain matrix representing the i-th stage,the coefficient matrix representing the i-th stage.
2.6 the update law in step 2.6 is used in step 2.1, and step 2.1 to step 2.6 are repeated until the optimal control law is obtained.
Claims (1)
1. A multi-model control method for a multi-stage intermittent industrial process is characterized by comprising the following steps:
designing a novel control system model of an intermittent industrial process; the method comprises the following steps:
1.1 considering system disturbance in the intermittent industrial process, establishing a novel system model to describe the dynamic interference of a control system:
wherein t and k are respectively time step and period index, and x (t, k) belongs to R n ，u(t,k)∈R 1 And y (t, k) e R 1 Respectively representing the system state, input and output at the kth period t moment; σ (t +1, k) represents the transformed signal of the system at the time of the kth cycle t +1, σ (t, k) represents the transformed signal of the system at the time of the kth cycle t,a constant matrix representing the corresponding dimension; w is a σ(t,k) (t, k) represents the unknown disturbance of the system at the moment of the kth period t; x is a radical of a fluorine atom 0.k Indicating the initial conditions of the system; Ω (σ (t, k), x (t, k)) represents a modelA transfer function;
1.2 determine the specific expression of the conversion function as:
wherein G is σ(t,k)+1 (x(t,k))&0, sigma (t, k) +1= {1,2, \8230;, p } represents the conversion condition of the sigma (t, k) +1 stage; σ (0, k) ≡ 1 denotes that the treatment process starts from the first stage every cycle, and p denotes the number of stages contained in every cycle;
1.3 introduction of switching time:
wherein, the first and the second end of the pipe are connected with each other,indicating the transition time of the phase i of the k-th cycle,indicating the transition time of the k-th cycle i-1 phase,represents the initial transition time of the system, G i (x(t,k))&0 represents the conversion condition of the i stage at the t moment of the kth period;
1.4 consider the influence of disturbance, and the system model is simplified as follows:
wherein x is i (t +1, k) represents the state vector at the moment of t +1 in the i stage of the kth period of the chemical intermittent process, x i (t, k) represents the state vector at the moment t of the i stage of the kth period of the chemical intermittent process, u i (t, k) represents the kth period i stage t of the chemical intermittent processSystem control input of time of day, y i (t, k) represents the system output at the moment t of the i stage of the kth period of the chemical intermittent process; w is a i (t, k) represents the unknown disturbance of the system at the moment t in the phase i of the kth period of the chemical intermittent process,a constant matrix representing the corresponding dimension;
1.5 introduce a state matrix:
in the formula (I), the compound is shown in the specification,is a state matrix, if the states of different stages are the same, J i = I, I denotes an identity matrix of the corresponding dimension;represents the k period i +1 stage T of the chemical intermittent process k i The state of the system at the time of day,represents the kth period i stage T of the chemical intermittent process k i The system state at the time;
designing a novel controller according to the novel control system model aiming at the intermittent industrial process in the step one;
2.1 introducing a control system control law according to the system model in the step 1.4:
u i (t,k)＝u i (t,k-1)+r i (t,k)
wherein u is i (t, k-1) represents the system control input at the i stage t moment of the k-1 period in the chemical intermittent process, r i (t, k) represents a system updating law at the moment of t in the kth period i in the chemical intermittent process;
2.2 introducing status errors in the control System
Wherein the content of the first and second substances,representing the state error x at the moment t of the i stage of the kth period of the chemical intermittent process i (t, k) represents the system state at the moment of i stage t of the kth period of the chemical intermittent process, x i (t, k-1) represents the system state at the time of t in the phase i of the k-1 th period of the chemical intermittent process;
2.3 step 1.4 combining steps 2.1 and 2.2 to give
Wherein, the first and the second end of the pipe are connected with each other,
showing the state error of the phase t in the kth period i of the chemical intermittent process,the state error of the k period i stage t +1 moment in the chemical intermittent process is shown; e.g. of a cylinder i (t +1, k) represents the tracking error at the moment of t +1 in the i stage of the kth period of the chemical intermittent process, e i (t +1, k-1) represents the tracking error at the moment of t +1 in the i stage of the k-1 th period of the chemical intermittent process, r i (t, k) represents the updating law of the k period i stage t moment in the chemical intermittent process(ii) a x (t, k-1) represents the system state at the moment of t in the k-1 th period of the chemical intermittent process,represents the dynamic interference, delta (Delta A), at the time t of the kth cycle i )，δ(△B i ) Represents the corresponding perturbation coefficient, δ (w) i (t, k)) represents an unknown disturbance;
2.4 extend the model of step 2.3 to:
in the formula
A matrix of constants representing the corresponding dimension is shown,representing a state estimation vector of a system at the moment of i +1 in the kth period of the chemical intermittent process;representing the state estimation vector of the system at the moment t of the phase i of the kth period of the chemical intermittent process,representing a state estimation vector of a system at the moment t +1 of the i stage in the k-1 th period of the chemical intermittent process;a matrix of coefficients each representing a corresponding dimension; a simplified coefficient matrix;
2.5 introduction of the update law of the control system:
wherein the content of the first and second substances,
wherein, the first and the second end of the pipe are connected with each other,a gain matrix representing the i-th stage,a coefficient matrix representing the i-th stage;
2.6 the update law in step 2.6 is used in step 2.1, and step 2.1 to step 2.6 are repeated until the optimal control law is obtained.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104317269A (en) * | 2014-10-30 | 2015-01-28 | 清华大学 | Comprehensive forecast and iterative learning control method based on 2D theory |
| CN104330972A (en) * | 2014-10-30 | 2015-02-04 | 清华大学 | Comprehensive prediction iterative learning control method based on model adaptation |
| CN104932263A (en) * | 2015-06-03 | 2015-09-23 | 辽宁石油化工大学 | Minimum operation time control method of multistage intermittent process |
| CN105607591A (en) * | 2015-12-10 | 2016-05-25 | 辽宁石油化工大学 | Control method enabling minimum operating time of batch process in controller asynchronous switching |
| CN105911868A (en) * | 2016-06-15 | 2016-08-31 | 南京工业大学 | Multi-batch intermittent reactor two-dimension iterative learning feedback control method |
-
2017
- 2017-12-13 CN CN201711328079.0A patent/CN107991874A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104317269A (en) * | 2014-10-30 | 2015-01-28 | 清华大学 | Comprehensive forecast and iterative learning control method based on 2D theory |
| CN104330972A (en) * | 2014-10-30 | 2015-02-04 | 清华大学 | Comprehensive prediction iterative learning control method based on model adaptation |
| CN104932263A (en) * | 2015-06-03 | 2015-09-23 | 辽宁石油化工大学 | Minimum operation time control method of multistage intermittent process |
| CN105607591A (en) * | 2015-12-10 | 2016-05-25 | 辽宁石油化工大学 | Control method enabling minimum operating time of batch process in controller asynchronous switching |
| CN105911868A (en) * | 2016-06-15 | 2016-08-31 | 南京工业大学 | Multi-batch intermittent reactor two-dimension iterative learning feedback control method |
Non-Patent Citations (1)
| Title |
|---|
| LIMIN WANG等: "Iterative learning fault-tolerant control for injection molding processes against actuator faults", 《JOURNAL OF PROCESS CONTROL》 * |
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Application publication date: 20180504 |