CN109491354A - A kind of full level of factory performance optimal control method of complex industrial process data-driven - Google Patents
A kind of full level of factory performance optimal control method of complex industrial process data-driven Download PDFInfo
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- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
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Abstract
A kind of full level of factory performance optimal control method of complex industrial process data-driven, it is related to a kind of industrial process optimal control method, the method includes following procedure: the full level of factory performance optimal control of multiple target multiple constraint, it include: a) objective function building, to realize that full level of factory production target tracks ideal value as target in near-optimization mode, objective function is made of the quadratic form of tracking error and operating index；B) constraint condition describes, and with production target dynamic, production target is limited, operating index is limited and exogenous disturbances are limited as constraint condition；C) multiple target multiconstraint optimization controls；The invention also includes the optimized operation index Design based on nonzero sum game, the design of the optimized operation index Design algorithm for estimating based on ADP, emulation and Physical Experiment verifyings；The technology path that the present invention takes theory analysis, analog simulation experiment and laboratory Physical Experiment to combine, so that the project had not only had stronger pure science meaning, but also keeps very strong practicability.
Description
Technical Field
The invention relates to an industrial data optimization control method, in particular to a complex industrial process data-driven plant-level performance optimization control method.
Background
At present, the research on the design of complex industrial application draws general attention, and the problems to be solved particularly in the aspect of operation optimization control comprise:
a) the production index dynamic state is of a nonlinear characteristic, and system dynamic state cannot be accurately modeled and unknown bounded interference exists;
b) how to solve the multi-objective performance optimization problem.
In the partial design, the problems that the whole plant level production index dynamic state of the complex industrial process cannot be accurately modeled, the coupling of multiple production indexes and a multi-unit process and multiple time scales exist. The optimum operation index of the analysis design guarantees the condition of realizing Nash equilibrium and the condition of zero steady-state tracking error of the production index, the optimum operation index of the analysis design requires that the dynamic information of the production index is known, but for the complex industrial process, the whole plant level production index dynamic state is difficult to accurately model. The problems to be solved at this stage include:
a) generating a track independent of production indexes;
b) the production index generation track is interfered by the outside;
c) under the condition that the ideal production index is nonzero, an optimal operation index design algorithm is approximated;
d) and analyzing the convergence of the algorithm and the stability of the track generated by the operation index.
Disclosure of Invention
The invention aims to provide a complex industrial process data-driven plant-wide performance optimization control method, and adopts a technical route combining theoretical analysis, simulation experiments and laboratory physical experiments, so that the project has strong theoretical scientific significance and keeps strong practicability.
The purpose of the invention is realized by the following technical scheme:
a complex industrial process data-driven plant-wide performance optimization control method, the method comprising the following processes:
a. multi-objective multi-constraint whole-plant performance optimization control
The method comprises the following steps: a) constructing an objective function, aiming at tracking an ideal value of a full plant-level production index in an approximately optimal mode, wherein the objective function is composed of a tracking error and a quadratic form of an operation index; b) describing constraint conditions, namely taking the dynamic production index, the limited operation index and the limited interference input as the constraint conditions; c) performing multi-objective multi-constraint optimization control, and working according to the first two parts to obtain a multi-objective multi-constraint optimization control problem;
b. optimal operation index design based on non-zero sum game
By adopting a non-zero sum game theory, an optimal operation index design scheme is given, and the Nash equilibrium of the production indexes of the whole plant is realized, and the part of work is two stages of multi-agent non-zero sum game, global Nash equilibrium and stability analysis;
c. optimal operation index design estimation algorithm design based on ADP
Under an ADP framework, a DP, RL and H infinity control method is organically integrated, an Off-ply (RL) algorithm which does not depend on the production index dynamics is provided, and an approximate optimal operation index is obtained;
d. simulation and physical experiment verification
The method adopts simulation software, a semi-physical simulation platform and a physical experiment platform to jointly verify the effectiveness of a theoretical method and a result; according to the simulation and physical experiment results, the theoretical method and the control technology are adjusted correspondingly.
The complex industrial process data-driven plant-wide performance optimization control method is characterized in that the multi-objective multi-constraint optimization control problem is as follows:
(1)
constraint conditions are as follows:
wherein,the method can be used for expressing the production index,the operation index is represented by the index of the operation,indicating the time of day taken by the terminal,in order to be a positive definite matrix,is an external disturbance.
According to the complex industrial process data-driven whole-plant-level performance optimization control method, the multi-agent non-zero sum game adopts a non-zero sum game theory to design an optimal operation index in a first stage, so that the Nash balance of whole-plant-level production indexes is realized.
According to the complex industrial process data-driven whole-plant-level performance optimization control method, the global Nash equilibrium and stability analysis is performed, in the second stage, a Lyapunov function is designed according to production indexes, and the stability and global Nash equilibrium analysis is performed on the basis of the Lyapunov stability theory and the Nash equilibrium theory.
According to the complex industrial process data-driven plant-level performance optimization control method, simulation and physical experiment verification are carried out, a sewage treatment physical experiment platform is adopted, an approximate optimal set value design algorithm and a bottom layer control loop approximate optimal controller design algorithm are executed, and whether the water quality and energy use of effluent is controlled in an ideal range is verified.
The invention has the advantages and effects that:
1. aiming at the operation optimization control problem with the nonlinear controlled process and the nonlinear operation index generation track, the invention adopts the establishment of a multi-target multi-constraint optimal performance optimization control scheme, and in the second stage, the conditions for ensuring the realization of Nash equilibrium by analyzing and designing the optimal operation index and the conditions for zero steady-state tracking error of the production index are analyzed and designed.
2. Designing a strategy iterative algorithm based on the optimal operation index obtained in the previous part to obtain an optimal operation index estimation; secondly, obtaining a new Bellman equation by adopting an Off-policy strategy and combining a value function Taylor expansion theory; then, introducing a variable matrix, and estimating an optimal operation index based on a minimum quadratic method;
3. the invention adopts vector isomorphic topological transformation, so that external interference information is not needed in the estimation of the optimal operation index;
4. the method introduces the discount factor into the production index optimization objective function.
5. The method is based on the Lyapuov stability theory, the optimal control theory, and the convergence of the analysis algorithm and the condition of the stability of the production index generation track.
Drawings
FIG. 1 is a non-zero and game-based optimal operation index diagram of the present invention;
FIG. 2 is a diagram of a near optimal tracking controller of the present invention;
FIG. 3 is a technical roadmap of the present invention.
Detailed Description
The present invention will be described in detail with reference to examples.
The implementation process of the invention is as follows:
one-target multi-constraint full plant-level performance optimization control
The invention adopts the construction of multi-target multi-constraint optimal performance optimization control, which comprises the following steps: a) and constructing an objective function, wherein the objective function is used for realizing that the full-plant-level production index tracks an ideal value in an approximately optimal mode, and the objective function is composed of a tracking error and a quadratic form of an operation index. At this stage, the scientific problem to be solved is the description of the optimal tracking problem at different time scales. The operation index change is a fast process, and the production index change is a slow process; b) describing constraint conditions, namely taking the dynamic production index, the limited operation index and the limited interference input as the constraint conditions; c) and (3) performing multi-objective multi-constraint optimization control, and working according to the first two parts to obtain a multi-objective multi-constraint optimization control problem:
(1)
constraint conditions are as follows:
wherein,the method can be used for expressing the production index,the operation index is represented by the index of the operation,indicating the time of day taken by the terminal,in order to be a positive definite matrix,is an external disturbance. Note that: the nonlinear dynamics of the production index are difficult to model as unknown nonlinear functions.
Second, based on non-zero sum game, optimum operation index design
In order to solve the stated multi-target multi-constraint optimization control problem, an optimal operation index needs to be designed, and the ideal value of the whole plant-level production index is tracked in an optimal mode. In view of the fact that the optimization problem is a multi-objective coupling problem, a plurality of decision variables (unit process operation indexes) exist, in the part, a non-zero and game theory is adopted, an optimal operation index design scheme is given, and the Nash balance of the whole plant-level production indexes is achieved, as shown in figure 1. This part of the work is in the following two phases:
multi-agent non-zero-sum gambling:
in the first stage, the multi-target multi-constraint optimization control problem is converted into a multi-agent game problem, the non-zero sum game theory is adopted, the optimal operation index is designed, and the Nash balance of the whole plant-level production index is realized. The scientific problems to be solved include: a) the production index is dynamically in a nonlinear characteristic, and unknown bounded interference exists; b) how to convert the problem of solving multiple targets and multiple constraints into a multi-agent game problem. In order to solve the problem a), a linear time-invariant system equation with bounded interference terms is obtained by assuming that a nonlinear function describing a production index is continuously differentiable in second order and then utilizing a Taylor expansion equation; in order to solve the problem b), each unit process is used as an intelligent agent, optimization of each production index depends on optimal response (designed optimal operation index) of the intelligent agent, a Nash equilibrium theory and an optimal control theory are organically fused to obtain the optimal operation index, and Nash equilibrium is finally realized. In solving the problem b), the method also relates to the processing of bounded unknown interference terms, fusing H ∞ control and LQT technology and adopting a topological transformation method.
Global nash equilibrium and stability analysis:
in the second stage, the scientific difficult problem to be solved is the condition that the optimum operation index of the analysis design guarantees the realization of Nash equilibrium and the condition that the production index has zero steady-state tracking error. In order to solve the problem, a Lyapunov function is designed according to production indexes, and stability and global Nash balance analysis is carried out on the basis of a Lyapunov stability theory and a Nash balance theory.
Third, optimal operation index design estimation algorithm design based on ADP
The optimal operation indexes of the previous part of design require that the dynamic information of the production indexes is known, but for complex industrial processes, the whole plant level production index dynamic is difficult to accurately model. Therefore, at this stage, methods such as DP, RL and H ∞ control are organically integrated under an ADP framework, and an Off-ply RL algorithm independent of production index dynamics is proposed to obtain an approximately optimal operation index. As shown in fig. 2.
The scientific challenges to be solved at this stage include: a) generating a track independent of production indexes; b) the production index generation track is interfered by the outside; c) under the condition that the ideal production index is nonzero, an optimal operation index design algorithm is approximated; d) and analyzing the convergence of the algorithm and the stability of the track generated by the operation index. To solve the problem a), a strategy iterative algorithm is designed based on the optimal operation index obtained in the previous part, and the optimal operation index estimation is obtained; secondly, obtaining a new Bellman equation by adopting an Off-policy strategy and combining a value function Taylor expansion theory; then, introducing a variable matrix, and estimating an optimal operation index based on a minimum quadratic method; to solve the problem b), vector isomorphic topological transformation is adopted, so that external interference information is not needed in the optimal operation index estimation; to solve problem c), a discount factor is introduced into the production index optimization objective function. To solve the problem d), based on the Lyapuov stability theory, the optimal control theory, the convergence of the analysis algorithm and the condition of the stability of the production index generation track.
Fourth, simulation and physical experiment verification
As shown in FIG. 3, the invention adopts simulation software, a semi-physical simulation platform and a physical experiment platform, and jointly verifies the effectiveness of a theoretical method and a result. According to the simulation and physical experiment results, the theoretical method and the control technology are adjusted correspondingly.
The simulation adopts Java, Matlab software, a compiler and a simulation verification operation optimization control algorithm.
And (3) adopting a sewage treatment physical experiment platform, executing an approximate optimal set value design algorithm and a bottom layer control ring approximate optimal controller design algorithm, and verifying whether the quality and the energy use of the effluent are controlled in an ideal range.
Claims (5)
1. A complex industrial process data-driven plant-wide performance optimization control method is characterized by comprising the following processes:
a. multi-objective multi-constraint whole-plant performance optimization control
The method comprises the following steps: a) constructing an objective function, aiming at tracking an ideal value of a full plant-level production index in an approximately optimal mode, wherein the objective function is composed of a tracking error and a quadratic form of an operation index; b) describing constraint conditions, namely taking the dynamic production index, the limited operation index and the limited interference input as the constraint conditions; c) performing multi-objective multi-constraint optimization control, and working according to the first two parts to obtain a multi-objective multi-constraint optimization control problem;
b. optimal operation index design based on non-zero sum game
By adopting a non-zero sum game theory, an optimal operation index design scheme is given, and the Nash equilibrium of the production indexes of the whole plant is realized, and the part of work is two stages of multi-agent non-zero sum game, global Nash equilibrium and stability analysis;
c. optimal operation index design estimation algorithm design based on ADP
Under an ADP framework, a DP, RL and H infinity control method is organically integrated, an Off-ply (RL) algorithm which does not depend on the production index dynamics is provided, and an approximate optimal operation index is obtained;
d. simulation and physical experiment verification
The method adopts simulation software, a semi-physical simulation platform and a physical experiment platform to jointly verify the effectiveness of a theoretical method and a result; according to the simulation and physical experiment results, the theoretical method and the control technology are adjusted correspondingly.
2. The complex industrial process data-driven plant-wide performance optimization control method according to claim 1, wherein the multi-objective multi-constraint optimization control problem is:
（1）
constraint conditions are as follows:
wherein,the method can be used for expressing the production index,the operation index is represented by the index of the operation,indicating the time of day taken by the terminal,in order to be a positive definite matrix,is an external disturbance.
3. The complex industrial process data-driven plant-wide performance optimization control method as claimed in claim 1, wherein the multi-agent non-zero sum game adopts non-zero sum game theory to design optimal operation indexes in the first stage, so as to realize the plant-wide production index nash balance.
4. The method as claimed in claim 1, wherein the global nash equilibrium and stability analysis, in the second stage, designs Lyapunov function according to production index, and performs stability and global nash equilibrium analysis based on Lyapunov stability theory and nash equilibrium theory.
5. The complex industrial process data-driven plant-wide performance optimization control method according to claim 1, characterized in that simulation and physical experiment verification are performed by adopting a sewage treatment physical experiment platform, and performing an approximate optimal set value design algorithm and a bottom-layer control loop approximate optimal controller design algorithm to verify whether the water quality and energy use of the effluent are controlled within an ideal range.
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