Control method and device of oil-water separation system based on event triggering

Abstract

The invention relates to the technical field of optimal control and intelligent control, in particular to a control method and device of an oil-water separation system based on event triggering. The method comprises the following steps: establishing an oil-water separation system mathematical model based on defined input saturation constraints, then converting a robust trajectory tracking control problem into a binary zero-sum game problem, defining a Hamilton function and a Bellman equation, and solving to obtain an optimal control gain and an optimal control strategy; based on a preset compensation controller and the optimal control strategy, an event triggering condition is defined, and an event triggering control strategy with input saturation constraints is obtained; and inputting the event trigger control strategy into the oil-water separation system through the zero-order holder for state updating, and obtaining the optimal event trigger control strategy in the current state according to the optimal control gain and the event trigger condition. The problems that in the prior art, after oil-water separation, the oil content of discharged water does not reach the standard, and an existing control mode does not meet the standard can be solved.

Description

Technical Field

[0001] This invention relates to the fields of optimal control and intelligent control technology, and in particular to a control method and apparatus for an event-triggered oil-water separation system. Background Technology

[0002] In offshore oil extraction, oil-water separation systems are key equipment for separating oil, gas, and water. The oil content in the discharged water after separation must be less than 30 mg / L to avoid polluting the marine environment. Efficient operation and green emissions are core requirements of the industry.

[0003] Currently, the proportional-integral-derivative (PID) control method is widely used in industry. It is applied to oil-water separation systems composed of three-phase separators, hydrocyclones, etc. By controlling the opening of relevant valves, water level regulation and pressure drop ratio (PDR) control are achieved, thereby completing the oil-water separation process.

[0004] However, the slug flow disturbance caused by gas accumulation in the oil riser, as well as the complex nonlinear coupling characteristics of the system itself, make it difficult for PID control to guarantee emission compliance. At the same time, existing control methods suffer from high system maintenance costs, wasted communication resources, frequent actuator valve changes, and high energy consumption, failing to fully meet the industry's development needs for intelligence, low cost, and green environmental protection. Summary of the Invention

[0005] This invention provides a control method and apparatus for an event-triggered oil-water separation system to solve the problems of substandard oil content in the discharged water after oil-water separation and the failure of existing control methods to meet standards in the prior art.

[0006] In a first aspect, embodiments of the present invention provide a control method for an event-triggered oil-water separation system, comprising: Based on the defined input saturation constraints, a mathematical model of the oil-water separation system is established. Based on the mathematical model of the oil-water separation system, a robust control method is used to define a cost function, and based on the cost function, the robust trajectory tracking control problem is transformed into a binary zero-sum game problem. Based on the aforementioned binary zero-sum game problem, the optimal control method is applied. The Hamiltonian function and the Bellman equation are defined. The partial derivative of the Hamiltonian function is used to obtain the optimal control law and the worst-case perturbation policy. The optimal control law and the worst-case perturbation policy are substituted into the Bellman equation, and the obtained iterative Bellman equation is solved to obtain the optimal control gain and the optimal control policy. Based on the preset compensation controller and the optimal control strategy, event triggering conditions are defined to obtain an event triggering control strategy with the input saturation constraint. The event-triggered control strategy is input to the oil-water separation system through a zero-order hold for state updates. Based on the optimal control gain and the event triggering conditions, the optimal event-triggered control strategy in the current state is obtained and executed cyclically to achieve control of the oil-water separation system.

[0007] In one possible implementation, the control signal corresponding to the input saturation constraint is defined as ; in, This represents the system control input with saturation constraints. The first control strategy generated by the design is represented by the second control strategy. One control signal, Indicates the first The upper limit of a control signal Indicates the first The lower limit of a control signal; The mathematical model of the oil-water separation system is constructed using a cascaded control method, consisting of a water level control subsystem corresponding to the three-phase separator and a PDR control subsystem corresponding to the hydrocyclone tube. The mathematical models of the water level control subsystem and the PDR control subsystem are uniformly adopted in an augmented form as follows: ; in, The time derivative of the augmented system's state vector. , This represents the state vector of the augmented system. Indicates the system status. Indicates a disturbance. Represents the system matrix. Represents the input matrix, Represents the perturbation input matrix. This indicates slug flow disturbance. Indicates tracking error. This represents the output matrix.

[0008] In one possible implementation, based on the mathematical model of the oil-water separation system, a robust control method is applied, a cost function is defined, and based on the cost function, the robust trajectory tracking control problem is transformed into a binary zero-sum game problem, including: Based on the mathematical model of the oil-water separation system, a robust control method is used to define the tracking error; Based on the tracking error, define the performance output; Based on the performance output, define Gain condition; Based on the above Gain condition, define cost function; Based on the cost function, the robust trajectory tracking control problem is transformed into a binary zero-sum game problem, where the system control input is the advantageous term that minimizes the cost function, and the slug flow disturbance is the disadvantageous term that maximizes the cost function.

[0009] In one possible implementation, the tracking error is defined as ;in, Indicates system output; The performance output is defined as ;in, Indicates performance output. , They represent positive definite matrices, respectively. The Gain condition is defined as ;in, It represents any moment within the time range from the initial moment to infinity. Indicates the discount factor. Indicates the attenuation coefficient; The cost function is defined as follows: ; The binary zero-sum game problem is defined as follows: ;in, This represents the optimal control strategy. This represents the worst-case perturbation strategy.

[0010] In one possible implementation, based on the binary zero-sum game problem, an optimal control method is applied. A Hamiltonian function and a Bellman equation are defined. Partial derivatives of the Hamiltonian function are obtained to yield the optimal control law and the worst-case perturbation policy. These are then substituted into the Bellman equation, and the resulting iterative Bellman equation is solved to obtain the optimal control gain and the optimal control policy, including: Based on the aforementioned binary zero-sum game problem, the Hamiltonian function and Bellman equation are defined using the optimal control method. By setting the Hamiltonian function to zero and taking the partial derivatives with respect to the system control input and the disturbance, the optimal control law and the worst-case disturbance policy are obtained. Substituting the optimal control law and the worst-case disturbance policy into the Bellman equation, we obtain the iterative Bellman equation; The iterative Bellman equation is transformed into a vector-like Riccati equation using the Kronecker product method and matrix vectorization method. Initialize the control strategy and control gain matrix of the oil-water separation system, wherein the control gain matrix includes control gain and slug flow disturbance gain; Collect the operating data of the oil-water separation system; Based on the control strategy, the control gain matrix, and the operating data, the solution of the Riccati-like equation, the control strategy, and the control gain matrix are iteratively updated using the least squares method until the absolute value of the difference between the solutions of two adjacent iterations meets the preset condition, at which point the learned optimal control gain is output. Based on the optimal control gain, the optimal control strategy is obtained.

[0011] In one possible implementation, the step of defining event triggering conditions based on a preset compensation controller and the optimal control strategy to obtain an event-triggered control strategy with the input saturation constraint includes: Obtain auxiliary variables; Based on the auxiliary variables and the system control input with saturation constraints, a preset compensation controller is defined; Define a system controller based on the preset compensation controller and the optimal control strategy; Define the event triggering error and the event triggering conditions; Based on the event triggering error and the event triggering condition, the control signal corresponding to the input saturation constraint is input to the defined system controller to obtain an event triggering control strategy with input saturation constraints.

[0012] In one possible implementation, the preset compensation controller is ; in, Denotes the derivative of the auxiliary variable. Represents auxiliary variables. This represents a preset constant. This represents the preset gain matrix. This represents a preset constant. Indicates the difference in system control input. Indicates the preset compensation controller. This represents the gain matrix of the design. This represents the optimal control strategy.

[0013] In one possible implementation, the event triggering error is defined as: ; in, Indicates the event triggering error. Indicates the time when the event was triggered. This represents the augmented system state vector at the moment the event is triggered. Indicates the first The augmented system state vector at time t. Indicates the first Each sampling time, Indicates the first Each sampling time; The event triggering condition is defined as follows: ; in, This represents a constant that is greater than 0 and less than 1. Representation matrix The smallest eigenvalue, To represent a scalar, This represents a positive constant that satisfies the Lipschitz condition.

[0014] Secondly, embodiments of the present invention provide a control device for an event-triggered oil-water separation system, comprising: The model building module is used to build a mathematical model of the oil-water separation system based on the defined input saturation constraints. The processing module is used to define a cost function based on the mathematical model of the oil-water separation system, using a robust control method, and based on the cost function, transform the robust trajectory tracking control problem into a binary zero-sum game problem. The solution module is used to solve the binary zero-sum game problem by applying the optimal control method, defining the Hamiltonian function and the Bellman equation, obtaining the optimal control law and the worst perturbation policy by taking the partial derivative of the Hamiltonian function, substituting the optimal control law and the worst perturbation policy into the Bellman equation, and solving the obtained iterative Bellman equation to obtain the optimal control gain and the optimal control policy. The event trigger definition module is used to define event trigger conditions based on the preset compensation controller and the optimal control strategy, and obtain an event trigger control strategy with the input saturation constraint. The control module is used to input the event-triggered control strategy into the oil-water separation system through a zero-order hold for state updates, and to obtain the optimal event-triggered control strategy in the current state according to the optimal control gain and the event triggering conditions, and to execute it cyclically to achieve control of the oil-water separation system.

[0015] Thirdly, embodiments of the present invention provide a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the control method for an event-triggered oil-water separation system as described in the first aspect or any possible implementation of the first aspect.

[0016] This invention provides a control method and apparatus for an event-triggered oil-water separation system. A mathematical model of the oil-water separation system is established based on defined input saturation constraints. Based on this mathematical model, a robust control method is applied, a cost function is defined, and the robust trajectory tracking control problem is transformed into a binary zero-sum game problem. Based on the binary zero-sum game problem, an optimal control method is applied, defining a Hamiltonian function and a Bellman equation. Partial derivatives of the Hamiltonian function are obtained to yield the optimal control law and the worst-case disturbance policy. These are then substituted into the Bellman equation, and the iterative Bellman equation is solved to obtain the optimal control gain and the optimal control policy. Based on a preset compensation controller and the optimal control policy, event triggering conditions are defined to obtain an event-triggered control policy with input saturation constraints. The event-triggered control policy is input to the oil-water separation system through a zero-order hold for state updates. Based on the optimal control gain and the event triggering conditions, the optimal event-triggered control policy in the current state is obtained, and this process is repeated to achieve control of the oil-water separation system.

[0017] This invention, through the introduction of a game theory approach between slug flow disturbance and control input, reduces the impact of slug flow disturbance on oil-water separation efficiency. It also introduces a compensation control strategy to mitigate the effects of input saturation, enabling collaborative control of an oil-water separation system with input saturation constraints in a model-free environment. This reduces the complexity and cost of oil-water separation system maintenance, decreases the frequency of control valve changes, lowers energy consumption, reduces communication costs, enhances system intelligence, improves environmental friendliness, avoids environmental pollution, and improves system performance while ensuring stability under event-triggered conditions. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the implementation of the control method for an event-triggered oil-water separation system provided in this embodiment of the invention. Figure 2 This is a schematic diagram of the oil-water separation system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the cascaded control flow of the water level control subsystem and the PDR control subsystem provided in the embodiments of the present invention; Figure 4This is a schematic diagram of the structure of the control device for an event-triggered oil-water separation system provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the terminal provided in an embodiment of the present invention. Detailed Implementation

[0020] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0021] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0022] Figure 1 A flowchart illustrating the implementation of a control method for an event-triggered oil-water separation system, as provided in this embodiment of the invention, is detailed below: Step 101: Based on the defined input saturation constraints, establish a mathematical model of the oil-water separation system.

[0023] like Figure 2 The diagram shows an oil-water separation system, which consists of a three-phase separator subsystem and a hydrocyclone tube system. After crude oil is extracted from offshore oil and gas fields, the oil-gas-water mixture passes through... The water is fed to a three-phase separator for initial separation. The water treated by the three-phase separator is called product water, which will be injected into a hydrocyclone for secondary separation.

[0024] During the initial separation process, based on the different densities of oil, gas, and water, the gas passes through the top under the influence of gravity. The valve collects the oil, which flows through the bottom. Valve collection. During the secondary separation process, the product water containing small oil droplets is further injected into a hydrocyclone. Under pressure, due to the physical structural characteristics of the hydrocyclone tube, vortices are generated inside. Then, the oil and water pass through valves separately. and The valve is discharging.

[0025] Specifically, slug flow disturbance occurs when gas accumulates in the oil riser and reaches the position of the oil horizontal pipe, causing a large number of bubbles to burst. This severely affects the water level in the three-phase separator, which in turn affects the pressure of the hydrocyclone tube, resulting in significant damage to the oil-water separation efficiency.

[0026] The mathematical model of the oil-water separation system consists of a water level control subsystem corresponding to the three-phase separator and a PDR control subsystem corresponding to the hydrocyclone, constructed using a cascaded control method. Under the disturbance of slug flow, to ensure ideal oil removal efficiency, the water level control subsystem aims to track the ideal water level. The goal of the PDR control subsystem is to track the ideal . Figure 3 The paper demonstrates the cascaded control process of the water level control subsystem and the PDR control subsystem.

[0027] In this embodiment, the PDR parameter is defined as: (1) in, This indicates pressure at the import point. Indicates overflow pressure. Indicates the underflow pressure. , These represent the corresponding pressure drops. Indicates the pressure drop ratio. The numerical value can be used to evaluate the efficiency of oil-water separation.

[0028] The control signal corresponding to the input saturation constraint is (2) in, This represents the system control input with saturation constraints. The first control strategy generated by the design is represented by the second control strategy. One control signal, Indicates the first The upper limit of a control signal Indicates the first The lower limit of a control signal.

[0029] The mathematical models for both the water level control subsystem and the PDR control subsystem are uniformly presented in an augmented form as follows: (3) Among them, among them, The time derivative of the augmented system's state vector. , This represents the state vector of the augmented system. Indicates the system status. Indicates a disturbance. Represents the system matrix. Represents the input matrix, Represents the perturbation input matrix. This indicates slug flow disturbance. Indicates tracking error. This represents the output matrix. , , , , , , , These represent the system matrices, Represents the identity matrix.

[0030] For the water level control subsystem, the system state , The water level in the three-phase separator is the system control input. for Valve opening, Indicates the undercurrent pressure drop rate, system output Reference trajectory For the PDR control subsystem, the system state System control input for Valve opening, Indicates the overflow pressure drop rate, system output Reference trajectory .

[0031] After introducing a non-policy reinforcement learning process, the system takes the following form: (4) in, This represents the augmented system state vector introduced during the non-policy reinforcement learning process. Indicates the number of learning iterations. This indicates unconstrained system control inputs. Indicates the first There are no constrained system control inputs in this iteration. Indicates the first Mid-stage slug flow disturbance in the second iteration This represents the system control input with saturation constraints.

[0032] Step 102: Based on the mathematical model of the oil-water separation system, a robust control method is used to define a cost function, and based on the cost function, the robust trajectory tracking control problem is transformed into a binary zero-sum game problem.

[0033] In this embodiment, based on the mathematical model of the oil-water separation system, a robust control method is applied, a cost function is defined, and based on the cost function, the robust trajectory tracking control problem is transformed into a binary zero-sum game problem, which may include: Based on the mathematical model of the oil-water separation system, robust control is applied. Method, define tracking error; tracking error is defined as follows: (5) in, Indicates system output, ; Based on the tracking error, the performance output is defined as follows: (6) in, Indicates performance output. , These are positive definite matrices, and represents the performance tuning parameters. Based on performance output, define Gain condition; Gain condition is defined as (7) in, Indicates from the initial time ( The time range extends to infinity, used to characterize the system's performance over its entire operating cycle. Indicates from the initial time ( (From) to any moment within an infinite time range. Indicates the discount factor. Indicates the attenuation coefficient; based on Gain condition, define cost function; cost function is defined as follows: (8) Based on the cost function, the robust trajectory tracking control problem is transformed into a binary zero-sum game problem, where the system control input is the favorable term that minimizes the cost function. Slug flow disturbance is a disadvantageous term in maximizing the cost function. Advantages and disadvantages conflict. The solution to this game is called the Nash equilibrium. , is the target value to be solved.

[0034] The binary zero-sum game problem is defined as follows: (9) in, This represents the optimal control strategy. This represents the worst-case perturbation.

[0035] Step 103: Based on the binary zero-sum game problem, the optimal control method is applied. The Hamiltonian function and the Bellman equation are defined. The partial derivative of the Hamiltonian function is obtained to obtain the optimal control law and the worst-case perturbation policy. The optimal control law and the worst-case perturbation policy are substituted into the Bellman equation, and the obtained iterative Bellman equation is solved to obtain the optimal control gain and the optimal control policy.

[0036] In one embodiment, based on a binary zero-sum game problem, an optimal control method is used. The Hamiltonian function and the Bellman equation are defined. Partial derivatives of the Hamiltonian function are obtained to yield the optimal control law and the worst-case perturbation policy. These are then substituted into the Bellman equation, and the resulting iterative Bellman equation is solved to obtain the optimal control gain and the optimal control policy. This may include: Based on the binary zero-sum game problem, this paper uses the optimal control method to define the Hamiltonian function and the Bellman equation. Setting the Hamiltonian function to zero, partial derivatives are calculated with respect to the system control input and disturbance to obtain the optimal control law and the worst-case disturbance policy. Substituting the optimal control law and the worst-case disturbance policy into the Bellman equation, iterative Bellman equations are obtained. Using the Kronecker product method and matrix vectorization, the iterative Bellman equations are transformed into a vector-form Licati-like equation. The control policy and control gain matrix of the oil-water separation system are initialized, where the control gain matrix includes the control gain and the slug flow disturbance gain. Operating data of the oil-water separation system are collected. Based on the control policy, control gain matrix, and operating data, the solution of the Licati-like equation, the control policy, and the control gain matrix are iteratively updated using the least squares method until the absolute value of the difference between the solutions of two adjacent iterations meets a preset condition. The learned optimal control gain is then output. Based on the optimal control gain, the optimal control policy is obtained.

[0037] Optionally, the Hamiltonian function is defined as: (10) in, Represents the Hamiltonian function. This indicates the output equivalent adjustment parameter. , Indicates the first The derivative of the performance function for each iteration.

[0038] Let Hamiltonian function (10) equal to 0, and then... and Find the partial derivatives and note that the cost function (8) has a quadratic form. , Given a positive definite matrix to be solved, the optimal control law can be obtained by taking the partial derivative. Worst perturbation strategy : (11) (12) The Bellman equation can be obtained from the Hamiltonian function (10): (13) in, , for The gradient.

[0039] According to the oil-water separation system (3), Differentiating and substituting into equation (13), we get: ;(14) remember ,in, For the first The control gain of the next iteration For the first The slug flow disturbance gain of the next iteration, with the superscript indicating the number of iterations and the subscript distinguishing the type of gain. Combining equations (11) and (12), equation (14) can be rewritten as: (15) From equation (15), the iterative Bellman equation can be obtained: ; (16) in, Represents a fixed time interval for iterative learning; This represents the current moment within a fixed time interval of iterative learning; Using the matrix vectorization method, we rewrite equation (16) to obtain... (17) in, Represented as the Kronecker product symbol, It is a vector that represents the columns of the matrix within the brackets stacked from left to right.

[0040] Equation (17) can be solved using the least squares method by defining the following matrix: (18) (19) (20) According to equation (17), the solution can be obtained. (21) The solution to the Riccati-like equation is obtained. Control gain slug flow disturbance gain .

[0041] The above provides the computational framework and method for solving the Riccati-like equations, the control strategy, and the control gain matrix. The following section details the iterative solution method: Initializing the control strategy and control gain matrix of the oil-water separation system may include: providing the initial permissible control strategy for the oil-water separation system. The initial control gain matrix .

[0042] Collect operational data from the oil-water separation system until the initial learning dimension is met, then update and iterate. Learning will stop when the following formula is met or the maximum number of learning iterations is exceeded: ;(twenty two) in, The error constant is set to be sufficiently small. The optimal control gain can be obtained upon completion of the learning process. And then according to (23) Obtain the optimal control strategy .

[0043] Step 104: Based on the preset compensation controller and the optimal control strategy, define the event triggering conditions to obtain the event triggering control strategy with input saturation constraints.

[0044] In one embodiment, based on a preset compensation controller and an optimal control strategy, event triggering conditions are defined to obtain an event-triggered control strategy with input saturation constraints, which may include: Obtain auxiliary variables; A preset compensation controller is defined based on auxiliary variables and system control inputs with saturation constraints. Define the system controller based on the preset compensation controller and the optimal control strategy; Define the event triggering error and the event triggering conditions; Based on the event triggering error and the event triggering condition, the control signal corresponding to the input saturation constraint is input to the defined system controller to obtain the event triggering control strategy with input saturation constraint.

[0045] Designed control strategy It consists of two parts: ;(twenty four) in, This is the optimal control strategy used to stabilize the system. This indicates a preset compensation controller used to handle saturation constraints.

[0046] In order to characterize Introducing auxiliary variables : (25) in, Denotes the derivative of the auxiliary variable. Represents auxiliary variables. This represents a preset constant. This represents the preset gain matrix. This represents a preset constant. Indicates the difference in system control input. , , yes Saturated input.

[0047] but The definition is as follows: (26) in, This represents the gain matrix of the design. satisfy: (27) Represents the preset gain matrix The smallest eigenvalue reflects the "positive definiteness" of the matrix and is a key indicator to ensure that the system energy does not diverge. Represents the gain matrix of the design The largest eigenvalue of the matrix reflects the matrix The "upper limit of gain intensity" is used to quantify the degree of its impact on the system state.

[0048] For sampling time The sequence formed, ,but This is the augmented system state vector at the trigger moment. The event trigger error is defined. for: (28) in, Indicates the event triggering error. Indicates the time when the event was triggered. This represents the augmented system state vector at the moment the event is triggered. Indicates the first The augmented system state vector at time t. Indicates the first Each sampling time, Indicates the first Each sampling time.

[0049] The event triggering condition is defined as follows: (29) in, This represents a constant that is greater than 0 and less than 1. Representation matrix The smallest eigenvalue, Describe a scalar that satisfies , This indicates that the Lipschitz condition is satisfied. (30) is a positive constant.

[0050] in, The optimal event-triggered control strategy is as follows: (31) Substituting equation (31) into equation (2) yields the event-triggered control strategy with saturation constraints. .

[0051] Step 105: The event-triggered control strategy is input to the oil-water separation system via a zero-order hold for state updates, and the optimal control gain is used as the basis for the update. And the event triggering conditions, to obtain the optimal event triggering control strategy in the current state. The process is repeated cyclically to control the oil-water separation system.

[0052] By introducing an initial permissible control strategy into the oil-water separation system, the system's operating data can be obtained. The event-triggered control method for the oil-water separation system provided in this embodiment, specifically the iterative learning method using an event-triggered and non-policy reinforcement learning algorithm with input saturation, can then be applied. All of these can iterate to the model-based optimal value with minimal error in a short time. Under slug flow disturbance, the optimal control strategy, after compensating for input saturation, can achieve this. Under the influence of the event triggering mechanism, both the water level and the PDR (Power Deflection Rate) remained within a good operating range, indicating that the proposed event-triggered control method for the oil-water separation system is effective. The optimal value based on the model can be obtained by first acquiring system parameters through system identification, and then further derived, thereby verifying the effectiveness of the algorithm.

[0053] This invention provides a control method for an event-triggered oil-water separation system. A mathematical model of the oil-water separation system is established based on defined input saturation constraints. Based on this mathematical model, a robust control method is applied, a cost function is defined, and the robust trajectory tracking control problem is transformed into a binary zero-sum game problem. Based on the binary zero-sum game problem, an optimal control method is applied, defining a Hamiltonian function and a Bellman equation. Partial derivatives of the Hamiltonian function are obtained to yield the optimal control law and the worst-case disturbance policy. These are then substituted into the Bellman equation, and the iterative Bellman equation is solved to obtain the optimal control gain and the optimal control policy. Based on a preset compensation controller and the optimal control policy, event triggering conditions are defined to obtain an event-triggered control policy with input saturation constraints. The event-triggered control policy is input to the oil-water separation system through a zero-order hold for state updates. Based on the optimal control gain and the event triggering conditions, the optimal event-triggered control policy in the current state is obtained, and this process is repeated cyclically to achieve control of the oil-water separation system. This invention, through the introduction of a game theory approach between slug flow disturbance and control input, reduces the impact of slug flow disturbance on oil-water separation efficiency. It also introduces a compensation control strategy to mitigate the effects of input saturation, enabling collaborative control of an oil-water separation system with input saturation constraints in a model-free environment. This reduces the complexity and cost of oil-water separation system maintenance, decreases the frequency of control valve changes, lowers energy consumption, reduces communication costs, enhances system intelligence, improves environmental friendliness, avoids environmental pollution, and improves system performance while ensuring stability under event-triggered conditions.

[0054] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0055] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0056] Figure 4 The diagram shows a schematic of a control device for an event-triggered oil-water separation system according to an embodiment of the present invention. For ease of explanation, only the parts relevant to the embodiment of the present invention are shown, and are described in detail below: like Figure 4 As shown, the control device of the event-triggered oil-water separation system includes: a model building module 41, a processing module 42, a solution module 43, an event trigger definition module 44, and a control module 45.

[0057] Model building module 41 is used to build a mathematical model of the oil-water separation system based on the defined input saturation constraints; Processing module 42 is used to define a cost function based on the mathematical model of the oil-water separation system, apply a robust control method, and transform the robust trajectory tracking control problem into a binary zero-sum game problem based on the cost function. The solution module 43 is used to solve a binary zero-sum game problem by applying the optimal control method. It defines the Hamiltonian function and the Bellman equation, obtains the optimal control law and the worst perturbation policy by taking the partial derivative of the Hamiltonian function, substitutes the optimal control law and the worst perturbation policy into the Bellman equation, and solves the obtained iterative Bellman equation to obtain the optimal control gain and the optimal control policy. The event trigger definition module 44 is used to define event trigger conditions based on the preset compensation controller and the optimal control strategy, and obtain an event trigger control strategy with input saturation constraints. The control module 45 is used to input the event-triggered control strategy into the oil-water separation system through the zero-order hold for state update, and obtain the optimal event-triggered control strategy in the current state according to the optimal control gain and event triggering conditions, and execute it cyclically to achieve control of the oil-water separation system.

[0058] In one possible implementation, the control signal corresponding to the input saturation constraint is defined as ; in, This represents the system control input with saturation constraints. The first control strategy generated by the design is represented by the second control strategy. One control signal, Indicates the first The upper limit of a control signal Indicates the first The lower limit of a control signal; The mathematical model of the oil-water separation system is constructed using a cascaded control method, consisting of a water level control subsystem corresponding to the three-phase separator and a PDR control subsystem corresponding to the hydrocyclone tube. The mathematical models for both the water level control subsystem and the PDR control subsystem are uniformly presented in an augmented form as follows: ; in, The time derivative of the augmented system's state vector. , This represents the state vector of the augmented system. Indicates the system status. Indicates a disturbance. Represents the system matrix. Represents the input matrix, Represents the perturbation input matrix. This indicates slug flow disturbance. Indicates tracking error. This represents the output matrix.

[0059] In one possible implementation, processing module 42, based on the mathematical model of the oil-water separation system, applies a robust control method, defines a cost function, and, based on the cost function, transforms the robust trajectory tracking control problem into a binary zero-sum game problem, is used for: Based on the mathematical model of the oil-water separation system, a robust control method is used to define the tracking error; Define the performance output based on the tracking error; Based on performance output, define Gain condition; based on Gain condition, define cost function; Based on the cost function, the robust trajectory tracking control problem is transformed into a binary zero-sum game problem, where the system control input is the advantageous term that minimizes the cost function, and the slug flow disturbance is the disadvantageous term that maximizes the cost function.

[0060] In one possible implementation, the tracking error is defined as... ;in, Indicates system output; Performance output is defined as ;in, Indicates performance output. , They represent positive definite matrices, respectively. Gain condition is defined as ;in, It represents any moment within the time range from the initial moment to infinity. Indicates the discount factor. Indicates the attenuation coefficient; The cost function is defined as ; The binary zero-sum game problem is defined as follows: ;in, This represents the optimal control strategy. This represents the worst-case perturbation.

[0061] In one possible implementation, the solution module 43, based on a binary zero-sum game problem, employs optimal control methods, defining the Hamiltonian function and the Bellman equation. Partial derivatives of the Hamiltonian function are obtained to yield the optimal control law and the worst-case disturbance policy. These are then substituted into the Bellman equation, and the resulting iterative Bellman equation is solved to obtain the optimal control gain and optimal control policy. This is then used for: Based on the binary zero-sum game problem, the Hamiltonian function and Bellman equation are defined using the optimal control method. By setting the Hamiltonian function to zero and taking the partial derivatives with respect to the system control input and disturbance, the optimal control law and the worst-case disturbance policy can be obtained. Substituting the optimal control law and the worst disturbance policy into the Bellman equation, we obtain the iterative Bellman equation. By using the Kronecker product method and matrix vectorization method, the iterative Bellman equation is transformed into a vector form of a Riccati-like equation; Initialize the control strategy and control gain matrix of the oil-water separation system, wherein the control gain matrix includes the control gain and the slug flow disturbance gain; Collect operational data from the oil-water separation system; Based on the control strategy, control gain matrix, and operating data, the solution, control strategy, and control gain matrix of the Riccati-like equation are iteratively updated using the least squares method until the absolute value of the difference between the solutions of two adjacent iterations meets the preset conditions, at which point the learned optimal control gain is output. The optimal control strategy is obtained based on the optimal control gain.

[0062] In one possible implementation, the event triggering definition module 44 defines event triggering conditions based on a preset compensation controller and an optimal control strategy. When obtaining an event triggering control strategy with input saturation constraints, it is used for: Obtain auxiliary variables; A preset compensation controller is defined based on auxiliary variables and system control inputs with saturation constraints. Define the system controller based on the preset compensation controller and the optimal control strategy; Define the event triggering error and the event triggering conditions; Based on the event triggering error and the event triggering condition, the control signal corresponding to the input saturation constraint is input to the defined system controller to obtain the event triggering control strategy with input saturation constraint.

[0063] In one possible implementation, the preset compensation controller is... ; in, Denotes the derivative of the auxiliary variable. Represents auxiliary variables. This represents a preset constant. This represents the preset gain matrix. This represents a preset constant. Indicates the difference in system control input. Indicates the preset compensation controller. This represents the gain matrix of the design. This represents the optimal control strategy.

[0064] In one possible implementation, the event triggering error is defined as: ; in, Indicates the event triggering error. Indicates the time when the event was triggered. This represents the augmented system state vector at the moment the event is triggered. Indicates the first The augmented system state vector at time t. Indicates the first Each sampling time, Indicates the first Each sampling time; The event triggering condition is defined as follows: ; in, This represents a constant that is greater than 0 and less than 1. Representation matrix The smallest eigenvalue, To represent a scalar, This represents a positive constant that satisfies the Lipschitz condition.

[0065] The above embodiments provide a control device for an event-triggered oil-water separation system. A model building module establishes a mathematical model of the oil-water separation system based on defined input saturation constraints. A processing module, based on the mathematical model, uses a robust control method to define a cost function and transforms the robust trajectory tracking control problem into a binary zero-sum game problem. A solution module, based on the binary zero-sum game problem, uses an optimal control method, defining a Hamiltonian function and a Bellman equation. Partial derivatives of the Hamiltonian function are obtained to achieve the optimal control law and worst-case disturbance policy. These are then substituted into the Bellman equation, and the iterative Bellman equation is solved to obtain the optimal control gain and optimal control policy. An event-triggered definition module, based on a preset compensation controller and the optimal control policy, defines event-triggered conditions to obtain an event-triggered control policy with input saturation constraints. The control module inputs the event-triggered control policy into the oil-water separation system through a zero-order hold for state updates. Based on the optimal control gain and event-triggered conditions, it obtains the optimal event-triggered control policy under the current state and executes iteratively to control the oil-water separation system. This invention, through the introduction of a game theory approach between slug flow disturbance and control input, reduces the impact of slug flow disturbance on oil-water separation efficiency. It also introduces a compensation control strategy to mitigate the effects of input saturation, enabling collaborative control of an oil-water separation system with input saturation constraints in a model-free environment. This reduces the complexity and cost of oil-water separation system maintenance, decreases the frequency of control valve changes, lowers energy consumption, reduces communication costs, enhances system intelligence, improves environmental friendliness, avoids environmental pollution, and improves system performance while ensuring stability under event-triggered conditions.

[0066] Figure 5 This is a schematic diagram of a terminal provided in an embodiment of the present invention. Figure 5 As shown, the terminal 5 in this embodiment includes a processor 50, a memory 51, and a computer program 52 stored in the memory 51 and executable on the processor 50. When the processor 50 executes the computer program 52, it implements the steps in the various event-triggered oil-water separation system control method embodiments described above, for example... Figure 1 Steps 101 to 105 are shown. Alternatively, when processor 50 executes computer program 52, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 4 The functions of each module / unit are shown.

[0067] For example, computer program 52 can be divided into one or more modules / units, one or more of which are stored in memory 51 and executed by processor 50 to complete the present invention. One or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 52 in terminal 5. For example, computer program 52 can be divided into... Figure 4 The modules / units shown are shown.

[0068] Terminal 5 may include, but is not limited to, a processor 50 and a memory 51. Those skilled in the art will understand that... Figure 5 This is merely an example of terminal 5 and does not constitute a limitation on terminal 5. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal may also include input / output devices, network access devices, buses, etc.

[0069] The processor 50 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0070] The memory 51 can be an internal storage unit of the terminal 5, such as the hard disk or memory of the terminal 5. The memory 51 can also be an external storage device of the terminal 5, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal 5. Furthermore, the memory 51 can include both internal storage units and external storage devices of the terminal 5. The memory 51 is used to store computer programs and other programs and data required by the terminal. The memory 51 can also be used to temporarily store data that has been output or will be output.

[0071] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0072] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0073] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0074] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0075] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0076] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0077] If integrated modules / units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-described embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various event-triggered oil-water separation system control method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0078] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A control method for an event-triggered oil-water separation system, characterized in that, include: Based on the defined input saturation constraints, a mathematical model of the oil-water separation system is established. Based on the mathematical model of the oil-water separation system, a robust control method is used to define a cost function, and based on the cost function, the robust trajectory tracking control problem is transformed into a binary zero-sum game problem. Based on the aforementioned binary zero-sum game problem, the optimal control method is applied. The Hamiltonian function and the Bellman equation are defined. The partial derivative of the Hamiltonian function is used to obtain the optimal control law and the worst-case perturbation policy. The optimal control law and the worst-case perturbation policy are substituted into the Bellman equation, and the obtained iterative Bellman equation is solved to obtain the optimal control gain and the optimal control policy. Based on the preset compensation controller and the optimal control strategy, event triggering conditions are defined to obtain an event triggering control strategy with the input saturation constraint. The event-triggered control strategy is input to the oil-water separation system through a zero-order hold for state updates. Based on the optimal control gain and the event triggering conditions, the optimal event-triggered control strategy in the current state is obtained and executed cyclically to achieve control of the oil-water separation system.

2. The control method for an event-triggered oil-water separation system according to claim 1, characterized in that, The control signal corresponding to the input saturation constraint is defined as follows: ； in, This represents the system control input with saturation constraints. The first control strategy generated by the design is represented by the second control strategy. One control signal, Indicates the first The upper limit of a control signal Indicates the first The lower limit of a control signal; The mathematical model of the oil-water separation system is constructed using a cascaded control method, consisting of a water level control subsystem corresponding to the three-phase separator and a PDR control subsystem corresponding to the hydrocyclone tube. The mathematical models of the water level control subsystem and the PDR control subsystem are uniformly adopted in an augmented form as follows: ; in, The time derivative of the augmented system's state vector. , This represents the state vector of the augmented system. Indicates the system status. Indicates a disturbance. Represents the system matrix. Represents the input matrix, Represents the perturbation input matrix. This indicates slug flow disturbance. Indicates tracking error. This represents the output matrix.

3. The control method for an event-triggered oil-water separation system according to claim 2, characterized in that, Based on the mathematical model of the oil-water separation system, a robust control method is applied, a cost function is defined, and based on the cost function, the robust trajectory tracking control problem is transformed into a binary zero-sum game problem, including: Based on the mathematical model of the oil-water separation system, a robust control method is used to define the tracking error; Based on the tracking error, define the performance output; Based on the performance output, define Gain condition; Based on the above Gain condition, define cost function; Based on the cost function, the robust trajectory tracking control problem is transformed into a binary zero-sum game problem, where the system control input is the advantageous term that minimizes the cost function, and the slug flow disturbance is the disadvantageous term that maximizes the cost function.

4. The control method for an event-triggered oil-water separation system according to claim 3, characterized in that, The tracking error is defined as ;in, Indicates system output; The performance output is defined as ;in, Indicates performance output. , They represent positive definite matrices, respectively. The Gain condition is defined as ;in, It represents any moment within the time range from the initial moment to infinity. Indicates the discount factor. Indicates the attenuation coefficient; The cost function is defined as follows: ; The binary zero-sum game problem is defined as follows: ;in, This represents the optimal control strategy. This represents the worst-case perturbation strategy.

5. The control method for an event-triggered oil-water separation system according to claim 4, characterized in that, Based on the aforementioned binary zero-sum game problem, an optimal control method is employed. The Hamiltonian function and the Bellman equation are defined. Partial derivatives of the Hamiltonian function are obtained to yield the optimal control law and the worst-case perturbation policy. These are then substituted into the Bellman equation, and the resulting iterative Bellman equation is solved to obtain the optimal control gain and the optimal control policy, including: Based on the aforementioned binary zero-sum game problem, the Hamiltonian function and Bellman equation are defined using the optimal control method. By setting the Hamiltonian function to zero and taking the partial derivatives with respect to the system control input and the disturbance, the optimal control law and the worst-case disturbance policy are obtained. Substituting the optimal control law and the worst-case disturbance policy into the Bellman equation, we obtain the iterative Bellman equation; The iterative Bellman equation is transformed into a vector-like Riccati equation using the Kronecker product method and matrix vectorization method. Initialize the control strategy and control gain matrix of the oil-water separation system, wherein the control gain matrix includes control gain and slug flow disturbance gain; Collect the operating data of the oil-water separation system; Based on the control strategy, the control gain matrix, and the operating data, the solution of the Riccati-like equation, the control strategy, and the control gain matrix are iteratively updated using the least squares method until the absolute value of the difference between the solutions of two adjacent iterations meets the preset condition, at which point the learned optimal control gain is output. Based on the optimal control gain, the optimal control strategy is obtained.

6. The control method for an event-triggered oil-water separation system according to claim 5, characterized in that, The event-triggered control strategy, defined based on the preset compensation controller and the optimal control strategy, with the input saturation constraint, includes: Obtain auxiliary variables; Based on the auxiliary variables and the system control input with saturation constraints, a preset compensation controller is defined; Define a system controller based on the preset compensation controller and the optimal control strategy; Define the event triggering error and the event triggering conditions; Based on the event triggering error and the event triggering condition, the control signal corresponding to the input saturation constraint is input to the defined system controller to obtain an event triggering control strategy with input saturation constraints.

7. The control method for an event-triggered oil-water separation system according to claim 6, characterized in that, The preset compensation controller is ； in, Denotes the derivative of the auxiliary variable. Represents auxiliary variables. This represents a preset constant. This represents the preset gain matrix. This represents a preset constant. Indicates the difference in system control input. Indicates the preset compensation controller. This represents the gain matrix of the design. This represents the optimal control strategy.

8. The control method for an event-triggered oil-water separation system according to claim 6, characterized in that, The event triggering error is defined as follows: ; in, Indicates the event triggering error. Indicates the time when the event was triggered. This represents the augmented system state vector at the moment the event is triggered. Indicates the first The augmented system state vector at time t. Indicates the first Each sampling time, Indicates the first Each sampling time; The event triggering condition is defined as follows: ; in, This represents a constant that is greater than 0 and less than 1. Representation matrix The smallest eigenvalue, To represent a scalar, This represents a positive constant that satisfies the Lipschitz condition.

9. A control device for an event-triggered oil-water separation system, characterized in that, include: The model building module is used to build a mathematical model of the oil-water separation system based on the defined input saturation constraints. The processing module is used to define a cost function based on the mathematical model of the oil-water separation system, using a robust control method, and based on the cost function, transform the robust trajectory tracking control problem into a binary zero-sum game problem. The solution module is used to solve the binary zero-sum game problem by applying the optimal control method, defining the Hamiltonian function and the Bellman equation, obtaining the optimal control law and the worst perturbation policy by taking the partial derivative of the Hamiltonian function, substituting the optimal control law and the worst perturbation policy into the Bellman equation, and solving the obtained iterative Bellman equation to obtain the optimal control gain and the optimal control policy. The event trigger definition module is used to define event trigger conditions based on the preset compensation controller and the optimal control strategy, and obtain an event trigger control strategy with the input saturation constraint. The control module is used to input the event-triggered control strategy into the oil-water separation system through a zero-order hold for state updates, and to obtain the optimal event-triggered control strategy in the current state according to the optimal control gain and the event triggering conditions, and to execute it cyclically to achieve control of the oil-water separation system.

10. A terminal, comprising a memory and a processor, the memory for storing a computer program, the processor for calling and running the computer program stored in the memory, characterized in that, When the processor executes the computer program, it implements the steps of the control method for the event-triggered oil-water separation system as described in any one of claims 1 to 8.

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