A method for optimizing power output control of an electrically operated shut-off valve

Abstract

The application provides a power output control optimization method for an electric locking valve, and belongs to the field of PID control optimization, and specifically relates to the following: current current and opening and closing displacement of the electric locking valve driving motor are collected in real time, and the electric locking valve locking process is started according to the target current of the driving motor; a current error signal is input into a current PID controller to perform closed-loop adjustment on the current of the driving motor; parameters of the current PID controller are set by using an improved beaver behavior algorithm: based on a competition index and a structural consistency tensor, the position of a population individual is updated through an exponential regulation factor; based on a reflection operator and a regulation parameter, the position of the population individual is updated through a direction modulation matrix; when the valve reaches a locking position, the target current of the driving motor is limited and maintained, so that the valve is kept in a stable locking state; and the power output of the electric locking valve is optimized by using the method.

Description

Technical Field

[0001] This invention belongs to the field of PID control optimization, and in particular relates to a method for optimizing the power output control of an electric locking valve. Background Technology

[0002] Electric shut-off valves are widely used in the opening, closing, and isolation control of various pipeline systems. The power output performance of their drive motors directly affects the reliability of valve operation and the stability of system operation. In actual operation, most electric shut-off valves use fixed parameter control or simple current drive, lacking a fine-grained adjustment mechanism for the drive motor output current. When the valve performs the shut-off action, due to factors such as load changes, friction disturbances, and ambient temperature fluctuations, there is a significant dynamic coupling relationship between the motor current and the valve displacement. If the control strategy is unreasonable, problems such as response lag, unstable closure, or end-point oscillation can easily occur. In addition, traditional control methods often focus on position determination while neglecting current regulation optimization during the power output process, failing to dynamically adjust the drive current according to the valve's operating status. When the load changes abruptly or the operating conditions change, fixed current or simple limiting control cannot balance response speed and stability, affecting shut-off accuracy and reliability. Therefore, it is necessary to propose a power output control optimization method that integrates operating status perception and current closed-loop regulation to improve the control accuracy and operational stability of electric shut-off valves.

[0003] The current PID controller is a classic closed-loop feedback control device widely used in motor drive systems for precise control of the output current. It dynamically generates control commands by integrating the proportional (P), integral (I), and derivative (D) control strategies through real-time acquisition of the error signal between the target current and the actual output current, thereby achieving stable current regulation. The proportional term is responsible for quickly responding to the current error, the integral term eliminates steady-state deviations, and the derivative term suppresses dynamic fluctuations in the system, making the overall control process both fast and stable with good anti-interference capabilities. The current PID controller has a simple structure and high adjustment accuracy, making it particularly suitable for applications requiring high response speed and precision.

[0004] The Beaver behavior optimizer is an emerging swarm intelligence optimization algorithm inspired by the dam-building behavior of beavers in nature. The algorithm simulates the entire process of a beaver finding a suitable location, collecting materials, constructing and reinforcing a dam to solve complex optimization problems. In the modeling process, individual beaver behaviors include location decisions, material selection and transportation, and structural repair. These behaviors are abstracted as the position updates, fitness assessments, and optimal feedback adjustments of individuals within the group in the search space. The algorithm possesses strong global search and local optimization capabilities, but it still faces challenges such as decreased search efficiency and high parameter sensitivity in complex problems. Summary of the Invention

[0005] This invention provides an optimized method for power output control of an electric locking valve. Addressing the problems of current regulation lag, power output fluctuation, and insufficient locking stability in existing electric locking valves during the locking action, it proposes a comprehensive power control mechanism integrating real-time acquisition of operating status, current PID closed-loop regulation, and intelligent parameter tuning. This invention constructs a feedback information system reflecting the valve's operating status by real-time acquisition of the current of the drive motor and the valve's opening and closing displacement, and initiates the electric locking valve locking control process based on the target current of the drive motor. During the valve's transition from the open to the locked state, a current error signal is constructed and... An input current PID controller enables continuous closed-loop regulation of the current of the drive motor, ensuring coordination between the power output process and valve displacement changes. For controller parameter tuning, an improved beaver behavior algorithm is used as the optimization engine. This algorithm constructs a competitive index and structural consistency tensor based on objective function differences, and combines exponential mapping, trace constraints, and a weighted fusion strategy using reflection operators to dynamically update the individual positions of the population, achieving efficient optimization of the current PID control parameters. This invention improves the stability and control accuracy of the power output during the locking process of the electric locking valve, enabling optimized regulation of the drive motor's output current.

[0006] To achieve the above objectives, the present invention employs an optimized method for power output control of an electric locking valve, the specific steps of which are as follows.

[0007] The current current of the electric locking valve drive motor and the opening and closing displacement of the valve are collected in real time, and the locking process of the electric locking valve is started according to the target current of the drive motor.

[0008] During the transition of the valve from the open state to the closed state, a current error signal is generated and input into the current PID controller to perform closed-loop regulation of the current of the drive motor.

[0009] The parameters of the current PID controller are tuned using an improved beaver behavior algorithm, specifically including: constructing a competitive index based on the difference in objective function values ​​among individuals in the population, and forming a structural consistency tensor by combining the spatial difference relationship between individuals; further updating the position of individuals in the population by constructing an exponential control factor through exponential mapping and trace constraints; constructing a reflection operator based on the spatial difference relationship between individuals, generating control parameters by combining the objective function value and spatial difference scale information, and updating the position of individuals in the population by weighted fusion of the reflection operator to form a direction modulation matrix.

[0010] When the valve reaches the locked position, the target current of the drive motor is limited and maintained to keep the valve in a stable locked state, thereby achieving optimized control of the power output of the electric locking valve.

[0011] Preferably, the current of the electric locking valve drive motor is obtained by collecting the instantaneous current value in the drive motor power supply circuit; the opening and closing displacement of the valve is obtained by non-contact acquisition of the valve stem stroke of the electric locking valve through a displacement sensor.

[0012] Preferably, the population individual positions are updated by constructing an exponential regulation factor through exponential mapping and trace constraints, specifically including: By performing difference discrimination processing on the objective function values ​​of candidate individuals and other individuals at corresponding positions, and mapping the difference discrimination results through a sign function; summarizing all difference discrimination results, and performing proportional constraint mapping according to the population size, a competition index is constructed. Based on the spatial difference relationship between candidate individuals and other individuals, the corresponding position difference terms are extracted, and the position difference terms are used to construct a structural quantity in the form of an outer product. A scale constraint term based on the square of the norm of the difference terms is introduced into the outer product structural quantity, and an amplitude constraint transformation is performed on the structural quantity. The structural quantity after scale constraint is aggregated as a whole to form a structural consistency tensor.

[0013] Preferably, an exponential mapping is performed on the competition index, and a trace operation is performed on the structural consistency tensor; the results of the exponential mapping and the trace operation are combined in fractional form, and a constant term is introduced on the denominator side to form a stable proportional constraint structure, thereby obtaining the exponential control factor; Under the influence of the exponential regulation factor, a matrix modulation term containing an identity matrix and a structural consistency tensor is constructed, and the scale constraint of the modulation term is applied based on the trace value of the structural consistency tensor. At the same time, the position difference term between the candidate individual and the other individuals is subjected to population aggregation processing to form a population orientation term. The matrix modulation term and the population orientation term are coupled by multiplication and superimposed with the current position to update the position of the individual in the population.

[0014] Preferably, the population individual position update is achieved by weighted fusion of reflection operators to form a direction modulation matrix, specifically including: Based on the spatial difference relationship formed between candidate individuals and reference individuals at corresponding positions, the spatial difference information is expressed in matrix form. Under the unified identity matrix constraint framework, proportional mapping and scale constraint processing are introduced to embed the difference relationship into the structured matrix form, thereby constructing the structured reflection operator. Based on the spatial difference relationship between candidate individuals and the optimal individual at their corresponding positions, the spatial difference information is expressed in a matrix form. Under the unified identity matrix constraint framework, proportional mapping and scale constraint processing are introduced to embed the difference relationship into a structured matrix form, thereby constructing the optimal reflection operator.

[0015] Preferably, based on the difference between the candidate individuals and the optimal individuals in the objective function values, and combined with the spatial difference scale information between the candidate individuals and the optimal individuals, continuous mapping processing is performed on the difference results to form control parameters; further, the control parameters are used as the basis for weight allocation to perform weighted fusion processing on the structural reflection operator and the optimal reflection operator, so that the two types of structural operators are combined in the same matrix space, thereby generating the direction modulation matrix; The matrix modulation operation is performed on the difference information between the current position and the optimal position of the candidate individual based on the direction modulation matrix, and the modulation result is superimposed on the current position of the candidate individual to realize the update of the individual position of the population.

[0016] Preferably, the parameters of the current PID controller are tuned using an improved beaver behavior algorithm, specifically including: Set the initial running parameters for the improved beaver behavior algorithm; The proportional coefficient, integral coefficient, and derivative coefficient in the current PID controller are introduced as independent dimensions into the individual position representation of the algorithm population to construct a mapping relationship between control parameters and the algorithm search space. During the algorithm's operation, the positions of individuals in the population are iteratively updated, allowing the individuals in the population to continuously evolve within the search space. Based on the pre-constructed objective evaluation function, the fitness of the individual positions in the population after each generation update is calculated, and the individual position with the best fitness is selected as the optimal solution for the current iteration cycle and stored. The algorithm iterative process is terminated by a condition check. When the number of iterations reaches the set upper limit, the position of the globally optimal individual in the population obtained in the historical iterations is output and decoded into the corresponding current PID controller parameters. If the termination condition is not met, the algorithm returns to the first step.

[0017] Preferably, the minimum sustaining current required to ensure stable system operation is used as the lower limit reference, and a gain modulation term related to the current position deviation of the valve is introduced into the lower limit reference. The gain modulation term is nonlinearly amplified based on the deviation between the current displacement of the valve and the preset static target displacement. At the same time, dynamic suppression is constructed in combination with the valve displacement disturbance change rate to constrain the over-modulation caused by rapid displacement fluctuation. Furthermore, an exponential decay term related to the degree of current disturbance is introduced to adaptively weaken the current adjustment amplitude, thereby obtaining the target sustaining current of the drive motor.

[0018] By adopting the above technical solution, this invention provides an optimized method for power output control of an electric locking valve. Based on real-time acquisition and feedback processing of the current current of the drive motor and the valve's opening and closing displacement, a closed-loop regulation foundation for the electric locking valve's operating state is constructed, and the valve locking process is initiated according to the target current of the drive motor. During the valve's transition from the open to the locked state, a current error signal is constructed and input into a current PID controller to achieve continuous closed-loop regulation of the drive motor's current, ensuring that the power output remains consistent with the valve's displacement change. Regarding controller parameter tuning, an improved beaver behavior algorithm is used for optimization. By constructing a competitive index and structural consistency tensor based on objective function differences, and combining exponential mapping, trace constraints, and reflection operator weighted fusion to form a direction modulation matrix, dynamic updates and efficient search of control parameters are achieved, improving the stability and convergence accuracy of parameter tuning. After the valve reaches the locked position, the target current of the drive motor is limited and maintained to ensure the valve is in a stable locked state, thereby achieving optimized control of the electric locking valve's power output process and improving the overall system's operational stability and control accuracy. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the steps of an optimized method for controlling the power output of an electric locking valve.

[0020] Figure 2 A flowchart outlining the specific steps for tuning the current PID controller parameters to improve the beaver behavior algorithm.

[0021] Figure 3 This is a comparison chart showing the changes in fitness values ​​during the optimization process of existing algorithms and the algorithm of this invention.

[0022] Figure 4 The process of tuning the current PID controller parameters for the existing algorithm is shown in the diagram.

[0023] Figure 5 This is a diagram illustrating the process of tuning the current PID controller parameters using the algorithm of this invention.

[0024] Figure 6 This is a comparison chart showing the control effects of the method of the present invention and existing methods in optimizing the power output control system of the electric locking valve. Detailed Implementation

[0025] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] This invention provides a technical solution: a method for optimizing the power output control of an electric locking valve, such as... Figure 1 As shown, the specific steps include:

[0027] The current current of the electric locking valve drive motor and the opening and closing displacement of the valve are collected in real time, and the locking process of the electric locking valve is started according to the target current of the drive motor.

[0028] Specifically, the current of the electric shut-off valve drive motor is obtained by collecting the instantaneous current value in the drive motor power supply circuit; the valve opening and closing displacement is obtained by non-contact acquisition of the valve stem stroke of the electric shut-off valve through a displacement sensor.

[0029] During the transition of the valve from the open state to the closed state, a current error signal is generated and input into the current PID controller to perform closed-loop regulation of the current of the drive motor.

[0030] Specifically, the mathematical model for implementing the current error signal is as follows: ; In the formula, This is the current error signal for the drive motor. The target current for driving the motor, This is the current driving current of the motor. For time.

[0031] Specifically, the mathematical model for inputting the current error signal into the current PID controller to perform closed-loop regulation of the current of the drive motor is as follows: ; In the formula, This is the output signal of the current PID controller. Let be the current error signal of the drive motor at time a. , and These are the proportional, integral, and differential coefficients, respectively.

[0032] The parameters of the current PID controller are tuned using an improved beaver behavior algorithm, specifically including: constructing a competitive index based on the difference in objective function values ​​among individuals in the population, and forming a structural consistency tensor by combining the spatial difference relationship between individuals; further updating the position of individuals in the population by constructing an exponential control factor through exponential mapping and trace constraints; constructing a reflection operator based on the spatial difference relationship between individuals, generating control parameters by combining the objective function value and spatial difference scale information, and updating the position of individuals in the population by weighted fusion of the reflection operator to form a direction modulation matrix.

[0033] Specifically, the population individual positions are updated by constructing an exponential regulation factor through exponential mapping and trace constraints, including: By performing difference discrimination processing on the objective function values ​​of candidate individuals and other individuals at corresponding positions, and mapping the difference discrimination results through a sign function, and summarizing all difference discrimination results, a proportional constraint mapping is performed according to the population size to construct a competition index. The specific mathematical model is as follows: ; In the formula, As a competitive indicator, For population size, Let $\mathbf{k}$ be the objective function value corresponding to the $j$-th position of individual $k$ in the $t$-th iteration. Let the objective function value be the value corresponding to the j-th dimension position of individual i in the t-th iteration; Based on the spatial difference relationship between candidate individuals and other individuals, corresponding positional difference terms are extracted, and a structural quantity in the form of an outer product is constructed for the positional difference terms. A scale constraint term, calculated by the square of the norm of the difference terms, is introduced into the outer product structural quantity, and an amplitude constraint transformation is performed on the structural quantity. The scale-constrained structural quantity is then aggregated to form a structural consistency tensor. The specific mathematical model is as follows: ; In the formula, For structural consistency tensors, Let j be the position of individual k in the t-th iteration. Let j be the position of individual i in the t-th iteration. This is a regularization stabilization term used to avoid a denominator of 0. Specifically, its value range is

[10] . -8 10 -4 The values ​​within this range can avoid numerical divergence caused by zero values ​​in the denominator, while not significantly perturbing the scale of the structural consistency tensor. In this embodiment, the specific value is 10. -6 .

[0034] Specifically, an exponential mapping is performed on the competition index, and a trace operation is performed on the structural consistency tensor; the results of the exponential mapping and trace operation are combined in fractional form, and a constant term is introduced on the denominator to form a stable proportional constraint structure, thereby obtaining the exponential control factor. The specific mathematical model is as follows: ; In the formula, As an index control factor, To perform trace operation on the structural consistency tensor; Under the influence of the exponential regulation factor, a matrix modulation term containing an identity matrix and a structural consistency tensor is constructed, and the scale of the modulation term is constrained based on the trace value of the structural consistency tensor. Simultaneously, the position difference terms between candidate individuals and other individuals are aggregated to form a population orientation term. The matrix modulation term and the population orientation term are product-coupled and superimposed with the current position to update the individual positions in the population. The specific mathematical model is as follows: ; ; In the formula, Let i be the position of the architect beaver individual in dimension j in the (t+1)th iteration. Let i be the position of the architect beaver individual in the j-th dimension during the t-th iteration. It is a 3×3 identity matrix. Let i be the position of the explorer beaver individual in dimension j in the (t+1)th iteration. Let i be the position of the explorer beaver individual i in the j-th dimension during the t-th iteration.

[0035] Specifically, the position update of individuals in the population is achieved by weighted fusion of reflection operators to form a direction modulation matrix, which includes: Based on the spatial difference relationship between candidate individuals and reference individuals at corresponding positions, the spatial difference information is expressed in a matrix form. Under a unified identity matrix constraint framework, scaling and scale constraints are introduced to embed the difference relationship into a structured matrix form, thereby constructing a structured reflection operator. The specific mathematical model is as follows: ; In the formula, For structural reflection operators; Based on the spatial difference relationship between candidate individuals and the optimal individual at corresponding positions, the spatial difference information is expressed in a matrix form. Under a unified identity matrix constraint framework, scaling and scale constraints are introduced to embed the difference relationship into a structured matrix form, thereby constructing the optimal reflection operator. The specific mathematical model is as follows: ; In the formula, For the optimal reflection operator, Let be the j-th dimension position of the optimal individual in the t-th iteration.

[0036] Specifically, based on the difference between candidate individuals and the optimal individual in the objective function values, and combined with the spatial difference scale information between candidate individuals and the optimal individual, continuous mapping processing is performed on the difference results to form control parameters. The specific mathematical model is as follows: ; In the formula, To adjust parameters, Let be the objective function value corresponding to the j-th dimension position of the optimal individual in the t-th iteration; Furthermore, using the control parameters as the basis for weight allocation, the structural reflection operator and the optimal reflection operator are weighted and fused to combine the two types of structural operators in the same matrix space, thereby generating the direction modulation matrix. The specific mathematical model is as follows: ; In the formula, This is the directional modulation matrix; Matrix modulation is performed on the difference between the current position and the optimal position of a candidate individual based on the direction modulation matrix, and the modulation result is superimposed on the current position of the candidate individual to achieve population individual position update. The specific mathematical model is as follows: ; In the formula, Let be the j-th dimension position of individual i in the (t+1)-th iteration.

[0037] Specifically, the competition index is used to measure the relative competitive relationship between an individual and other reference individuals. By mapping the difference in objective function values ​​to a sign function, it effectively guides the population to balance diversity and search efficiency. The structural consistency tensor reflects the spatial structural distribution characteristics of individuals in the population. By structurally representing the outer product of difference terms, it improves the spatial structural consistency among groups, thus enabling individuals to have more targeted search directions at different stages. By introducing an exponential adjustment factor, the algorithm can dynamically adjust the search intensity and direction of individuals. Based on the combination of the competition index and the structural consistency tensor, the population can adjust its search strategy more flexibly and adaptively during the optimization process. The direction modulation matrix integrates optimization information and precisely controls the update direction of individuals, achieving a more efficient fusion of global and local searches and avoiding the local optimum dilemma. Through synergistic effects, the algorithm of this invention can achieve higher accuracy and faster convergence speed in multi-dimensional and complex constraint optimization environments.

[0038] Specifically, in the power output control system of the electric shut-off valve, the competitive index ensures that the output is effectively adjusted according to the deviation between the current and the target current during the current regulation process, thereby avoiding excessive current fluctuations; the structural consistency tensor enables the control of the drive motor to balance under multiple factors and reduce the system's response delay; the algorithm of this invention can achieve precise control of the target current in the system. When the current changes abruptly, the system can respond quickly and maintain a stable current output, avoiding energy loss and equipment overheating caused by excessive current; when the target current value changes, the system can complete the stable regulation of the current in a short time through dynamically adjusted exponential control factors and directional modulation matrices, improving the system's accuracy and adaptability, thereby achieving more efficient power output control of the electric shut-off valve; the algorithm of this invention not only improves the performance of the electric shut-off valve, but also optimizes the energy utilization efficiency of the entire control system and extends the service life of the equipment.

[0039] Specifically, the parameters of the current PID controller are tuned using an improved beaver behavior algorithm, such as... Figure 2 As shown, it specifically includes: Set the initial running parameters for the improved beaver behavior algorithm; The proportional coefficient, integral coefficient, and derivative coefficient in the current PID controller are introduced as independent dimensions into the individual position representation of the algorithm population to construct a mapping relationship between control parameters and the algorithm search space. During the algorithm's operation, the positions of individuals in the population are iteratively updated, allowing the individuals in the population to continuously evolve within the search space. Based on the pre-constructed objective evaluation function, the fitness of the individual positions in the population after each generation update is calculated, and the individual position with the best fitness is selected as the optimal solution for the current iteration cycle and stored. The algorithm iterative process is terminated by a condition check. When the number of iterations reaches the set upper limit, the position of the globally optimal individual in the population obtained in the historical iterations is output and decoded into the corresponding current PID controller parameters. If the termination condition is not met, the algorithm returns to the first step.

[0040] Specifically, the initial operating parameters for setting the improved beaver behavior algorithm include population size N, maximum number of iterations T, problem dimension D, and upper and lower bounds of the search space ub and lb.

[0041] Specifically, the proportional coefficient, integral coefficient, and derivative coefficient of the current PID controller are introduced as independent dimensions into the representation of the individual positions of the algorithm population, where the value of each dimension represents a specific value of a controller parameter. As the algorithm iterates, updating the individual positions of the population updates the solution of the algorithm's search space, which in turn updates the control parameters of the current PID controller. The specific mathematical model for this implementation is as follows: .

[0042] Specifically, the objective evaluation function, also known as the fitness function, is implemented using the following mathematical model: ; In the formula, f is the objective function value, i.e., the fitness value.

[0043] When the valve reaches the locked position, the target current of the drive motor is limited and maintained to keep the valve in a stable locked state, thereby achieving optimized control of the power output of the electric locking valve.

[0044] Specifically, the minimum sustaining current required to ensure stable system operation is used as the lower limit reference, and a gain modulation term related to the current position deviation of the valve is introduced into the lower limit reference. The gain modulation term is nonlinearly amplified based on the deviation amplitude between the current valve displacement and the preset static target displacement. At the same time, dynamic suppression is constructed in conjunction with the valve displacement disturbance change rate to constrain the over-modulation caused by rapid displacement fluctuations. Furthermore, an exponential decay term related to the degree of current disturbance is introduced to adaptively weaken the current adjustment amplitude, thereby obtaining the target sustaining current of the drive motor. The specific implementation mathematical model is as follows: ; In the formula, To maintain the target current for the drive motor, The minimum holding current for the drive motor is specifically set within the range of [0.3, 0.8] A. This range ensures that the drive motor provides sufficient holding torque during the valve's locking and holding phase to prevent rebound or loosening, while avoiding excessive current that could lead to coil overheating and increased energy consumption. In this embodiment, the specific value is set to 0.5 A. The target displacement for valve locking. This refers to the opening and closing displacement of the valve. This represents the change in valve displacement during opening and closing. This represents the change in current of the drive motor; where... and The specific mathematical model for implementation is as follows: ; ; In the formula, The time interval is 1 second. This represents the valve opening / closing displacement in the previous second. This represents the drive motor current in the previous second.

[0045] Specifically, the dynamic response process of the drive motor current is described by establishing a current response model based on a second-order linear system. The model input is the output signal of the current PID controller, and the output is the current of the drive motor. The specific mathematical model is as follows: ; In the formula, The damping ratio parameter describes the damping characteristics of a current-response system, determining whether the system oscillates and its decay rate during dynamic adjustment. Specifically, its value range is [0.6, 1.0], corresponding to the underdamped to critically damped state. This range ensures current response speed while suppressing significant oscillations, balancing dynamic response speed and current stability. The natural angular frequency of the system is used to describe the dynamic speed characteristics of the current response system and reflect the response speed level of the current loop. Specifically, the value range is [5, 20] rad / s. This range allows the current system to complete the main dynamic adjustment within a short time scale, which not only meets the action response requirements of the electric locking valve, but also avoids the system from being too sensitive to noise and disturbance due to excessively high frequencies.

[0046] Specifically, the current response model of the second-order linear system is converted into an s-domain transfer function mathematical model for Simulink simulation: ; In the formula, Let be the transfer function, and s be a complex frequency domain variable. The specific value is set to 0.7. The specific value is set to 10 rad / s; the transfer function of the final Simulink simulation model is obtained as follows: .

[0047] Specifically, to verify the application effect of the improved beaver behavior algorithm of this invention in the power output control system of an electric locking valve, a joint simulation test using Matlab and Simulink was conducted. The mathematical model of the traditional beaver behavior algorithm was structurally improved based on the Matlab platform, and a simulation module integrating optimization strategies was constructed. An electric locking valve power output control system including a current PID controller was built in Simulink, completing the mapping between the algorithm control parameters and the three parameters of the current PID controller. The objective evaluation function was connected with the Simulink system output error signal function, thus forming a complete optimization closed-loop path. The algorithm initialization parameters in the simulation experiment were set as follows: population size N=30, maximum number of iterations T=30, problem variable dimension D=3, upper limit of search space ub=100, and lower limit lb=0.

[0048] Specifically, further comparative experimental results are as follows: Figure 3As shown, the optimal fitness of the existing algorithm is 91, while the optimal fitness of the algorithm of this invention is 76. When fitness is used as an evaluation index of optimization performance, a lower fitness value represents better control performance, indicating that the algorithm of this invention is significantly better than the existing algorithm in terms of overall optimization capability. The algorithm of this invention can adaptively adjust the search strategy, guide the population evolution trend, and significantly improve the convergence efficiency and the quality of the global optimal solution.

[0049] Specifically, Figure 4 and Figure 5 The comparison of controller parameters shows that the optimal PID parameters obtained by the algorithm of this invention are Kp=9.87, Ki=0.52, and Kd=3.78, while the optimization results of the existing algorithm are Kp=83.82, Ki=5.08, and Kd=9.57. The optimal parameter configurations were applied to the Simulink simulation platform for a 20-second control task response test. In a simulation environment where the initial target current of the electric locking valve drive motor was set to 1A, and then suddenly increased to 4A after 10 seconds, the results were analyzed. Figure 6 This indicates that the method of the present invention can quickly track the new set value after a sudden change in the target current, exhibiting lower overshoot and better stability, and is superior to existing methods in terms of adjustment accuracy and adaptability. The method of the present invention can effectively improve the dynamic response speed and stability in the electric shut-off valve control system, and can achieve more efficient and accurate valve control under complex working conditions, ensuring the stable operation and safety of the electric shut-off valve.

Claims

1. A method for optimizing the power output control of an electric locking valve, characterized in that, The specific steps are as follows: real-time acquisition of the current current of the electric locking valve drive motor and the valve opening and closing displacement, and initiation of the electric locking valve locking process according to the target current of the drive motor. During the process of the valve transitioning from the open state to the closed state, a current error signal is generated and input into the current PID controller to perform closed-loop regulation of the current of the drive motor. The parameters of the current PID controller are tuned using an improved beaver behavior algorithm, specifically including: constructing a competitive index based on the difference in objective function values ​​among individuals in the population, and forming a structural consistency tensor by combining the spatial difference relationship between individuals; further updating the position of individuals in the population by constructing an exponential control factor through exponential mapping and trace constraints; constructing a reflection operator based on the spatial difference relationship between individuals, generating control parameters by combining the objective function value and spatial difference scale information, and updating the position of individuals in the population by weighted fusion of the reflection operator to form a direction modulation matrix; When the valve reaches the locked position, the target current of the drive motor is limited and maintained to keep the valve in a stable locked state, thereby achieving optimized control of the power output of the electric locking valve.

2. The method for optimizing the power output control of an electric locking valve according to claim 1, characterized in that, The current of the electric locking valve drive motor is obtained by collecting the instantaneous current value in the drive motor power supply circuit; the opening and closing displacement of the valve is obtained by non-contact acquisition of the valve stem stroke of the electric locking valve by a displacement sensor.

3. The method for optimizing the power output control of an electric locking valve according to claim 1, characterized in that, The method of updating the position of individuals in the population by constructing an exponential regulation factor through exponential mapping and trace constraint specifically includes: By performing difference discrimination processing on the objective function values ​​of candidate individuals and other individuals at corresponding positions, and mapping the difference discrimination results through a sign function; summarizing all difference discrimination results, and performing proportional constraint mapping according to the population size, a competition index is constructed. Based on the spatial difference relationship between candidate individuals and other individuals, the corresponding position difference terms are extracted, and the position difference terms are used to construct a structural quantity in the form of an outer product. A scale constraint term based on the square of the norm of the difference terms is introduced into the outer product structural quantity, and an amplitude constraint transformation is performed on the structural quantity. The structural quantity after scale constraint is aggregated as a whole to form a structural consistency tensor.

4. The method for optimizing the power output control of an electric locking valve according to claim 3, characterized in that, An exponential mapping is performed on the competition index, and a trace operation is performed on the structural consistency tensor. The results of the exponential mapping and trace operation are combined in fractional form, and a constant term is introduced on the denominator side to form a stable proportional constraint structure, thereby obtaining the exponential control factor. Under the influence of the exponential regulation factor, a matrix modulation term containing an identity matrix and a structural consistency tensor is constructed, and the scale constraint of the modulation term is applied based on the trace value of the structural consistency tensor. At the same time, the position difference term between the candidate individual and the other individuals is subjected to population aggregation processing to form a population orientation term. The matrix modulation term and the population orientation term are coupled by multiplication and superimposed with the current position to update the position of the individual in the population.

5. The method for optimizing the power output control of an electric locking valve according to claim 1, characterized in that, The method of updating the position of individuals in the population by weighted fusion of reflection operators to form a direction modulation matrix specifically includes: Based on the spatial difference relationship formed between candidate individuals and reference individuals at corresponding positions, the spatial difference information is expressed in matrix form. Under the unified identity matrix constraint framework, proportional mapping and scale constraint processing are introduced to embed the difference relationship into the structured matrix form, thereby constructing the structured reflection operator. Based on the spatial difference relationship between candidate individuals and the optimal individual at their corresponding positions, the spatial difference information is expressed in a matrix form. Under the unified identity matrix constraint framework, proportional mapping and scale constraint processing are introduced to embed the difference relationship into a structured matrix form, thereby constructing the optimal reflection operator.

6. The method for optimizing the power output control of an electric locking valve according to claim 5, characterized in that, Based on the difference between candidate individuals and optimal individuals in the objective function value, and combined with the spatial difference scale information between candidate individuals and optimal individuals, continuous mapping processing is performed on the difference results to form control parameters; further, the control parameters are used as the basis for weight allocation to perform weighted fusion processing on the structural reflection operator and the optimal reflection operator, so that the two types of structural operators are combined in the same matrix space, thereby generating the direction modulation matrix. The matrix modulation operation is performed on the difference information between the current position and the optimal position of the candidate individual based on the direction modulation matrix, and the modulation result is superimposed on the current position of the candidate individual to realize the update of the individual position of the population.

7. The method for optimizing the power output control of an electric locking valve according to claim 1, characterized in that, The parameters of the current PID controller are tuned using an improved beaver behavior algorithm, specifically including: Set the initial running parameters for the improved beaver behavior algorithm; The proportional coefficient, integral coefficient, and derivative coefficient in the current PID controller are introduced as independent dimensions into the individual position representation of the algorithm population to construct a mapping relationship between control parameters and the algorithm search space. During the algorithm's operation, the positions of individuals in the population are iteratively updated, allowing the individuals in the population to continuously evolve within the search space. Based on the pre-constructed objective evaluation function, the fitness of the individual positions in the population after each generation update is calculated, and the individual position with the best fitness is selected as the optimal solution for the current iteration cycle and stored. The algorithm iterative process is terminated by a condition check. When the number of iterations reaches the set upper limit, the position of the globally optimal individual in the population obtained in the historical iterations is output and decoded into the corresponding current PID controller parameters. If the termination condition is not met, the algorithm returns to the first step.

8. The method for optimizing the power output control of an electric locking valve according to claim 1, characterized in that, The minimum sustaining current required to ensure stable system operation is used as the lower limit reference, and a gain modulation term related to the current position deviation of the valve is introduced into the lower limit reference. The gain modulation term is nonlinearly amplified based on the deviation between the current displacement of the valve and the preset static target displacement. At the same time, dynamic suppression is constructed in combination with the valve displacement disturbance change rate to constrain the over-modulation caused by rapid displacement fluctuation. Furthermore, an exponential decay term related to the degree of current disturbance is introduced to adaptively weaken the current adjustment amplitude, thereby obtaining the target sustaining current of the drive motor.

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