A method for reducing flow-induced noise of a blunt body based on double-signal iterative feedback regulation

Abstract

The present application relates to the technical field of flow control, in particular to a method for reducing bluff body flow-induced noise based on double signal iterative feedback tuning, comprising: obtaining flow field parameter error and obtaining actuator action signal; obtaining response value of flow field flow to actuator action; constructing cost function of double signal iterative feedback tuning algorithm; obtaining response value of open loop control output; obtaining descending gradient of double signal iterative tuning method and initial step length in gradient descending process; obtaining optimal PID control parameter and controlling closed loop control system. The present application can obtain better control effect by combining different cost functions and feedback signals.

Description

Technical Field

[0001] This invention relates to the field of flow control technology, and specifically to a method for reducing flow-induced noise in blunt bodies based on dual-signal iterative feedback tuning. Background Technology

[0002] If flow separation occurs as it flows over an object, that object can be considered a bluff body. Flow around a bluff body is often accompanied by complex flow phenomena such as vortex shedding, transition, and reattachment, radiating intense noise outwards. Radiated noise from bluff body flows is a widespread problem in engineering, such as aircraft landing gear noise, underwater vehicle appendage noise, and car rearview mirror noise. Reducing radiated noise from bluff body flows has significant engineering value. Based on the fundamental principle of flow-induced noise—that noise is generated by unsteady flow—the flow structure can be modulated through flow control, thereby reducing far-field radiated noise. In 2019, an article applying Iterative Feedback Tuning (IFT) to wake control was published in *IEEE Transactions on Control Systems Technology*, issue 27, pages 1385-1396, marking the first introduction of iterative feedback tuning into the field of fluid mechanics. Iterative feedback tuning is a method for finding the optimal parameter combination in PID closed-loop control, especially suitable for situations where it is difficult to accurately model the transfer function. Examples include iterative feedback tuning control of a rotating inverted pendulum and its robust optimization method, and a UDE control system design method based on iterative feedback tuning. These techniques demonstrate that iterative feedback tuning has achieved significant results in the fields of industrial control and robot optimization control.

[0003] However, in the original iterative feedback tuning method, the feedback signal is used synchronously for the evaluation of the cost function, which is not effective when far-field noise is used as the feedback signal in this field.

[0004] Therefore, there is a need to provide a method for reducing flow-induced noise in blunt bodies based on dual-signal iterative feedback tuning to solve the above problems. Summary of the Invention

[0005] To address the issue that the original iterative feedback tuning method, in which the feedback signal is synchronously used for cost function evaluation, is ineffective when far-field noise is used as the feedback signal, this invention provides a method for reducing flow-induced noise in blunt bodies based on dual-signal iterative feedback tuning. By decoupling the feedback signal and the cost function evaluation through the proposed dual-signal iterative feedback tuning method, the noise reduction effect is improved by setting the feedback signal and the cost function, thereby solving the existing problems.

[0006] This invention provides a method for reducing flow-induced noise in blunt bodies based on dual-signal iterative feedback tuning, employing the following technical solution: A closed-loop control system is constructed based on sensors, PID controllers, and actuators; The error of the flow field parameters is obtained based on the flow field parameters and the preset target flow field parameters; based on the error of the flow field parameters, the actuator's actuation signal is obtained using a PID control algorithm. The flow field parameters include flow field velocity and flow field pressure. The actuator's actuation signal is input into the actuator of the closed-loop control system to obtain the response value of the flow field output by the closed-loop control system to the actuator's action; Based on the flow field parameter error and the gain parameter of the closed-loop control system, and combined with the iterative feedback tuning algorithm, a cost function for the dual-signal iterative feedback tuning algorithm is constructed. The response value of the flow field at the previous moment to the actuator action is output by the closed-loop control system as the actuation signal of the open-loop control in the dual-signal iterative tuning method, and the response value of the open-loop control output is obtained based on the actuation signal of the open-loop control. The gradient of the cost function and the pseudo-Hessian matrix are obtained based on the response value of the open-loop control output; the descent gradient of the two-signal iterative tuning method is obtained based on the gradient of the cost function and the pseudo-Hessian matrix; the initial step size is obtained based on the initial cost function value, the gradient of the cost function, and the pseudo-Hessian matrix. Under the current descent gradient, the optimal step size is obtained based on the initial step size and using the golden section method. Convergence conditions are set based on the relative error and relative error threshold between the cost function value corresponding to the optimal step size under the current descent gradient and the cost function value corresponding to the optimal step size under the previous descent gradient. The control parameters under the current descent gradient that meet the convergence conditions are used as the optimal PID control parameters. The closed-loop control system is controlled based on the optimal PID control parameters.

[0007] A further technical solution of the present invention is that the expression of the actuator's actuation signal is:

[0008] In the formula, express The actuation signal of the actuator at any given moment; Indicates the proportionality coefficient; express Time of day Errors in the flow field parameters; Indicates the integral coefficient; express Time of day Errors in the flow field parameters; Indicates the time interval for applying control; Represents the differential coefficient; express Time of day Errors in various flow field parameters.

[0009] A further technical solution of the present invention is that the expression for the cost function of the dual-signal iterative feedback tuning algorithm is:

[0010] In the formula, Indicates iteration to Cost function value under the next-time gain parameter; Indicates the start time; Indicates the end time; express Time and iteration to The gain parameter at the next time Errors in the flow field parameters; Indicates the time interval for applying control.

[0011] A further technical solution of the present invention is that the gradient expression of the cost function is:

[0012] In the formula, The gradient of the cost function is represented. Indicates the start time; Indicates the end time; express Time and iteration to The gain parameter at the next time Errors in the flow field parameters; Indicates the time interval for applying control; Indicates iteration to The gain parameter at this time.

[0013] A further technical solution of the present invention is that the expression for the pseudo-Hessian matrix is:

[0014] In the formula, Represents the pseudo-Hessian matrix; Indicates the start time; Indicates the end time; express Time and iteration to The gain parameter at the next time Errors in the flow field parameters; Indicates the time interval for applying control; Indicates iteration to The gain parameter at this time.

[0015] A further technical solution of the present invention is as follows: The expression is:

[0016] In the formula, express The response value of the open-loop control output at any given time; express The response value of the open-loop control output at any given time.

[0017] A further technical solution of the present invention is that the expression for the initial step size is:

[0018] In the formula, Indicates iteration to Cost function value under the next-time gain parameter; Represents the pseudo-Hessian matrix; This represents the gradient of the cost function.

[0019] A further technical solution of the present invention is that the convergence condition is:

[0020] In the formula, Indicates iteration to The optimal gain parameter corresponding to the optimal step size at this time. The cost function value below; Indicates iteration to The optimal gain parameter corresponding to the optimal step size at this time. The cost function value below; This represents the convergence threshold.

[0021] The beneficial effects of this invention are: This invention is based on the fundamental principle of flow-induced noise. It reduces noise at the source by modulating the flow structure that generates the noise. Therefore, the frequency range of noise reduction is unaffected by actuator size. The invention employs a dual-signal iterative feedback tuning method to estimate the descent gradient, avoiding precise modeling of the transfer function of complex nonlinear flow systems. The dual-signal iterative feedback tuning method does not rely on the feedback signal to evaluate the cost function; therefore, it can achieve better control performance by combining different cost functions and feedback signals. In summary, the core idea of ​​this invention is to reduce noise by modulating the near-field flow structure. Because the single-signal iterative feedback tuning method uses a pressure signal far from the cylinder, changes in the near-field flow structure cannot be reflected in the pressure signal in a timely manner, resulting in a lower noise reduction effect compared to the dual-signal iterative feedback tuning method that uses lift as the feedback signal. Compared to when no control is applied, the optimal control parameters obtained by the dual-signal method reduce the far-field noise by 76% overall. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, 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.

[0023] Figure 1 This is a flowchart illustrating a method for reducing flow-induced noise in blunt bodies based on dual-signal iterative feedback tuning according to the present invention. Figure 2 This is a schematic diagram of PID control in an embodiment of the present invention; Figure 3 This is a schematic diagram of a dual-signal assisted loop in an embodiment of the present invention; Figure 4 This is a flowchart of the iterative feedback tuning process in an embodiment of the present invention; Figure 5 This is a schematic diagram of PID control for reducing radiated noise around a cylinder in an embodiment of the present invention; Figure 6 This is a comparison chart of the method of the present invention, the iterative feedback tuning method for a single signal, and the noise reduction effect of an uncontrolled method. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0025] An embodiment of the present invention, based on dual-signal iterative feedback tuning, for reducing flow-induced noise in blunt bodies, is as follows: Figure 1 As shown, it includes: S1. Obtain the flow field parameter error and the actuator's actuation signal; Specifically, a closed-loop control system is constructed based on sensors, controllers, and actuators; the flow field parameter error is obtained based on the flow field parameters and preset target flow field parameters; and the actuator actuation signal is obtained based on the flow field parameter error and using a PID control algorithm. The flow field parameters include flow field velocity and flow field pressure.

[0026] For example, in one specific embodiment, the PID controller is composed of a linear combination of three parts: proportional control, integral control, and derivative control. The goal of PID control is to reduce the feedback signal. f i ( t ) and preset target signalr ( t ) error e n ( t ),Right now e n ( t ) = f n ( t ) - r ( t The three key parameters for PID control are defined as follows: k P , k I and k D Then, the output of the PID control in the discrete-time system is denoted as the actuator's actuation signal, that is, the expression of the actuator's actuation signal is:

[0027] In the formula, express The actuation signal of the actuator at any given moment; Indicates the proportionality coefficient; express Time of day Errors in the flow field parameters; Indicates the integral coefficient; express Time of day Errors in the flow field parameters; Indicates the time interval for applying control; Represents the differential coefficient; express Time of day Errors in various flow field parameters.

[0028] S2. Obtain the response value of the flow field to the actuator action; Specifically, the actuator's actuation signal is input into the actuator of the closed-loop control system to obtain the response value of the flow field output by the closed-loop control system to the actuator's action.

[0029] S3. Construct the cost function for the dual-signal iterative feedback tuning algorithm; Specifically, a cost function for a dual-signal iterative feedback tuning algorithm is constructed based on the flow field parameter error and the gain parameters of the closed-loop control system, combined with an iterative feedback tuning algorithm.

[0030] For example, such as Figure 2 and Figure 3 As shown, in a specific embodiment, the transfer function corresponding to the PID controller is denoted as... CThe actuation signal generated in step 1 can be represented as: Ω ( t i ) = Ce n ( t i The transfer function of the flow field is denoted as... G p ,Will Ω ( t i The transfer function input to the flow field G p The response of the flow to the actuator action can be denoted as: e n ( t i The gain parameter of the closed-loop control system is defined as follows: β ( k ) = [ k P , k I , k D ] T Iterative feedback tuning continuously optimizes the control parameters by changing the gain parameter, running closed-loop control under the corresponding control parameters, and synchronously calculating the magnitude of the cost function. Therefore, the expression for the cost function of the dual-signal iterative feedback tuning algorithm in this embodiment is:

[0031] In the formula, Indicates iteration to Cost function value under the next-time gain parameter; Indicates the start time; Indicates the end time; express Time and iteration to The gain parameter at the next time Errors in the flow field parameters; Indicates the time interval for applying control.

[0032] S4. Obtain the response value of the open-loop control output; Specifically, the response value of the flow field at the previous moment to the actuator action is output by the closed-loop control system as the actuation signal of the open-loop control in the dual-signal iterative tuning method, and the response value of the open-loop control output is obtained based on the actuation signal of the open-loop control. S5. Obtain the descent gradient of the dual-signal iterative tuning method and the initial step size during the gradient descent process; Specifically, the gradient of the cost function and the pseudo-Hessian matrix are obtained based on the response value of the open-loop control output; the descent gradient of the two-signal iterative tuning method is obtained based on the gradient of the cost function and the pseudo-Hessian matrix; and the initial step size is obtained based on the initial cost function value, the gradient of the cost function, and the pseudo-Hessian matrix.

[0033] For example, in one specific embodiment, the gradient expression of the cost function is:

[0034] In the formula, The gradient of the cost function is represented. Indicates the start time; Indicates the end time; express Time and iteration to The gain parameter at the next time Errors in the flow field parameters; Indicates the time interval for applying control; Indicates iteration to The gain parameter at this time.

[0035] For example, in one specific embodiment, the expression for the pseudo-Hessian matrix is:

[0036] In the formula, Represents the pseudo-Hessian matrix; Indicates the start time; Indicates the end time; express Time and iteration to The gain parameter at the next time Errors in the flow field parameters; Indicates the time interval for applying control; Indicates iteration to The gain parameter at this time.

[0037] The core advantage of the iterative feedback tuning method lies in the fact that it does not require consideration of the flow environment. G p By performing modeling, you can obtain That is, only the output of the closed-loop control from the previous iteration needs to be used as the target value. r ( t The closed-loop control is then run again with the same gain, i.e., the response value of the actuator action to the previous flow. e j ( t i The response value of the open-loop control output can be obtained by directly using it as the actuation signal in the open-loop control of the dual-signal iterative tuning method.e j, parallel ( t i Based on the response value of the open-loop control output. You can get , The expression is:

[0038] In the formula, express The response value of the open-loop control output at any given time; express The response value of the open-loop control output at any given time.

[0039] Therefore, the descent gradient of the two-signal iterative tuning method is: .

[0040] For example, in one specific embodiment, the expression for the initial step size is:

[0041] In the formula, Indicates iteration to Cost function value under the next-time gain parameter; Represents the pseudo-Hessian matrix; This represents the gradient of the cost function.

[0042] It should be noted that iterative feedback tuning involves two processes: running the main loop to obtain the cost function corresponding to the current gain, and running the auxiliary loop to obtain the descent gradient. The original single-signal iterative feedback tuning algorithm requires maintaining consistency between the input of the cost function and the feedback signal of the closed-loop control in the auxiliary loop. However, the proposed dual-signal iterative feedback tuning method decouples these signals. In dual-signal iterative feedback tuning, an auxiliary loop is also used to obtain the descent gradient, but the difference lies in that the auxiliary loop in the dual-signal method executes open-loop control. In the original iterative feedback tuning algorithm, since the auxiliary loop is closed-loop control, it is necessary to ensure that the signal evaluating the cost function is consistent with the feedback signal. However, the current dual-signal method, by executing open-loop control, skips this restriction.

[0043] S6. Obtain the optimal PID control parameters and control the closed-loop control system. Specifically, under the current descent gradient, the optimal step size is obtained based on the initial step size and using the golden section method; the convergence condition is set based on the relative error and relative error threshold between the cost function value corresponding to the optimal step size under the current descent gradient and the cost function value corresponding to the optimal step size under the previous descent gradient; the control parameters under the current descent gradient that meet the convergence condition are taken as the optimal PID control parameters; and the closed-loop control system is controlled based on the optimal PID control parameters.

[0044] For example, in one specific embodiment, the convergence condition is:

[0045] In the formula, Indicates iteration to The optimal gain parameter corresponding to the optimal step size at this time. The cost function value below; Indicates iteration to The optimal gain parameter corresponding to the optimal step size at this time. The cost function value below; This represents the relative error threshold.

[0046] The embodiments of the present invention will be described below with reference to specific examples and accompanying drawings: In this embodiment, a cylinder is used as a blunt body to control the flow radiation noise around the cylinder. The specific implementation of the invention is illustrated through numerical simulation. Specifically, a cylinder with a diameter of... L A cylinder with velocity 0 is submerged in water with velocity 0. U In a uniform inflow of 0, the dimensionless Reynolds number is Re = U 0 L 0 / ν = 150, where ν Let be the kinematic viscosity of the fluid, and let the dimensionless Mach number of the incoming flow be . Ma = U 0 / c s ,in c s The velocity of sound in the current fluid. The computational domain size is 200. L 0, numerical simulation was performed using the lattice Boltzmann method.

[0047] Step 1: Obtain feedback signals through numerical simulation and apply actions: The current implementation of flow control is rotational control, achieved by modifying the boundary conditions of the cylinder. The flow field parameter is the lift coefficient of the cylinder. C L and in the flow field p * (0, 20 L0) The dimensionless instantaneous pressure at position 0, where the lift coefficient and instantaneous pressure are defined as:

[0048] In the formula, The lift force on the cylinder was obtained through numerical simulation; The lift coefficient; Instantaneous pressure; The fluid density is the target fluid density; the goal of the control is to reduce the lift coefficient or dimensionless instantaneous pressure to 0, i.e., the preset target signal is 0. For example... Figure 4 As shown, the flow field parameters are input to the PID controller to generate the rotational speed of the cylinder. To highlight the improvement effect of dual-signal iterative feedback tuning, both the cost function and feedback signal of the single signal are instantaneous pressure signals. That is, existing iterative feedback tuning methods are all for single signals. The specific expression is as follows:

[0049]

[0050] In the formula, This refers to the actuator's actuation signal in the single-signal iterative feedback tuning algorithm. This is the proportionality coefficient; When the flow field parameter is instantaneous pressure in the single-signal iterative feedback tuning algorithm Instantaneous pressure error at any given moment; The integral coefficient; When the flow field parameter is instantaneous pressure in the single-signal iterative feedback tuning algorithm Instantaneous pressure error at any given moment; These are the differential coefficients; Indicates the start time; Indicates the end time; Indicates the time interval for applying control; For the single-signal iterative feedback tuning algorithm, at iteration... Cost function under the gain parameter at this time; When the flow field parameter is instantaneous pressure in the single-signal iterative feedback tuning algorithm Time and iteration to Instantaneous pressure error under the gain parameters at this time; For iteration to Gain parameters at this time; The cost function of the dual-signal iterative feedback tuning algorithm uses instantaneous pressure as the input signal and lift coefficient as the feedback signal. That is, it uses both instantaneous pressure and lift coefficient as the dual signals, resulting in:

[0051]

[0052] In the formula, The actuator's actuation signal in the dual-signal iterative feedback tuning algorithm; When the flow field parameter is the lift coefficient in the dual-signal iterative feedback tuning algorithm The lift coefficient error at time t; For the dual-signal iterative feedback tuning algorithm, at iteration... Cost function under the gain parameter at this time; When the flow field parameter is the instantaneous pressure in the dual-signal iterative feedback tuning algorithm Time and iteration to Instantaneous pressure error under the gain parameters at this time; For iteration to The gain parameter at this time.

[0053] Step 2: Perform dual-signal iterative feedback tuning to obtain the optimal PID control parameters: Both the two-signal method and the single-signal method employ Figure 4 The optimization process shown differs only in the execution of the auxiliary loop. The initial gain is set to 0 for all iterations, and the start and end times for the statistics are as follows: T a = 50 T 0 and T b = 150 T 0. T 0 represents the characteristic time, defined as T 0= L 0 / U 0; First, the main loop executes once, outputting the corresponding... J And the pressure signal. In the auxiliary loop of single-signal iterative feedback tuning, the pressure signal is input to the closed-loop PID control system, and the output is a discrete pressure signal. p * parallel ( t This is used to obtain the descent gradient, i.e., in the iterative feedback tuning method for a single signal:

[0054] The descent gradient of the iterative feedback tuning method for a single signal is then... In the iterative feedback tuning method for a single signal, the gradient of the cost function is:

[0055] The pseudo-Hessian matrix is:

[0056] In the formula, In the single-signal iterative feedback tuning method The response value of the closed-loop control output at any given time; For single-signal iterative feedback tuning method The response value of the closed-loop control output at any given time; The gradient of the cost function in the iterative feedback tuning method for a single signal; for Time and iteration to Error of instantaneous pressure under the gain parameters at this time; This refers to the pseudo-Hessian matrix in the iterative feedback tuning method for a single signal. This is the cost function of the iterative feedback tuning method for a single signal.

[0057] In the dual-signal iterative feedback tuning method of this invention, the auxiliary loop uses the output of the main loop as the rotational speed and independently performs three open-loop controls, i.e.:

[0058] In the formula, For the dual-signal iterative feedback tuning method The response value of the open-loop control output at any given time; For the dual-signal iterative feedback tuning method The single input signal at any given time is the response value of the open-loop control output when the instantaneous pressure is applied.

[0059] Subsequently, the descent gradient of the dual-signal iterative feedback tuning is obtained as follows: The gradient of the cost function for dual-signal iterative feedback tuning is:

[0060] The pseudo-Hessian matrix is:

[0061] In the formula, It is the pseudo-Hessian matrix tuned by iterative feedback of two signals.

[0062] Therefore, we can use the current descent gradient. The optimal step size is obtained using the classic one-dimensional search method, namely the golden section method. The initial step size is obtained from the gradient of the cost function and the pseudo-Hessian matrix, satisfying the following:

[0063] As the step size is updated, the gain parameter, i.e. the PID control parameter, changes continuously. The corresponding cost function is obtained by executing closed-loop control under the current gain.

[0064] For example, in one specific embodiment, the convergence condition is determined based on the cost function value corresponding to the optimal step size under the current descent gradient and the cost function value corresponding to the optimal step size under the previous descent gradient. The control parameters under the current descent gradient when the convergence condition is met are taken as the optimal PID control parameters. The steps for controlling the closed-loop control system based on the optimal PID control parameters are as follows: During the optimization process, the descent direction is changed so that the relative error of the cost function is less than or equal to the relative error threshold (in this embodiment, it is taken as...). A value of 0.05 indicates that the convergence condition is met, meaning the convergence condition is satisfied when the following expression is met:

[0065] In the formula, Indicates iteration to The optimal gain parameter corresponding to the optimal step size at this time. The cost function value below; Indicates iteration to The optimal gain parameter corresponding to the optimal step size at this time. The cost function value below; This represents the convergence threshold.

[0066] Step 3: Evaluate the noise reduction effect: The control parameters after convergence of single-signal iterative feedback tuning are: k P = -0.21、 k D = 0.05, the control parameters for convergence of dual-signal iterative feedback tuning are: k P = 7.42. With the center of the cylinder as the center, the radius is 75. L Pressure measurement points were placed at position 0 to monitor far-field noise, and 200 characteristic time points were statistically analyzed. L 0 / U The distribution of the effective value of pressure pulsation obtained from 0 is as follows: Figure 6 As shown, both the single-signal and dual-signal methods achieved significant noise reduction effects. However, the core idea of ​​this invention is to reduce noise by modulating the near-field flow structure. Since the single-signal iterative feedback tuning method uses a pressure signal far from the cylinder, changes in the near-field flow structure cannot be reflected in the pressure signal in a timely manner, resulting in a lower noise reduction effect compared to the dual-signal iterative feedback tuning method that uses lift as the feedback signal. Compared to the case where no control is applied, the optimal control parameters obtained by the dual-signal method reduce the far-field noise by 76% overall.

[0067] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for reducing flow-induced noise in blunt bodies based on dual-signal iterative feedback tuning, characterized in that, include: A closed-loop control system is constructed based on sensors, PID controllers, and actuators; The error of the flow field parameters is obtained based on the flow field parameters and the preset target flow field parameters; Based on the error of the flow field parameters, and using the PID control algorithm to obtain the actuator's actuation signal, the flow field parameters include flow field velocity and flow field pressure; The actuator's actuation signal is input into the actuator of the closed-loop control system to obtain the response value of the flow field output by the closed-loop control system to the actuator's action; Based on the flow field parameter error and the gain parameter of the closed-loop control system, and combined with the iterative feedback tuning algorithm, a cost function for the dual-signal iterative feedback tuning algorithm is constructed. The response value of the flow field at the previous moment to the actuator action is output by the closed-loop control system as the actuation signal of the open-loop control in the dual-signal iterative tuning method, and the response value of the open-loop control output is obtained based on the actuation signal of the open-loop control. The gradient of the cost function and the pseudo-Hessian matrix are obtained based on the response value of the open-loop control output; the descent gradient of the two-signal iterative tuning method is obtained based on the gradient of the cost function and the pseudo-Hessian matrix; the initial step size is obtained based on the initial cost function value, the gradient of the cost function, and the pseudo-Hessian matrix. Under the current descent gradient, the optimal step size is obtained based on the initial step size and using the golden section method. Convergence conditions are set based on the relative error and relative error threshold between the cost function value corresponding to the optimal step size under the current descent gradient and the cost function value corresponding to the optimal step size under the previous descent gradient. The control parameters under the current descent gradient that meet the convergence conditions are used as the optimal PID control parameters. The closed-loop control system is controlled based on the optimal PID control parameters.

2. The method for reducing flow-induced noise in blunt bodies based on dual-signal iterative feedback tuning according to claim 1, characterized in that, The expression for the actuator's actuation signal is: In the formula, express The actuation signal of the actuator at any given moment; Indicates the proportionality coefficient; express Time of day Errors in the flow field parameters; Indicates the integral coefficient; express Time of day Errors in the flow field parameters; Indicates the time interval for applying control; Represents the differential coefficient; express Time of day Errors in various flow field parameters.

3. The method for reducing flow-induced noise in blunt bodies based on dual-signal iterative feedback tuning according to claim 1, characterized in that, The expression for the cost function of the dual-signal iterative feedback tuning algorithm is: In the formula, Indicates iteration to Cost function value under the next-time gain parameter; Indicates the start time; Indicates the end time; express Time and iteration to The gain parameter at the next time Errors in the flow field parameters; Indicates the time interval for applying control.

4. The method for reducing flow-induced noise in blunt bodies based on dual-signal iterative feedback tuning according to claim 1, characterized in that, The gradient expression for the cost function is: In the formula, The gradient of the cost function is represented. Indicates the start time; Indicates the end time; express Time and iteration to The gain parameter at the next time Errors in the flow field parameters; Indicates the time interval for applying control; Indicates iteration to The gain parameter at this time.

5. The method for reducing flow-induced noise in blunt bodies based on dual-signal iterative feedback tuning according to claim 1, characterized in that, The expression for the pseudo-Hessian matrix is: In the formula, Represents the pseudo-Hessian matrix; Indicates the start time; Indicates the end time; express Time and iteration to The gain parameter at the next time Errors in the flow field parameters; Indicates the time interval for applying control; Indicates iteration to The gain parameter at this time.

6. A method for reducing flow-induced noise in blunt bodies based on dual-signal iterative feedback tuning according to claim 4 or 5, characterized in that, The expression is: In the formula, express The response value of the open-loop control output at any given time; Indicate The response value of the open-loop control output at any given time.

7. The method for reducing flow-induced noise in blunt bodies based on dual-signal iterative feedback tuning according to claim 1, characterized in that, The expression for the initial step size is: In the formula, Indicates iteration to Cost function value under the next-time gain parameter; Represents the pseudo-Hessian matrix; This represents the gradient of the cost function.

8. The method for reducing flow-induced noise in blunt bodies based on dual-signal iterative feedback tuning according to claim 1, characterized in that, The convergence criterion is: In the formula, Indicates iteration to The optimal gain parameter corresponding to the optimal step size at this time The cost function value below; Indicates iteration to The optimal gain parameter corresponding to the optimal step size at this time. The cost function value below; This represents the convergence threshold.

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