Generalized Internal Model Control Method, Device and Medium for a Voice Coil Motor Fast Steering Mirror

Abstract

An embodiment of the present application discloses a generalized internal model control method, device and medium for a voice coil motor fast steering mirror, belonging to the technical field of voice coil motor fast steering mirrors, and solving the problem of controller performance degradation caused by system parameter perturbation. It includes analyzing the input and output data of the voice coil motor fast steering mirror system to obtain the corresponding nominal model. The nominal model is loop-shaped through a pre-compensator, and the loop shaping controller is solved based on the standard H ∞ control algorithm to obtain a performance controller. The additive uncertainty between the nominal model and the model under the actual working condition is determined, and the upper bound function corresponding to the additive uncertainty is obtained through curve fitting. The preset controller is iteratively processed to obtain a robust controller. Based on the performance controller and the robust controller, the inner loop controller is solved to achieve the generalized internal model control of the voice coil motor fast steering mirror.

Description

Technical Field

[0001] The present application relates to the technical field of voice coil motor fast steering mirrors, and particularly to a generalized internal model control method, device, and medium for a voice coil motor fast steering mirror. Background Art

[0002] The voice coil motor fast steering mirror is driven by four voice coil motors, and a flexible hinge is used as the support structure inside. Due to its large resonance characteristics and coupling between axes, it will be difficult for general control algorithms to meet the high-speed and high-precision control requirements of the voice coil motor fast steering mirror. And it generally works on airborne or shipborne platforms, and the atmospheric turbulence and platform vibration in the working environment will cause certain parameter perturbations to the voice coil motor fast steering mirror. Under such perturbations, if the controller does not have strong robust stability, the tracking performance of the system will decline or even the system will become unstable.

[0003] In the current control technology of voice coil motor fast steering mirrors, most adopt the traditional PID control method based on the transfer function model of the voice coil motor fast steering mirror. It has a fixed controller structure, and the controller parameters are adjusted by drawing root loci. It has the advantages of few parameters and easy implementation of the control structure, and is the most widely used control method in the industrial field.

[0004] However, the traditional PID control method is difficult to effectively suppress the influence brought by the resonance characteristics in the voice coil motor fast steering mirror, and the PID control method is designed according to a single-input single-output system, while the voice coil motor fast steering mirror is a multi-input multi-output system, and the PID control method is difficult to effectively solve the adverse influence brought by the coupling to the system.

[0005] In addition, in the traditional control structure, performance and robustness are contradictory indicators. If the controller has good performance, its robustness will not be good, and vice versa. If a controller has good robustness, its performance will definitely not be good. Therefore, in the industrial field, the parameters of the controller are often adjusted to balance performance and robustness, so that both performance and robustness reach a moderate level. However, this method of parameter compromise is difficult to meet the high-speed and high-precision control requirements in a parameter perturbation system, and thus it is still difficult to solve the problem of controller performance degradation caused by system parameter perturbations. Summary of the Invention

[0006] The embodiments of the present application provide a generalized internal model control method, device, and medium for a voice coil motor fast steering mirror to solve the following technical problem: the problem of controller performance degradation caused by system parameter perturbations in the prior art.

[0007] The embodiments of the present application adopt the following technical solutions:

[0008] An embodiment of the present application provides a generalized internal model control method for a voice coil motor fast steering mirror, including analyzing and processing the input and output data of the voice coil motor fast steering mirror system obtained through the Matlab identification toolbox to obtain the nominal model corresponding to the voice coil motor fast steering mirror. The nominal model is loop-shaped through a pre-compensator, and the loop shaping controller is solved based on the standard H ∞ control algorithm to obtain a performance controller. The additive uncertainty between the nominal model and the model under the real working condition is determined, and the upper bound function corresponding to the additive uncertainty is obtained through curve fitting. Based on the upper bound function, the preset controller is iteratively processed through the D-K iteration algorithm to obtain a robust controller. Based on the performance controller and the robust controller, the inner loop controller is solved to achieve the generalized internal model control of the voice coil motor fast steering mirror.

[0009] In an embodiment of the present application, the performance controller of the voice coil motor fast steering mirror is designed through the H ∞ loop shaping method, which effectively suppresses the influence of the flexible mode in the system on the system tracking accuracy, and effectively suppresses the cross-axis coupling of the voice coil motor fast steering mirror based on the multi-input multi-output design method. Secondly, an embodiment of the present application designs a μ-synthesis robust controller and implements it using the architecture of generalized internal model control, achieving both system performance and robust stability, and meeting the performance requirements of the voice coil motor fast steering mirror under complex working conditions such as vibration. An embodiment of the present application can improve the tracking accuracy and dynamic response speed of the voice coil motor fast steering mirror, and at the same time can suppress the influence of parameter perturbation on the system tracking accuracy and response speed. It ensures the stable and high-performance operation of the voice coil motor fast steering mirror.

[0010] In an implementation manner of the present application, before loop shaping the nominal model through the pre-compensator, the method further includes: performing a bilinear transformation on the nominal model; based on the bilinear transformation, presetting the target open-loop singular value, and performing internal and external factorization on the transformed nominal model to obtain a pre-compensator, so as to compensate the nominal model through the pre-compensator to make the singular value of the controlled object shaped into the desired open-loop shape.

[0011] In an implementation manner of the present application, the low-frequency gain corresponding to the preset target open-loop singular value is greater than the preset gain threshold; and the high-frequency gain attenuation rate corresponding to the preset target open-loop singular value is greater than the preset speed threshold; and the slope at the cut-off frequency corresponding to the preset target open-loop singular value is -20dB.

[0012] In an implementation manner of the present application, the nominal model is loop-shaped through the pre-compensator, and based on the standard H ∞The control algorithm performs loop shaping controller resolution to obtain a performance controller, specifically including: changing the controlled object model into a state-space form to perform a normalized left-coprime factorization on the controlled object model; during the loop shaping controller resolution process, obtaining an optimal controller based on a preset robust performance index function; combining the optimal controller with a pre-compensation controller to obtain a performance controller.

[0013] In an implementation manner of the present application, based on an upper bound function, the preset controller is iteratively processed through the D-K iteration algorithm to obtain a robust controller, specifically including: determining the parameter perturbation and external disturbance corresponding to the controlled object system to construct an uncertain controlled object model based on the parameter perturbation and external disturbance; performing a transformation on the uncertain controlled object system in the form of a linear fractional transformation; based on the upper bound function and the representation form of the uncertain controlled object obtained after the transformation, performing iterative processing on the preset controller through the D-K iteration algorithm to obtain a robust controller; wherein, the representation form of the uncertain controlled object obtained after the transformation includes the uncertain controlled object model and the error weighting function.

[0014] In an implementation manner of the present application, based on the performance controller and the robust controller, an inner loop controller is solved, specifically including: performing a standard left-coprime factorization on the controlled object and the performance controller based on Youla parameterization to obtain an inner loop controller expression; solving the inner loop controller expression to obtain the inner loop controller.

[0015] In an implementation manner of the present application, performing a standard left-coprime factorization on the controlled object and the performance controller based on Youla parameterization to obtain an inner loop controller expression, specifically including: based on Youla parameterization and the function:

[0016]

[0017] obtaining a derived function:

[0018]

[0019] where K is the controller; both and Q are minimum-phase stable transfer functions; is the nominal model is the left-coprime factorization of is the left-coprime factorization of the performance controller K0; analyzing and processing the derived function to obtain the inner loop controller expression.

[0020] In an implementation manner of the present application, analyzing and processing the derived function to obtain the inner loop controller expression, specifically including: moving the terms of the derived function and extracting the common factor to obtain:

[0021]

[0022] The inner-loop controller expression is obtained as follows:

[0023]

[0024] where K is the controller; and Q are both stable transfer functions with minimum phase; is the nominal model of the left-coprime factorization, is the left-coprime factorization of the performance controller K0.

[0025] An embodiment of the present application provides a generalized internal model control device for a voice coil motor fast steering mirror, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: analyze and process the input and output data of the voice coil motor fast steering mirror system obtained through the Matlab identification toolbox to obtain the nominal model corresponding to the voice coil motor fast steering mirror; perform loop shaping on the nominal model through a pre-compensator, and perform loop shaping controller calculation based on the standard H ∞ control algorithm to obtain a performance controller; determine the additive uncertainty between the nominal model and the model under the actual working condition, and obtain the upper bound function corresponding to the additive uncertainty through curve fitting; based on the upper bound function, perform iterative processing on a preset controller through the D-K iteration algorithm to obtain a robust controller; solve for the inner-loop controller based on the performance controller and the robust controller to achieve generalized internal model control of the voice coil motor fast steering mirror.

[0026] A non-volatile computer storage medium provided by an embodiment of the present application stores computer-executable instructions, and the computer-executable instructions are set to: analyze and process the input and output data of the voice coil motor fast steering mirror system obtained through the Matlab identification toolbox to obtain the nominal model corresponding to the voice coil motor fast steering mirror; perform loop shaping on the nominal model through a pre-compensator, and perform loop shaping controller calculation based on the standard H ∞ control algorithm to obtain a performance controller; determine the additive uncertainty between the nominal model and the model under the actual working condition, and obtain the upper bound function corresponding to the additive uncertainty through curve fitting; based on the upper bound function, perform iterative processing on a preset controller through the D-K iteration algorithm to obtain a robust controller; solve for the inner-loop controller based on the performance controller and the robust controller to achieve generalized internal model control of the voice coil motor fast steering mirror.

[0027] The above at least one technical solution adopted in the embodiments of the present application can achieve the following beneficial effects: In the embodiments of the present application, through the H ∞ loop shaping method, a performance controller of the voice coil motor fast steering mirror is designed, which effectively suppresses the influence of the flexible mode in the system on the system tracking accuracy, and effectively suppresses the cross-axis coupling of the voice coil motor fast steering mirror based on the multi-input multi-output design method. Secondly, in the embodiments of the present application, a μ-synthesis robust controller is designed and implemented by adopting the architecture of the generalized internal model control, achieving the balance between system performance and robust stability, and meeting the performance requirements of the voice coil motor fast steering mirror under complex working conditions such as vibration. The embodiments of the present application can improve the tracking accuracy and dynamic response speed of the voice coil motor fast steering mirror, and at the same time can suppress the influence of parameter perturbation on the system tracking accuracy and response speed, ensuring the stable and high-performance operation of the voice coil motor fast steering mirror. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings. In the drawings:

[0029] Figure 1 It is a standard control structure diagram provided by the embodiments of the present application;

[0030] Figure 2 It is a schematic diagram of the Youla parameterization of the controller provided by the embodiments of the present application;

[0031] Figure 3 It is a generalized internal model control structure diagram provided by the embodiments of the present application;

[0032] Figure 4 It is a flowchart of the generalized internal model control method for the voice coil motor fast steering mirror provided by the embodiments of the present application;

[0033] Figure 5 It is a schematic diagram of the structure of the performance controller K0 provided by the embodiments of the present application;

[0034] Figure 6 It is a structure diagram of the uncertain feedback control system provided by the embodiments of the present application;

[0035] Figure 7 It is a schematic diagram of the linear fractional transformation provided by the embodiments of the present application;

[0036] Figure 8Schematic diagram of a generalized internal model control device for a voice coil motor fast steering mirror provided by an embodiment of the present application.

[0037] Reference numerals:

[0038] 200 Generalized internal model control device for voice coil motor fast steering mirror, 201 Processor, 202 Memory. Detailed implementation manners

[0039] Embodiments of the present application provide a generalized internal model control method, device and medium for a voice coil motor fast steering mirror.

[0040] In order to enable those skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0041] The technical solutions proposed in the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0042] Under the standard control structure, the system performance and robustness exist in a mutually exclusive form, and it is impossible to ensure the strong robustness of the system on the premise of having good system performance. Therefore, under the limitation conditions of the standard control structure, it is almost impossible to design a controller that can make the system have both high performance and strong robustness at the same time. The multi-objective complementary control breaks this limitation. It introduces an inner loop under the standard control structure and can achieve the coexistence of high performance and strong robustness.

[0043] Figure 1 A standard control structure diagram provided by an embodiment of the present application is shown as Figure 1 shown, where r is the reference signal, e is the deviation signal, d is the disturbance signal, y is the output signal, K is the controller, and G is the controlled object. Assume that the controller K0 and the nominal model have a standard left-coprime factorization:

[0044]

[0045] In the standard feedback structure, tracking the reference input r, suppressing the disturbance d and the perturbation d Δ are all completed by one controller. However, in actual control, this problem often occurs. The controller designed based on the nominal model is of good performance when there is no parameter perturbation in the system. However, due to factors such as noise and vibration in the working environment, the system undergoes parameter perturbation, and the model changes from becomes G. At this time, according to the nominal model The performance of the designed controller will be greatly reduced. This problem of controller performance degradation caused by internal parameter perturbations of the system is called the robust performance problem. When there are large parameter perturbations in the system, the problem of how to design a controller to still ensure the stability of the system is called the robust stability problem. These two problems can be collectively referred to as the robustness problem. Currently, these two problems widely exist in the industrial field. In the standard feedback structure, the controller performance problem and the robustness problem are contradictory problems.

[0046] In industry, engineers often need to adjust the parameters of the controller to pursue the balance between controller performance and robustness. This idea of "compromise" is the mainstream idea in the current industrial field. However, improving robustness through this "compromise" idea comes at the cost of sacrificing controller performance. This way of solving problems is not optimal.

[0047] To solve the contradiction problem between performance and robustness, the embodiment of this application proposes a generalized internal model control strategy. Under this strategy, performance and robustness can be complementary rather than "compromised". The generalized internal model control framework can be derived from the classical feedback control structure with the help of Youla parameterization. Figure 1 The Youla parameterization of the controller K in

[0048]

[0049] Figure 2 is a schematic diagram of the Youla parameterization of a controller provided by the embodiment of this application. Figure 1 After the Youla parameterization of the controller in Figure 2 is as shown in and consists of five parts: Q, where and Q are both minimum-phase stable transfer functions. is the left coprime factorization of the nominal model , is the left coprime factorization of the controller K0, as shown in formula (3). For Q ∈ H ∞ , it is necessary to satisfy

[0050]

[0051] Figure 3 is a generalized internal model control structure diagram provided by the embodiment of this application. By changing the position of the input signal, the Figure 3 shown multi-objective complementary control structure can be obtained. This structure is the same as Figure 2The structures shown are equivalent because the transfer function from the output signal y to the control quantity u has not changed.

[0052] In Figure 3 , when the output q of Q is 0, this structure is exactly equivalent to the classical feedback control structure shown in Figure 1 . Therefore, the controller design method in the classical feedback control structure can be used to design the controller K0. Usually, the controller K0 is designed as a high-performance controller, and its main task is to ensure the tracking accuracy and tracking bandwidth. Figure 3 The K in Δ is a robust controller designed considering the model parameter perturbation Δ, and its role is to ensure the robust stability and robust performance of the system in the case of large parameter perturbations in the system. When there are no parameter perturbations and disturbances in the system (d □ = 0, d = 0), the input f of the inner loop Q is 0, and the robust controller does not work. When there are parameter perturbations or disturbances in the system, the robust controller intervenes in the system to complete the compensation control for d and d

[0053]

[0054] Formula (4) shows the influence of the reference signal r, the perturbation d □ and the disturbance d on the control quantity u under the generalised internal model control structure. By comparing formula (1), it can be seen that the generalised internal model control structure separates the two tasks of tracking and suppressing the influence of parameter perturbations on the system to a certain extent. The tracking task is completed by the performance controller K0, and the anti-disturbance and suppression of the influence brought by parameter perturbations are achieved through the design of the inner loop Q.

[0055] The generalised internal model control design in the embodiments of this application can be divided into the following three steps:

[0056] (1) Design the performance controller K0 based on the nominal model. This controller K0 can meet the performance requirements of the system, has a high closed-loop tracking bandwidth and strong anti-interference ability.

[0057] (2) Design the robust controller K based on the model considering uncertainties. This controller K has strong robustness and can effectively cope with the influence brought by uncertainties such as internal parameter perturbations in the system.

[0058] (3) Solve the inner loop controller Q based on the designed performance controller K0 and robust controller K.

[0059] Figure 4 is a flowchart of a generalised internal model control method for a voice coil motor fast steering mirror provided by the embodiments of this application. As shown in Figure 4 , the generalised internal model control method for a voice coil motor fast steering mirror includes the following steps:

[0060] Step 101: Analyze and process the input and output data of the voice coil motor fast steering mirror system obtained through the Matlab identification toolbox to obtain the nominal model corresponding to the voice coil motor fast steering mirror.

[0061] In an embodiment of the present application, the embodiment of the present application first performs system identification on the voice coil motor fast steering mirror, and uses the Matlab identification toolbox according to the input and output data of the system to obtain a high-precision nominal model of the voice coil motor fast steering mirror.

[0062] Specifically, first, it is necessary to collect the input and output data of the voice coil motor fast steering mirror system. These data can be obtained through experimental measurements to ensure the accuracy and integrity of the data. Using mathematical tools such as Matlab and the system identification toolbox, a mathematical model of the voice coil motor fast steering mirror is constructed according to the collected data. This step usually includes selecting a suitable model structure, such as a transfer function, a state space model, etc., and using optimization algorithms, such as the least squares method, the genetic algorithm, etc., to fit the data to obtain a high-precision nominal model.

[0063] Step 102: Perform loop shaping on the nominal model through a pre-compensator, and solve the loop shaping controller based on the standard H ∞ control algorithm to obtain a performance controller.

[0064] In an embodiment of the present application, a bilinear transformation is performed on the nominal model. Based on the bilinear transformation, the preset target open-loop singular value is used to perform internal and external factorization on the transformed nominal model to obtain a pre-compensator, so as to compensate the nominal model through the pre-compensator and make the singular value of the controlled object take the shape of the desired open-loop shape.

[0065] Specifically, in the embodiment of the present application, the performance controller adopts the H ∞ loop shaping design method based on the nominal model. The H ∞ loop shaping control algorithm can design the cut-off frequency of the system by designing the open-loop loop shape, and ensure the stability of the system in the frequency band where the amplitude decays rapidly.

[0066] A compensator is used to compensate the nominal model, and a pre-compensator W1 is designed to make the singular value of the controlled object G s take the shape of the desired open-loop shape, where

[0067] Figure 5 is a schematic diagram of the structure of a performance controller K0 provided by the embodiment of the present application. As Figure 5 shown, in the design of the controller K0, the standard H ∞ control algorithm is used to solve it. When the nominal model When designing the circuit, consider designing it to have a large low-frequency gain and a small high-frequency gain.

[0068] Furthermore, in the design of the pre-compensator, first, use the bilinear transformation as shown in Equation (5) to transform into R, where and the frequency characteristic curves of R are approximately the same between frequencies ω1 - ω2.

[0069]

[0070] where, is the original untransformed operator; S is the new transformed operator; ω1 is the approximate start frequency; ω2 is the approximate cut-off frequency.

[0071] And assume the ideal open-loop singular value is G d using the bilinear transformation, and perform internal and external factorization on R.

[0072] R = N1M1 -1 , (6)

[0073] where, N1 is the all-pass transfer matrix, and M1 -1 is the external factor of R. Perform internal and external factorization on M1 T :

[0074] M1 T = N2M2 -1 , (7)

[0075] where, N2 is the all-pass transfer matrix, and M2 -1 is the external factor of M1 T . The pre-compensator W1 can be obtained from Equation (8).

[0076] W1 = IM2 -T G d , (8)

[0077] where, I is the identity matrix with all main diagonal elements equal to 1.

[0078] In an embodiment of the present application, the gain of the preset target open-loop singular value corresponding to the low-frequency band is greater than the preset gain threshold. And, the attenuation rate of the gain of the preset target open-loop singular value corresponding to the high-frequency band is greater than the preset speed threshold. And, the slope at the cut-off frequency of the preset target open-loop singular value is -20 dB.

[0079] In an embodiment of the present application, in the solution of the performance controller K0, to meet the requirements of high steady-state accuracy and high dynamic characteristics, the ideal open-loop shape G dIt is required to have a high gain in the low-frequency band, a rapid attenuation of the gain in the high-frequency band, and a slope of -20 dB at the cut-off frequency. To meet the design requirements, G d is selected in the form shown in Equation (9):

[0080]

[0081] where k is a proportionality coefficient, and the selection of k needs to be combined with the actual controlled object. Since there are constraints on the control voltage input of the VCA-FSM, the value of k needs to be reasonably selected according to experiments.

[0082] In an embodiment of the present application, the controlled object model is changed to the state-space form to perform a normalized left-coprime factorization on the controlled object model. During the solution process of the loop shaping controller, an optimal controller is obtained based on a preset robust performance index function. The optimal controller is combined with the pre-compensator to obtain the performance controller.

[0083] Specifically, the controlled object model G of the system s is written in the state-space form, and let the matrix L make A s + LC s stable, then G s can be normalized left-coprime factorized as follows:

[0084]

[0085] where (M s N s ) satisfies:

[0086] M s (jω)M s * (jω)+N s (jω)N s * (jω)=I, (11)

[0087] And L = -YCs*, we can get:

[0088]

[0089] where Y ≥ 0 is the stabilizing solution of the following Riccati equation:

[0090] A s Y + YA s * -YC s * C s Y + B s B s *= 0. (13)

[0091] During the controller calculation process, by solving γ min it is possible to make K ∞ be the optimal controller, but the optimal controller may not exist, so mostly the sub-optimal controller is chosen to be solved:

[0092]

[0093] where λ max (YQ) is the largest eigenvalue of YQ, and Q is the solution of the following Lyapunov equation:

[0094] Q(A s - YC s * C s ) + (A s - YC s * C s ) * Q + C s * C s = 0. (15)

[0095] Select γ > γ min , and by solving, the sub-optimal controller K ∞ can be obtained as follows:

[0096]

[0097] where

[0098]

[0099] Combine the solved controller K ∞ and the designed compensator W1 to obtain the final loop shaping performance controller K0 as shown in formula (18).

[0100] K0 = W1K ∞ . (18)

[0101] Step 103: Determine the additive uncertainty between the nominal model and the model under the real working condition, and obtain the upper bound function corresponding to the additive uncertainty through curve fitting.

[0102] In one embodiment of the present application, under given input conditions, a nominal model is used for simulation or calculation to obtain a series of output values. Under the same input conditions, the output values of the real system are measured or recorded. For each input point, the difference between the nominal output and the real output is calculated, that is, the additive uncertainty is obtained. According to the characteristics of the data and the requirements of the problem, a suitable fitting function is selected, and the least squares method, non-linear least squares method or other optimization methods are used to determine the parameters of the fitting function, and the goodness-of-fit index (such as mean square error MSE, root mean square error RMSE, etc.) is used to evaluate the fitting effect.

[0103] Furthermore, the function obtained by curve fitting describes the average value or trend of the additive uncertainty. However, in order to design a robust controller, it is usually necessary to know the upper bound of the additive uncertainty, that is, a value larger than the actual uncertainty, to ensure that the controller remains stable in the worst case. For example, if the fitting function itself has an upper bound, the maximum value or upper bound that can be directly used can be used as the upper bound function; in order to estimate the uncertainty more conservatively, a safety margin can be added to the fitting function f(x); if the additive uncertainty varies greatly with time or input conditions, a dynamic upper bound function, that is, a function that varies with time or input conditions, can be considered.

[0104] Step 104: Based on the upper bound function, the preset controller is iteratively processed through the D-K iteration algorithm to obtain a robust controller.

[0105] In one embodiment of the present application, the parameter perturbation and external disturbance corresponding to the controlled object system are determined to construct an uncertain controlled object model based on the parameter perturbation and external disturbance. The uncertain controlled object system is transformed in the form of linear fractional transformation. Based on the upper bound function and the representation form of the uncertain controlled object obtained after transformation, the preset controller is iteratively processed through the D-K iteration algorithm to obtain a robust controller. Among them, the representation form of the uncertain controlled object obtained after transformation includes the uncertain controlled object model and the error weighting function.

[0106] Specifically, this embodiment of the present application considers the upper bound of parameter perturbation uncertainty and adopts the μ-synthesis method to design the robust controller K. μ-synthesis can keep the system stable under the action of parameter perturbation and external disturbance, and the control performance will not decrease too much, which can effectively reduce the adverse effects brought by parameter perturbation to the controller.

[0107] Specifically:

[0108]

[0109] Figure 6 It is a structural diagram of an uncertain feedback control system provided by an embodiment of the present application. Figure 6The feedback control system shown can be represented in the form of (Linear Fraction Transformation, LFT), where W p is the weighting function for the error, and M is the plant model considering uncertainties.

[0110] Figure 7 This is a schematic diagram of linear fractional transformation provided by an embodiment of the present application. As Figure 7 shown, in the LFT form:

[0111]

[0112] As Figure 6 shown in the LFT, a stable controller K needs to be found to minimize the μ-norm in Equation (20):

[0113]

[0114] where

[0115] F l (P, K) = P 11 + P 12 K(I - P 22 K) -1 P 21 (21)

[0116] The problem of solving the μ-synthesis robust controller is solved by D-K iteration, and can be solved by the dksyn function in Matlab.

[0117] Step 105: Based on the performance controller and the robust controller, solve to obtain the inner-loop controller to achieve the generalized internal model control of the voice coil motor fast steering mirror.

[0118] In an embodiment of the present application, based on Youla parameterization, a standard left-coprime factorization of the plant and the performance controller is performed to obtain the expression of the inner-loop controller. Solve the expression of the inner-loop controller to obtain the inner-loop controller.

[0119] Specifically, in Figure 3 the multi-objective complementary control structure shown, the inner-loop controller Q is mainly used to improve the robustness of the system. According to the design principle of multi-objective complementary control, it is solved based on the identified nominal model the performance controller K0 and the robust controller K.

[0120] Furthermore, according to the Youla parameterization principle of the controller, as well as Equations (2) and (3), it can be further deduced that:

[0121]

[0122] Obtain the derived function:

[0123]

[0124] where K is a controller; and Q are both stable transfer functions with minimum phase; is the nominal model of the left coprime factorization, is the left coprime factorization of the performance controller K0;

[0125] Analyze and process the derived function to obtain the inner loop controller expression.

[0126] Furthermore, move the terms of the derived function and extract the common factor to obtain:

[0127]

[0128] The obtained inner loop controller expression is:

[0129]

[0130] where K is a controller; and Q are both stable transfer functions with minimum phase; is the nominal model of the left coprime factorization, is the left coprime factorization of the performance controller K0.

[0131] Through the above method, the design of the generalized internal model control strategy for the fast steering mirror of the voice coil motor can be realized. The finally obtained controller architecture is as Figure 3 shown.

[0132] Figure 8 This is the structural schematic diagram of a generalized internal model control device for a fast steering mirror of a voice coil motor provided by an embodiment of the present application. As Figure 8 shown, the generalized internal model control device 200 for a fast steering mirror of a voice coil motor includes: at least one processor 201; and a memory 202 communicatively connected to the at least one processor 201; wherein, the memory 202 stores instructions executable by the at least one processor 201, and the instructions are executed by the at least one processor 201 to enable the at least one processor 201 to: analyze and process the input and output data of the fast steering mirror system of the voice coil motor obtained through the Matlab identification toolbox to obtain the nominal model corresponding to the fast steering mirror of the voice coil motor; perform loop shaping on the nominal model through a pre-compensator, and based on the standard H ∞The control algorithm performs loop shaping controller resolution to obtain a performance controller; determines the additive uncertainty between the nominal model and the model under the actual working conditions, and obtains the upper bound function corresponding to the additive uncertainty through curve fitting; based on the upper bound function, iteratively processes the preset controller through the D-K iteration algorithm to obtain a robust controller; based on the performance controller and the robust controller, solves for the inner loop controller to achieve the generalized internal model control of the voice coil motor fast steering mirror.

[0133] A non-volatile computer storage medium provided by an embodiment of the present application stores computer-executable instructions, and the computer-executable instructions are set to: analyze and process the input and output data of the voice coil motor fast steering mirror system obtained through the Matlab identification toolbox to obtain the nominal model corresponding to the voice coil motor fast steering mirror; perform loop shaping on the nominal model through a pre-compensator, and based on the standard H ∞ The control algorithm performs loop shaping controller resolution to obtain a performance controller; determines the additive uncertainty between the nominal model and the model under the actual working conditions, and obtains the upper bound function corresponding to the additive uncertainty through curve fitting; based on the upper bound function, iteratively processes the preset controller through the D-K iteration algorithm to obtain a robust controller; based on the performance controller and the robust controller, solves for the inner loop controller to achieve the generalized internal model control of the voice coil motor fast steering mirror.

[0134] The various embodiments in the present application are all described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments. In particular, for the embodiments of the device, equipment, and non-volatile computer storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0135] The above are only the embodiments of the present application and are not used to limit the present application. For those skilled in the art, the embodiments of the present application can have various changes and modifications. These modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A generalized internal model control method for a voice coil motor fast steering mirror, characterized in that The method includes: Analyzing and processing the input and output data of the voice coil motor fast steering mirror system obtained through the Matlab identification toolbox to obtain the nominal model corresponding to the voice coil motor fast steering mirror; The nominal model is loop-shaped by a pre-compensator, and a performance controller is obtained by solving a loop-shaping controller based on a standard H ∞ control algorithm Determining the additive uncertainty between the nominal model and the model under actual working conditions, and obtaining the upper bound function corresponding to the additive uncertainty through curve fitting; Based on the upper bound function, iteratively processing the preset controller through the D-K iteration algorithm to obtain a robust controller; Based on the performance controller and the robust controller, solving to obtain an inner loop controller to achieve the generalized internal model control of the voice coil motor fast steering mirror.

2. The generalized internal model control method for a voice coil motor fast steering mirror according to claim 1, wherein Before shaping the loop of the nominal model through the pre-compensator, the method further includes: Performing a bilinear transformation on the nominal model; Based on the bilinear transformation to preset the target open-loop singular value, performing internal and external factorization on the transformed nominal model to obtain the pre-compensator, so as to compensate the nominal model through the pre-compensator to make the singular value of the controlled object take the shape of the desired open-loop.

3. A generalized internal model control method for a voice coil motor fast steering mirror according to claim 2, characterized in that The low-frequency gain corresponding to the preset target open-loop singular value is greater than the preset gain threshold; And, the high-frequency gain attenuation rate corresponding to the preset target open-loop singular value is greater than the preset speed threshold; And, the slope at the cut-off frequency corresponding to the preset target open-loop singular value is -20 dB.

4. A generalized internal model control method for a voice coil motor fast steering mirror according to claim 1, characterized in that The nominal model is loop-shaped by a pre-compensator, and a loop shaping controller is solved based on the standard H ∞ control algorithm to obtain a performance controller, specifically including: Changing the controlled object model to the state-space form to perform a normalized left-coprime factorization on the controlled object model; During the solution process of the loop shaping controller, obtaining the optimal controller based on the preset robust performance index function; Combining the optimal controller and the pre-compensator to obtain the performance controller.

5. A generalized internal model control method for a voice coil motor fast steering mirror according to claim 1, characterized in that The step of obtaining the robust controller by iteratively processing the preset controller through the D-K iteration algorithm based on the upper bound function specifically includes: Determining the parameter perturbation and external disturbance corresponding to the controlled object system, and constructing an uncertain controlled object model based on the parameter perturbation and the external disturbance; Transforming the uncertain controlled object system in the form of a linear fractional transformation; Based on the upper bound function and the representation form of the uncertain controlled object obtained after transformation, iteratively processing the preset controller through the D-K iteration algorithm to obtain the robust controller; Wherein, the representation form of the uncertain controlled object obtained after transformation includes the uncertain controlled object model and the error weighting function.

6. The generalized internal model control method for a voice coil motor fast steering mirror according to claim 1, wherein The step of solving to obtain the inner loop controller based on the performance controller and the robust controller specifically includes: Performing a standard left-coprime factorization on the controlled object and the performance controller based on Youla parameterization to obtain the inner loop controller expression; Solving the inner loop controller expression to obtain the inner loop controller.

7. A generalized internal model control method for a voice coil motor fast steering mirror according to claim 6, characterized in that The step of performing a standard left-coprime factorization on the controlled object and the performance controller based on Youla parameterization to obtain the inner loop controller expression specifically includes: Based on Youla parameterization and the function: Obtaining the derivation function: where K is a controller; Both and Q are stable transfer functions with minimum phase; is the nominal model of the left-coprime factorization, is the left-coprime factorization of the performance controller K0; Analyzing and processing the derivation function to obtain the inner loop controller expression.

8. A generalized internal model control method for a voice coil motor fast steering mirror according to claim 7, characterized in that Analyzing and processing the derived function to obtain the inner-loop controller expression specifically includes: Transposing the derived function and extracting the common factor to obtain: The obtained inner-loop controller expression is: where K is a controller; and Q are both stable transfer functions of minimum phase; is the nominal model of the left coprime factorization, is the left coprime factorization of the performance controller K0.

9. A generalized internal model control device for a voice coil motor fast steering mirror, characterized in that, The device includes a memory for storing computer program instructions and a processor for executing the program instructions. Wherein, when the computer program instructions are executed by the processor, the device is triggered to execute the method described in any one of claims 1-8.

10. A non-volatile computer storage medium stores computer-executable instructions, characterized in that, The computer-executable instructions can execute the method described in any one of claims 1-8.

Images