A dynamic control allocation method based on actuator frequency characteristics

Abstract

A dynamic control allocation method based on the frequency characteristics of actuators is disclosed, belonging to the field of aircraft control technology. This invention aims to improve the allocation efficiency of multiple actuators. The method includes establishing a dynamic model of multiple actuators, discretizing the model, and setting physical constraints for each actuator; designing a control allocation mapping function based on a control efficiency matrix to describe the control allocation problem of multiple actuators; constructing a dynamic control allocation method based on the frequency characteristics of multiple actuators; constructing a preliminary set of feasible control inputs that satisfy the actuator constraints; designing a dynamic control allocation optimization objective function with a time-varying matrix; and using a fixed-point iterative algorithm to find the optimal solution for dynamic control allocation based on the actuator frequency characteristics. The proposed algorithm is convergent, providing a guarantee for the subsequent design of virtual control laws.

Description

Technical Field

[0001] This invention belongs to the field of aircraft control technology, specifically relating to a dynamic control allocation method based on the frequency characteristics of actuators. Background Technology

[0002] For aircraft attitude control systems, traditional layouts primarily consist of three sets of control surfaces: ailerons, rudders, and elevators, used to control roll, yaw, and pitch. Within the permissible range of these control surfaces, the deflection of the control surfaces corresponding to the required control torque is uniquely determined. To pursue higher maneuverability, controllability, and aerodynamic characteristics, aircraft layouts are gradually moving towards multi-control surface configurations. Compared to traditional layouts, multi-control surface configurations offer stronger redundancy, providing missile flight control with many flexible implementation methods, and significantly improving the missile's reconfigurability under flight failure conditions and its survivability under battlefield conditions.

[0003] Due to the complexity of the flight environment and the changing flight states of aircraft, the aerodynamic parameters of the controlled object exhibit strong time-varying characteristics. Traditional classical control theory often designs based on a nominal model when parameter variations are small. However, if the parameter variation range is large, the controller needs to adapt to changes in flight motion pressure, angle of attack, etc., to achieve relatively satisfactory control quality. Control allocation technology can rationally combine control surfaces to maximize the achievement of desired control objectives, effectively solve the control problem of actuator redundancy, and enable the aircraft to have superior flight performance and improve system reliability. With control allocation technology, given virtual commands can be distributed to each control surface according to the allocation algorithm, reducing the coupling characteristics of multiple control surfaces and accelerating control calculation efficiency. Simultaneously, the introduction of control allocation technology enables modular design of flight control systems. This involves designing robust control law modules that generate virtual control inputs and control allocation modules that distribute virtual control commands to each control surface. Modular design significantly reduces the design difficulty of multi-control-surface aircraft control systems. When the aircraft experiences failure or even complete failure in some control surfaces, control reconfiguration can be achieved simply by modifying some parameters in the control allocater, eliminating the need to redesign a complex controller and thus reducing the design difficulty of the controller. Therefore, control allocation technology is of great significance for improving the performance of multi-control-face aircraft. How to achieve coordination among multiple control surfaces is the primary focus of aircraft control allocation research. For an overdriven system, the number of actuators is usually greater than the dimension of virtual control inputs. When using a common multivariable attitude control system design, weighted control of singular values ​​can solve the actuator saturation problem, but at the same time, it reduces system performance and causes certain impacts and losses. Because multiple actuators have position and rate constraints, different control surface efficiencies, and different dynamic characteristics, the dynamic changes of the actuators cannot be ignored. Currently, most control allocation methods ignore the impact of multi-actuator bandwidth on the system and do not fully utilize the dynamic and redundancy characteristics of the actuators. This results in the actuators not fully leveraging their own dynamic characteristics during the allocation process, leading to low allocation efficiency and actuator saturation. Summary of the Invention

[0004] The problem this invention aims to solve is to improve the allocation efficiency of multiple actuators, and proposes a dynamic control allocation method based on the frequency characteristics of actuators.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A dynamic control allocation method based on the frequency characteristics of an actuator includes the following steps:

[0007] S1. Establish a dynamic model of multiple actuators, then discretize the dynamic model of multiple actuators, and set physical constraints for multiple actuators;

[0008] S2. Design a control allocation mapping function based on the control efficiency matrix to describe the control allocation problem of multiple actuators;

[0009] S3. Construct a dynamic control allocation method based on the frequency characteristics of multiple actuators;

[0010] This includes constructing a set of feasible control inputs that initially satisfy the constraints of the actuator based on the control allocation mapping function obtained in step S2, and then designing a dynamic control allocation optimization objective function with a time-varying matrix;

[0011] The time-varying matrix is ​​analyzed, the rate of change of the desired virtual control command is defined and normalized; the bandwidth characteristics of the actuator are defined, and then a partial matrix based on the influence of the actuator bandwidth characteristics on the rate of change and a partial matrix based on the influence of the allocation efficiency of a single actuator on the allocation result are constructed to obtain the complete time-varying matrix; the complete time-varying matrix is ​​substituted into the dynamic control allocation optimization objective function with the time-varying matrix to obtain the final dynamic control allocation optimization objective function with the time-varying matrix.

[0012] S4. Using the final dynamic control allocation optimization objective function with time-varying matrix obtained in step S3, the optimal solution for dynamic control allocation based on the frequency characteristics of the actuator is obtained using a fixed-point iterative algorithm.

[0013] Furthermore, the specific implementation method of step S1 includes the following steps:

[0014] S1.1. Establish a dynamic system model with multiple actuators;

[0015] For multi-channel, multi-control surface control of an aircraft, assuming there are The first executive agency and the first The order of each executive body is denoted as , ;

[0016] The dynamic system model of a multi-actuator system is then represented as:

[0017] ,

[0018] in, Represents the status of the implementing agency. , Indicates the input of the actuator, , This represents the actual servo deflection angle. , , and These represent the system matrix, control matrix, and output matrix, respectively. , , , , This represents the sum of the orders of the m actuators. Represents the status of the implementing agency The first derivative;

[0019] S1.2. Discretize the multi-actuator dynamic system model obtained in step S1.1 to obtain the multi-actuator discrete system model as follows:

[0020]

[0021]

[0022] in, , and These correspond to the system matrix, control matrix, and output matrix of a discrete system, respectively. For the first The status of the step actuator, Indicates the first The input to the actuator; to ensure the stability of the multi-actuator dynamic system, assume the system... , It is controllable. , It is considerable; and It is a non-singular matrix;

[0023] S1.3. Set the physical constraints for multiple actuators as follows:

[0024]

[0025]

[0026] in, and These represent the minimum and maximum values ​​of the position constraints for each actuator, respectively. The maximum value corresponding to the rate constraint of each actuator; The actual deflection rate of the multiple actuators;

[0027] Combining the physical constraints of the multiple actuators, the expression for the rudder deflection angle constraint of the multiple actuators at the current sampling time is obtained as follows:

[0028]

[0029]

[0030]

[0031] in, and These represent the minimum and maximum actual deflections after combining position and rate constraints across multiple actuators; T is the sampling time; and t is the time independent variable in the continuous time domain.

[0032] Then, the rudder deflection angle constraint of the multiple actuators at the current sampling time is transformed into... Expressing the constraints for the variables, we get:

[0033]

[0034]

[0035]

[0036] in, and These are the minimum and maximum values ​​output by the control allocation algorithm at time k, after combining position and rate constraints for multiple actuators.

[0037] Furthermore, step S2, based on the control efficiency matrix, designs the expression for the control allocation mapping function as follows:

[0038]

[0039] in, These are the desired virtual control commands. It is the control efficiency matrix;

[0040]

[0041] in, It is a matrix The element in the nth row and mth position is usually related to the aerodynamic coefficients of the aircraft, where n is the virtual control command. The order of.

[0042] Furthermore, the specific implementation method of step S3 includes the following steps:

[0043] S3.1. Construct a preliminary set of feasible control inputs that satisfy the actuator constraints based on the control allocation mapping function obtained in step S2. The expression is:

[0044]

[0045] in, It is a constant weighted matrix;

[0046] S3.2. Design a dynamic control allocation optimization objective function with a time-varying matrix, and obtain:

[0047]

[0048] in, It is the expected deflection of the implementing agency. It is a constant matrix. It is a time-varying dynamic diagonal matrix;

[0049] Then to Perform analysis;

[0050] S3.2.1. Considering the position and rate constraints of multiple actuators, define the rate of change of the desired virtual control law, and obtain:

[0051]

[0052] in, Let the rate of change of the virtual control law be... Then, the rate of change of the virtual control law is normalized to obtain:

[0053]

[0054] in, Let j be the rate of change of the j-th virtual control law. Let j be the rate of change of the virtual control law. The normalized parameters, and These are the maximum and minimum values ​​of the rate of change of the virtual control law, respectively;

[0055] S3.2.2. Define the bandwidth characteristics of the actuator, sort the bandwidth of the actuator, and define the first... The bandwidth of each actuator is Two parameters related to the actuator frequency are introduced. and Its expression is:

[0056]

[0057] ;

[0058] S3.2.3. Construct a partial matrix based on the influence of actuator bandwidth characteristics on the rate of change. When the bandwidths of the actuators are arranged in descending order, the matrix expression is as follows:

[0059]

[0060] When the actuator bandwidths are arranged in ascending order The form becomes:

[0061]

[0062] in, For design parameters, Is with and The relevant design parameters, namely ;

[0063] S3.2.4. Construct a partial matrix based on the impact of allocation efficiency of a single executor on the allocation result. The resulting expression is:

[0064]

[0065] S3.2.5. Design a complete time-varying matrix Based on considerations of both instruction change rate and executor efficiency, targeting Virtual torque and 3D actuator, complete time-varying matrix Designed as The diagonal matrix formed by multiplying the diagonal elements is expressed as:

[0066]

[0067] in, Representation matrix The Middle Line 1 Column elements, ;

[0068] S3.2.6. Substitute the complete time-varying matrix into the dynamic control allocation optimization objective function with the time-varying matrix to obtain the final dynamic control allocation optimization objective function with the time-varying matrix.

[0069] Furthermore, the specific implementation method of step S4 includes the following steps:

[0070] S4.1. Integrate the preliminary feasible control input set that satisfies the actuator constraints obtained in step S3 with the dynamic control allocation optimization objective function with a time-varying matrix to obtain the constrained quadratic programming expression as follows:

[0071]

[0072]

[0073] Where H is a linear combination of the constant matrix and the time-varying matrix in S3.1 and S3.2. These are variables related to the desired virtual control command and the desired actuator deflection, and their specific expressions are as follows:

[0074]

[0075]

[0076] in, Representative matrix No. Line 1 The elements of the column.

[0077] S4.2. Solve the constrained quadratic programming expression obtained in step S4.1 using a globally convergent fixed-point iterative algorithm;

[0078] Define saturation function for:

[0079]

[0080] in, The independent variable of the function, and They are and The One element;

[0081] Then, within one sampling period, a fixed-point equation of the following form is given. The optimal solution is obtained iteratively based on the fixed-point equation, and its expression is:

[0082]

[0083] in, It is about The function, It is a saturation function related to the output of the control allocation algorithm. This represents the reciprocal of the square root of the sum of squares of the elements in matrix H, and its specific expression is as follows:

[0084]

[0085]

[0086] in, It is the identity matrix; the fixed-point equation converges iteratively to the optimal solution of the optimization problem at the current sampling time.

[0087] The beneficial effects of this invention are:

[0088] This invention discloses a dynamic control allocation method based on the frequency characteristics of actuators. Considering the position and rate constraints of multiple actuators, and leveraging the bandwidth differences among them, a designed dynamic weighting matrix is ​​incorporated. This matrix can, to a certain extent, alter the control efficiency allocation ratio, thereby preventing actuator saturation and effectively handling situations where actuators are constrained. By fully utilizing the dynamic characteristics of different actuators, control allocation efficiency is improved, ensuring the system's rapid response capability. Furthermore, for special forms of quadratic programming, a fixed-point iterative algorithm is used to reduce the computational load and ensure algorithm convergence, providing a guarantee for the subsequent design of virtual control laws. Attached Figure Description

[0089] Figure 1 This is a flowchart of a dynamic control allocation method based on the frequency characteristics of an actuator, as described in this invention. Detailed Implementation

[0090] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described specific embodiments are merely a part of the embodiments of the invention, and not all of them. The components of the specific embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations, and the invention may also have other embodiments.

[0091] Therefore, the following detailed description of specific embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected specific embodiments of the invention. All other specific embodiments obtained by those skilled in the art based on these specific embodiments without inventive effort are within the scope of protection of this invention.

[0092] To further understand the invention's content, features, and effects, the following specific embodiments are provided, along with accompanying drawings. Figure 1 Detailed explanation is as follows:

[0093] Example 1:

[0094] A dynamic control allocation method based on the frequency characteristics of an actuator includes the following steps:

[0095] S1. Establish a dynamic model of multiple actuators, then discretize the dynamic model of multiple actuators, and set physical constraints for multiple actuators;

[0096] Furthermore, the specific implementation method of step S1 includes the following steps:

[0097] S1.1. Establish a dynamic system model with multiple actuators;

[0098] For multi-channel, multi-control surface control of an aircraft, assuming there are The first executive agency and the first The order of each executive body is denoted as , ;

[0099] The dynamic system model of a multi-actuator system is then represented as:

[0100] ,

[0101] in, Represents the status of the implementing agency. , Indicates the input of the actuator, , This represents the actual servo deflection angle. , , and These represent the system matrix, control matrix, and output matrix, respectively. , , , , This represents the sum of the orders of the m actuators. Represents the status of the implementing agency The first derivative;

[0102] S1.2. Discretize the multi-actuator dynamic system model obtained in step S1.1 to obtain the multi-actuator discrete system model as follows:

[0103]

[0104]

[0105] in, , and These correspond to the system matrix, control matrix, and output matrix of a discrete system, respectively. For the first The status of the step actuator, Indicates the first The input to the actuator; to ensure the stability of the multi-actuator dynamic system, assume the system... , It is controllable. , It is considerable; and It is a non-singular matrix;

[0106] S1.3. Set the physical constraints for multiple actuators as follows:

[0107]

[0108]

[0109] in, and These represent the minimum and maximum values ​​of the position constraints for each actuator, respectively. The maximum value corresponding to the rate constraint of each actuator; The actual deflection rate of the multiple actuators;

[0110] Combining the physical constraints of the multiple actuators, the expression for the rudder deflection angle constraint of the multiple actuators at the current sampling time is obtained as follows:

[0111]

[0112]

[0113]

[0114] in, and These represent the minimum and maximum actual deflections after combining position and rate constraints across multiple actuators; T is the sampling time; and t is the time independent variable in the continuous time domain.

[0115] Then, the rudder deflection angle constraint of the multiple actuators at the current sampling time is transformed into... Expressing the constraints for the variables, we get:

[0116]

[0117]

[0118]

[0119] in, and These are the minimum and maximum values ​​output by the control allocation algorithm at time k, after combining position and rate constraints for multiple actuators.

[0120] S2. Design a control allocation mapping function based on the control efficiency matrix to describe the control allocation problem of multiple actuators;

[0121] Furthermore, step S2, based on the control efficiency matrix, designs the expression for the control allocation mapping function as follows:

[0122]

[0123] in, These are the desired virtual control commands. It is the control efficiency matrix;

[0124]

[0125] in, It is a matrix The element in the nth row and mth position is usually related to the aerodynamic coefficients of the aircraft, where n is the virtual control command. The order of.

[0126] S3. Construct a dynamic control allocation method based on the frequency characteristics of multiple actuators;

[0127] This includes constructing a set of feasible control inputs that initially satisfy the constraints of the actuator based on the control allocation mapping function obtained in step S2, and then designing a dynamic control allocation optimization objective function with a time-varying matrix;

[0128] The time-varying matrix is ​​analyzed, the rate of change of the desired virtual control command is defined and normalized; the bandwidth characteristics of the actuator are defined, and then a partial matrix based on the influence of the actuator bandwidth characteristics on the rate of change and a partial matrix based on the influence of the allocation efficiency of a single actuator on the allocation result are constructed to obtain the complete time-varying matrix; the complete time-varying matrix is ​​substituted into the dynamic control allocation optimization objective function with the time-varying matrix to obtain the final dynamic control allocation optimization objective function with the time-varying matrix.

[0129] Furthermore, the specific implementation method of step S3 includes the following steps:

[0130] S3.1. Construct a preliminary set of feasible control inputs that satisfy the actuator constraints based on the control allocation mapping function obtained in step S2. The expression is:

[0131]

[0132] in, It is a constant weighted matrix;

[0133] S3.2. Design a dynamic control allocation optimization objective function with a time-varying matrix, and obtain:

[0134]

[0135] in, It is the expected deflection of the implementing agency. It is a constant matrix. It is a time-varying dynamic diagonal matrix;

[0136] Then to Perform analysis;

[0137] S3.2.1. Considering the position and rate constraints of multiple actuators, define the rate of change of the desired virtual control law, and obtain:

[0138]

[0139] in, Let the rate of change of the virtual control law be... Then, the rate of change of the virtual control law is normalized to obtain:

[0140]

[0141] in, Let j be the rate of change of the j-th virtual control law. Let j be the rate of change of the virtual control law. The normalized parameters, and These are the maximum and minimum values ​​of the rate of change of the virtual control law, respectively;

[0142] S3.2.2. Define the bandwidth characteristics of the actuator, sort the bandwidth of the actuator, and define the first... The bandwidth of each actuator is Two parameters related to the actuator frequency are introduced. and Its expression is:

[0143]

[0144] ;

[0145] S3.2.3. Construct a partial matrix based on the influence of actuator bandwidth characteristics on the rate of change. When the bandwidths of the actuators are arranged in descending order, the matrix expression is as follows:

[0146]

[0147] When the actuator bandwidths are arranged in ascending order The form becomes:

[0148]

[0149] in, For design parameters, Is with and The relevant design parameters, namely ;

[0150] S3.2.4. Construct a partial matrix based on the impact of allocation efficiency of a single executor on the allocation result. The resulting expression is:

[0151]

[0152] S3.2.5. Design a complete time-varying matrix Based on considerations of both instruction change rate and executor efficiency, targeting Virtual torque and 3D actuator, complete time-varying matrix Designed as The diagonal matrix formed by multiplying the diagonal elements is expressed as:

[0153]

[0154] in, Representation matrix The Middle Line 1 Column elements, ;

[0155] S3.2.6. Substitute the complete time-varying matrix into the dynamic control allocation optimization objective function with the time-varying matrix to obtain the final dynamic control allocation optimization objective function with the time-varying matrix.

[0156] S4. Using the final dynamic control allocation optimization objective function with time-varying matrix obtained in step S3, the optimal solution for dynamic control allocation based on the frequency characteristics of the actuator is obtained using a fixed-point iterative algorithm.

[0157] Furthermore, the specific implementation method of step S4 includes the following steps:

[0158] S4.1. Integrate the preliminary feasible control input set that satisfies the actuator constraints obtained in step S3 with the dynamic control allocation optimization objective function with a time-varying matrix to obtain the constrained quadratic programming expression as follows:

[0159]

[0160]

[0161] Where H is a linear combination of the constant matrix and the time-varying matrix in S3.1 and S3.2. These are variables related to the desired virtual control command and the desired actuator deflection, and their specific expressions are as follows:

[0162]

[0163]

[0164] in, Representative matrix No. Line 1 The elements of the column.

[0165] S4.2. Solve the constrained quadratic programming expression obtained in step S4.1 using a globally convergent fixed-point iterative algorithm;

[0166] Define saturation function for:

[0167]

[0168] in, The independent variable of the function, and They are and The One element;

[0169] Then, within one sampling period, a fixed-point equation of the following form is given. The optimal solution is obtained iteratively based on the fixed-point equation, and its expression is:

[0170]

[0171] in, It is about The function, It is a saturation function related to the output of the control allocation algorithm. This represents the reciprocal of the square root of the sum of squares of the elements in matrix H, and its specific expression is as follows:

[0172]

[0173]

[0174] in, It is the identity matrix; the fixed-point equation converges iteratively to the optimal solution of the optimization problem at the current sampling time.

[0175] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0176] Although this application has been described above with reference to specific embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of this application. In particular, as long as there is no structural conflict, the features in the specific embodiments disclosed in this application can be combined with each other in any way. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, this application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method of dynamic control allocation based on actuator frequency characteristics, characterized in that, Includes the following steps: S1. Establish a dynamic model of multiple actuators, then discretize the dynamic model of multiple actuators, and set physical constraints for multiple actuators; S2. Design a control allocation mapping function based on the control efficiency matrix to describe the control allocation problem of multiple actuators; S3. Construct a dynamic control allocation method based on the frequency characteristics of multiple actuators; This includes constructing a set of feasible control inputs that initially satisfy the constraints of the actuator based on the control allocation mapping function obtained in step S2, and then designing a dynamic control allocation optimization objective function with a time-varying matrix; The time-varying matrix is ​​analyzed, the rate of change of the desired virtual control command is defined and normalized; Define the bandwidth characteristics of the actuator, and then construct a partial matrix based on the influence of the actuator bandwidth characteristics on the rate of change and a partial matrix based on the influence of the allocation efficiency of a single actuator on the allocation result, to obtain the complete time-varying matrix; Substituting the complete time-varying matrix into the dynamic control allocation optimization objective function with the time-varying matrix, we obtain the final dynamic control allocation optimization objective function with the time-varying matrix. S4. Using the final dynamic control allocation optimization objective function with time-varying matrix obtained in step S3, the optimal solution for dynamic control allocation based on the frequency characteristics of the actuator is obtained using a fixed-point iterative algorithm.

2. The method of claim 1, wherein the dynamic control allocation is based on a frequency characteristic of the actuator. The specific implementation method of step S1 includes the following steps: S1.

1. Establish a dynamic system model with multiple actuators; For the multi-channel multi-control surface control of aircraft, it is assumed that there are actuators and the order of the first actuator is denoted as , ; The dynamic system model of a multi-actuator system is then represented as: , in, Represents the status of the implementing agency. , Indicates the input of the actuator, , This represents the actual servo deflection angle. , , and These represent the system matrix, control matrix, and output matrix, respectively. , , , , This represents the sum of the orders of the m actuators. Represents the status of the implementing agency The first derivative; S1.

2. Discretize the multi-actuator dynamic system model obtained in step S1.1 to obtain the multi-actuator discrete system model as follows: in, , and These correspond to the system matrix, control matrix, and output matrix of a discrete system, respectively. For the first The status of the step actuator, Indicates the first The input to the actuator; to ensure the stability of the multi-actuator dynamic system, assume the system... , It is controllable. , It is considerable; and It is a non-singular matrix; S1.

3. Set the physical constraints for multiple actuators as follows: in, and These represent the minimum and maximum values ​​of the position constraints for each actuator, respectively. The maximum value corresponding to the rate constraint of each actuator; The actual deflection rate of the multiple actuators; Combining the physical constraints of the multiple actuators, the expression for the rudder deflection angle constraint of the multiple actuators at the current sampling time is obtained as follows: in, and These represent the minimum and maximum actual deflections after combining position and rate constraints across multiple actuators; T is the sampling time; and t is the time independent variable in the continuous time domain. Then, the rudder deflection angle constraint of the multiple actuators at the current sampling time is transformed into... Expressing the constraints on the variables, we get: in, and These are the minimum and maximum values ​​output by the control allocation algorithm at time k, after combining position and rate constraints for multiple actuators.

3. The dynamic control allocation method based on the frequency characteristics of the actuator according to claim 2, characterized in that, Step S2, based on the control efficiency matrix, designs the expression for the control allocation mapping function as follows: in, These are the desired virtual control commands. It is the control efficiency matrix; in, It is a matrix The element in the nth row and mth position is usually related to the aerodynamic coefficients of the aircraft, where n is the virtual control command. The order of.

4. The dynamic control allocation method based on the frequency characteristics of the actuator according to claim 3, characterized in that, The specific implementation method of step S3 includes the following steps: S3.

1. Construct a preliminary set of feasible control inputs that satisfy the actuator constraints based on the control allocation mapping function obtained in step S2. The expression is: in, It is a constant weighted matrix; S3.

2. Design a dynamic control allocation optimization objective function with a time-varying matrix, and obtain: in, It is the expected deflection of the implementing agency. It is a constant matrix. It is a time-varying dynamic diagonal matrix; Then to Perform analysis; S3.2.

1. Considering the position and rate constraints of multiple actuators, define the rate of change of the desired virtual control law, and obtain: in, Let the rate of change of the virtual control law be... Then, the rate of change of the virtual control law is normalized to obtain: in, Let j be the rate of change of the j-th virtual control law. Let j be the rate of change of the virtual control law. The normalized parameters, and These are the maximum and minimum values ​​of the rate of change of the virtual control law, respectively; S3.2.

2. Define the bandwidth characteristics of the actuator, sort the bandwidth of the actuator, and define the first... The bandwidth of each actuator is Two parameters related to the actuator frequency are introduced. and Its expression is: ； S3.2.

3. Construct a partial matrix based on the influence of actuator bandwidth characteristics on the rate of change. When the bandwidths of the actuators are arranged in descending order, the matrix expression is as follows: When the actuator bandwidths are arranged in ascending order The form becomes: in, For design parameters, Is with and The relevant design parameters, namely ; S3.2.

4. Construct a partial matrix based on the impact of allocation efficiency of a single executor on the allocation result. The resulting expression is: S3.2.

5. Design a complete time-varying matrix Based on considerations of both instruction change rate and executor efficiency, targeting Virtual torque and 3D actuator, complete time-varying matrix Designed as The diagonal matrix formed by multiplying the diagonal elements is expressed as: in, Representation matrix The Middle Line number Column elements, ; S3.2.

6. Substitute the complete time-varying matrix into the dynamic control allocation optimization objective function with the time-varying matrix to obtain the final dynamic control allocation optimization objective function with the time-varying matrix.

5. The dynamic control allocation method based on the frequency characteristics of the actuator according to claim 4, characterized in that, The specific implementation method of step S4 includes the following steps: S4.

1. Integrate the preliminary feasible control input set that satisfies the actuator constraints obtained in step S3 with the dynamic control allocation optimization objective function with a time-varying matrix to obtain the constrained quadratic programming expression as follows: Where H is a linear combination of the constant matrix and the time-varying matrix in S3.1 and S3.

2. These are variables related to the desired virtual control command and the desired actuator deflection, and their specific expressions are as follows: in, Representative matrix No. Line number Column elements, S4.

2. Solve the constrained quadratic programming expression obtained in step S4.1 using a globally convergent fixed-point iterative algorithm; Define saturation function for: in, The independent variable of the function, and They are and The One element; Then, within one sampling period, a fixed-point equation of the following form is given. The optimal solution is obtained iteratively based on the fixed-point equation, and its expression is: in, It is about The function, It is a saturation function related to the output of the control allocation algorithm. This represents the reciprocal of the square root of the sum of squares of the elements in matrix H, and its specific expression is as follows: in, It is the identity matrix; the fixed-point equation converges iteratively to the optimal solution of the optimization problem at the current sampling time.

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