Green anti-interference control method and device for cmg frame servo system

Abstract

This application relates to the field of servo system control technology, and provides a green disturbance rejection control method and device for a CMG frame servo system. The method establishes a mathematical model of the CMG frame servo system under multi-source disturbances based on a high-order full-drive method, and models the multi-source disturbances as external sources. Based on the external source model and the system mathematical model, a fine disturbance separation estimator is determined. The fine disturbance separation estimator is used to determine the equivalent estimate of the multi-source disturbances. Based on this equivalent estimate, a state observer is determined, and then the state observer is used to determine the estimate of the higher-order state variables. Finally, a backstepping controller based on a barrier Lyapunov function is established using the estimates of the multi-source disturbances and the higher-order state variables. This backstepping controller controls the CMG frame servo system, providing more adjustable degrees of freedom for control performance optimization, enhancing the fine disturbance rejection capability of the CMG frame servo system under multiple constraints, and exhibiting simple design and low energy consumption.

Description

Technical Field

[0001] This application relates to the field of servo system control technology, and in particular to a green anti-disturbance control method and device for a CMG frame servo system. Background Technology

[0002] Control Moment Gyro (CMG) has become a key actuator in spacecraft attitude control systems due to its advantages such as simple structure, large output torque, fast response speed, and long lifespan. It is widely used in platforms such as the Tianhe core module and the WorldView series of high-resolution Earth imaging satellites. Based on the principle of conservation of angular momentum, the CMG generates the reaction torque required to control the spacecraft's attitude by changing the direction of the high-speed rotor's angular momentum through a frame servo system. The magnitude and direction of its output torque directly depend on the precise control of the frame rotation speed. As space missions become increasingly complex and sophisticated, the requirements for spacecraft attitude pointing accuracy, stability, low energy consumption, and long lifespan are becoming increasingly stringent. Therefore, the actuators of spacecraft attitude control systems need to possess superior performance. This requires the CMG frame servo system to achieve higher rotation speed control accuracy while realizing green (energy-saving, long-life operation) control under multiple constraints and multi-source disturbances.

[0003] However, multi-source interference in the CMG frame servo system directly constrains the core performance requirements of the spacecraft. These diverse interferences, with varying mathematical representations, not only significantly increase the difficulty of control algorithm design, but also cause frame rotation speed fluctuations that directly degrade the spacecraft's attitude pointing accuracy. Simultaneously, the presence of multi-source interference increases system energy consumption, contradicting the low-energy-consumption design goal: nonlinear friction leads to heat dissipation; rotor dynamic imbalance interference forces the system to consume additional power to suppress vibration; armature torque interference reduces motor efficiency and generates additional energy consumption—all three causing unnecessary power losses and increasing system energy consumption.

[0004] In addition, to ensure the stable and long-term operation of the CMG, the frame servo system must meet multiple constraints on angular position, rotational speed, and angular acceleration. Angular position constraints are used for singularity avoidance to prevent the system from completely losing its torque output capability; rotational speed constraints are used to suppress the "peak" phenomenon that occurs when multiple sources of interference occur, avoiding CMG failure caused by accelerated bearing wear; angular acceleration constraints are used to limit shaft current values ​​to prevent overcurrent damage to components.

[0005] Therefore, how to design a high-precision speed tracking anti-disturbance control method is an urgent technical problem to be solved. Summary of the Invention

[0006] In view of this, embodiments of this application provide a green anti-disturbance control method and apparatus for a CMG frame servo system, in order to solve the problem of how to design an anti-disturbance control method for high-precision speed tracking in the prior art.

[0007] A first aspect of this application provides a green disturbance rejection control method for a CMG framework servo system, comprising:

[0008] A mathematical model of the CMG framework servo system under multi-source interference is established based on a high-order full-drive method; the system mathematical model is used to characterize the actual performance of the CMG framework servo system. shaft current, actual shaft current, actual Axis voltage, actual The shaft voltage and the actual rotational speed must satisfy an equal relationship;

[0009] Multi-source interference is modeled as an external source model. Based on the external source model and the system mathematical model, a fine interference separation estimator is determined to achieve accurate separation and estimation of multi-source interference.

[0010] The equivalent estimate of multi-source interference is determined by using a fine interference separation estimator, and the state observer is determined based on the equivalent estimate of multi-source interference.

[0011] The state observer is used to determine the estimated values ​​of higher-order state variables, including the actual rotational speed and actual angular acceleration of the CMG frame servo system.

[0012] A backstepping controller based on the barrier Lyapunov function is established using the estimates of multi-source disturbances and higher-order state variables. The backstepping controller is then used to control the CMG frame servo system.

[0013] A second aspect of this application provides a green anti-interference control device for a CMG framework servo system, comprising:

[0014] The model building module is configured to establish a mathematical model of the CMG framework servo system under multi-source interference based on a high-order full-drive method; the system mathematical model is used to characterize the actual CMG framework servo system. shaft current, actual shaft current, actual Axis voltage, actual The shaft voltage and the actual rotational speed must satisfy an equal relationship;

[0015] The interference separation module is configured to model multi-source interference as an external source model, and determine a fine interference separation estimator based on the external source model and the system mathematical model to achieve accurate separation and estimation of multi-source interference;

[0016] The state observation module is configured to determine the equivalent estimate of multi-source interference using a fine interference separation estimator, and to determine the state observer based on the equivalent estimate of multi-source interference.

[0017] The estimation module is configured to use a state observer to determine estimates of higher-order state variables, including the actual rotational speed and actual angular acceleration of the CMG frame servo system.

[0018] The control module is configured to establish a backstepping controller based on the barrier Lyapunov function using estimates of multi-source disturbances and higher-order state variables, and to control the CMG frame servo system using the backstepping controller.

[0019] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0020] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0021] The beneficial effects of this application embodiment compared with the prior art are as follows: This application embodiment establishes a mathematical model of the CMG frame servo system under multi-source interference based on a high-order full-drive method, and models the multi-source interference as an external source model. Based on the external source model and the system mathematical model, a fine interference separation estimator is determined. The fine interference separation estimator is used to determine the equivalent estimate of the multi-source interference. Based on the equivalent estimate, a state observer is determined, and then the state observer is used to determine the estimate of the higher-order state variables. Finally, a backstepping controller based on the barrier Lyapunov function is established using the estimate of the multi-source interference and the estimate of the higher-order state variables. The backstepping controller is used to control the CMG frame servo system, which can provide more adjustable degrees of freedom for control performance optimization, enhance the fine disturbance rejection capability of the CMG frame servo system under multiple constraints, and has a simple design and low energy consumption. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application, 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 this application. 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 green anti-interference control method for a CMG framework servo system provided in an embodiment of this application.

[0024] Figure 2 This is a flowchart illustrating the method for determining the estimated value of multi-source interference using a fine interference separation estimator provided in the embodiments of this application.

[0025] Figure 3 This is a flowchart illustrating another green anti-disturbance control method for a CMG framework servo system provided in this application embodiment.

[0026] Figure 4 This is a flowchart illustrating another green anti-interference control method for a CMG framework servo system provided in this application embodiment.

[0027] Figure 5 This is a schematic diagram of the rotational speed tracking curve of the green anti-disturbance control method for the CMG frame servo system provided in the embodiments of this application.

[0028] Figure 6 This is the CMG framework servo system provided in the embodiments of this application. Shaft current waveform diagram.

[0029] Figure 7 This is a schematic diagram of the multi-source interference estimation curve of the CMG frame servo system provided in the embodiments of this application.

[0030] Figure 8 This is a schematic diagram of the angular acceleration estimation curve of the CMG frame servo system provided in the embodiments of this application.

[0031] Figure 9 This is a schematic diagram of a green anti-interference control device for a CMG frame servo system provided in an embodiment of this application.

[0032] Figure 10 This is a schematic diagram of the electronic device provided in the embodiments of this application. Detailed Implementation

[0033] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0034] The following will describe in detail, with reference to the accompanying drawings, a green anti-interference control method and apparatus for a CMG frame servo system according to an embodiment of this application.

[0035] As mentioned above, multi-source interference in the CMG frame servo system directly constrains the core performance requirements of the spacecraft. These diverse interferences, with varying mathematical representations, not only significantly increase the difficulty of control algorithm design, but also cause frame rotation speed fluctuations that directly degrade the spacecraft's attitude pointing accuracy. Furthermore, the presence of multi-source interference increases system energy consumption, contradicting the low-energy-consumption design goal: nonlinear friction leads to heat dissipation; rotor dynamic imbalance interference forces the system to consume additional power to suppress vibration; armature torque interference reduces motor efficiency and generates additional energy consumption. All three cause unnecessary power losses, increasing system energy consumption.

[0036] In addition, to ensure the stable and long-term operation of the CMG, the frame servo system must meet multiple constraints on angular position, rotational speed, and angular acceleration. Angular position constraints are used for singularity avoidance to prevent the system from completely losing its torque output capability; rotational speed constraints are used to suppress the "peak" phenomenon that occurs when multiple sources of interference occur, avoiding CMG failure caused by accelerated bearing wear; angular acceleration constraints are used to limit shaft current values ​​to prevent overcurrent damage to components.

[0037] In related technologies, some solutions use first-order state-space models to solve the control problem of CMG framework servo systems. First-order state-space methods focus on state vectors and are suitable for state solving and estimation, but they do not provide sufficient convenience for solving control inputs. Some solutions utilize iterative algorithms to adjust the optimal control law when designing the state-space model; however, these methods either have high computational complexity or do not consider multi-source disturbances and system constraints, and may face additional challenges in real-time performance, energy consumption, and reliability in practical engineering applications, while still not achieving sufficient control accuracy.

[0038] In view of this, this application provides a green disturbance rejection control method for a CMG frame servo system. It establishes a mathematical model of the CMG frame servo system under multi-source disturbances based on a high-order full-drive method, and models the multi-source disturbances as external sources. Based on the external source model and the system mathematical model, a fine disturbance separation estimator is determined. The fine disturbance separation estimator is used to determine the equivalent estimate of the multi-source disturbances. Based on this equivalent estimate, a state observer is determined, and then the state observer is used to determine the estimate of the higher-order state variables. Finally, a backstepping controller based on a barrier Lyapunov function is established using the estimates of the multi-source disturbances and the higher-order state variables. This backstepping controller controls the CMG frame servo system, providing more adjustable degrees of freedom for control performance optimization, enhancing the fine disturbance rejection capability of the CMG frame servo system under multiple constraints, and exhibiting simple design and low energy consumption.

[0039] Figure 1 This is a flowchart illustrating a green anti-interference control method for a CMG framework servo system provided in an embodiment of this application. Figure 1 As shown, the method includes the following steps:

[0040] In step S101, a mathematical model of the CMG frame servo system under multi-source interference is established based on the high-order full-drive method.

[0041] Among them, the system mathematical model is used to characterize the actual CMG framework servo system. shaft current, actual shaft current, actual Axis voltage, actual The shaft voltage and the actual rotational speed must satisfy an equal relationship.

[0042] In step S102, the multi-source interference is modeled as an external source model. Based on the external source model and the system mathematical model, a fine interference separation estimator is determined to achieve accurate separation and estimation of multi-source interference.

[0043] In step S103, the equivalent estimate of the multi-source interference is determined using the fine interference separation estimator, and the state observer is determined based on the equivalent estimate of the multi-source interference.

[0044] In step S104, the state observer is used to determine the estimated values ​​of the higher-order state variables.

[0045] Among them, the higher-order state variables include the actual rotational speed and actual angular acceleration of the CMG frame servo system.

[0046] In step S105, a backstepping controller based on the barrier Lyapunov function is established using the estimated values ​​of multi-source disturbances and higher-order state variables, and the backstepping controller is used to control the CMG frame servo system.

[0047] In some embodiments of this application, the method can be executed by a server or by a terminal device with certain processing capabilities to achieve green anti-interference control of the CMG framework servo system. The CMG framework servo system is a CMG framework servo system that includes multi-source interference.

[0048] In some embodiments of this application, a mathematical model of a CMG framework servo system under multi-source interference can be established based on a high-order full-drive method. The high-order full-drive method refers to constructing a high-order full-drive model of the system and designing a controller to directly cancel the original dynamics such as nonlinearity in the system, thereby explicitly achieving complete linearization of the closed-loop system and arbitrary pole configuration. In one example, key parameters of the CMG framework servo system can be extracted, such as the actual parameters of the CMG framework servo system. shaft current, actual shaft current, actual Axis voltage, actual The shaft voltage and the actual rotational speed of the CMG frame servo system are determined, and then the equivalent relationships that need to be satisfied between these key parameters are determined based on the high-order full drive method, thereby obtaining the mathematical model of the CMG frame servo system.

[0049] Among them, the CMG framework servo system Axis refers to the motor in a CMG frame servo system. A shaft, also known as a straight shaft, is an axis aligned with the direction of the rotor's magnetic field; in CMG frame servo systems... Axis refers to the motor in a CMG frame servo system. The axis, also known as the cross axis, is the leading axis. The orthogonal axes with an electrical angle of 90° form a rotating coordinate system used to decouple control flux and torque.

[0050] In some embodiments of this application, multi-source interference can also be modeled as an external source model, and then a fine interference separation estimator can be determined based on the external source model and the system mathematical model to achieve accurate separation and estimation of multi-source interference.

[0051] In some embodiments of this application, a defined fine interference separation estimator can be used to separate and estimate multi-source interference to obtain an equivalent estimate of the multi-source interference, and then a state observer can be determined based on the equivalent estimate of the multi-source interference.

[0052] In some embodiments of this application, the state observer can be used to determine the estimated values ​​of higher-order state variables, which may include the actual rotational speed and actual angular acceleration of the CMG frame servo system.

[0053] Finally, a backstepping controller based on the barrier Lyapunov function can be established using the estimates of multi-source disturbances and higher-order state variables, and the backstepping controller can be used to control the CMG frame servo system.

[0054] According to the technical solution provided in the embodiments of this application, a mathematical model of a CMG frame servo system under multi-source disturbance is established based on a high-order full-drive method, and the multi-source disturbance is modeled as an external source model. A fine disturbance separation estimator is determined based on the external source model and the system mathematical model. The equivalent estimate of the multi-source disturbance is determined using the fine disturbance separation estimator. A state observer is determined based on the equivalent estimate, and then the state observer is used to determine the estimate of the higher-order state variables. Finally, a backstepping controller based on the barrier Lyapunov function is established using the estimate of the multi-source disturbance and the estimate of the higher-order state variables. The CMG frame servo system is controlled using this backstepping controller, which can provide more adjustable degrees of freedom for control performance optimization, enhance the fine disturbance rejection capability of the CMG frame servo system under multiple constraints, and has a simple design and low energy consumption.

[0055] In some embodiments of this application, the mathematical model of the CMG frame servo system under multi-source interference can be:

[0056] ;

[0057] in, For the actual CMG framework servo system shaft current, For the actual CMG framework servo system shaft current, For the actual CMG framework servo system shaft voltage, For the actual CMG framework servo system shaft voltage, This refers to the actual rotational speed of the CMG framework servo system.

[0058] for The first derivative with respect to time, for The first derivative with respect to time, for The first derivative with respect to time.

[0059] coefficient , , , and It can be defined in the following way: , , , , .

[0060] For the torque coefficient of the CMG frame servo system, ; The back electromotive force coefficient of the CMG frame servo system. .

[0061] , , , , and These represent the pole pair number, flux linkage, moment of inertia, coefficient of viscous friction, stator resistance, and stator inductance of the CMG frame servo system. In one example, , , , , and The values ​​can be set to respectively. , , , , and .

[0062] The equivalent value of multi-source interference is given in the mathematical model of the CMG framework servo system under multi-source interference established by the high-order full-drive method. This is the inherent perturbation term obtained from the equivalent transformation of the original multi-source interference. .

[0063] For multi-source interference, For the first derivative of multi-source interference with respect to time, and , For rotor dynamic imbalance interference, For lumped low-frequency interference, nonlinear friction and armature torque can be included.

[0064] In some embodiments of this application, after establishing a mathematical model of the CMG frame servo system under multi-source interference, a fine interference separation estimator can be designed to achieve accurate separation and estimation of multi-source interference. The fine interference separation estimator can accurately separate and estimate rotor dynamic imbalance interference with known frequency information. and lumped low-frequency interference Separate estimation is performed to obtain a more accurate equivalent estimate of multi-source interference.

[0065] When designing a fine-grained interference separation estimator, multi-source interference can first be modeled as an external source model, which is as follows:

[0066] ;

[0067] in, As auxiliary state variables, for The first derivative with respect to time, for The first derivative with respect to time, for The first derivative with respect to time, for The first derivative with respect to time.

[0068] The second derivative of the lumped low-frequency interference with respect to time. The state matrix of the external model, The frequency of the dynamic imbalance disturbance is known from prior information. In one example, The value can be (radians per second).

[0069] , This is the output matrix of the external model. For uncertain input matrices, This is the transpose symbol.

[0070] Next, a refined interference separation estimator can be designed based on the external source model and the system mathematical model to separate and estimate multi-source interference. In one example, the refined interference separation estimator could be:

[0071] ;

[0072] in, To The estimated value, For the fine disturbance separation estimator, for The first derivative with respect to time.

[0073] for The estimated value; for The estimated value.

[0074] This refers to the actual rotational speed status of the CMG framework servo system, which includes the actual rotational speed. It can be used to characterize the actual rotational speed in a mathematical model, and also to characterize the physical quantity of the system's actual rotational speed. It can be understood as a variable. It is both a state in the mathematical model and an actual physical quantity of the system. When When it is the actual physical quantity of the system, When the variable When it is a state in a mathematical model, The value of and The values ​​are the same. Using This indicates that the actual rotational speed of the CMG frame servo system can fully describe the system dynamics.

[0075] For the gain of the fine interference separation estimator, , , , The four gain components of the fine interference separation estimator must satisfy the following conditions: , , and In one example, , , and The value can be , , , ,and .

[0076] Figure 2 This is a flowchart illustrating the method for determining the estimated value of multi-source interference using a fine interference separation estimator, as provided in an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0077] In step S201, the estimated value of multi-source interference is determined using a fine interference separation estimator.

[0078] In step S202, the equivalent estimate of the multi-source interference is determined based on the estimate of the multi-source interference.

[0079] In some embodiments of this application, the estimated value of multi-source interference can be determined first using the constructed fine interference separation estimator, and then the formula can be used. Based on the estimated value of the multi-source interference, the equivalent estimated value of the multi-source interference is determined; where, This is an equivalent estimate of the multi-source interference.

[0080] In some embodiments of this application, a state observer can be designed using a determined equivalent estimate of multi-source disturbances to achieve accurate estimation of higher-order state variables. The state observer can be:

[0081] .

[0082] This refers to the actual rotational speed of the CMG frame servo system. When it is the actual physical quantity of the system, ; To monitor the system state The estimated value, for The first derivative with respect to time.

[0083] This refers to the actual angular acceleration state of the CMG frame servo system, which includes the actual angular acceleration. It can be used to characterize the state of actual angular acceleration in a mathematical model, and also to characterize the physical quantity of the actual angular acceleration of a system. It can be understood as a variable. It is both the state in the mathematical model and the actual physical quantity of the system, when When it is the actual physical quantity of the system, When the variable When it is a state in a mathematical model, The value of and The values ​​are the same. Using This indicates that the actual angular acceleration state of the CMG frame servo system can fully describe the system dynamics. To monitor the system state The estimated value, for The first derivative with respect to time.

[0084] , For the gain of the state observer to be designed, it must satisfy the following: and In one example, and The value can be , ,and .

[0085] In some embodiments of this application, a backstepping controller based on the obstacle Lyapunov function can be established using the estimated values ​​of determined multi-source disturbances and higher-order state variables to achieve high-precision tracking of CMG frame rotation speed under multiple constraints and multi-source disturbances.

[0086] In some implementations, the designed backstepping controller can be:

[0087] ;

[0088] in, For angular position tracking error, and These represent the desired angular position and the actual angular position of the CMG frame servo system, respectively.

[0089] For speed tracking error, For virtual speed controller, , The desired rotational speed for the CMG framework servo system.

[0090] The tracking error is the first derivative of rotational speed with respect to time. For virtual controllers, , for The first derivative with respect to time, Angular acceleration estimated by the state observer. for The first derivative with respect to time.

[0091] , Expectations for CMG framework servo systems shaft current; for The first derivative with respect to time.

[0092] , , and These are respectively variables that affect the system state. , , and Constraints, , , and .at the same time, This can be considered as a characterization error. Boundary constants, This can be considered as a characterization error. Boundary constants, This can be considered as a characterization error. Boundary constants, This can be considered as a characterization error. Boundary constants.

[0093] Indicates the interference suppression gain. , , and All are backstepping controller parameters based on the barrier Lyapunov function. , , and .

[0094] When using a backstepping controller to control a CMG frame servo system, the controller derivation can be performed by designing multiple obstacle Lyapunov functions to derive the mathematical model of the CMG frame servo system. The following section details the implementation method of using a backstepping controller to control a CMG frame servo system, using the design of four obstacle Lyapunov functions as an example.

[0095] In some embodiments of this application, the first barrier Lyapunov function can be determined first. , ,in, The symbol for the natural logarithm. The value can be .

[0096] Can be calculated First derivative with respect to time ; for The first derivative with respect to time, for The first derivative with respect to time.

[0097] set up to make In one example, it can be set .

[0098] Determine the rewritten for .

[0099] if ,but At this point, it can be confirmed that the CMG framework servo system is stable. Otherwise, further processing is required.

[0100] In some embodiments of this application, in Then, the second barrier Lyapunov function can be determined. , In one example, it can be set .

[0101] Can be calculated First derivative with respect to time ; for The first derivative with respect to time.

[0102] set up In one example, it can be set .

[0103] Determine the rewritten for ;

[0104] if ,but At this point, it can be confirmed that the CMG framework servo system is stable. Otherwise, further processing is required.

[0105] In some embodiments of this application, in Then, the third barrier Lyapunov function can be determined. , In one example, it can be set .

[0106] Can be calculated First derivative with respect to time ; for The first derivative with respect to time.

[0107] set up In one example, it can be set , .

[0108] Determine the rewritten for .

[0109] The estimation error of the fine disturbance separation estimator is bounded; the boundary of the disturbance estimation error can be described as... , The absolute value of the equivalent estimation error for multi-source interference The conservative upper limit, at this time It can be rewritten as ,in, and For interval ( All of them Both are true. That is to say, through rewriting... It can be organized into a standardized, ultimately bounded form; furthermore, by rewriting it again... Then the complex The expression is reconstructed into a clear standard form. Based on this standard form, it can be proven that the estimation error of the fine-grained disturbance separation estimator is bounded.

[0110] Specifically, when the systematic error increases, that is... When it is larger, the rewritten version The right side of the inequality is This leads to the guarantee System energy It will decrease; at the same time, the system's energy will not ultimately dissipate, its energy... It will be restricted to a place by Within the determined boundaries. That is to say, by the rewritten... The expression can be deduced as follows: ; Indicates all .

[0111] therefore, It is bounded, which means , , All are bounded variables, and for all All are satisfied .

[0112] Furthermore, due to the embodiments selected in this application... , and Both are barrier Lyapunov functions, whose form inherently contains state constraints. The mathematical property of barrier Lyapunov functions is that as long as the function value is bounded, its internal error can never reach the boundary. Therefore, with... For example, because when hour, .because It includes and Furthermore, the barrier Lyapunov function itself is non-negative, therefore , It is also bounded.

[0113] Therefore, it can be rigorously and mathematically proven that all tracking errors , , The preset constraints are strictly met throughout the entire control process: ( This proves that the system is stable.

[0114] In some embodiments of this application, a fourth barrier Lyapunov function can also be determined. , In one example, it can be set .

[0115] Can be calculated First derivative with respect to time ; for The first derivative with respect to time.

[0116] set up In one example, it can be set .

[0117] Determine the rewritten for Therefore, the stability of the CMG framework servo system is confirmed.

[0118] in, and It consists of two independent control channels in the CMG frame servo system, which can independently control the CMG frame servo system to achieve stable and high-precision speed tracking.

[0119] The green disturbance rejection control method for CMG frame servo systems provided in this application aims to meet the high-precision speed tracking requirements of CMG frames under multiple constraints (speed, angular acceleration) and multiple source disturbances (rotor dynamic imbalance disturbance, nonlinear friction, armature torque), while achieving green (energy-saving, long-life operation) control. This method first establishes a system model containing multiple source disturbances based on a high-order full-drive method, increasing the control degrees of freedom; then, a fine disturbance separation estimator is designed to achieve accurate separation and estimation of multiple source disturbances; next, a state observer is designed based on the multi-source disturbance estimates to accurately estimate high-order state variables; finally, based on the multi-source disturbances and high-order state estimates, a backstepping controller based on a barrier Lyapunov function is designed to achieve high-precision speed tracking of the CMG frame under multiple constraints.

[0120] The technical solution provided in this application can enhance the fine anti-interference capability of the CMG frame servo system under multiple constraints. It has the advantages of simple design and low energy consumption, and completes the green anti-interference control of the CMG frame servo system.

[0121] Figure 3 This is a flowchart illustrating another green anti-interference control method for a CMG framework servo system provided in this application embodiment. Figure 3 As shown, a mathematical model of the CMG framework servo system under multi-source interference can be established first based on a high-order full-drive method. Next, a refined interference separation estimator can be designed for multi-source interference, and this estimator can be used to obtain estimates of the multi-source interference. and On the other hand, an extended state observer based on a high-order full-drive method can be designed for higher-order state variables. Equivalent estimates of the multi-source disturbances and estimates of the higher-order state variables can be obtained based on the estimates of the multi-source disturbances. Then, a backstepping controller based on a barrier Lyapunov function can be designed by combining the equivalent estimates of the multi-source disturbances and the estimates of the higher-order state variables. Finally, this backstepping controller is used to implement green disturbance rejection control for the CMG framework servo system.

[0122] In other words, a system model with multi-source disturbances can be established first based on a high-order full-drive method to increase the control degrees of freedom. Then, a fine-grained disturbance separation estimator can be designed to achieve accurate separation and estimation of multi-source disturbances. Next, a state observer can be designed based on the multi-source disturbance estimates to accurately estimate high-order state variables. Finally, based on the multi-source disturbances and high-order state estimates, a backstepping controller based on the barrier Lyapunov function can be designed to achieve high-precision speed tracking of the CMG frame under multiple constraints.

[0123] Figure 4 This is a flowchart illustrating another green anti-interference control method for a CMG framework servo system provided in this application embodiment. Figure 4 As shown, a CMG framework servo system can be constructed, which has multi-source interference. .

[0124] A fine-grained interference separation estimator can be constructed based on the CMG framework servo system. Multi-source disturbances are estimated using parameters such as actual shaft current and actual rotational speed, yielding estimates of the multi-source disturbances and their first derivatives. and The estimated value of the multi-source interference and its first derivative. and The inputs can be fed into a backstepping controller built on a high-order full-drive method. The inputs to the backstepping controller can also include high-order state variables of the CMG frame servo system, such as the actual angular acceleration of the CMG frame servo system. And the barrier Lyapunov function. The barrier Lyapunov function can be constructed based on the difference between the actual motor speed output by the CMG frame servo system and the desired motor speed. The backstep controller outputs the voltage of the CMG frame servo system. ,Should and It can be collectively referred to as .

[0125] An extended state observer is constructed based on a high-order full-drive method to receive the voltage. It also receives estimates of multi-source interference and its first derivative. and The estimated values ​​of the higher-order state variables of the CMG framework servo system are obtained by taking the actual speed of the motor in the CMG framework servo system and providing these estimated values ​​to the backstepping controller.

[0126] The CMG frame servo system receives this voltage. Output Shaft current, actual motor speed, etc.

[0127] Figure 5 This is a schematic diagram of the speed tracking curve of the green disturbance rejection control method for the CMG framework servo system provided in this application embodiment. For example... Figure 5 As shown, compared to the feedback (FC) control method (FC / HOFA+ESO / HOFA) and proportional-integral (PI) control method based on the full drive method (HOFA) and extended state observer (ESO), the frame rotation speed of the CMG frame servo system controlled by the method provided in this application embodiment can quickly and accurately track the desired frame rotation speed without exceeding the constraint range.

[0128] Figure 6 This is the CMG framework servo system provided in the embodiments of this application. Shaft current waveform diagram. (Example) Figure 6 As shown, compared to the feedback control (FC) method (FC / HOFA+ESO / HOFA) and proportional-integral control (PI) method based on the full drive method (HOFA) and extended state observer (ESO), the backstepping controller based on the barrier Lyapunov function provided in the embodiments of this application has the ability to constrain current and save energy.

[0129] Figure 7 This is a schematic diagram of the multi-source interference estimation curve of the CMG framework servo system provided in the embodiments of this application. Figure 7 As shown, compared to the Extended State Observer (ESO / HOFA) based on the full-drive method, the fine interference separation estimator provided in this application embodiment can accurately separate, estimate, and compensate for actual multi-source interference.

[0130] Figure 8 This is a schematic diagram of the angular acceleration estimation curve of the CMG frame servo system provided in an embodiment of this application. Figure 8 As shown, compared to the extended state observer (ESO / HOFA) based on the full-drive method, the state observer provided in this application embodiment can accurately estimate the desired angular acceleration.

[0131] This application establishes a mathematical model of the CMG frame servo system under multi-source disturbances based on a high-order full-drive method. This model provides more adjustable degrees of freedom for control performance optimization. Simultaneously, a fine disturbance separation estimator, a state observer, and a backstepping controller based on the barrier Lyapunov function are designed to enhance the fine disturbance rejection capability of the CMG frame servo system under multiple constraints, offering advantages such as simple design and low energy consumption.

[0132] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.

[0133] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0134] Figure 9 This is a schematic diagram of a green anti-interference control device for a CMG frame servo system provided in an embodiment of this application. Figure 9 As shown, the device includes:

[0135] Model building module 901 is configured to establish a mathematical model of the CMG framework servo system under multi-source interference based on a high-order full-drive method; the system mathematical model is used to characterize the actual CMG framework servo system. shaft current, actual shaft current, actual Axis voltage, actual The shaft voltage and the actual rotational speed must satisfy an equal relationship.

[0136] The interference separation module 902 is configured to model multi-source interference as an external source model, determine a fine interference separation estimator based on the external source model and the system mathematical model, and achieve accurate separation estimation of multi-source interference.

[0137] The state observation module 903 is configured to determine the equivalent estimate of multi-source interference using a fine interference separation estimator, and to determine the state observer based on the equivalent estimate of multi-source interference.

[0138] The estimation module 904 is configured to use a state observer to determine estimates of higher-order state variables, including the actual rotational speed and actual angular acceleration of the CMG frame servo system.

[0139] Control module 905 is configured to establish a backstepping controller based on the barrier Lyapunov function using estimates of multi-source disturbances and estimates of higher-order state variables, and to control the CMG frame servo system using the backstepping controller.

[0140] According to the technical solution provided in the embodiments of this application, a mathematical model of a CMG frame servo system under multi-source disturbance is established based on a high-order full-drive method, and the multi-source disturbance is modeled as an external source model. A fine disturbance separation estimator is determined based on the external source model and the system mathematical model. The equivalent estimate of the multi-source disturbance is determined using the fine disturbance separation estimator. A state observer is determined based on the equivalent estimate, and then the state observer is used to determine the estimate of the higher-order state variables. Finally, a backstepping controller based on the barrier Lyapunov function is established using the estimate of the multi-source disturbance and the estimate of the higher-order state variables. The CMG frame servo system is controlled using this backstepping controller, which can provide more adjustable degrees of freedom for control performance optimization, enhance the fine disturbance rejection capability of the CMG frame servo system under multiple constraints, and has a simple design and low energy consumption.

[0141] In some implementations, the mathematical model of the CMG frame servo system under multi-source interference is as follows: ,in, For the actual CMG framework servo system shaft current, For the actual CMG framework servo system shaft current, For the actual CMG framework servo system shaft voltage, For the actual CMG framework servo system shaft voltage, This refers to the actual rotational speed of the CMG frame servo system; for The first derivative with respect to time, for The first derivative with respect to time, for The first derivative with respect to time; , , , , ; For the torque coefficient of the CMG frame servo system, ; The back electromotive force coefficient of the CMG frame servo system. ; , , , , and These are the pole pairs, flux linkage, moment of inertia, coefficient of viscous friction, stator resistance, and stator inductance of the CMG frame servo system. This is the equivalent value of multi-source interference. , For multi-source interference, For the first derivative of multi-source interference with respect to time, and , For rotor dynamic imbalance interference, This is for lumped low-frequency interference.

[0142] In some implementations, the external model is: ,in, As auxiliary state variables, for The first derivative with respect to time, for The first derivative with respect to time, for The first derivative with respect to time, for The first derivative with respect to time; The second derivative of the lumped low-frequency interference with respect to time. The state matrix of the external model, The frequency of the dynamic imbalance disturbance is known from prior information; , This is the output matrix of the external model. For uncertain input matrices, This is the transpose symbol.

[0143] In some implementations, the fine interference separation estimator is: ,in, To The estimated value, For the fine disturbance separation estimator, for The first derivative with respect to time; for The estimated value; for The estimated value; This refers to the actual rotational speed of the CMG frame servo system. Used to characterize the actual rotational speed in a mathematical model, and to characterize the physical quantity of the actual rotational speed of the system. ; For the gain of the fine interference separation estimator, , , , The four gain components of the fine interference separation estimator , , and .

[0144] In some implementations, the fine interference separation estimator is used to determine the estimate of the multi-source interference, including: using the fine interference separation estimator to determine the estimate of the multi-source interference; and determining the equivalent estimate of the multi-source interference based on the estimate of the multi-source interference.

[0145] In some implementations, the state observer is: ,in, This is an equivalent estimate of multi-source interference; This refers to the actual rotational speed of the CMG frame servo system. Used to characterize the actual rotational speed in a mathematical model, and to characterize the physical quantity of the actual rotational speed of the system. ; To The estimated value, for The first derivative with respect to time; This represents the actual angular acceleration state of the CMG frame servo system. Used to characterize the state of actual angular acceleration in a mathematical model, and to characterize the physical quantity of actual angular acceleration of a system. ; To The estimated value, for The first derivative with respect to time; , For the gain of the state observer to be designed, and .

[0146] In some implementations, the backstepping controller is: ,in, For speed tracking error, For virtual speed controller, , For the desired rotational speed of the CMG framework servo system, For angular position tracking error, and These represent the desired angular position and the actual angular position of the CMG frame servo system, respectively. The tracking error is the first derivative of rotational speed with respect to time. For virtual controllers, , for The first derivative with respect to time, Angular acceleration estimated by the state observer. for The first derivative with respect to time; , Expectations for CMG framework servo systems shaft current; for The first derivative with respect to time; , , and These are variables that represent the system state. , , and Constraints, , , and ; Indicates the interference suppression gain. , , and All are backstepping controller parameters based on the barrier Lyapunov function. , , and .

[0147] In some implementations, a backstepping controller is used to control the CMG frame servo system, including: determining a first obstacle Lyapunov function. , ,in, The symbol for the natural logarithm; calculation First derivative with respect to time ; for The first derivative with respect to time, for First derivative with respect to time; setting to make ; Determine the rewritten for ; Response to determination ,but This confirms the stability of the CMG framework servo system.

[0148] In some implementations, in response to determining The system uses a backstepping controller to control the CMG frame servo system and also includes: determining the second obstacle Lyapunov function. , ;calculate First derivative with respect to time ; for First derivative with respect to time; setting ; Determine the rewritten for ; Response to determination ,but This confirms the stability of the CMG framework servo system.

[0149] In some implementations, in response to determining The system uses a backstepping controller to control the CMG frame servo system and also includes: determining the Lyapunov function for the third obstacle. , ;calculate First derivative with respect to time ; for First derivative with respect to time; setting ; Determine the rewritten for Obtain the perturbation estimation error boundary of the fine perturbation separation estimator. , The absolute value of the equivalent estimation error for multi-source interference The conservative upper bound, based on the perturbation estimation error boundary, will Rewritten as ,in, and For interval ( All of them Both are true; by the rewritten The expression is derived as follows: ;in , , All are bounded variables. Indicates all For all All are satisfied This confirms the stability of the CMG framework servo system.

[0150] In some implementations, using a backstepping controller to control the CMG frame servo system further includes: determining the fourth obstacle Lyapunov function. , ;calculate First derivative with respect to time ; for The first derivative with respect to time;

[0151] set up ; Determine the rewritten for Therefore, the stability of the CMG framework servo system is confirmed.

[0152] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0153] Figure 10 This is a schematic diagram of an electronic device provided in an embodiment of this application. Figure 10 As shown, the electronic device 10 of this embodiment includes: a processor 1001, a memory 1002, and a computer program 1003 stored in the memory 1002 and executable on the processor 1001. When the processor 1001 executes the computer program 1003, it implements the steps in the various method embodiments described above. Alternatively, when the processor 1001 executes the computer program 1003, it implements the functions of each module / unit in the various device embodiments described above.

[0154] Electronic device 10 may be a desktop computer, laptop, handheld computer, cloud server, or other electronic device. Electronic device 10 may include, but is not limited to, a processor 1001 and a memory 1002. Those skilled in the art will understand that... Figure 10 This is merely an example of electronic device 10 and does not constitute a limitation on electronic device 10. It may include more or fewer components than shown, or different components.

[0155] The processor 1001 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0156] The memory 1002 can be an internal storage unit of the electronic device 10, such as a hard disk or RAM of the electronic device 10. The memory 1002 can also be an external storage device of the electronic device 10, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, FlashCard, etc., equipped on the electronic device 10. The memory 1002 can also include both internal and external storage units of the electronic device 10. The memory 1002 is used to store computer programs and other programs and data required by the electronic device.

[0157] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0158] If an integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program may include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0159] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A green anti-disturbance control method for a CMG frame servo system, characterized in that, include: A mathematical model of the CMG framework servo system under multi-source interference is established based on a high-order full-drive method. The system mathematical model is used to characterize the actual CMG framework servo system. shaft current, actual shaft current, actual Axis voltage, actual The shaft voltage and the actual rotational speed must satisfy an equal relationship; The multi-source interference is modeled as an external source model, and a fine interference separation estimator is determined based on the external source model and the system mathematical model to achieve accurate separation and estimation of multi-source interference; The equivalent estimate of the multi-source interference is determined using the fine interference separation estimator, and the state observer is determined based on the equivalent estimate of the multi-source interference. The state observer is used to determine the estimated values ​​of higher-order state variables, including the actual rotational speed and actual angular acceleration of the CMG frame servo system. A backstepping controller based on the barrier Lyapunov function is established using the estimated values ​​of the multi-source disturbances and the higher-order state variables, and the backstepping controller is used to control the CMG frame servo system.

2. The method according to claim 1, characterized in that, The mathematical model of the CMG framework servo system under multi-source interference is as follows: ； in, For the actual CMG framework servo system shaft current, For the actual CMG framework servo system shaft current, For the actual CMG framework servo system shaft voltage, For the actual CMG framework servo system shaft voltage, This refers to the actual rotational speed of the CMG frame servo system. for The first derivative with respect to time, for The first derivative with respect to time, for The first derivative with respect to time; , , , , ; The torque coefficient of the CMG frame servo system. ; The back electromotive force coefficient of the CMG frame servo system is... ; , , , , and These are the number of pole pairs, flux linkage, moment of inertia, coefficient of viscous friction, stator resistance, and stator inductance of the CMG frame servo system, respectively. This is the equivalent value of the multi-source interference. , For the aforementioned multi-source interference, Let be the first derivative of the multi-source interference with respect to time, and , For rotor dynamic imbalance interference, This is for lumped low-frequency interference.

3. The method according to claim 2, characterized in that, The external model is: ； in, As auxiliary state variables, for The first derivative with respect to time, for The first derivative with respect to time, for The first derivative with respect to time, for The first derivative with respect to time; The second derivative of the lumped low-frequency interference with respect to time. The state matrix of the external model, The frequency of the dynamic imbalance disturbance is known from prior information; , The output matrix of the external model is... For uncertain input matrices, It is the transpose symbol; The fine interference separation estimator is: ； in, for The estimated value, For the fine disturbance separation estimator, for The first derivative with respect to time; for The estimated value; for The estimated value; The actual rotational speed state of the CMG frame servo system Used to characterize the actual rotational speed in a mathematical model, and to characterize the physical quantity of the actual rotational speed of the system. ; For the gain of the fine interference separation estimator, , , , The four gain components of the fine interference separation estimator , , and .

4. The method according to claim 1, characterized in that, Determining the estimated value of the multi-source interference using the fine interference separation estimator includes: The estimated value of the multi-source interference is determined using the fine interference separation estimator. ; Use formula The equivalent estimate of the multi-source interference is determined based on the estimated value of the multi-source interference; in, This is the equivalent estimate of the multi-source interference. , and These are the rotational inertia, stator resistance, and stator inductance of the CMG frame servo system, respectively. The first derivative of the multi-source interference with respect to time The estimated value; The state observer is: ； in, This is the equivalent estimate of the multi-source interference; This refers to the actual rotational speed of the CMG frame servo system. Used to characterize the actual rotational speed in a mathematical model, and to characterize the physical quantity of the actual rotational speed of the system. ; To The estimated value, for The first derivative with respect to time; This refers to the actual angular acceleration state of the CMG frame servo system. Used to characterize the state of actual angular acceleration in a mathematical model, and to characterize the physical quantity of actual angular acceleration of a system. ; To The estimated value, for The first derivative with respect to time; , For the gain of the state observer to be designed, and .

5. The method according to claim 2, characterized in that, The backstep controller is: ； in, For speed tracking error, For virtual speed controller, , The desired rotational speed for the CMG framework servo system; For angular position tracking error, and These are the desired angular position and the actual angular position of the CMG frame servo system, respectively. The tracking error is the first derivative of rotational speed with respect to time. For virtual controllers, , for The first derivative with respect to time, Angular acceleration estimated by the state observer. for The first derivative with respect to time; , Expectations for CMG framework servo systems shaft current; for The first derivative with respect to time; , , and These are variables that represent the system state. , , and Constraints, , , and ; Indicates the interference suppression gain. , , and All are backstepping controller parameters based on the barrier Lyapunov function. , , and .

6. The method according to claim 5, characterized in that, Using the backstep controller to control the CMG frame servo system includes: Determine the first barrier Lyapunov function , ,in, The symbol for the natural logarithm; calculate First derivative with respect to time ; for The first derivative with respect to time, for The first derivative with respect to time; set up to make ; Determine the rewritten for ; Response to determination ,but This confirms the stability of the CMG framework servo system. Response to determination Determine the second barrier Lyapunov function , ; calculate First derivative with respect to time ; for The first derivative with respect to time; set up ; Determine the rewritten for ; Response to determination ,but This confirms the stability of the CMG framework servo system. Response to determination Determine the third barrier Lyapunov function , ; calculate First derivative with respect to time ; for The first derivative with respect to time; set up ; Determine the rewritten for ; Obtain the perturbation estimation error boundary of the fine perturbation separation estimator The absolute value of the equivalent estimation error for multi-source interference The conservative upper bound, based on the perturbation estimation error boundary, will Rewritten as ; in, and For interval ( All of them Both are true; From the rewritten Expression derivation ,in , , All are bounded variables. Indicates all For all All are satisfied The stability of the CMG framework servo system was confirmed.

7. The method according to claim 6, characterized in that, Using the backstep controller to control the CMG frame servo system further includes: Determine the fourth barrier Lyapunov function , ; calculate First derivative with respect to time ; for The first derivative with respect to time; set up ; Determine the rewritten for Therefore, the stability of the CMG framework servo system is confirmed.

8. A green anti-interference control device for a CMG frame servo system, characterized in that, include: The model building module is configured to establish a mathematical model of the CMG framework servo system under multi-source interference based on a high-order full-drive method; The system mathematical model is used to characterize the actual CMG framework servo system. shaft current, actual shaft current, actual Axis voltage, actual The shaft voltage and the actual rotational speed must satisfy an equal relationship; The interference separation module is configured to model the multi-source interference as an external source model, determine a fine interference separation estimator based on the external source model and the system mathematical model, and achieve accurate separation estimation of multi-source interference; The state observation module is configured to determine the equivalent estimate of the multi-source interference using the fine interference separation estimator, and to determine the state observer based on the equivalent estimate of the multi-source interference. The estimation module is configured to use the state observer to determine estimated values ​​of higher-order state variables, including the actual rotational speed and actual angular acceleration of the CMG frame servo system. The control module is configured to establish a backstepping controller based on the barrier Lyapunov function using the estimates of the multi-source disturbances and the estimates of the higher-order state variables, and to use the backstepping controller to control the CMG frame servo system.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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