A method for inertia identification and control based on two-axis optoelectronic devices

Abstract

This invention discloses an inertia identification and control method based on a two-axis optoelectronic device, belonging to the field of electromechanical servo control. The method includes: system parameter acquisition and initialization; automatic identification of friction torque; automatic identification of pitch axis inertia; automatic identification of azimuth axis inertia function; and disturbance feedforward compensation control based on the identification results, achieving real-time suppression of disturbances such as friction and imbalance. This invention effectively solves the problem that platform inertia identification requires external measuring instruments and cannot perform disturbance feedforward compensation based on inertia.

Description

Technical Field

[0001] This invention belongs to the field of electromechanical servo control, specifically relating to an inertia identification and control method based on a two-axis optoelectronic device. Background Technology

[0002] Two-axis optoelectronic devices (typically including azimuth and pitch axes) are the core carriers for optoelectronic detection and other tasks. They are widely used to mount sensors such as laser illuminators and visible / infrared detectors to achieve target servo stabilization, tracking, and precise pointing. Their performance directly determines the success or failure of optoelectronic detection, observation, aiming, and ranging tasks.

[0003] In traditional control strategies, two-axis photoelectric devices often employ a three-loop feedback control system: position-velocity-current. This control method is essentially a passive response mechanism, unable to identify the platform's own inertia and lacking inertia-based control. It only corrects errors caused by frictional torque, unbalanced torque, and external environmental disturbances. When the platform experiences high frictional torque, poor uniformity, or significant environmental disturbances, accuracy drops drastically, severely impacting platform performance.

[0004] Existing technologies are either limited to offline torque detection and mechanical leveling, or rely on adding hardware to suppress specific external disturbances, failing to offer a solution to the fundamental problem at the core level of the control system—namely, identifying the system's time-varying dynamic parameters (inertia) online and automatically, and then performing proactive, real-time disturbance feedforward compensation accordingly. Summary of the Invention

[0005] The technical problem to be solved:

[0006] To avoid the shortcomings of existing technologies, this invention provides an inertia identification and control method based on a two-axis optoelectronic device, and a disturbance compensation method accordingly. This effectively solves the problem that platform inertia identification requires external measuring instruments and cannot perform disturbance feedforward compensation based on inertia.

[0007] The technical solution of this invention is: an inertia identification and control method based on a two-axis optoelectronic device, the specific steps of which are as follows:

[0008] Step 1, System Parameter Acquisition and Initialization: Obtain the motor torque coefficient K for the azimuth and pitch axes of the optoelectronic device. T Upper limit of angular velocity ω m And the pitch axis angle range A~B; initialize the control program, set the sampling period, and prepare to read the current feedback value and angle feedback value from the motor driver and shaft encoder in real time;

[0009] Step 2, Automatic identification of friction torque: based on the upper limit of angular velocity ω of each axis. mThe control angular velocity is divided into N segments; one axis is fixed at zero position, and the other axis is controlled to move at a constant speed according to the segmented speeds; after each uniform speed movement stabilizes, the current feedback value of the motor driver is collected, combined with the motor torque coefficient K. T The frictional torque of the shaft at each speed range is calculated, and the average value of the frictional torques in N ranges is taken as the final frictional torque f of the shaft.

[0010] Step 3: Automatic identification of pitch axis inertia: Control the azimuth axis to be fixed at zero, and control the pitch axis to have a peak value not exceeding ω. m The motion follows a sinusoidal velocity pattern; after the motion stabilizes, the driving torque data K is collected over a period of time. T • i(k) and angular velocity data ω(k); Based on the impulse theorem, using the collected data and the pitch axis friction torque f identified in step 2, the moment of inertia J of the pitch axis is calculated. 俯仰 ;

[0011] Step 4: Automatic identification of azimuth axis inertia function: Divide the range of motion of the pitch axis into M angular positions; sequentially control the pitch axis to be fixed at each angular position, and control the azimuth axis to have a peak value not exceeding ω. m The sinusoidal velocity motion; at each pitch angle position, the driving torque K of the azimuth axis is collected over a period of time. T Based on the impulse theorem and the azimuth axis friction torque identified in step 2, the moment of inertia J of the azimuth axis at this pitch angle is calculated using the angular velocity data ω(k) and i(k). i Finally, using the azimuth axis inertia data calculated at all M angular positions, a curve fitting algorithm is used to obtain the inertia at the pitch angle θ. 俯仰 The azimuth axis inertia function J is a variable 方位 =F(θ 俯仰 );

[0012] Step 5: Disturbance feedforward compensation control based on identification results: The rotational inertia J of the pitch axis is... 俯仰 and azimuth axis inertia function J 方位 =F(θ 俯仰 The pitch angle θ is stored in the storage medium and read in real time during device operation. 俯仰 Based on this, the corresponding azimuth axis inertia value is retrieved from the inertia function; simultaneously, the angular acceleration α is calculated in real time from the angle value fed back by the encoder; according to formula T d =T i The disturbance moment T is calculated using Jα. d T i Input torque to the current loop, where J is the inertia of the azimuth or pitch axis called in real time; calculate the disturbance torque T. dAs a feedforward quantity to compensate for the control torque given to the current loop, it enables real-time suppression of disturbances such as friction and imbalance.

[0013] A further technical solution of the present invention is as follows: In step 1, real-time reading is achieved by connecting the shaft encoder to the control board, and then connecting the control board to the host computer via a serial port or bus; when the power-on of the control board is detected, the dedicated test software in the host computer opens the communication port and automatically completes the preparation work for system initialization and data acquisition.

[0014] A further technical solution of the present invention is: in steps 3 and 4, the specific formula for calculating the moment of inertia based on the impulse theorem is as follows:

[0015]

[0016] Among them, T 采样 Let n be the sampling period, n be the number of samples within the calculation time period, and k be the discrete sampling time. T denoted as the motor torque coefficient, i(k) is the current feedback value of the motor driver at the sampling time, f is the friction torque, and Δω is the difference between the first and last angular velocities in the calculation time period t.

[0017] A further technical solution of the present invention is: in steps 3 and 4, the calculation time period is selected as one-quarter of the cycle between the zero point of the angular velocity in the sinusoidal velocity motion waveform and the adjacent peak point.

[0018] A further technical solution of the present invention is: in step 4, the curve fitting algorithm adopts the least squares method, and the fitted azimuth axis inertia function J 方位 =F(θ 俯仰 () is about the pitch angle θ 俯仰 A second-order or higher-order polynomial function.

[0019] A further technical solution of the present invention is: in step 5, the angular acceleration α is obtained by performing a second-order differential calculation on the angle value fed back by the encoder.

[0020] A line-of-sight stabilization accuracy detection device for implementing the method, comprising:

[0021] The data acquisition module is used to perform step 1, acquire and initialize system parameters, and read sensor data in real time;

[0022] The torque identification module is used to execute step 2, control the motor to perform segmented uniform speed motion and calculate the friction torque of each shaft;

[0023] The inertia identification module is used to execute steps 3 and 4, control the motor to perform sinusoidal motion, and calculate the pitch axis inertia and azimuth axis inertia functions based on the impulse theorem.

[0024] The compensation control module is used to execute step 5, which calculates the disturbance torque in real time based on the identified inertia and performs feedforward compensation.

[0025] An electronic device, comprising:

[0026] One or more processors;

[0027] Memory;

[0028] and one or more computer programs;

[0029] The one or more computer programs are stored in the memory and configured to be executed by the one or more processors, and the one or more computer programs include instructions for implementing the method.

[0030] A computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the method.

[0031] A two-axis optoelectronic platform, integrating the electronic device or the computer-readable storage medium, and capable of automatically executing the method to complete the platform's own inertia identification and disturbance feedforward compensation control.

[0032] Beneficial effects

[0033] The beneficial effects of this invention are as follows: This invention achieves automatic inertia identification and compensation control through a program on the two-axis photoelectric device itself, eliminating the need for external specialized measuring instruments, thus improving identification efficiency and economy. Furthermore, compensation control based on the identified inertia improves the control accuracy of the photoelectric device. Specific effects are analyzed below:

[0034] 1. This invention, through real-time identification of inertia and calculation of angular acceleration, can predict disturbance torque in advance and use it as a feedforward for active compensation. This control strategy fundamentally suppresses the impact of disturbances (especially frictional torque and unbalanced torque) on line-of-sight stability and tracking accuracy, enabling the system to maintain extremely high control accuracy and stability even under high-speed, high-dynamic, or externally disturbed conditions.

[0035] 2. This invention creatively proposes the concept and identification method of "azimuth axis inertia function". By performing multiple identifications and fitting the functional relationship at different pitch angles, the control system can perceive and adapt to changes in dynamic characteristics caused by pitch angle changes in real time.

[0036] 3. The key parameters involved in this invention, such as frictional torque and moment of inertia, are all automatically identified using the inherent motor, driver, and encoder of the photoelectric device itself. This eliminates the need for expensive and bulky external equipment such as torque meters and inertial measurement units, significantly reducing system cost, size, and testing complexity.

[0037] 4. This invention employs the "impulse theorem" for calculation. By integrating over a quarter cycle of sinusoidal velocity motion, it effectively smooths measurement noise and torque fluctuations, improving the signal-to-noise ratio and identification accuracy. This method, based on dynamic excitation and integral calculation, reflects the true dynamic characteristics of the system better than static or quasi-static methods, resulting in more accurate and reliable identification results. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of friction torque identification and control in an embodiment of the present invention;

[0039] Figure 2 This is a schematic diagram of the inertia identification data calculation curve in an embodiment of the present invention;

[0040] Figure 3 This is a schematic diagram of disturbance torque compensation in an embodiment of the present invention;

[0041] Figure 4 This is a simulation diagram of disturbance compensation in an embodiment of the present invention. Detailed Implementation

[0042] The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the invention, and should not be construed as limiting the invention.

[0043] In traditional control strategies, two-axis photoelectric devices often employ a three-loop feedback control system: position-velocity-current. This control method is essentially a "passive response" mechanism; that is, when the system experiences errors due to frictional torque, unbalanced torque, or external environmental disturbances, feedback is used for correction. However, this control mode has inherent drawbacks:

[0044] 1. Traditional control models treat the system's moment of inertia as a fixed or empirical value. In reality, the moment of inertia of the photoelectric platform, especially the azimuth axis, changes significantly with the pitch angle. This time-varying dynamic characteristic is not recognized and utilized by the controller, leading to a mismatch between the control model and the actual object. Under high-speed, high-dynamic, or large-disturbance conditions, control accuracy and stability drop sharply.

[0045] 2. Frictional torque and unbalanced torque are the most significant internal disturbances affecting the stability and accuracy of the line of sight. Existing technologies, such as CN202410493445.1A, provide a method for separating frictional torque and unbalanced torque using reciprocating motion current information and performing mechanical balancing. However, this is an offline, post-hoc mechanical compensation method that cannot adapt to the dynamic changes in frictional torque during motion, and the balancing accuracy is affected by the frictional torque. Another existing technology, CN201811193249.3B, uses a gyroscope sensor to suppress external disturbances to the platform's rolling axis. While effective, this method increases the hardware complexity and cost of the system.

[0046] 3. Accurate inertia identification is a prerequisite for achieving more advanced feedforward control. However, traditional inertia acquisition methods often rely on external precision measuring instruments such as torque meters and dedicated inertial measurement benches for offline, disassembly-based measurements. This process is not only complex, inefficient, and uneconomical, but also measures static inertia, which cannot reflect the dynamic inertia characteristics of the equipment in its actual assembly state and at different pitch angles. As CN202510664182.0A points out, the complexity of the rotational inertia acquisition process leads to inaccurate test results.

[0047] To address the aforementioned problems, this invention proposes an inertia identification and control method based on a two-axis optoelectronic device, the specific steps of which are as follows:

[0048] Step 1, System Parameter Acquisition and Initialization: Obtain the motor torque coefficient K for the azimuth and pitch axes of the optoelectronic device. T Upper limit of angular velocity ω m And the pitch axis angle range A~B; initialize the control program, set the sampling period, and prepare to read the current feedback value and angle feedback value from the motor driver and shaft encoder in real time;

[0049] Step 2, Automatic identification of friction torque: based on the upper limit of angular velocity ω of each axis. m The control angular velocity is divided into N segments; one axis is fixed at zero position, and the other axis is controlled to move at a constant speed according to the segmented speeds; after each uniform speed movement stabilizes, the current feedback value of the motor driver is collected, combined with the motor torque coefficient K. T The frictional torque of the shaft at each speed range is calculated, and the average value of the frictional torques in N ranges is taken as the final frictional torque f of the shaft.

[0050] Step 3: Automatic identification of pitch axis inertia: Control the azimuth axis to be fixed at zero, and control the pitch axis to have a peak value not exceeding ω. m The motion follows a sinusoidal velocity pattern; after the motion stabilizes, the driving torque data K is collected over a period of time. T• i(k) and angular velocity data ω(k); Based on the impulse theorem, using the collected data and the pitch axis friction torque f identified in step 2, the moment of inertia J of the pitch axis is calculated. 俯仰 ;

[0051] Step 4: Automatic identification of azimuth axis inertia function: Divide the range of motion of the pitch axis into M angular positions; sequentially control the pitch axis to be fixed at each angular position, and control the azimuth axis to have a peak value not exceeding ω. m The sinusoidal velocity motion; at each pitch angle position, the driving torque K of the azimuth axis is collected over a period of time. T Based on the impulse theorem and the azimuth axis friction torque identified in step 2, the moment of inertia J of the azimuth axis at this pitch angle is calculated using the angular velocity data ω(k) and i(k). i Finally, using the azimuth axis inertia data calculated at all M angular positions, a curve fitting algorithm is used to obtain the inertia at the pitch angle θ. 俯仰 The azimuth axis inertia function J is a variable 方位 =F(θ 俯仰 );

[0052] Step 5: Disturbance feedforward compensation control based on identification results: The rotational inertia J of the pitch axis is... 俯仰 and azimuth axis inertia function J 方位 =F(θ 俯仰 The pitch angle θ is stored in the storage medium and read in real time during device operation. 俯仰 Based on this, the corresponding azimuth axis inertia value is retrieved from the inertia function; simultaneously, the angular acceleration α is calculated in real time from the angle value fed back by the encoder; according to formula T d =T i The disturbance moment T is calculated using Jα. d T i Input torque to the current loop, where J is the inertia of the azimuth or pitch axis called in real time; calculate the disturbance torque T. d As a feedforward quantity to compensate for the control torque given to the current loop, it enables real-time suppression of disturbances such as friction and imbalance.

[0053] The present invention also proposes a line-of-sight stabilization accuracy detection device for implementing the method, comprising:

[0054] The data acquisition module is used to perform step 1, acquire and initialize system parameters, and read sensor data in real time;

[0055] The torque identification module is used to execute step 2, control the motor to perform segmented uniform speed motion and calculate the friction torque of each shaft;

[0056] The inertia identification module is used to execute steps 3 and 4, control the motor to perform sinusoidal motion, and calculate the pitch axis inertia and azimuth axis inertia functions based on the impulse theorem.

[0057] The compensation control module is used to execute step 5, which calculates the disturbance torque in real time based on the identified inertia and performs feedforward compensation.

[0058] The present invention also proposes an electronic device, comprising:

[0059] One or more processors;

[0060] Memory;

[0061] and one or more computer programs;

[0062] The one or more computer programs are stored in the memory and configured to be executed by the one or more processors, and the one or more computer programs include instructions for implementing the method.

[0063] The present invention also proposes a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the method.

[0064] The present invention also proposes a two-axis optoelectronic platform that integrates the electronic device or the computer-readable storage medium and can automatically execute the method to complete the platform's own inertia identification and disturbance feedforward compensation control.

[0065] The above technical solution will be further analyzed below with reference to the accompanying drawings and examples:

[0066] In one embodiment, the specific steps of an inertia identification and control method based on a two-axis optoelectronic device are as follows:

[0067] Step 1: Obtain data from optoelectronic devices:

[0068] The motor torque coefficients K of the azimuth and pitch axes of the optoelectronic equipment are respectively... T Angular range A~B, upper limit of angular velocity ω m The parameters are set into the control program, and the 1ms sampling period T is set through the program. 采样 Read the motor driver current feedback value i(k), the encoder feedback angle value θ(k), and differentiate the angle value to obtain the angular velocity ω(k).

[0069] Step 2: Identify frictional torque:

[0070] Based on the upper limit of angular velocity ω of each axis m The control angular velocity is evenly divided into 5 segments, namely ω m / 5、2ωm / 5、3ω m / 5、4ω m / 5、ω m Each axis is controlled to move at a uniform speed according to the set segment. After the speed stabilizes, the current feedback value is read for 10 seconds, and the motor torque output value K is calculated for each uniform speed movement. T *i(k), from which we obtain the frictional torque f=K T *i(k), and the average value of the frictional torque obtained from each speed segment is taken as the frictional torque of that axis. When controlling the movement of one axis, the other axis is controlled to be fixed at zero position.

[0071] Step 3: Identify pitch axis moment of inertia

[0072] like Figure 1 In (b), the azimuth axis is fixed at zero, and the pitch axis is controlled to move at ω=0.5ω. m The motion is sinusoidal, with velocity sin(0.25πt), where t is the motion time. After 15 seconds of uniform pitch axis motion, the sampled data are as follows: Figure 2 As shown, when the velocity ω(k)×ω(k-1)<0, that is, when the value of ω passes through 0, it is recorded as t0. The data of one-quarter motion cycle t1 is recorded and calculated, and the impulse is calculated based on the current value i(k). , where n is the number of samples from t0 to t1, and T 采样 Let n be the sampling period, n be the number of samples within the calculation time period, and k be the discrete sampling time. T Let i(k) be the motor torque coefficient, i(k) be the current feedback value of the motor driver at the sampling time, and f be the friction torque; calculate the pitch axis moment of inertia J according to the impulse theorem. 俯仰 =I / Δω, where Δω is the difference in angular velocity between t1 and t0. The average value of the inertia calculated by sampling three times is the pitch axis rotational inertia.

[0073] Step 4: Identify the moment of inertia of the azimuth axis

[0074] like Figure 1 In section (a), the pitch axis angle is divided into six set values ​​according to A to B: A, A+(BA) / 5, A+2(BA) / 5, A+3(BA) / 5, A+4(BA) / 5, and B. The pitch axis is fixed at each set value, and the azimuth axis inertia is identified. The identification method is the same as that for the pitch axis, and the azimuth axis inertia J of the pitch axis at different set values ​​is obtained. i The azimuth axis inertia function J is obtained by fitting using the least squares method. 方位 =F(θ 俯仰 ).

[0075] Step 5: Store inertia and compensate

[0076] The inertia identification results of the azimuth and pitch axes are stored in the storage medium. When the input torque of the current loop is Ti, the angular acceleration α is obtained from the second derivative of the feedback angle. Based on the inertia information, the external disturbance T experienced by the axis is obtained. d =Ti-Jα, after discretization, we get This is compensated for in the control torque, such as Figure 3 As shown, this achieves compensation control for disturbances such as frictional torque.

[0077] Taking a single axis as an example, the input acceleration is 10° / s². 2 The target speed is 10° / s. A sinusoidal speed disturbance of 10sin(2π)(° / s) is input to the shaft base to introduce frictional torque disturbance. Without changing the control parameters, the tracking curves without and with this compensation method are simulated respectively. Figure 4 As shown, with this compensation method added, the deviation can be corrected in a shorter time when disturbed compared to when it is not added, and the tracking accuracy is higher. This improves the response speed of the optoelectronic equipment to disturbance correction and effectively enhances the anti-interference capability of the optoelectronic equipment.

[0078] All of the above implementation methods are automatically run through the control program and can be completed based on the optoelectronic equipment itself, which effectively improves identification efficiency, control accuracy and economy.

[0079] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention without departing from the principles and spirit of the present invention.

Claims

1. A method for inertia identification and control based on a two-axis optoelectronic device, characterized in that... The specific steps are as follows: Step 1, System Parameter Acquisition and Initialization: Obtain the motor torque coefficient K for the azimuth and pitch axes of the optoelectronic device. T Upper limit of angular velocity ω m And the pitch axis angle range A~B; initialize the control program, set the sampling period, and prepare to read the current feedback value and angle feedback value from the motor driver and shaft encoder in real time; Step 2, Automatic identification of friction torque: based on the upper limit of angular velocity ω of each axis. m The control angular velocity is divided into N segments; one axis is fixed at zero position, and the other axis is controlled to move at a constant speed according to the segmented speeds; after each uniform speed movement stabilizes, the current feedback value of the motor driver is collected, combined with the motor torque coefficient K. T The frictional torque of the shaft at each speed range is calculated, and the average value of the frictional torques in N ranges is taken as the final frictional torque f of the shaft. Step 3: Automatic identification of pitch axis inertia: Control the azimuth axis to be fixed at zero, and control the pitch axis to have a peak value not exceeding ω. m The motion follows a sinusoidal velocity pattern; after the motion stabilizes, the driving torque data K is collected over a period of time. T • i(k) and angular velocity data ω(k); Based on the impulse theorem, using the collected data and the pitch axis friction torque f identified in step 2, the moment of inertia J of the pitch axis is calculated. 俯仰 ; Step 4: Automatic identification of azimuth axis inertia function: Divide the range of motion of the pitch axis into M angular positions; sequentially control the pitch axis to be fixed at each angular position, and control the azimuth axis to have a peak value not exceeding ω. m The sinusoidal velocity motion; at each pitch angle position, the driving torque K of the azimuth axis is collected over a period of time. T Based on the impulse theorem and the azimuth axis friction torque identified in step 2, the moment of inertia J of the azimuth axis at this pitch angle is calculated using the angular velocity data ω(k) and i(k). i Finally, using the azimuth axis inertia data calculated at all M angular positions, a curve fitting algorithm is used to obtain the inertia at the pitch angle θ. 俯仰 The azimuth axis inertia function J is a variable 方位 =F(θ 俯仰 ); Step 5: Disturbance feedforward compensation control based on identification results: The rotational inertia J of the pitch axis is... 俯仰 and azimuth axis inertia function J 方位 =F(θ 俯仰 Stored in a storage medium; During device operation, the current pitch angle θ is read in real time. 俯仰 Based on this, the corresponding azimuth axis inertia value is retrieved from the inertia function; simultaneously, the angular acceleration α is calculated in real time from the angle value fed back by the encoder; according to formula T d =T i The disturbance moment T is calculated using Jα. d T i Input torque to the current loop, where J is the inertia of the azimuth or pitch axis called in real time; calculate the disturbance torque T. d As a feedforward quantity to compensate for the control torque given to the current loop, it enables real-time suppression of disturbances.

2. The inertia identification and control method based on a two-axis optoelectronic device according to claim 1, characterized in that: In step 1, real-time reading is achieved by connecting the shaft encoder to the control board, and then connecting the control board to the host computer via a serial port or bus. When the power-on of the control board is detected, the dedicated test software in the host computer opens the communication port and automatically completes the preparation work for system initialization and data acquisition.

3. The inertia identification and control method based on a two-axis optoelectronic device according to claim 1, characterized in that: In steps 3 and 4, the specific formula for calculating the moment of inertia based on the impulse theorem is as follows: Among them, T 采样 Let n be the sampling period, n be the number of samples within the calculation time period, and k be the discrete sampling time. T denoted as the motor torque coefficient, i(k) is the current feedback value of the motor driver at the sampling time, f is the friction torque, and Δω is the difference between the first and last angular velocities in the calculation time period t.

4. The inertia identification and control method based on a two-axis optoelectronic device according to claim 3, characterized in that: In steps 3 and 4, the calculation time period is selected as one-quarter of the cycle between the zero point of the angular velocity in the sinusoidal velocity motion waveform and the adjacent peak point.

5. The inertia identification and control method based on a two-axis optoelectronic device according to claim 1, characterized in that: In step 4, the curve fitting algorithm uses the least squares method to obtain the azimuth axis inertia function. J 方位 =F(θ 俯仰 () is about the pitch angle θ 俯仰 A second-order or higher-order polynomial function.

6. The inertia identification and control method based on a two-axis optoelectronic device according to claim 1, characterized in that: In step 5, the angular acceleration α is obtained by performing a second-order differential calculation on the angle value fed back by the encoder.

7. A line-of-sight stabilization accuracy detection device for implementing the inertia identification and control method according to any one of claims 1-6, characterized in that, include: The data acquisition module is used to perform step 1, acquire and initialize system parameters, and read sensor data in real time; The torque identification module is used to execute step 2, control the motor to perform segmented uniform speed motion and calculate the friction torque of each shaft; The inertia identification module is used to execute steps 3 and 4, control the motor to perform sinusoidal motion, and calculate the pitch axis inertia and azimuth axis inertia functions based on the impulse theorem. The compensation control module is used to execute step 5, which calculates the disturbance torque in real time based on the identified inertia and performs feedforward compensation.

8. An electronic device, characterized in that, include: One or more processors; Memory; and one or more computer programs; The one or more computer programs are stored in the memory and configured to be executed by the one or more processors, the one or more computer programs including instructions for implementing the inertia identification and control method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed on a computer, cause the computer to perform the inertia identification and control method according to any one of claims 1-6.

10. A two-axis photoelectric platform, characterized in that, The device integrates the electronic device of claim 8 or the computer-readable storage medium of claim 9, and is capable of automatically executing the method of any one of claims 1-6 to complete the platform's own inertia identification and disturbance feedforward compensation control.

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