A servo system speed loop parameter self-tuning method based on moment of inertia identification

Abstract

The application belongs to the technical field of motion control, and discloses a servo system speed loop parameter self-tuning method based on rotational inertia identification, which comprises the following steps: a reference adaptive model based on a variable gain model is constructed; a parameter relationship equation group of a speed loop open loop cutoff frequency and a phase margin is constructed, and a function relationship between a time domain proportional gain, a time domain integral gain and a total rotational inertia identification value is obtained based on the parameter relationship equation group; a double linear method is used to discretize a time domain transfer function of the speed loop, and a discrete domain proportional gain and a discrete domain integral gain are obtained; a speed and an electromagnetic torque under a motor operation condition are collected in real time, and a total rotational inertia identification value is output in real time, and a speed loop proportional gain and a speed loop integral gain are dynamically adjusted according to the discrete domain proportional gain and the discrete domain integral gain. The application solves the problems that a traditional model reference adaptive rotational inertia identification algorithm cannot simultaneously consider speed and accuracy, and a fusion type self-tuning algorithm has high calculation complexity.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of motion control, and particularly relates to a servo system speed loop parameter self-tuning method based on rotational inertia identification. BACKGROUND

[0002] Permanent magnet synchronous servo motor (PMSM) has become the core driving component in the fields of industrial automation, robots, electric vehicles, etc. due to its high power density, fast dynamic response, high control precision, etc. The control performance is highly dependent on the coordinated optimization of the current loop, speed loop and position loop in the vector control technology. As the core part of each control loop, the rationality of the parameter setting of the PI controller (Proportional-Integral Controller) directly determines the steady-state accuracy and dynamic response characteristics of the system. However, the parameter setting of the traditional PI controller relies on manual experience and trial-and-error, which is not only inefficient, but also difficult to ensure the robustness of the control performance when the load inertia or working conditions change. In this context, parameter self-tuning technology has become a key breakthrough to improve the intelligent level and adaptability of the servo system.

[0003] Rotational inertia, as the core parameter of the dynamic model of the servo system, its identification accuracy directly affects the effectiveness of the PI controller parameter self-tuning. The dynamic response (such as acceleration, braking characteristics) of the motor drive system is closely related to the load rotational inertia: too large rotational inertia will cause slow system response, and too small rotational inertia will easily cause overshoot or oscillation. However, the traditional model reference adaptive rotational inertia identification algorithm uses fixed gain switching according to the set threshold, and improper threshold setting may cause gain oscillation, affecting the identification speed, and cannot well balance speed and accuracy.

[0004] In the current parameter self-tuning algorithm based on model and rule fusion, most algorithms need to perform model parameter identification and data-driven rule optimization online at the same time, which leads to a sharp increase in computing power demand, causing a significant burden on embedded platforms, and a long time is needed to complete the tuning process, causing slow drive control response. SUMMARY

[0005] The application aims to provide a servo system speed loop parameter self-tuning method based on rotational inertia identification, to solve the problems that the traditional model reference adaptive rotational inertia identification algorithm cannot simultaneously balance speed and accuracy, and the fusion type self-tuning algorithm has high computational complexity.

[0006] To achieve the above-mentioned purpose, the technical solution adopted by the application is:

[0007] A servo system speed loop parameter self-tuning method based on rotational inertia identification, comprising:

[0008] The reference adaptive model based on the variable gain model is constructed to identify the total rotational inertia of the servo system;

[0009] The open loop transfer function of the speed loop of the servo system is established, parameter relationship equations of the open loop cutoff frequency and phase margin of the speed loop are constructed, and a function relationship between the time domain proportional gain and the time domain integral gain and the total rotational inertia identification value is obtained based on the parameter relationship equations;

[0010] The time domain transfer function of the speed loop is discretized by using the bilinear method to obtain the discrete domain proportional gain and the discrete domain integral gain;

[0011] The speed and electromagnetic torque under the running condition of the motor are collected in real time, and the total rotational inertia identification value is output in real time by using the reference adaptive model based on the variable gain model, and the speed loop proportional gain and the speed loop integral gain are dynamically adjusted according to the discrete domain proportional gain and the discrete domain integral gain.

[0012] The following also provides several optional modes, but not as an additional limitation to the above overall scheme, just a further supplement or preferred, without technical or logical contradiction, each optional mode can be combined with the above overall scheme, and can also be combined between multiple optional modes.

[0013] As preferred, the reference adaptive model based on the variable gain model maintains an adaptive gain, and the adjustment process of the adaptive gain is as follows:

[0014] If the relative change rate of the current total rotational inertia identification value is greater than the sum of the relative change rate threshold and the hysteresis width, the maximum gain value is taken as the latest adaptive gain;

[0015] If the relative change rate of the current total rotational inertia identification value is less than or equal to the difference between the relative change rate threshold and the hysteresis width, the difference between the maximum gain value and the minimum gain value is calculated, and the product of the relative change rate of the current total rotational inertia identification value and the difference is taken, and then added to the minimum gain value to obtain the latest adaptive gain;

[0016] Otherwise, the adaptive gain of the last time is taken as the latest adaptive gain.

[0017] As preferred, the function relationship between the time domain proportional gain and the time domain integral gain and the total rotational inertia identification value obtained based on the parameter relationship equations includes:

[0018]

[0019] In the formula, is the time domain proportional gain, is the total rotational inertia identification value, is the carrier frequency of the inverter, a motor torque coefficient, a time-domain integral gain.

[0020] As a preference, the discrete-domain proportional gain and the discrete-domain integral gain comprise:

[0021]

[0022] wherein, is a discrete-domain proportional gain, is a discrete-domain integral gain, is a time-domain proportional gain, is a time-domain integral gain, is a speed loop control period.

[0023] As a preference, the dynamic adjustment of the speed loop proportional gain and the speed loop integral gain according to the discrete-domain proportional gain and the discrete-domain integral gain comprises:

[0024] If the total moment of inertia identification value is stable, the next step is entered; otherwise, the speed and the electromagnetic torque under the motor operation are re-collected, and a reference adaptive model based on a variable gain model is used to output the total moment of inertia identification value in real time;

[0025] The speed error and the dynamic error reference value are calculated according to the target speed and the actual speed collected in real time.

[0026] The dynamic adjustment of the speed loop proportional gain and the speed loop integral gain is as follows:

[0027]

[0028] wherein, represents the speed loop proportional gain of the i-th speed loop control period, represents the speed loop integral gain of the i-th speed loop control period, is a discrete-domain proportional gain, is a discrete-domain integral gain, is the speed error of the i-th speed loop control period, is an absolute value operation, is the dynamic error reference value of the i-th speed loop control period, is the speed error change rate of the i-th speed loop control period, is an integral adjustment factor.

[0029] As a preference, the stability judgment process of the total moment of inertia identification value is as follows:

[0030] ​​​​​The relative change rate of the total moment of inertia identification value is calculated, and if the relative change rate in each of the continuous speed loop control periods is less than a change threshold value, it is determined that the total moment of inertia identification value is stable; otherwise, the total moment of inertia identification value is unstable.

[0031] As preferred, the speed error is a difference between a target speed and an actual speed collected in real time.

[0032] The dynamic error reference value is obtained by taking the absolute value of the current speed error and the difference between the half of the last dynamic error reference value, taking the product of the learning rate and the difference, and then superimposing the product to the last dynamic error reference value to obtain the current dynamic error reference value.

[0033] Compared with the prior art, the present application has the following advantages: 1) an adaptive gain switching mechanism based on the relative change rate of the identified inertia is proposed, when the identified inertia changes greatly, the high gain mode is automatically switched to realize fast tracking; and in the steady state or slightly variable working condition, the identified inertia is smoothly adjusted according to the identified inertia change to improve the steady state identification accuracy and effectively balance the speed and accuracy; meanwhile, the hysteresis width is introduced to suppress the oscillation of the gain near the threshold value. 2) the parameter function relationship established by the moment of inertia identification result is used to generate an initial solution domain, a dynamic continuous adjustment mechanism is further designed to realize the smooth transition of transient acceleration convergence and steady state anti-disturbance, which can realize online adjustment, especially suitable for embedded scenes with limited computing power, and solves the problem of high computational complexity of the traditional fusion algorithm. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 A flowchart of the present application is shown in the figure.

[0035] Figure 2 A principle diagram of the present application is shown in the figure.

[0036] Figure 3 A flowchart of the present application is shown in the figure. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0038] ​Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0039] Embodiment 1: This embodiment provides a servo system speed loop parameter self-tuning method based on moment of inertia identification, as shown in Figure 1 , including the following steps:

[0040] Step 1, a variable gain model reference adaptive method is constructed for identifying the total moment of inertia of the servo system, and the specific process is as shown in Figure 2 .

[0041] Neglecting the motor friction torque, the permanent magnet synchronous motor rotor motion equation is as follows:

[0042]

[0043] In the formula, represents the electromagnetic torque of the motor, represents the load torque of the motor, represents the motor speed is differentiated with respect to time , and represents the total moment of inertia of the servo system.

[0044] Discretizing the permanent magnet synchronous motor rotor motion equation at and , we get:

[0045]

[0046] In the formula, represents the electromagnetic torque of the motor in the th speed loop control period, represents the electromagnetic torque of the motor in the th speed loop control period, represents the load torque of the motor in the th speed loop control period, represents the load torque of the motor in the th speed loop control period, is the speed loop control period, represents the motor speed in the th speed loop control period, represents the motor speed in the th speed loop control period, represents the motor speed in the th speed loop control period.

[0047] Generally, during the process of motor running, the load torque is considered to be constant at the time of and . Subtracting the two equations, we have:

[0048]

[0049] Define the electromagnetic torque difference , , then we have the reference model:

[0050]

[0051] Based on the reference model, we build the adjustable model to estimate the speed at the time of , using the speed and electromagnetic torque of the first speed loop control cycle, we have:

[0052]

[0053] In the equation, the is the estimated speed of the first speed loop control cycle, the is the estimated value related to the first speed loop control cycle and , that is, the estimated value of .

[0054] Subtracting the reference model and the adjustable model, we get the speed error expression of the two models:

[0055]

[0056] During the identification process, the goal is to reduce the deviation between the reference model and the adjustable model. When the deviation between the reference model and the adjustable model gradually decreases and approaches zero, that is, when it meets the allowable error range, the estimated value can be used to estimate the true value, thereby achieving the identification purpose.

[0057] According to the Popov hyperstability theory, the formula of the reference adaptive model based on the variable gain model is as follows:

[0058]

[0059] In the equation, the is the estimated value related to the first speed loop control cycle and , the is the adaptive gain of the first speed loop control cycle. For the The motor electromagnetic torque error during a speed loop control cycle is: For the The speed error of a speed loop control cycle.

[0060] When the adaptive gain When the adaptive gain increases, the convergence speed of inertia identification is accelerated, but the dynamic fluctuation amplitude will be larger; on the contrary, when the adaptive gain When it decreases, the convergence speed of inertia identification becomes slower, but the dynamic fluctuation amplitude of the system is smaller.

[0061] The traditional model reference adaptive moment of inertia identification algorithm performs fixed gain switching according to the set threshold. Improper threshold setting may cause gain oscillation, affect the identification speed, and fail to strike a good balance between speed and accuracy. Therefore, this embodiment proposes an adaptive gain switching mechanism based on the relative change rate of the identified inertia. When a large change in the identified inertia is detected, it automatically switches to the high gain mode to achieve fast tracking; in steady-state or slightly changing working conditions, it performs smooth adjustment according to the change in the identified inertia, improves the steady-state identification accuracy, and effectively balances speed and accuracy; at the same time, it also introduces the hysteresis loop width , suppressing the gain from oscillating near the threshold, the adaptive change law adjustment process of the gain coefficient is as follows:

[0062] If the relative change rate of the current total moment of inertia identification value is greater than the sum of the relative change rate threshold and the hysteresis band width, the maximum gain is taken as the latest adaptive gain; if the relative change rate of the current total moment of inertia identification value is less than or equal to the difference between the relative change rate threshold and the hysteresis band width, the difference between the maximum gain and the minimum gain is calculated, and the product of the relative change rate of the current total moment of inertia identification value and the difference is taken and added to the minimum gain to obtain the latest adaptive gain; otherwise, the last adaptive gain is taken as the latest adaptive gain. The formula is as follows:

[0063]

[0064] Where, is the relative rate of change of the identified value of the total moment of inertia, and , Indirectly reflects the degree of change of system inertia, For the The total moment of inertia identification value of a speed loop control cycle, For the Total moment of inertia identification value of a speed loop control cycle, relative change rate threshold , used to determine the degree of change in identification inertia and the width of the hysteresis loop make exist Lock the gain within the range to avoid adaptive gain Frequent switching. It is the maximum value of the adaptive gain (i.e. the maximum gain), which ensures the system's rapid response to large inertia changes and serves as the initial value of the adaptive gain. It is the minimum value of the adaptive gain (i.e. the minimum gain), which prevents the system from being overly sensitive to small inertia fluctuations.

[0065] Step 2: Establish the open-loop transfer function of the servo system speed loop , build the speed loop open loop cutoff frequency and phase margin The time domain proportional gain is analytically derived based on the parameter relationship equations of and time domain integral gain and total moment of inertia identification value The functional relationship is as follows:

[0066] Open-loop transfer function The formula is as follows:

[0067]

[0068] Where, is a complex variable, is the motor torque coefficient.

[0069] Open-loop cutoff frequency The frequency value corresponding to the amplitude of the open-loop frequency response function is 1; the phase margin is the open loop cutoff frequency The angle difference between the phase of the open-loop cutoff frequency response function and the critical phase (-180°) has an impact on the open-loop transfer function. Perform frequency response analysis, Substitution , and construct the speed loop open loop cutoff frequency according to the above definition and phase margin The mapping relationship equation between:

[0070]

[0071] Where, represents the open-loop frequency response function The amplitude of represents the open-loop frequency response function The phase angle, represents the imaginary unit, represents the arctan function. The time domain proportional gain can be derived as and time domain integral gain and total moment of inertia identification value The functional relationship is as follows:

[0072]

[0073] In the formula, represents the cotangent function, and the embodiment is set according to experience , The total moment of inertia identification value is substituted, and the time domain parameter expression of the speed PI controller is as follows:

[0074]

[0075] In the formula, is the frequency of the inverter carrier.

[0076] Step 3: The time domain transfer function of the speed loop is discretized by using the bilinear method to obtain the discrete domain proportional gain and the discrete domain integral gain , which are as follows:

[0077] The time domain transfer function of the speed PI controller is , which is discretized by using the bilinear method, that is, the is brought into to obtain the Z-domain expression:

[0078]

[0079] In the formula, represents the Z-domain output expression of the discretized speed PI controller, represents the Z-domain input expression of the discretized speed PI controller.

[0080] The Z-domain expression is cross-multiplied and inverse Z-transformed to obtain the difference equation of the discretized speed PI controller:

[0081]

[0082] In the formula, represents the PI controller output in the n-th speed loop control period, represents the PI controller output in the n-th speed loop control period, represents the speed error signal input into the PI controller in the n-th speed loop control period, represents the speed error signal input into the PI controller in the n-th speed loop control period. According to the difference equation, the time domain proportional gain

[0083] ​​​​and a discrete domain integral gain and a discrete domain parameter and The relationship is as follows:

[0084]

[0085] Step 4, drive the motor to run, real-time collect the speed and torque under the motor running condition, and use the variable gain model reference adaptive method to output the total moment of inertia identification value in real time, and dynamically adjust the speed loop proportional gain and the speed loop integral gain according to the discrete domain proportional gain and the discrete domain integral gain. As shown in Figure 3 , the specific steps are as follows:

[0086] Step 4-1, drive the motor to run, real-time collect the speed and electromagnetic torque, and use the variable gain model reference adaptive method to output the total moment of inertia identification value , judge whether the total moment of inertia identification value is stable, if the relative change rate in continuous (e.g. 6) speed loop control periods is less than the change threshold (e.g. 2%, which can be adjusted by 1% left and right), it is judged that the total moment of inertia identification value is stable; otherwise, the total moment of inertia identification value is not stable. If is stable, go to the next step; otherwise, repeat step 4-1.

[0087] Step 4-2, get the speed command (target speed) and the feedback actual speed every speed loop control period , calculate the speed error and the dynamic error reference value , is the dynamic error reference value of the first speed loop control period, and is the dynamic error reference value of the first speed loop control period, the learning rate controls the convergence speed and limits , wherein is the error expected by the user, and is used as the initial value of the dynamic error reference value , and (e.g. 5) speed loop control period sliding average filter is used to calculate the smoothed speed error change rate , a length of 5 error queue is maintained to store historical values to , the oldest data is removed and the latest is added every time, and the average value of the adjacent error difference is calculated.

[0088] Step 4-3: Dynamically adjust the speed loop proportional gain and speed loop integral gain :

[0089]

[0090] Among them, the integral adjustment factor , integral adjustment factor The denominator 10 is not a fixed value, but an empirical parameter that can be flexibly adjusted according to the system characteristics: when the denominator is reduced, The value increases, thereby increasing the speed loop integral gain Increase, the inhibitory effect is weakened, allowing stronger integral compensation to improve the dynamic response speed; on the contrary, increasing the denominator The value decreases, the speed loop integral gain It reduces and enhances the suppression effect, which can effectively suppress transient overshoot but may sacrifice response speed.

[0091] Dynamic error reference value Track the actual error amplitude through online learning to replace the fixed threshold. When the system is in a highly dynamic condition (such as a sudden load change), the dynamic error reference value Automatically increase and expand the gain adjustment range of large error areas; when the system is in steady state, the dynamic error reference value Reduce and improve the control accuracy in small error areas.

[0092] Transient process ( ): The system is in the fast response stage, the dynamic error reference value Because online learning lags behind speed error , has not yet been updated to a high position. At this time, the speed loop proportional gain Approaching , greatly improving the proportional effect to accelerate convergence; at the same time Increase, speed loop integral gain Reduce, suppress integral accumulation, and prevent overshoot. When it increases, the integral adjustment factor Increase, partially offset The influence of the integral action is allowed to compensate for nonlinear disturbances.

[0093] Steady-state process ( ): The system approaches the target state, the speed error Very small, dynamic error reference value It has converged to the minimum value through online learning. At this time, the speed loop proportional gain Approximate discrete domain proportional gain , restore the basic proportional gain to avoid oscillation caused by over-adjustment; Decrease, speed loop integral gain Approach to discrete domain integral gain , restore integral action, eliminate steady state error; dynamic error reference value Decrease, resulting in integral adjustment factor Decrease, strengthen integral suppression, avoid steady-state micro-oscillation.

[0094] The application proposes an adaptive gain switching mechanism with hysteresis characteristics, which dynamically adjusts the identification gain coefficient by monitoring the relative change rate of inertia, and takes into account the dynamic response speed and steady-state accuracy; The application solves the problems of parameter mismatch and high optimization algorithm calculation complexity of traditional methods when the moment of inertia suddenly changes, and is particularly suitable for parameter setting of embedded servo systems, with the characteristics of fast dynamic response, high steady-state accuracy and low calculation resource occupation.

[0095] Embodiment 2: The application also provides a servo system speed loop parameter self-tuning device based on rotational inertia identification, comprising a processor and a memory storing a plurality of computer instructions, wherein the computer instructions are executed by the processor to realize the steps of the servo system speed loop parameter self-tuning method based on rotational inertia identification.

[0096] For specific limitations of the servo system speed loop parameter self-tuning device based on rotational inertia identification, please refer to the limitations of the servo system speed loop parameter self-tuning method based on rotational inertia identification in the above, which will not be repeated here.

[0097] The memory and the processor are directly or indirectly electrically connected to realize the transmission or interaction of data. For example, these elements can be electrically connected to each other through one or more communication buses or signal lines. The memory stores a computer program that can run on the processor, and the processor realizes the method of the application by running the computer program stored in the memory.

[0098] Among them, the memory can be, but is not limited to, random access memory (Random Access Memory, RAM), read-only memory (Read Only Memory, ROM), programmable read-only memory (Programmable Read-Only Memory, PROM), erasable read-only memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable read-only memory (Electric Erasable Programmable Read-Only Memory, EEPROM) and the like.

[0099] The processor can be an integrated circuit chip with data processing capability. The processor can be a general purpose processor, including a central processing unit (CPU), a network processor (NP), or the like. The processor can implement or execute the methods, steps, and logical block diagrams disclosed in the embodiments of the present application. The general purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0100] Any combination of the above-described technical features of the embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above-described embodiments are not described, however, as long as the combinations of the technical features do not exist, they should be considered as the scope of the present application.

[0101] The above-described embodiments only express several implementation manners of the present application, the description is relatively specific and detailed, however, it should not be understood as a limitation on the scope of the present application. It should be pointed out that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for self-tuning speed loop parameters of a servo system based on rotational inertia identification, characterized in that, The servo system speed loop parameter self-tuning method based on the moment of inertia identification comprises: a reference adaptive model based on a variable gain model is constructed to identify the total moment of inertia of the servo system; an open-loop transfer function of the servo system speed loop is established, a parameter relationship equation group of the speed loop open-loop cutoff frequency and phase margin is constructed, and a function relationship between the time-domain proportional gain and the time-domain integral gain and the total moment of inertia identification value is obtained based on the parameter relationship equation group; a double linear method is used to discretize the time-domain transfer function of the speed loop to obtain the discrete-domain proportional gain and the discrete-domain integral gain; the speed and the electromagnetic torque under the motor operation are collected in real time, the reference adaptive model based on the variable gain model is used to output the total moment of inertia identification value in real time, and the speed loop proportional gain and the speed loop integral gain are dynamically adjusted according to the discrete-domain proportional gain and the discrete-domain integral gain. The reference adaptive model based on the variable gain model maintains an adaptive gain, and the adjustment process of the adaptive gain is as follows: if the relative change rate of the current total moment of inertia identification value is greater than the sum of the relative change rate threshold and the hysteresis width, the maximum gain value is taken as the latest adaptive gain; if the relative change rate of the current total moment of inertia identification value is less than or equal to the difference between the relative change rate threshold and the hysteresis width, the difference between the maximum gain value and the minimum gain value is calculated, the product of the relative change rate of the current total moment of inertia identification value and the difference is taken, and the latest adaptive gain is obtained by superimposing the product on the minimum gain value; otherwise, the adaptive gain of the last time is taken as the latest adaptive gain.

2. The method of claim 1, wherein, The function relationship between the time-domain proportional gain and the time-domain integral gain and the total moment of inertia identification value obtained based on the parameter relationship equation group comprises: ； wherein is a time domain proportional gain, is a total moment of inertia recognition value, is an inverter carrier frequency, is a motor torque coefficient, is a time domain integral gain.

3. The method of claim 1, wherein, The discrete-domain proportional gain and the discrete-domain integral gain comprise: ； wherein is a discrete domain proportional gain, is a discrete domain integral gain, is a time domain proportional gain, is a time domain integral gain, is a speed loop control period.

4. The method of claim 1, wherein, The dynamic adjustment of the speed loop proportional gain and the speed loop integral gain according to the discrete-domain proportional gain and the discrete-domain integral gain comprises: if the total moment of inertia identification value is stable, the next step is entered; otherwise, the speed and the electromagnetic torque under the motor operation are re-collected, and the reference adaptive model based on the variable gain model is used to output the total moment of inertia identification value in real time; the speed error and the dynamic error reference value are calculated according to the target speed and the actual speed collected in real time; the speed loop proportional gain and the speed loop integral gain are dynamically adjusted as follows: ； wherein represents a speed loop proportional gain of the th speed loop control period, represents a speed loop integral gain of the th speed loop control period, is a discrete domain proportional gain, is a discrete domain integral gain, is a speed error of the th speed loop control period, is an absolute value operation, is a dynamic error reference value of the th speed loop control period, is a speed error change rate of the th speed loop control period, is an integral adjustment factor.

5. The method of claim 4, wherein, The stability judgment process of the total moment of inertia identification value is as follows: The relative change rate of the total moment of inertia identification value is calculated, and if the relative change rate in each of the continuous speed loop control periods is less than a change threshold value, it is determined that the total moment of inertia identification value is stable. The relative change rate of the total moment of inertia identification value is calculated, and if the relative change rate in each of the continuous speed loop control periods is less than a change threshold value, it is determined that the total moment of inertia identification value is stable. otherwise, the total moment of inertia identification value is unstable.

6. The method of claim 4, wherein, The speed error is the difference between the target speed and the actual speed collected in real time. The acquisition process of the dynamic error reference value is as follows: the difference between the absolute value of the current speed error and half of the last dynamic error reference value is taken, the product of the learning rate and the difference is taken, and the current dynamic error reference value is obtained by superimposing the product on the last dynamic error reference value.

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