阅读说明：本技术 一种永磁同步电机驱动系统的惯量辨识方法 (Inertia identification method for permanent magnet synchronous motor driving system ) 是由 宋宝 杨承博 唐小琦 周向东 李虎 钟靖龙 姜茂文 李渝 张泽之 于 2021-08-30 设计创作，主要内容包括：本发明提供一种永磁同步电机驱动系统的惯量辨识方法,方法包括：将惯量扩展为新的系统状态,构建扩展的机械运动方程；将扩展滑模观测器技术引入惯量辨识领域,基于构建的机械运动方程开发一种具有时变反馈增益的新型扩展滑模观测器以辨识惯量；设计一个扩张状态观测器,进而为新型扩展滑模观测器提供所需的集总扰动信息以确保其成功估计惯量。本发明将扩展滑模观测器技术成功应用于惯量辨识领域,确保了惯量辨识过程对噪声干扰的强大鲁棒能力；另外,该方法不涉及任何矩阵运算,因而计算负担小。总的来讲,本发明所提供的方法在具有优越的抗噪声干扰性能的同时拥有低计算负担的特性。(The invention provides an inertia identification method of a permanent magnet synchronous motor driving system, which comprises the following steps: expanding the inertia into a new system state, and constructing an expanded mechanical motion equation; introducing an extended sliding mode observer technology into the field of inertia identification, and developing a novel extended sliding mode observer with time-varying feedback gain based on a constructed mechanical motion equation to identify inertia; an extended state observer is designed, and then required lumped disturbance information is provided for the novel extended sliding mode observer to ensure that the inertia is successfully estimated. The invention successfully applies the technology of expanding the sliding-mode observer to the field of inertia identification, and ensures the strong robust capability of the inertia identification process on noise interference; in addition, the method does not involve any matrix operation, and thus the computational burden is small. In general, the method provided by the invention has the characteristic of low computational burden while having excellent anti-noise interference performance.)

1. An inertia identification method for a permanent magnet synchronous motor driving system is characterized by comprising the following steps:

step 1, expanding inertia into a new system state, and constructing an expanded mechanical motion equation of a permanent magnet synchronous motor driving system;

step 2, constructing a novel extended sliding-mode observer with time-varying feedback gain based on the extended mechanical motion equation;

and 3, constructing an extended state observer, estimating lumped disturbance information of the permanent magnet synchronous motor driving system, and providing the lumped disturbance information to the developed novel extended sliding mode observer so that the novel extended sliding mode observer can successfully identify the inertia of the permanent magnet synchronous motor driving system.

2. The inertia identification method according to claim 1, wherein the step 1 comprises:

the mechanical equation of motion of a permanent magnet synchronous motor drive system is described as follows:

wherein, ω is_mIs the rotational speed, B is the coefficient of viscous friction, J is the inertia, T_lFor load torque, C is the Coulomb coefficient of friction，T_eFor electromagnetic torque, T_eAnd ω_mProvided by a permanent magnet synchronous motor driving system;

defining the reciprocal of inertia as J to be 1/J, expanding the inertia to a new system state, and constructing an expanded mechanical motion equation of the permanent magnet synchronous motor driving system as follows:

wherein D is_LIs lumped disturbance information of a permanent magnet synchronous motor driving system, which is defined asd_jThe derivative of j is indicated.

3. The inertia identification method of claim 2, wherein the step 2 comprises:

constructing an extended sliding-mode observer for estimating inertia of a permanent magnet synchronous motor driving system:

wherein the content of the first and second substances,respectively represent omega_m，j，D_LF is the feedback gain; u shape_ESMORepresents a sliding mode observer signal, which is designed to:

U_ESMO＝g·sign(S)； (4)

where g is the sliding mode switching gain and S is the sliding mode surface, which is designed as

4. The inertia identification method of claim 3, wherein the step 1 further comprises determining a sliding mode switching gain g and a feedback gain f, comprising:

calculating an error equation of the extended sliding-mode observer:

wherein the content of the first and second substances,

let e_D、e_jAnd T_eAre all bounded and define Selecting a Lyapunov function as V ═ 0.5S²The following can be obtained:

according to equation (6), the gain g is designed to be when sliding mode switchingCan deriveIs satisfied, which means that the sliding mode accessibility condition is satisfied;

after the sliding form occurs, e_ωAnd derivatives thereofWill converge to 0 in a finite time, i.e.Equation (5) is rewritten as:

from equation (7), one can obtain:

wherein the variation frequency of the inertia is far lower than the sampling frequency of the driving system, and d is satisfied in a short time_jThen equation (8) can be rewritten as:

when fe_DIs controlled to be fe_DWhen the value is approximately equal to 0, the following can be obtained:

the analytical solution of equation (10) can be derived as:

wherein C is a constant when fT is satisfied_eInertia estimation error e > 0_jWill gradually go to 0, so the feedback gain is designed to be f mT_e(m > 0) to satisfy fT_e> 0, notably fe_DThe key point for the conclusion is that the value is approximately equal to 0, and fe can be ensured by selecting a proper value of m_DSmall enough, i.e. able to ensure fe_D≈0；

Therefore, the final expression of the designed extended sliding-mode observer with time-varying feedback gain is as follows:

5. the inertia identification method of claim 2, wherein the step 3 comprises:

considering the lumped disturbance as a new system state, the following extended mechanical motion equation is obtained based on equation (1):

based on equation (13) above, the designed linear extended state observer can be expressed as follows:

where p is the desired pole of the extended state observer, satisfying p > 0, and is extended by the output of the sliding-mode observer given that j is unknownInstead, the final expression of the designed linear extended state observer is as follows:

Technical Field

The invention relates to the field of motor driving, in particular to an inertia identification method of a permanent magnet synchronous motor driving system.

Background

The permanent magnet synchronous motor has the excellent characteristics of good reliability, large torque inertia ratio, high power density, high efficiency and the like, so the permanent magnet synchronous motor is widely applied in the industrial field. Meeting the requirement of high performance control is a key sign of the driving system of the modern permanent magnet synchronous motor, and the speed ring is an important factor for determining the performance of the driving system. Currently, some algorithms for improving the control performance of the speed ring cannot keep accurate inertia identification results, such as speed ring self-tuning and load torque observation. High precision inertia data is critical to ensure superior performance of these methods. Therefore, it is very important and necessary to accurately estimate the inertia of the drive system.

Inertia identification techniques can be largely divided into two main categories: offline identification and online identification. Offline identification techniques can only be used during drive system commissioning, and such methods are only applicable to fixed inertia systems. Compared with offline recognition, online recognition technology is widely concerned due to its applicability to more complex situations. Model reference adaptive method, recursive least square method and gradient algorithm are common inertia online identification algorithms, and are popular because of low calculation burden. However, these mentioned inertia online identification techniques do not take into account friction torques, which makes their estimation less accurate. Fortunately, the orthogonal principle based methods (I.Awaya, Y.Kato, I.Miyake, and M.Ito, "New motion control with inertia identification function using interference generator," in Proc.IECON, San Diego, CA, USA,1992, pp.77-81.) and the method based On optimal parameter estimation (L.Niu, D.Xu, M.Yang, X.Gui, and Z.Liu, "On-line inertia identification algorithm for PI parameter optimization in speed loop," IEEE ns.Power Electron, vol.30, No.2, pp.849-859, Feb.2015.) overcome the above deficiencies. It should be noted that conventional approaches based on the orthogonal principle are limited by the periodic speed limitation. Although the latest improved quadrature-principle-based approach (y. chen, m. yang, j. long, w. qu, d. xu, and f. blaabjerg, "a model only line controller parameter selected-tuning method vision variable-periodic inertia identification," IEEE trans. power electron, vol.34, No.12, pp.12165-12180, dec.2019.) overcomes this limitation, it cannot be used in drive systems that require or only allow unidirectional rotation. In contrast, the method based on optimal parameter estimation does not have these limitations. Meanwhile, the method has excellent robustness to noise interference. Despite the attractive advantages mentioned above of an optimal parameter estimation based approach, such an approach still faces challenges in practical applications: this method involves a large number of matrix operations, so that its industrial implementation is rather time-consuming.

Disclosure of Invention

The invention provides an inertia identification method of a permanent magnet synchronous motor driving system aiming at the technical problems in the prior art, which comprises the following steps:

step 1, expanding inertia into a new system state, and constructing an expanded mechanical motion equation of a permanent magnet synchronous motor driving system;

step 2, constructing a novel extended sliding-mode observer with time-varying feedback gain based on the extended mechanical motion equation;

and 3, constructing an extended state observer, estimating lumped disturbance information of the permanent magnet synchronous motor driving system, and providing the lumped disturbance information to the developed novel extended sliding mode observer so that the novel extended sliding mode observer can successfully identify the inertia of the permanent magnet synchronous motor driving system.

The invention provides an inertia identification method of a permanent magnet synchronous motor driving system, which expands inertia into a new system state and constructs an expanded mechanical motion equation; introducing an extended sliding mode observer technology into the field of inertia identification, and developing a novel extended sliding mode observer with time-varying feedback gain based on a constructed mechanical motion equation to identify inertia; an extended state observer is designed, and then required lumped disturbance information is provided for the novel extended sliding mode observer to ensure that the inertia is successfully estimated. The invention successfully applies the technology of expanding the sliding-mode observer to the field of inertia identification, and ensures the strong robust capability of the inertia identification process on noise interference; in addition, the method does not involve any matrix operation, and thus the computational burden is small. In general, the method provided by the invention has the characteristic of low computational burden while having excellent anti-noise interference performance.

Drawings

Fig. 1 is a flowchart of an inertia identification method for a driving system of a permanent magnet synchronous motor according to the present invention;

FIG. 2 is a schematic block diagram of a designed extended sliding-mode observer;

FIG. 3 is a schematic block diagram of an inertia online identification technique provided by the present invention;

FIG. 4(a) is a comparative simulation result of the inertia estimation technique, the model reference adaptive method, and the optimal parameter estimation-based method provided by the present invention in the absence of noise;

FIG. 4(b) is a comparative simulation result of the inertia estimation technique, the model reference adaptive method, and the method based on the optimal parameter estimation provided by the present invention in the presence of slight noise interference;

FIG. 4(c) is a comparative simulation result of the inertia estimation technique, the model reference adaptive method, and the method based on the optimal parameter estimation provided by the present invention in the presence of severe noise interference;

fig. 5 is a schematic diagram showing the comparison of the actual execution time of the inertia estimation technique and the method based on the optimal parameter estimation in the STM32F103 microprocessor.

Detailed Description

The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.

Fig. 1 is a flowchart of an inertia identification method for a driving system of a permanent magnet synchronous motor according to the present invention, and as shown in fig. 1, the method includes: step 1, expanding inertia into a new system state, and constructing an expanded mechanical motion equation of a permanent magnet synchronous motor driving system; step 2, constructing an extended sliding mode observer with time-varying feedback gain based on the extended mechanical motion equation; and 3, constructing an extended state observer, estimating lumped disturbance information of the permanent magnet synchronous motor driving system, and providing the lumped disturbance information to the developed novel extended sliding mode observer so that the novel extended sliding mode observer can successfully identify the inertia of the permanent magnet synchronous motor driving system.

It can be understood that, based on the defects in the background art, the embodiment of the invention provides an online inertia identification technology for a permanent magnet synchronous motor driving system, which is resistant to noise interference and has low calculation burden. According to the method, inertia is firstly expanded to be a new system state, then the technology of the expanded sliding-mode observer is introduced into the field of inertia identification, a novel expanded sliding-mode observer with time-varying feedback gain is developed to identify the inertia, and finally an expanded state observer is constructed to provide required lumped disturbance information for the novel expanded sliding-mode observer so as to successfully estimate the inertia. The inertia identification technology provided by the invention solves the problem that the prior art cannot give consideration to both the anti-noise performance and the calculated amount, namely the inertia identification technology has low calculation burden while having excellent anti-noise performance.

In one possible embodiment, the step 1 of constructing the extended mechanical motion equation comprises the following steps:

the mechanical equations of motion for a permanent magnet synchronous motor drive system can be described as follows:

wherein ω is_mIs the rotational speed, B is the coefficient of viscous friction, J is the inertia, T_lFor load torque, C is the Coulomb coefficient of friction, T_eIs an electromagnetic torque.

Defining the reciprocal of inertia as J ═ 1/J, and expanding the reciprocal to a new system state, and then constructing an expanded mechanical motion equation based on equation (1) as follows:

wherein D is_LRepresents a lumped perturbation, which is defined asd_jThe derivative of j is indicated.

Step 2, introducing the technology of the extended sliding-mode observer into the field of inertia identification, and developing a novel extended sliding-mode observer with time-varying feedback gain based on the mechanical motion equation constructed in the step 1 to identify inertia, wherein the specific method comprises the following steps:

an extended sliding mode observer for estimating inertia is developed based on the extended mechanical motion equation constructed in the step 1 as follows:

whereinRespectively represent omega_m，j，D_LAn estimated value of (d); f is the feedback gain; u shape_ESMORepresents a sliding mode observer signal, which is designed to:

U_ESMO＝g·sign(S)；(4)

wherein g is sliding mode switching gain; s is a slip form surface designed as

In order to determine the sliding mode switching gain g and the feedback gain f, the invention carries out the following specific design process:

first subtracting equation (2) from equation (3), the error equation of the extended sliding-mode observer can be derived as:

wherein

Let e_D、e_jAnd T_eAre all bounded and define Selecting a Lyapunov function as V ═ 0.5S²It is possible to obtain:

from equation (6), when the sliding mode switching gain g is designed to beWhen we can deriveIs satisfied, which means that the sliding mode reachable condition is satisfied. Thus, after sliding mode has taken place, e_ωAnd derivatives thereofWill converge to 0 in a finite time, i.e.Then, equation (5) can be rewritten as:

from equation (7), one can obtain:

typically, the frequency of the inertia change is much lower than the sampling frequency of the drive system. Therefore, it can be reasonably considered that d is satisfied in a short time_j0. Then, equation (8) can be rewritten as:

when fe_DIs controlled to be fe_DWhen the value is approximately equal to 0, the following can be obtained:

then, the analytical solution of equation (10) can be derived as:

where C is a constant, it can be seen that when fT is satisfied_eInertia estimation > 0Error e_jWill asymptotically approach 0. Therefore, the invention designs the feedback gain as f ═ mT_e(m > 0) to satisfy fT_eIs greater than 0. It can be seen that the provided extended sliding-mode observer has a time-varying feedback gain, and the design ensures that the inertia estimation error gradually converges to 0. Notably, fe_DThe key point for the conclusion is that the value is approximately equal to 0, and fe can be ensured by selecting a proper value of m_DSmall enough, i.e. able to ensure fe_D≈0。

In summary, the final expression of the extended sliding-mode observer provided by the present invention is:

wherein, T in the above formula (12)_eAnd ω_mIs provided by a permanent magnet synchronous motor driving system which is frequently adoptedSo that the electromagnetic torque T_eCan be calculated as T_e＝1.5P_nψ_fi_q(P_nIs a logarithm of poles,. psi_fIs a flux linkage, i_qQ-axis current), the rotation speed ω_mAnd obtaining through an encoder. Fig. 2 shows a schematic block diagram of the extended sliding-mode observer provided.

It should be noted that, after the sliding mode occurs, the rotation speed ω is_mCan be described asTherefore, equation (3) can be rewritten as:

combining equation (2) and equation (13), one can obtain:

wherein G ═ fT_e,It is noted that equation (14) can be equivalent to a low-pass filter with a cut-off frequency G, and the transfer function can be expressed as:

therefore, the inertia estimation effect of the designed extended sliding mode observer is equivalent to the output of a low-pass filter. This means that the extended sliding mode observer itself is designed with an equivalent low pass filter function, and therefore sliding mode buffeting can be suppressed without additional buffeting suppression.

It can be understood that, when the extended sliding mode observer is used for estimating and identifying the inertia of the driving system of the permanent magnet synchronous motor, see formula (12), wherein one important variable is lumped disturbance information of the driving system. The specific method for designing the extended state observer comprises the following steps:

to provide a real-time lumped disturbance estimate (i.e., to the extended sliding-mode observer developed in step 2)) In order to ensure that the inertia is successfully identified, the invention designs a linear extended state observer. First, considering the lumped disturbance as a new system state, the following extended mechanical motion equation is obtained based on equation (1):

based on equation (16) above, the designed linear extended state observer can be expressed as follows:

wherein p is an expected pole of the extended state observer and satisfies p > 0. Considering that j is unknown, it is extended by the output of the sliding-mode observerInstead, the final expression of the designed linear extended state observer is as follows:

in summary, a schematic block diagram of the provided inertia online identification technology can be obtained, as shown in fig. 3.

In order to verify the feasibility and the effectiveness of the inertia identification technology provided by the invention, a corresponding simulation model is set up for research, and the simulation model adoptsThe vector control strategy of (1). In simulation, the robustness of the provided inertia identification technology to noise is evaluated, and a model reference self-adaption method and an optimal parameter estimation method are selected to compare the inertia identification technology provided by the invention. The relevant simulation parameters are set as follows: number of pole pairs P_n4; resistance R_s0.801 Ω; inductance L ═ 3.675 mH; rotor flux linkage psi_f0.278 Wb; the speed command is selected as a periodic square wave with an amplitude of 500r/min and a period of 0.1 s. In addition, the purpose of the simulation is to compare the noise-resistant performance of the three methods, and thus to eliminate the influence of other factors on the inertia recognition result, the viscous friction coefficient, the load torque, and the coulomb friction coefficient are all set to 0. The gain factor of the model reference adaptive method is chosen to be 0.001; observer in method based on optimal parameter estimationThe coefficient l is-0.06, and the initial value of the matrix P is designed to be P (0) ═ diag {0.2,0,0,0.1 }. The parameter design of the inertia identification technology provided by the invention is that m is 0.1, P is 20, and g is-1000. Fig. 4(a) is the estimation result under the noise-free condition, from which it can be found that the inertia can be accurately estimated by all three methods in the absence of noise. Fig. 4(b) is an estimation result under slight noise interference, it can be found that a model reference adaptive method has obvious errors, and the inertia can still be accurately estimated based on the method of optimal parameter estimation and the method provided by the present invention. When the drive system suffers from severe noise interference, fig. 4(c) shows that the model reference adaptive method estimates have diverged, while the other two methods only show slight errors. The results of fig. 4 illustrate that the noise immunity of the method provided by the present invention is comparable to the method based on optimal parameter estimation. In addition, fig. 5 compares the actual execution time of the method based on the optimal parameter estimation and the inertia estimation technique provided by the present invention in the driving system based on the STM32F103 microprocessor. It can be found that the actual execution time of the inertia identification technology provided by the invention is far lower than that of the method based on the optimal parameter estimation, which means that the invention has low calculation load while having excellent anti-noise performance.

According to the inertia identification method of the permanent magnet synchronous motor driving system provided by the embodiment of the invention, the inertia is expanded into a new system state, and an expanded mechanical motion equation is constructed; introducing an extended sliding mode observer technology into the field of inertia identification, and developing a novel extended sliding mode observer with time-varying feedback gain based on a constructed mechanical motion equation to identify inertia; an extended state observer is designed, and then required lumped disturbance information is provided for the novel extended sliding mode observer to ensure that the inertia is successfully estimated. The invention successfully applies the technology of expanding the sliding-mode observer to the field of inertia identification, and ensures the strong robust capability of the inertia identification process on noise interference; in addition, the method does not involve any matrix operation, and thus the computational burden is small. In general, the method provided by the invention has the characteristic of low computational burden while having excellent anti-noise interference performance.

While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.