Motor multi-parameter online identification and robust control method based on multi-observer cascade

Abstract

The invention discloses a motor multi-parameter online identification and robust control method based on multi-observer cascade. The method comprises the steps of obtaining real-time operation data of the permanent magnet synchronous motor; obtaining a preliminary observation value through a sliding mode observer group; the sliding-mode observer group is constructed based on a mathematical model of the permanent magnet synchronous motor and is used for receiving real-time operation data and synchronously outputting initial observation values of a permanent magnet flux linkage, q-axis inductance and d-axis inductance; then analyzing the preliminary observation value to obtain a flux linkage accurate value, a q-axis inductance accurate value and a d-axis inductance accurate value, and finally performing adaptive control on the permanent magnet synchronous motor according to the flux linkage accurate value, the q-axis inductance accurate value and the d-axis inductance accurate value. According to the method, multiple motor parameters can be synchronously estimated, the limitation that other parameters are assumed to be accurate when a single parameter is identified in a traditional identification method is avoided, adaptive current reference and voltage decoupling are further generated based on the estimated parameters, and robust and stable field weakening control under variable working conditions can be achieved.

Description

Technical Field

[0001] This invention relates to the field of permanent magnet synchronous motor control technology, and more specifically to a method for online identification of multiple parameters of a motor based on cascaded multi-observers. Background Technology

[0002] Permanent magnet synchronous motors are widely used in various fields such as transportation, mining, and manufacturing due to their high efficiency, high power density, and low energy consumption. However, their parameters are easily affected by operating conditions (temperature changes or dynamic loads), which can lead to a series of consequences such as current command distortion, field weakening capability attenuation, torque performance deterioration, and reduced energy efficiency. This can also negatively impact the overall system performance and reliability.

[0003] Currently, field weakening control methods are commonly used to control parameters, such as current vector control, voltage closed-loop control, and prediction- or optimization-based control strategies. Among these, high-speed operation is achieved by injecting negative d-axis current to offset back electromotive force, offering advantages such as simplicity and reliability. Voltage closed-loop methods directly limit the stator voltage amplitude within the inverter's capacity range through feedback, improving adaptability to parameter perturbations and voltage utilization. Furthermore, field weakening methods based on model prediction and maximum torque per voltage can further enhance the dynamic response and torque capability in the deep field weakening region.

[0004] While the aforementioned methods each have their advantages in practical applications, their control effectiveness heavily relies on the accuracy of the motor model. Specifically, the d-axis reference current is determined based on motor parameters such as stator inductance and permanent magnet flux linkage. Any deviation in these parameters directly affects the accuracy of the reference current, leading to a decrease in torque output and voltage utilization. However, in actual electric vehicle applications, the complex and variable operating environment makes it difficult to maintain parameter accuracy. Factors such as increased temperature, magnetic saturation, and manufacturing tolerances can cause stator resistance, dq-axis inductance, and permanent magnet flux linkage to deviate from their rated values. Such parameter disturbances can lead to inaccurate current commands, reduced field weakening capability, torque ripple, and even system instability at high speeds.

[0005] Therefore, improving the robustness of field weakening control to parameter perturbations has become a key research direction for ensuring the reliable and high-speed operation of electric vehicle traction systems.

[0006] To address the performance degradation caused by parameter mismatch in field weakening control, existing solutions can be broadly categorized into two types: compensation-based and parameter identification-based. Compensation-based methods indirectly mitigate the impact of parameter perturbations by estimating or compensating for the combined effects of parameter uncertainties and external disturbances in the control loop. They typically employ disturbance observers, sliding mode compensators, or extended state observers to construct correction terms, thereby improving current and torque stability. While this method improves robustness without requiring precise knowledge of each parameter, it does not directly solve the fundamental problem of parameter mismatch. The compensation accuracy heavily relies on observer bandwidth and gain adjustment, and residual torque errors or sluggish dynamic response may still exist under rapidly changing operating conditions. In contrast, parameter identification-based methods address parameter mismatch more directly by estimating stator resistance, inductance, and flux linkage in real time. Traditional parameter identification methods often assume that other parameters are known and fixed when estimating a particular parameter, but this is difficult to apply in practical electric vehicle applications. Since these parameters change synchronously with temperature, magnetic saturation, and operating conditions, this interdependence leads to the accumulation of estimation errors, resulting in unreliable identification results. For example, traditional methods estimate the flux linkage of a permanent magnet synchronous motor using a single sliding mode observer, while assuming that the inductance and resistance remain constant. Such traditional methods rely on prior fixed parameters, which can lead to inaccurate estimation results.

[0007] Overall, proposing an identification method that can accurately and simultaneously estimate multiple key parameters is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0008] In view of the above problems, the present invention is proposed to provide a method for online identification and robust control of multiple parameters of a motor based on cascaded multi-observers to overcome or at least partially solve the above problems.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] This invention provides a method for online multi-parameter identification and robust control of motors based on cascaded multi-observer systems, comprising the following steps: S1. Obtain real-time operating data of the permanent magnet synchronous motor; obtain preliminary observation values ​​through a sliding mode observer group; the sliding mode observer group is constructed based on the mathematical model of the permanent magnet synchronous motor and is used to receive the real-time operating data and synchronously output the preliminary observation values ​​of the permanent magnet flux linkage, q-axis inductance and d-axis inductance. S2. Analyze the preliminary observation values ​​to obtain the precise values ​​of magnetic flux, q-axis inductance, and d-axis inductance. S3. Adaptive control of the permanent magnet synchronous motor is performed based on the precise values ​​of the flux linkage, q-axis inductance, and d-axis inductance.

[0011] Preferably, the mathematical model of the permanent magnet synchronous motor includes the voltage balance equation and the angular velocity differential equation of the permanent magnet synchronous motor in the dq rotating coordinate system.

[0012] The sliding mode observer group includes a time-cascaded sliding mode flux observer, a q-axis inductance observer, and a d-axis inductance observer.

[0013] In an optional implementation, stability analysis of the sliding mode observer group includes: After running at the rated speed under no-load and reaching steady state, monitor the estimated parameters generated by the sliding mode observer group and record the maximum steady-state flux fluctuation amplitude. Response time is defined by step load excitation; The cost is determined based on the maximum steady-state flux fluctuation amplitude and the response time. Under the condition that the gain constraints of each observer are satisfied, the gain value with the minimum cost is taken as the optimal gain in the stable region.

[0014] As a preferred option, the cost is determined according to the following cost function:

[0015] In the formula, At the cost, For response time, This represents the maximum steady-state flux linkage fluctuation amplitude. The maximum allowable convergence period, To control the cycle, For offline measurement of magnetic flux.

[0016] Preferably, the gain constraints for each observer include:

[0017]

[0018]

[0019] In the formula, This represents the gain coefficient of the sliding mode flux observer. For offline measurement of magnetic flux, This represents the gain coefficient of the q-axis inductor observer. For offline measurement of q-axis inductance, The gain coefficient of the d-axis inductor observer. This is the d-axis inductance measured offline.

[0020] In an optional implementation, the preliminary observations are analyzed to obtain the precise values ​​of flux linkage, q-axis inductance, and d-axis inductance, using the following analytical formula:

[0021]

[0022]

[0023] In the formula, For the precise value of the magnetic flux linkage, For extreme logarithms, This is the actual angular velocity of the motor. , These represent the currents along the d and q axes in the dq rotating coordinate system, respectively, in the actual operating data. For offline measurement of d-axis inductance, For offline measurement of q-axis inductance, These are preliminary observations of the d-axis inductance. These are preliminary observations of the q-axis inductance. These are preliminary observations of the magnetic flux linkage; This is the precise value of the q-axis inductance. For offline measurement of magnetic flux; This is the precise value of the d-axis inductance.

[0024] In an optional implementation, adaptive control of the permanent magnet synchronous motor based on the precise values ​​of the flux linkage, q-axis inductance, and d-axis inductance includes: Based on the precise values ​​of magnetic flux, q-axis inductance, and d-axis inductance, calculate the d-axis reference current, q-axis reference current, and reference rotation speed. By incorporating various reference values ​​into the voltage regulation loop, control of the permanent magnet synchronous motor is achieved. The steps include: Through closed-loop regulation, the actual d-axis current tracks the d-axis reference current, the actual q-axis current tracks the q-axis reference current, and the actual angular velocity of the motor tracks the reference speed.

[0025] Based on the adjusted d-axis current, q-axis current, and rotational speed, the voltage before decoupling is calculated, and deviation decoupling is performed. Based on the decoupled voltage, the motor multi-parameters are identified online, and the decoupling calculation is updated based on the identified parameters to output the final control voltages for the d-axis and q-axis.

[0026] As a preferred option, the deviation decoupling processing formula is:

[0027] In the formula, and These are the reference currents for the d-axis and q-axis, respectively. and These represent the currents along the d and q axes in the dq rotating coordinate system, respectively, in the actual operating data. and These are the control voltages determined based on the adjusted d-axis current, q-axis current, and rotational speed. For extreme logarithms, This is the actual angular velocity of the motor. , These are the inductances of the motor's d-axis and q-axis. For motor flux linkage.

[0028] Preferably, the d-axis reference current, q-axis reference current, and reference speed satisfy the following relationship to ensure that the field weakening reference current always matches the actual parameters of the motor;

[0029] In the formula, and These are the d-axis and q-axis reference currents, respectively. This is the DC bus voltage. For reference speed, For the precise value of the magnetic flux linkage, This is the precise value of the q-axis inductance. This is the precise value of the d-axis inductance.

[0030] This invention provides a method for online identification of multiple parameters of a motor based on cascaded multi-observers, aiming to achieve robust and stable field weakening control under varying operating conditions.

[0031] Compared with existing technologies, this invention first constructs a mathematical model of a permanent magnet synchronous motor and builds a sliding mode observer group to observe the permanent magnet flux linkage, q-axis inductance, and d-axis inductance of the motor. Then, it collects real-time voltage, current, and speed signals of the motor, runs the sliding mode observer group, and obtains preliminary observation values ​​including parameter coupling errors. Finally, it performs error analysis, constructs a three-variable linear equation system based on the steady-state equation of the observer, and analytically calculates the accurate flux linkage, q-axis inductance, and d-axis inductance values ​​after eliminating coupling errors.

[0032] This invention can simultaneously estimate multiple motor parameters, avoiding the limitation of traditional identification methods that "assuming other parameters are accurate when identifying a single parameter", and generates an adaptive current reference based on the estimated parameters to achieve robust and stable field weakening control under varying operating conditions. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0034] Figure 1The flowchart is provided in the embodiment of the present invention for an online identification method of multiple parameters of a motor based on cascaded multiple observers. Figure 2 This is a flowchart of a robust control method for an online multi-parameter identification method for motors based on cascaded multi-observers, as described in a preferred embodiment of the present invention. Figure 3 This is a comparison of the experimental results of the online multi-parameter identification method for motors based on cascaded multi-observers in this embodiment of the invention with that of the traditional sliding mode observer. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms "an" or "a" and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms "connected" or "linked" and similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up," "down," "left," "right," etc., are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship also changes accordingly.

[0037] For permanent magnet synchronous motors, this invention discloses a robust field weakening control strategy to resist multi-parameter perturbations. This method can simultaneously estimate multiple motor parameters, avoiding the limitation of traditional identification methods that "assume other parameters are accurate when identifying a single parameter," and generates an adaptive current reference based on the estimated parameters, achieving robust and stable field weakening control under varying operating conditions. This invention provides an effective solution to the problem of field weakening control parameter mismatch and has significant implications for electric vehicle applications.

[0038] In one embodiment, please refer to Figure 1 , Figure 1 This is a schematic diagram of the online multi-parameter identification method for motors based on cascaded multi-observers, as described in this application.

[0039] In this embodiment, the method steps include: S1. Obtain real-time operating data of the permanent magnet synchronous motor; obtain preliminary observation values ​​through a sliding mode observer group; the sliding mode observer group is constructed based on the mathematical model of the permanent magnet synchronous motor and is used to receive the real-time operating data and synchronously output the preliminary observation values ​​of the permanent magnet flux linkage, q-axis inductance and d-axis inductance. S2. Analyze the preliminary observation values ​​to obtain the precise values ​​of flux linkage, q-axis inductance, and d-axis inductance. S3. Adaptive control of the permanent magnet synchronous motor is performed based on the precise values ​​of the flux linkage, q-axis inductance, and d-axis inductance.

[0040] This invention first estimates multiple motor parameters synchronously using a sliding mode observer group. Then, it derives explicit stability constraints for the sliding mode observer group using Lyapunov theory to ensure reliable operation in the field weakening scenario of electric vehicles. Finally, based on the sliding mode observer group, it proposes an error analysis and extraction process, constructing a ternary linear equation system by combining the observer output and offline measurements. This allows for the analytical calculation of the precise flux linkage, q-axis inductance, and d-axis inductance values ​​after eliminating coupling errors. This application enables permanent magnet synchronous motor control systems to have high reliability and achieve robust and stable field weakening control under varying operating conditions.

[0041] In one specific embodiment, a mathematical model is first constructed for the permanent magnet synchronous motor; In some implementation schemes, the steps for constructing a mathematical model include: S11. Construct the voltage balance equation of the permanent magnet synchronous motor in the dq rotating coordinate system. The expression is:

[0042]

[0043] In the formula, , It is the voltage in the dq rotating coordinate system. , It refers to the current in the same coordinate system. , These are the inductances of the motor's d-axis and q-axis. It is the resistance of the motor windings. For motor magnetic flux, This is the actual angular velocity of the motor. It is an extreme logarithm.

[0044] S12. Construct the electromagnetic torque equation and calculate the electromagnetic torque of the permanent magnet synchronous motor to integrate the magnetic torque and reluctance torque components. The formula is as follows:

[0045] In the formula, It is electromagnetic torque.

[0046] Furthermore, based on the electromagnetic torque equation, the differential equation for angular velocity is constructed as follows:

[0047] In the formula, and These are the rotor's moment of inertia and damping coefficient, respectively. This represents the load torque.

[0048] Secondly, a sliding mode observer group is constructed based on the above mathematical model; the sliding mode observer group includes three time-cascaded d-axis inductance observers, q-axis inductance observers and sliding mode flux linkage observers.

[0049] In some implementation schemes, The d-axis inductance observer is constructed based on variable structure theory and satisfies the following relationship:

[0050]

[0051]

[0052] In the formula Estimate the rotational speed for the observer. For offline measurement of magnetic flux, This is the gain coefficient. To estimate the error between the rotational speed and the actual rotational speed, For switching functions, To measure the q-axis inductance offline, when the system reaches equilibrium, These are preliminary observations of the d-axis inductance.

[0053] The q-axis inductance observer satisfies the following relationship:

[0054]

[0055]

[0056] In the formula, The d-axis current estimated by the observer; This is the gain coefficient; To estimate the error between the d-axis current and the actual d-axis current, These are preliminary observations of the q-axis inductance.

[0057] The sliding mode flux observer satisfies the following relationship:

[0058]

[0059]

[0060]

[0061] In the formula, and For offline measurement of d-axis and q-axis inductance, The q-axis current estimated by the observer. This is the gain coefficient. To estimate the error between the q-axis current and the actual q-axis current, For switching functions, These are preliminary observations of the magnetic flux linkage.

[0062] In a preferred embodiment, a stability analysis is performed on the sliding mode observer group to ensure its reliable operation in a weak magnetic field environment. In this embodiment, the stability analysis steps include: The gain constraints for each observer are defined as follows: for ,have At this point, taking the intersection yields... ; Similarly, Depend on , can be obtained ; Depend on , can be obtained .

[0063] In the formula, This represents the gain coefficient of the sliding mode flux observer. For offline measurement of magnetic flux, , These represent the d-axis and q-axis currents in the dq rotating coordinate system, respectively, in the real-time operating data. This represents the gain coefficient of the q-axis inductor observer. For offline measurement of q-axis inductance, The gain coefficient of the d-axis inductor observer. The d-axis inductance was measured offline. To estimate the error between the rotational speed and the actual rotational speed.

[0064] Typically, the optimal observer gain is obtained through experimental testing procedures. Therefore, this application proposes a parameter tuning method based on torque variation experiments, including: The motor is run at its rated speed under no-load conditions. After accelerating to the rated speed and reaching steady state, the estimated parameters generated by the sliding mode observer group are monitored, and the maximum steady-state flux fluctuation amplitude is recorded. ; Response time is defined by step load excitation. That is, by suddenly applying a unit step load torque of 1 Nm, measuring the time required for the estimated parameters to transition from the disturbed state to the new steady state, and defining this time as the response time. ; Substitute the maximum steady-state fluctuation amplitude and the response time into the cost function to determine the cost, and use the cost... The minimum gain value is taken as the optimal gain within the stable region.

[0065] Optionally, in this embodiment, the cost function expression is:

[0066] In the formula, At the cost, For response time, This represents the maximum steady-state fluctuation amplitude. The maximum allowable convergence period, To control the cycle, For offline measurement of magnetic flux.

[0067] Furthermore, in one embodiment, the preliminary observation values ​​are analyzed based on the steady-state equation of the observer to obtain accurate values ​​of flux linkage, q-axis inductance, and d-axis inductance.

[0068] The specific analytical formula is as follows:

[0069] Therefore, we can conclude that:

[0070]

[0071]

[0072] In the formula, For the precise value of the magnetic flux linkage, For extreme logarithms, This is the actual angular velocity of the motor. , These represent the currents along the d and q axes in the dq rotating coordinate system, respectively, in the actual operating data. The d-axis inductance was measured offline. For offline measurement of q-axis inductance, These are preliminary observations of the d-axis inductance. These are preliminary observations of the q-axis inductance. These are preliminary observations of the magnetic flux linkage; This is the precise value of the q-axis inductance. For offline measurement of magnetic flux; This is the precise value of the d-axis inductance.

[0073] To further optimize the above technical solution, adaptive control of the permanent magnet synchronous motor is performed based on the precise values ​​of the flux linkage, q-axis inductance, and d-axis inductance. One embodiment includes: The d-axis reference current, q-axis reference current, and reference speed are calculated based on real-time updated precise parameters. And by incorporating various reference values ​​into the voltage regulation loop, robust control of the permanent magnet synchronous motor is achieved, such as... Figure 2 The steps include: The d-axis current is adjusted using a d-axis current controller based on the d-axis current and d-axis reference current in the dq rotating coordinate system from the actual operating data. The motor speed is adjusted by a speed regulator based on the actual angular velocity and reference speed of the motor. Then, the q-axis current is adjusted by a q-axis current controller based on the q-axis current and q-axis reference current in the dq rotating coordinate system from the actual operating data. This application achieves high-performance decoupled control by independently adjusting the current components of the d-axis and q-axis current controllers, which can effectively suppress cross-coupling.

[0074] Furthermore, based on the adjusted d-axis current, q-axis current, and rotational speed, the voltage before decoupling is first calculated. and Then, by combining the d-axis reference current and the q-axis reference current, deviation decoupling is performed, and the control voltages for the d-axis and q-axis are output. u q and u q The deviation decoupling formula is:

[0075] The decoupled voltage is used as input in the parameter identification observer, and multiple motor parameters are identified online according to steps S1 and S2. Subsequently, the identified parameter inputs are decoupled due to bias.

[0076] In a preferred embodiment of this invention, the d-axis reference current, q-axis reference current, and reference speed are made to satisfy the following relationship to ensure that the field weakening reference current always matches the actual parameters of the motor.

[0077] In the formula, and These are the d-axis and q-axis reference currents, respectively. This is the DC bus voltage. For reference speed, For the precise value of the magnetic flux linkage, This is the precise value of the q-axis inductance. This is the precise value of the d-axis inductance.

[0078] To verify the effectiveness of the proposed field weakening control strategy under multiple parameter variations, an experiment was conducted based on a three-phase permanent magnet synchronous motor. The motor parameters are shown in Table 1.

[0079]

[0080] Ignoring the effects of magnetic saturation and thermal changes, the parameters obtained through reliable offline measurement technology in Table 1 can be considered accurate values. The inverter consists of an insulated gate bipolar transistor module and a drive module, with a control frequency of 10kHz, a sampling time of 0.1ms, and a DC bus voltage of 150V. A 1024-line rotary encoder is used to detect the rotor position and speed. Hall sensors LA25-NP and LV25-P are used to measure the phase current and DC bus voltage, respectively. The load motor is an induction motor, driven by an AutomationDrive PM240-2, operating in torque control mode to provide the required load torque for the test motor. The proposed control algorithm is implemented on the control board of the electromechanical control development platform. Data is acquired through an interface, and the measured current and voltage signals are transmitted to the host computer via an adapter board for data recording and subsequent analysis. Figure 3 The experimental setup is as follows: The motor speed and load are set to be constant. When the motor operates stably under the preset speed and current reference, the q-axis current remains at a certain stable value; at the observer gain... , and When selecting based on the analytical results, the three observers maintain stable operation, and the estimation error of all states is close to zero. Finally, the flux linkage, q-axis inductance, and d-axis inductance identified by the traditional observer are compared with the estimated flux linkage, q-axis inductance, and d-axis inductance obtained by applying the proposed parameter calculation strategy. Figure 3 (1) shows the observation results of the traditional sliding mode observer. Figure 3 (2) shows the results of the multi-parameter identification method for motors based on multi-observer cascade. In comparison, the proposed multi-parameter identification method for motors based on multi-observer cascade has significantly higher accuracy and stronger robustness to parameter changes.

[0081] In summary, the online multi-parameter identification and robust control method for motors based on multi-observer cascades achieves optimized control of the robust operation of permanent magnet synchronous motors by using multiple observers to identify multiple motor parameters online. This is of great significance for improving the reliability of motor operation and maintaining the safety of the drive system.

[0082] Based on the same inventive concept, this application also provides a multi-observer cascaded online multi-parameter motor identification system, including a sliding mode observer group, a current vector control model, and a computer program stored in a memory and executable on a processor. When the processor executes the computer program, it implements the steps of the above-described method. The multi-observer cascaded online multi-parameter motor identification system can implement all embodiments of the above-described multi-observer cascaded online multi-parameter motor identification method and achieve the same effects; therefore, further details are omitted here.

[0083] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0084] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for online multi-parameter identification and robust control of a motor based on multi-observer cascade, characterized in that, include: S1. Obtain real-time operating data of the permanent magnet synchronous motor; Preliminary observation values ​​are obtained through a sliding mode observer group; the sliding mode observer group is constructed based on the mathematical model of the permanent magnet synchronous motor and is used to receive the real-time operating data and synchronously output the preliminary observation values ​​of the permanent magnet flux linkage, q-axis inductance and d-axis inductance. S2. Analyze the preliminary observation values ​​to obtain the precise values ​​of magnetic flux, q-axis inductance, and d-axis inductance. S3. Adaptive control of the permanent magnet synchronous motor is performed based on the precise values ​​of the flux linkage, q-axis inductance, and d-axis inductance.

2. The online multi-parameter identification and robust control method for motors according to claim 1, characterized in that, The mathematical model of the permanent magnet synchronous motor includes the voltage balance equation and the angular velocity differential equation of the permanent magnet synchronous motor in the dq rotating coordinate system.

3. The online multi-parameter identification and robust control method for motors according to claim 1, characterized in that, The sliding mode observer group includes a time-cascaded sliding mode flux observer, a q-axis inductance observer, and a d-axis inductance observer.

4. The online multi-parameter identification and robust control method for motors according to claim 1 or 3, characterized in that, Stability analysis of the sliding mode observer group includes: After running at the rated speed under no-load and reaching steady state, monitor the estimated parameters generated by the sliding mode observer group and record the maximum steady-state flux fluctuation amplitude. Response time is defined by step load excitation; The cost is determined based on the maximum steady-state flux fluctuation amplitude and the response time. Under the condition that the gain constraints of each observer are satisfied, the gain value with the minimum cost is taken as the optimal gain in the stable region.

5. The online multi-parameter identification and robust control method for motors according to claim 4, characterized in that, The cost is determined based on the following cost function: In the formula, At the cost, For response time, This represents the maximum steady-state flux linkage fluctuation amplitude. The maximum allowable convergence period, To control the cycle, For offline measurement of magnetic flux.

6. The online multi-parameter identification and robust control method for motors according to claim 4, characterized in that, Gain constraints for each observer, including: In the formula, This represents the gain coefficient of the sliding mode flux observer. For offline measurement of magnetic flux, This represents the gain coefficient of the q-axis inductor observer. For offline measurement of q-axis inductance, The gain coefficient of the d-axis inductor observer. This is the d-axis inductance measured offline.

7. The online multi-parameter identification and robust control method for motors according to claim 1, characterized in that, The preliminary observations were analyzed to obtain precise values ​​for flux linkage, q-axis inductance, and d-axis inductance. The analytical formulas are as follows: In the formula, For the precise value of the magnetic flux linkage, For extreme logarithms, This is the actual angular velocity of the motor. , These represent the currents along the d and q axes in the dq rotating coordinate system, respectively, in the actual operating data. For offline measurement of d-axis inductance, For offline measurement of q-axis inductance, These are preliminary observations of the d-axis inductance. These are preliminary observations of the q-axis inductance. These are preliminary observations of the magnetic flux linkage; This is the precise value of the q-axis inductance. For offline measurement of magnetic flux; This is the precise value of the d-axis inductance.

8. The online multi-parameter identification and robust control method for motors according to claim 1, characterized in that, Adaptive control of the permanent magnet synchronous motor based on the precise values ​​of flux linkage, q-axis inductance, and d-axis inductance includes: Based on the precise values ​​of magnetic flux, q-axis inductance, and d-axis inductance, calculate the d-axis reference current, q-axis reference current, and reference rotation speed. By incorporating various reference values ​​into the voltage regulation loop, control of the permanent magnet synchronous motor is achieved. The steps include: Through closed-loop regulation, the actual d-axis current tracks the d-axis reference current, the actual q-axis current tracks the q-axis reference current, and the actual angular velocity of the motor tracks the reference speed. Based on the adjusted d-axis current, q-axis current, and rotational speed, the voltage before decoupling is calculated, and deviation decoupling is performed. Based on the decoupled voltage, the motor multi-parameters are identified online, and the decoupling calculation is updated based on the identified parameters to output the final control voltages for the d-axis and q-axis.

9. The online multi-parameter identification and robust control method for motors according to claim 8, characterized in that, The formula for deviation decoupling is: In the formula, and These are the reference currents for the d-axis and q-axis, respectively. and These represent the currents along the d and q axes in the dq rotating coordinate system, respectively, in the actual operating data. and These are the control voltages determined based on the adjusted d-axis current, q-axis current, and rotational speed. For extreme logarithms, This is the actual angular velocity of the motor. , These are the inductances of the motor's d-axis and q-axis. For motor flux linkage.

10. The online multi-parameter identification and robust control method for motors according to claim 8, characterized in that, The d-axis reference current, q-axis reference current, and reference speed satisfy the following relationship: In the formula, and These are the d-axis and q-axis reference currents, respectively. This is the DC bus voltage. For reference speed, For the precise value of the magnetic flux linkage, This is the precise value of the q-axis inductance. This is the precise value of the d-axis inductance.

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