A method, system and device for adaptive control of time constant of an induction motor rotor

Abstract

The application relates to the technical field of motor control, and discloses an induction motor rotor time constant adaptive control method, system and equipment, wherein the method comprises the following steps: determining a rotor time constant estimation value based on a reactive power deviation of a model reference adaptive system; determining a rotor resistance estimation value according to the rotor time constant estimation value and a real-time acquired rotor inductance parameter value; performing low-pass filtering on the rotor resistance estimation value to generate a filtered value; updating a rotor time constant feedforward value based on the rotor inductance parameter value and the filtered value; and optimizing a convergence process of the rotor time constant estimation value based on the rotor time constant feedforward value to perform indirect vector control of the induction motor. The method has the beneficial effect of improving the convergence speed and robustness of rotor time constant adaptive identification.

Description

Technical Field

[0001] This application relates to the field of motor control technology, and in particular to an adaptive control method, system and device for the rotor time constant of an induction motor. Background Technology

[0002] In induction motor indirect vector control systems, the accuracy of the rotor time constant directly affects the system's current and torque control performance. The rotor time constant reflects the speed of the rotor excitation process. During motor operation, the rotor inductance is affected by magnetic saturation, and the rotor resistance is affected by rotor temperature, slip frequency, and skin effect. These factors cause the rotor time constant to deviate from its nominal value, with maximum variations exceeding 50%, and the resistance is in a state of dynamic change. Related technologies use offline calibration with a fixed rotor time constant, which cannot adapt to the drastic changing operating conditions of automotive motors. Summary of the Invention

[0003] This application provides an adaptive control method, system, and device for the rotor time constant of an induction motor, which solves the technical problem of insufficient adaptive dynamic performance of rotor time constant in related technologies and achieves the technical effect of improving convergence speed and stability.

[0004] To achieve the above objectives, the main technical solutions adopted in this application include:

[0005] In a first aspect, embodiments of this application provide an adaptive control method for the rotor time constant of an induction motor. The method includes: determining an estimated rotor time constant based on the reactive power deviation of a model reference adaptive system; determining an estimated rotor resistance based on the estimated rotor time constant and real-time acquired rotor inductance parameter values; performing low-pass filtering on the estimated rotor resistance to generate a filtered value; updating the rotor time constant feedforward value based on the rotor inductance parameter values ​​and the filtered value; and optimizing the convergence process of the estimated rotor time constant based on the rotor time constant feedforward value to perform indirect vector control of the induction motor.

[0006] This application proposes an adaptive control method for the rotor time constant of an induction motor. By adding a feedforward value for the rotor time constant, a complete adaptive control strategy for the rotor time constant is proposed. The rotor resistance estimate is obtained synchronously based on the online identification result of the rotor time constant, and the rotor resistance estimate is filtered in the feedforward loop. The feedforward value of the rotor time constant is calculated based on the filtered value of the rotor resistance estimate and the rotor inductance parameter value. Feedforward compensation is performed on the rotor time constant estimate, which solves the technical problem of insufficient adaptive dynamic performance of the rotor time constant in related technologies. It achieves the technical effect of improving convergence speed and stability, and can avoid the problem that the rotor time constant estimate will deviate from the true value instantaneously during current switching, requiring adaptive readjustment.

[0007] Optionally, the method further includes: multiplying the reactive power deviation by the reciprocal of the synchronization frequency to obtain the input error; inputting the input error into the PI controller to obtain the adaptive rate output value; and updating the rotor time constant estimate based on the adaptive rate output value and the rotor time constant feedforward value.

[0008] Optionally, the method further includes: the adaptive rate output value includes an integral term and a proportional term; if the input error is less than a first error threshold and the integral term is less than the integral term threshold, then it is determined to be in a steady state, the integral coefficient is set to zero, and the proportional coefficient is set to be less than the first base value.

[0009] Optionally, the method further includes: if the input error is greater than a second error threshold, then it is determined to be a disturbance state, and the proportional coefficient is set to be greater than the first base value.

[0010] Optionally, low-pass filtering of the rotor resistance estimate includes: determining the cutoff frequency of the low-pass filter based on a given current angle, and performing low-pass filtering on the rotor resistance estimate based on the cutoff frequency, wherein the larger the given current angle, the lower the cutoff frequency.

[0011] Optionally, the method further includes: if the given current is less than a preset threshold, stopping the adaptive adjustment of the rotor time constant, reverting the rotor resistance estimate to a preset initial value at a set rate, and setting the rotor time constant estimate as the rotor time constant feedforward value.

[0012] Optionally, the method further includes: low-pass filtering and the calculation of the rotor time constant feedforward value are performed in the speed loop or torque loop cycle; the calculation of the rotor time constant estimate is performed in the current loop cycle.

[0013] Optionally, the method further includes: looking up a table based on a given current angle to obtain the rotor inductance parameter value; wherein the table lookup execution function is executed in the speed loop or torque loop cycle.

[0014] Secondly, embodiments of this application provide an adaptive control system for the rotor time constant of an induction motor, capable of implementing the aforementioned adaptive control method for the rotor time constant of an induction motor, comprising: a first estimation module, used to determine an estimated value of the rotor time constant based on the reactive power deviation of the model reference adaptive system; a second estimation module, used to determine an estimated value of the rotor resistance based on the estimated value of the rotor time constant and the rotor inductance parameter value acquired in real time; a filtering module, used to perform low-pass filtering on the estimated value of the rotor resistance to generate a filtered value; a feedforward module, used to update the feedforward value of the rotor time constant based on the rotor inductance parameter value and the filtered value; and used to optimize the convergence process of the estimated value of the rotor time constant based on the feedforward value of the rotor time constant to perform indirect vector control of the induction motor.

[0015] Thirdly, embodiments of this application provide a computer device, including: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the above-described adaptive control method for the rotor time constant of an induction motor by executing the computer instructions.

[0016] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer instructions, which are used to cause a computer to execute the above-described adaptive control method for the rotor time constant of an induction motor.

[0017] Fifthly, embodiments of this application provide a computer program product, including computer instructions, which are used to cause a computer to execute the above-described adaptive control method for the rotor time constant of an induction motor. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a structural diagram of an indirect vector control system for an induction motor.

[0020] Figure 2 A flowchart of the adaptive control method for the rotor time constant of an induction motor provided in the embodiments of this application;

[0021] Figure 3 This is a trend graph of a given current angle versus cutoff frequency proposed in the embodiments of this application;

[0022] Figure 4 This is a schematic diagram of the induction motor control system proposed in an embodiment of this application;

[0023] Figure 5 A schematic diagram of the execution cycle provided for embodiments of this application;

[0024] Figure 6 This is a block diagram illustrating the adaptive adjustment of the rotor time constant proposed in an embodiment of this application.

[0025] Figure 7 A comparison chart of torque step response test results provided in the embodiments of this application;

[0026] Figure 8 A schematic diagram of an adaptive control system for the rotor time constant of an induction motor provided in an embodiment of this application;

[0027] Figure 9 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] Indirect Field Oriented Control (IFOC) is widely used in induction motor drives due to its ease of implementation and high reliability. Please refer to [link / reference]. Figure 1 , Figure 1 This is a structural diagram of an indirect vector control system for an induction motor. In an indirect vector control system for an induction motor, as shown... Figure 1As shown, the MTPA / Fluxweaken functional module (i.e., the maximum torque-to-current ratio / field weakening control module) looks up the given torque, voltage, and speed information in a table to obtain the given current for the d and q axes. The indirect vector control module calculates the given slip based on the given current for the d and q axes and the rotor time constant set in the program. The slip value is added to the rotor frequency to obtain the synchronous frequency. The synchronous frequency is used to establish a synchronous rotation coordinate system for the d and q axes. Finally, closed-loop control of the d and q axis currents is performed based on this coordinate system. When the rotor time constant is accurate, the actual rotor flux linkage is aligned with the positive direction of the d-axis, the magnetic field orientation is accurate, and the actual d- and q-axis currents are consistent with the given values. However, when there is a deviation between the rotor time constant and the actual value, the calculated slip and synchronous frequency will deviate from the required values. Based on this slip, the actual rotor flux linkage induced will have a phase difference with the d-axis, the magnetic field orientation will be inaccurate, and the actual d- and q-axis currents will be inconsistent with the given values. The magnitude of this difference is determined by different operating conditions. Therefore, under complex operating conditions, the actual current of the motor cannot operate exactly according to the calibrated MTPA / field weakening curve, resulting in performance problems such as lower torque output and increased energy consumption.

[0030] In induction motor indirect vector control systems, the accuracy of the rotor time constant directly affects the system's current and torque control performance. The rotor time constant is defined as the time constant of the induced electromotive force in the rotor circuit, reflecting the speed of the rotor excitation process. It is calculated by dividing the rotor's self-inductance by its resistance. During motor operation, the rotor inductance is affected by magnetic saturation, and the rotor resistance is affected by rotor temperature, slip frequency, and skin effect. These factors cause the rotor time constant to deviate from its nominal value, with maximum variations exceeding 50%, and the resistance is dynamically changing. Currently, the most widely used method for selecting the rotor time constant is to obtain the stator and rotor inductance and resistance through offline parameter identification. The rotor time constant is then set based on this offline identification result, and the entire operating condition of the motor is calibrated based on the set rotor time constant to achieve stable torque mode control. However, this approach cannot avoid rotor time constant deviations caused by factors such as rotor temperature changes, and therefore cannot achieve ideal torque output and energy efficiency during continuous system operation.

[0031] Rotor time constant adaptive identification technology aims to obtain real-time and accurate rotor time constants through software algorithms. Related online rotor time constant identification technologies mainly fall into three categories:

[0032] The first type involves offline calibration of rotor resistance and inductance parameters using a lookup table method, generating a lookup table function through fitting. However, this method is labor-intensive and time-consuming.

[0033] The second type is the observer method, which is theoretically complex, highly influenced by motor parameters, and computationally intensive, making it difficult to apply in practice at present. For example, patent document CN103051278A calculates the magnitude and position of the actual rotor flux linkage based on a voltage-current model in a two-phase stationary coordinate system. After coordinate transformation, the actual d-axis and q-axis currents are obtained, and the rotor time constant is estimated using the error relationship between the current in the inaccurate coordinate system and the actual d-axis and q-axis currents. This scheme must ensure the accuracy of the rotor flux linkage calculation, but due to the initial value and error accumulation issues in the integral term of the voltage-current model, as well as the inclusion of stator resistance and inductance parameters, the implementation of this scheme is quite difficult. Patent document CN119030394A uses a complex coefficient rotor flux estimator in a two-phase stationary coordinate system to obtain the rotor flux, avoiding the saturation offset caused by a pure integrator. Based on the flux estimate, the d-axis and q-axis rotor flux are obtained through transformation. Using this as a known quantity, a sliding mode observer is constructed to estimate the rotor time constant. No speed information is required, realizing sensorless control and parallel identification of the rotor time constant. However, the dynamic tracking performance of the rotor time constant still needs to be verified in practice.

[0034] The third category is Model Reference Adaptive (MRAS), which uses the error between the output of the reference model and the adjustable model to adaptively adjust the target parameters in the adjustable model, achieving online parameter estimation. It has the advantages of simplicity and reliability, but its convergence accuracy and speed still have room for improvement. For example, patent document CN105227022A uses a reactive power-based MRAS scheme, dividing the reactive power deviation by the synchronous frequency as the input error of the adaptive rate, proposing a synchronous frequency limiting operation, and introducing an initial value for the rotor time constant. However, all motor parameters used are obtained through offline identification; in practical applications, excessive parameter deviation can cause divergence, and the tracking performance of the adaptive rate in dynamic operating conditions needs improvement. Patent document CN117614330A, based on the reactive power-based MRAS scheme, uses a Kalman filter to filter the rotor time constant estimate, effectively reducing the fluctuation of the rotor time constant estimate. However, the improvement in stability and convergence is limited, and it requires relatively complex filter calculations. Patent document CN110138299A constructs an MRAS scheme based on a reactive power calculation method in a two-phase stationary coordinate system to achieve online identification of rotor resistance, but it requires the introduction of a flux linkage observer, which reduces the robustness of the system.

[0035] Based on the reactive power MRAS scheme, this application proposes an adaptive control method for the rotor time constant of an induction motor based on a rotor resistance feedforward reactive power MRAS model. The method obtains inductance information through point scanning calibration, uses reactive power deviation to identify the rotor time constant and rotor resistance estimate, and establishes a feedforward link based on the rotor resistance estimate, thereby improving the convergence speed and stability of the identification system.

[0036] This application provides an adaptive control method for the rotor time constant of an induction motor. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0037] Please refer to Figure 2 , Figure 2 A flowchart of the adaptive control method for the rotor time constant of an induction motor provided in the embodiments of this application is shown below. Figure 2 As shown, the process includes the following steps:

[0038] Step S1: Determine the estimated value of the rotor time constant based on the reactive power deviation of the model reference adaptive system.

[0039] The reactive power deviation is the difference between the actual reactive power and the expected reactive power. The expected reactive power is the actual reactive power under accurate orientation. If there is a deviation in the rotor time constant leading to inaccurate orientation, there will be a deviation between the actual reactive power and the expected reactive power, i.e., the reactive power deviation. Based on Lyapunov stability theory, multiplying the reactive power deviation by the reciprocal of the synchronization frequency as the input to the adaptive rate yields an estimated value for the rotor time constant.

[0040] Step S3: Determine the rotor resistance estimate based on the rotor time constant estimate and the real-time acquired rotor inductance parameter value.

[0041] The rotor inductance parameter values ​​can be obtained by looking up a table. The estimated rotor resistance value is obtained by dividing the rotor inductance parameter value by the estimated rotor time constant value.

[0042] Step S5: Perform low-pass filtering on the estimated rotor resistance value to generate a filtered value.

[0043] The purpose of low-pass filtering the rotor resistance estimate is to enhance the stability of the convergence process and prevent oscillations in the adaptive system. The bandwidth of the low-pass filter can affect the dynamic performance of the system.

[0044] Step S7: Update the rotor time constant feedforward value based on the rotor inductance parameter value and the filter value.

[0045] The rotor time constant feedforward value is calculated by dividing the rotor inductance parameter value by the filtered value of the rotor resistance estimate. By using the rotor time constant feedforward value to compensate for the rotor time constant estimate, the problem of the rotor time constant estimate momentarily deviating from the true value during current switching, requiring adaptive readjustment, can be avoided.

[0046] Step S9: Based on the rotor time constant feedforward value, optimize the convergence process of the rotor time constant estimate to perform indirect vector control of the induction motor.

[0047] During the convergence of the rotor time constant estimate, i.e., as the estimated rotor time constant approaches the true rotor time constant, the rotor resistance estimate can synchronously approach the true rotor resistance. The rotor resistance estimate, after low-pass filtering, is used to calculate the feedforward term. That is, the rotor time constant feedforward value is updated based on the latest rotor resistance estimate. The updated rotor time constant feedforward value further compensates for the convergence result of the rotor time constant estimate. Through this recursive update method, the adaptive identification of the rotor time constant quickly achieves convergence.

[0048] This application proposes an adaptive control method for the rotor time constant of an induction motor. By adding a feedforward value for the rotor time constant, a complete adaptive control strategy for the rotor time constant is proposed. The rotor resistance estimate is obtained synchronously based on the online identification result of the rotor time constant, and the rotor resistance estimate is filtered in the feedforward loop. The feedforward value of the rotor time constant is calculated based on the filtered value of the rotor resistance estimate and the rotor inductance parameter value. Feedforward compensation is performed on the rotor time constant estimate, which can avoid the problem that the rotor time constant estimate deviates from the true value instantaneously during current switching and requires adaptive readjustment. This improves the convergence speed and robustness of the adaptive identification of the rotor time constant.

[0049] In some embodiments, the reactive power of the motor is defined as the cross product of the stator voltage and the stator current. In the dq-axis two-phase synchronous rotating coordinate system, the actual reactive power of the motor can be expressed as:

[0050]

[0051] In the formula, Q1 is the actual reactive power of the motor, and U S I is the phase voltage vector. S i is the phase current vector. ds i qs For the d-axis and q-axis stator currents, u ds u qs These are the d-axis and q-axis stator voltages.

[0052] Based on the stator voltage equation under steady state:

[0053]

[0054] In the formula, R s L is the stator resistance. s For stator inductance, σ is the leakage inductance coefficient, and L is the stator inductance. a For the equivalent inductance, σL sIndicates total leakage, L m For the magnetizing inductor, L r For rotor inductance, ψ dr ψ qr For the rotor flux linkages of the d and q axes, ω e This is the synchronization frequency.

[0055] Substituting equation (2) into equation (1) yields another expression for the actual reactive power, which is represented by Q2:

[0056]

[0057] If the rotor time constant value T used in the program rset Equal to the true value T r If the magnetic field orientation is accurate, then the d-axis current, q-axis current, and rotor flux linkage in the program are all true values, satisfying the following relationship:

[0058]

[0059] Substituting equation (4) into equation (3), we can obtain the actual reactive power when the rotor time constant is equal to the true value (denoted by Q3):

[0060]

[0061] In the formula, Q3 is called the expected reactive power because it is the actual reactive power under the condition of accurate orientation. If there is a deviation in the rotor time constant, resulting in inaccurate orientation, then there will be a deviation between the actual reactive power Q1 calculated according to formula (1) and the expected value Q3. This reactive power deviation is expressed as:

[0062]

[0063] The reactive power deviation ΔQ reflects the deviation of the rotor time constant, and the rotor time constant can be adjusted online based on this reactive power deviation. In practical engineering, the current loop adjustment error can be ignored, and the d-axis and q-axis stator voltages and currents in equation (6) can be represented by known given values, and the inductance parameter L s L a The values ​​are obtained by looking up a table based on the given currents along the d and q axes.

[0064] The formula for estimating the rotor time constant based on Lyapunov stability theory is as follows:

[0065]

[0066] In the formula, k ω =1 / ω e In order to prevent ω in the project e A value of 0 results in a denominator of 0, for k ωAmplitude limiting is applied; T r0 The feedforward value of the rotor time constant set in the program is used to reduce the adjustment margin.

[0067] In some embodiments, the reactive power deviation is multiplied by the reciprocal of the synchronization frequency to obtain the input error; the input error is input into the PI controller to obtain the adaptive rate output value; and the rotor time constant estimate is updated based on the adaptive rate output value and the rotor time constant feedforward value.

[0068] To address the rapid torque response and continuous torque output conditions of induction motors, and to ensure stability under various operating conditions, a hierarchical PI parameter tuning strategy is implemented based on the input error of the adaptive rate (i.e., reactive power deviation multiplied by the reciprocal of the synchronous frequency). This involves hierarchical adjustment of the PI parameters, including: first, setting PI parameter base values ​​(Kp_Base, Ki_Base), multiple error thresholds, and an integral term threshold. The system is considered to have reached steady state only when the input error is below the minimum error threshold (i.e., the first error threshold) and the integral term is below the integral term threshold. At this point, integration stops, and a smaller proportional gain is set to maintain stability. Otherwise, the system is considered to be in a non-converged state, and the proportion of the PI parameters relative to the base values ​​needs to be determined based on the input error and threshold range. This logic ensures both dynamic response speed and steady-state stability, reduces overshoot and fluctuations in the estimated values, and improves the smoothness of the output torque. During steady-state operation, the integral term of the adaptive rate is kept at 0 to prevent it from affecting the adjustment at the next moment, significantly improving the convergence speed and robustness of the rotor time constant adaptive identification.

[0069] In some embodiments, the adaptive rate output value includes an integral term and a proportional term. If the input error is less than a first error threshold and the integral term is less than an integral term threshold, then it is determined to be in a steady state, and the integral coefficient k is set... i Set to zero, the proportionality constant k p Set it to be less than the first base value.

[0070] In some embodiments, if the input error is greater than the second error threshold, it is determined to be a disturbance state, and the proportional coefficient is set to be greater than the first base value.

[0071] The principle for setting the PI parameter is as follows: when the input error is large, set a larger proportional coefficient Kp and a smaller integral coefficient Ki; when the input error is small, set a smaller proportional coefficient Kp and a larger integral coefficient Ki, so as to ensure the dynamic response speed while taking into account the stability in the steady state stage.

[0072] In some embodiments, low-pass filtering of the rotor resistance estimate includes: determining a cutoff frequency for the low-pass filter based on a given current angle, and performing low-pass filtering on the rotor resistance estimate based on the cutoff frequency, wherein the larger the given current angle, the lower the cutoff frequency.

[0073] The system's response capability and stability can be optimized by adjusting the cutoff frequency under different operating conditions. The most direct and effective way to adjust the cutoff frequency is to adjust it according to the given current angle, taking into account both fast response capability and stability in the weak magnetic region.

[0074] The bandwidth of the low-pass filter significantly impacts the system's dynamic performance. A larger bandwidth leads to faster follow-up of the feedforward term and quicker convergence of the rotor time constant estimate, but it can also cause overshoot and oscillations. To balance the rapid response of the low-speed MTPA condition with the convergence stability of the Tr value in the high-speed field weakening condition, the cutoff frequency of the low-pass filter is tuned online by setting a given current angle. This causes the filter bandwidth to decrease as the given current angle increases. Please refer to [reference needed]. Figure 3 , Figure 3 This is a trend graph of a given current angle versus cutoff frequency as proposed in an embodiment of this application.

[0075] The formula between a given current angle and a cutoff frequency is as follows:

[0076] f c =k r *θ ref +f c0 (8)

[0077] In the above formula, k r f c With a given current angle θ ref The slope of the change, f c0 This represents the cutoff frequency when the given current angle is 0 degrees. The above scheme qualitatively analyzes f. c The tuning approach can be used to optimize the tuning curve in practical applications.

[0078] The filter bandwidth of the rotor resistance estimate is adjusted online based on the given current angle. This ensures both dynamic response speed and stability in the steady state, reduces overshoot and fluctuations in the rotor time constant estimate, and improves the smoothness of the motor output torque, which is beneficial for practical engineering applications.

[0079] Specifically, different asynchronous motors have different MTPA and field weakening characteristic curves. Under the same given torque command, the corresponding given current angle is different. The range of the given current angle is 0-90°. The given current angle in the MTPA region is generally smaller than the given current angle in the field weakening region. The given current angle is generally between about 40-85°.

[0080] In some embodiments, the rotor resistance estimate This is determined by the rotor inductance parameter value L. r Divide by the estimated rotor time constant The filtered value is obtained after filtering. The formula used for calculating the rotor time constant feedforward value is as follows:

[0081]

[0082] Please refer to Figure 4 , Figure 4 This is a schematic diagram of the induction motor control system proposed in an embodiment of this application. The above calculation strategy is used to construct... Figure 4 The induction motor control system shown:

[0083] During the adaptive convergence of the rotor time constant, i.e. To the true value T r During the approach, Able to be based on The estimated value synchronously approximates the true value R. r After being low-pass filtered, it is used for the calculation of the feedforward term, i.e., the rotor time constant feedforward value T. r0 The value of T is updated based on the latest rotor resistance estimate. r0 Will further The convergence result is used for feedforward compensation. This recursive update method enables the system to quickly reach convergence, and after convergence, The integral term is approximately 0, which avoids the integral value of the previous working condition affecting the convergence of the next time step.

[0084] Once convergence is achieved, the rotor resistance estimate is... It also converges to the true value R. r Under the current condition at the next moment, since the rotor resistance changes relatively slowly and can be considered constant within a millisecond time period, while the inductance parameter can be obtained in real time by looking up the current table, the feedforward value T of the rotor time constant calculated in subsequent moments will be... r0 It is close to the true value T r No excessive adjustments are needed; this method utilizes time-varying parameters. Dynamically adjust T r0 The strategy shortened The convergence time during dynamic operation is reduced, decreasing the dynamic torque response delay, making it suitable for applications where automotive motor parameters change drastically.

[0085] Theoretically, we do not consider the change in rotor resistance over a short period of time, and assume the actual R... r =20mΩ remains constant, R r0=15mΩ, convergence has occurred at time k, and the inductance lookup table is accurate. The comparison of the adjustment margin between the technical solution provided in this application and the traditional solution during the Tr adjustment process in three adjacent speed / torque loop adjustment cycles is shown in Table 1 below:

[0086] Table 1

[0087]

[0088] In some embodiments, if the given current is less than a preset threshold, the adaptive adjustment of the rotor time constant is stopped, the rotor resistance estimate is returned to the preset initial value at a set rate, and the rotor time constant estimate is set as the rotor time constant feedforward value.

[0089] In practical applications, the output voltage is low in the low-current region, which is greatly affected by inverter nonlinearity, resulting in a large error between the given voltage and the actual voltage. Furthermore, the rotor inductance in the low-current region is difficult to accurately identify. All of these factors affect the accuracy of reactive power calculation, and consequently, the convergence accuracy of rotor time constant estimation in the low-current region. Considering that even if there is a deviation in the given current angle under low-current conditions, the resulting torque deviation is relatively small, a preset threshold (with hysteresis) can be set for the given current based on actual engineering needs. This sets the executable region for the Tr adaptive function. Exceeding the preset threshold disables the module and stops Tr adaptive adjustment (the part executed synchronously with the current loop). Simultaneously, to prevent abrupt changes in the used Tr, a rotor resistance estimation regression strategy is executed in this state, performing a transitional process. That is, the rotor resistance estimation value regresses to the preset initial value at a set rate, simulating the cooling process of the motor under low-current conditions. Meanwhile, the inductance lookup table and the calculation of the rotor time constant feedforward value continue, directly assigning the preset initial value to the rotor time constant estimation value. This enhances the reliability of the rotor time constant estimation value under conditions where the adaptive function is not executed. Based on the engineering requirements, the stability of the system is ensured throughout the entire current operating range by setting current thresholds and using a rotor resistance estimation regression strategy.

[0090] The selection of the current preset threshold is first based on the current lower limit of the inductance lookup table module. If the current is lower than the current lower limit, the inductance will be inaccurate. Therefore, the preset threshold must be at least higher than the current lower limit. Based on this, it is adjusted according to the project requirements to ensure that the rotor time constant can work normally under the condition of being higher than the preset threshold.

[0091] In some embodiments, low-pass filtering and the calculation of the rotor time constant feedforward value are performed in the speed loop or torque loop cycle; the calculation of the rotor time constant estimate is performed in the current loop cycle.

[0092] In some embodiments, the rotor inductance parameter value is obtained by looking up a table based on a given current angle; wherein the table lookup execution function is executed in the speed loop or torque loop cycle.

[0093] Please refer to Figure 5 , Figure 5 This is a schematic diagram of the execution cycle provided for an embodiment of this application. For example... Figure 5 As shown, in motor control software, interrupt response frequencies can be mainly divided into current loop and speed / torque loop, with the current loop being the underlying layer and having a high execution frequency. To enhance the stability of the system convergence process while considering the software load rate, the execution functions for inductance lookup, rotor resistance estimation (with filtering), and rotor time constant feedforward calculation are placed on the speed loop or torque loop to maintain a slower execution frequency. Meanwhile, the core function for rotor time constant estimation is placed in an interrupt function that executes synchronously with the inner current loop to maintain a faster execution frequency.

[0094] In some embodiments, please refer to Figure 6 , Figure 6 This is a block diagram of the rotor time constant adaptive adjustment proposed in this application. This application incorporates output limiting and anti-saturation integral operations into the PI control section, forming a complete rotor time constant adaptive adjustment scheme. The execution block diagram of the core component (the part executed synchronously with the current loop) is shown below. Figure 6 As shown.

[0095] like Figure 6 The execution steps shown are summarized as follows:

[0096] ① Determine whether the given current exceeds the preset threshold and decide whether to enable the adaptive function.

[0097] ② If the function is not enabled, the rotor resistance estimation regression strategy and rotor time constant feedforward value calculation are executed. The preset initial value is directly assigned to the rotor time constant estimation value and released to the indirect vector control module for slip calculation.

[0098] ③ If the function is enabled, the PI parameter hierarchical tuning strategy will be executed.

[0099] ④ Calculate the adaptive rate output value (i.e., PI output) by combining the anti-saturation integral strategy.

[0100] ⑤ Calculate the estimated value of the rotor time constant based on the adaptive rate output value and the rotor time constant feedforward value, and perform a limiting operation in combination with the threshold of the upper and lower limits of the rotor time constant. Release the estimated value of the rotor time constant to the indirect vector control module for slip calculation.

[0101] ⑥ Calculate the rotor resistance estimate based on the rotor time constant estimate and the rotor inductance lookup table value, and set thresholds for the upper and lower limits of the rotor resistance for limiting operation. Finally, release the rotor resistance estimate to the outer feedforward loop for filtering and calculation of the rotor time constant feedforward value.

[0102] Please refer to Figure 7 , Figure 7 This is a comparison chart of torque step response test results provided in an embodiment of this application. Related technologies use a fixed Tr for calibration and control, while this embodiment employs a Tr adaptive control strategy, which can improve torque output performance under extreme conditions such as high temperature and high cold. The external characteristic torque step response test results of the two schemes under the same high-pressure 4000RPM condition are compared as follows: Figure 7 As shown, given a torque ramp of 6 Nm / ms, the same test bench was used for testing, with a torque recording interval of 30 ms. Figure 7 The adaptive Tr technology solution provided in the embodiments of this application shows that there is no obvious torque response delay and no obvious fluctuation in the steady state stage, which meets the requirements of engineering applications.

[0103] This application provides an adaptive control method for the rotor time constant of an induction motor, applicable to the estimation of all rotor time constants. For example, MRAS control based on rotor flux linkage or estimation of the rotor time constant using an observer can be optimized by combining the proposed scheme.

[0104] Accordingly, please refer to Figure 8 , Figure 8 This is a schematic diagram of an adaptive control system for the rotor time constant of an induction motor provided in an embodiment of this application, as shown below. Figure 8 As shown, it includes: a first estimation module for determining a rotor time constant estimate based on the reactive power deviation of the model reference adaptive system; a second estimation module for determining a rotor resistance estimate based on the rotor time constant estimate and the real-time acquired rotor inductance parameter value; a filtering module for performing low-pass filtering on the rotor resistance estimate to generate a filtered value; a feedforward module for updating the rotor time constant feedforward value based on the rotor inductance parameter value and the filtered value; and a module for optimizing the convergence process of the rotor time constant estimate based on the rotor time constant feedforward value to perform indirect vector control of the induction motor.

[0105] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0106] In this embodiment, the adaptive control system for the rotor time constant of the induction motor is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit), a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0107] Please see Figure 9 , Figure 9 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application, such as... Figure 9 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 9 Take a processor 10 as an example.

[0108] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0109] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0110] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0111] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0112] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0113] This application also provides a computer-readable storage medium. The methods described in this application can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the methods shown in the above embodiments are implemented.

[0114] This application provides a computer program product including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method of any embodiment of this application.

[0115] The systems or modules described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0116] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0117] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0118] This application is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0119] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0120] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0121] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0122] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0123] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

[0124] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An adaptive control method for the rotor time constant of an induction motor, characterized in that, The method includes: Based on the reactive power deviation of the model reference adaptive system, the estimated value of the rotor time constant is determined. Based on the estimated rotor time constant and the real-time acquired rotor inductance parameter values, the estimated rotor resistance value is determined; The estimated rotor resistance is low-pass filtered to generate a filtered value; Based on the rotor inductance parameter value and the filter value, update the rotor time constant feedforward value; Based on the rotor time constant feedforward value, the convergence process of the rotor time constant estimate is optimized to perform indirect vector control of the induction motor.

2. The method according to claim 1, characterized in that, The method further includes: Multiply the reactive power deviation by the reciprocal of the synchronization frequency to obtain the input error; The input error is input into the PI controller to obtain the adaptive rate output value; The rotor time constant estimate is updated based on the adaptive rate output value and the rotor time constant feedforward value.

3. The method according to claim 2, characterized in that, The method further includes: the adaptive rate output value includes an integral term and a proportional term; if the input error is less than a first error threshold and the integral term is less than the integral term threshold, then it is determined to be in a steady state, the integral coefficient is set to zero, and the proportional coefficient is set to be less than the first base value.

4. The method according to claim 3, characterized in that, The method further includes: If the input error is greater than the second error threshold, it is determined to be a disturbance state, and the proportional coefficient is set to be greater than the first base value.

5. The method according to claim 1, characterized in that, The estimated rotor resistance is low-pass filtered, including: Based on a given current angle, the cutoff frequency of the low-pass filter is determined, and the rotor resistance estimate is low-pass filtered based on the cutoff frequency. The larger the given current angle, the lower the cutoff frequency.

6. The method according to claim 1, characterized in that, The method further includes: If the given current is less than the preset threshold, the adaptive adjustment of the rotor time constant is stopped, the estimated rotor resistance value is returned to the preset initial value at a set rate, and the estimated rotor time constant value is set as the feedforward value of the rotor time constant.

7. The method according to claim 1, characterized in that, The method further includes: Low-pass filtering and the calculation of the rotor time constant feedforward value are performed in the speed loop or torque loop cycle; the calculation of the rotor time constant estimate is performed in the current loop cycle.

8. The method according to claim 1, characterized in that, The method further includes: The rotor inductance parameter value is obtained by looking up a table based on a given current angle; wherein the table lookup execution function is executed in the speed loop or torque loop cycle.

9. An adaptive control system for the rotor time constant of an induction motor, capable of implementing the adaptive control method for the rotor time constant of an induction motor as described in any one of claims 1-8, characterized in that, include: The first estimation module is used to determine the estimated value of the rotor time constant based on the reactive power deviation of the model reference adaptive system. The second estimation module is used to determine the rotor resistance estimate based on the rotor time constant estimate and the rotor inductance parameter value acquired in real time. The filtering module is used to perform low-pass filtering on the rotor resistance estimate to generate a filtered value; The feedforward module is used to update the rotor time constant feedforward value based on the rotor inductance parameter value and the filter value; and to optimize the convergence process of the rotor time constant estimate based on the rotor time constant feedforward value, so as to perform indirect vector control of the induction motor.

10. A computer device, comprising: The system includes a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the adaptive control method for the rotor time constant of an induction motor as described in any one of claims 1-8.

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