Steering motor position and speed estimation method and related device

Abstract

The application provides a steering motor position and speed estimation method and related equipment, including: obtaining three-phase electric signals and motor operating parameters of a target steering motor, and converting the three-phase electric signals into two-phase electric signals in a stationary alpha-beta two-phase coordinate system; substituting the two-phase electric signals and the motor operating parameters into a back electromotive force extended state observer to obtain a stationary two-phase back electromotive force estimation value in the stationary alpha-beta two-phase coordinate system; obtaining a rotating two-phase back electromotive force estimation value in a rotating dq two-phase coordinate system through a rotating coordinate transformation; constructing a half-sine integral type position error signal by using the rotating two-phase back electromotive force estimation value; inputting the position error signal into an integral type filter to obtain a speed estimation value of the target steering motor, and performing integral operation on the speed estimation value to obtain a position estimation value of the target steering motor. Thus, high-precision and high-stability position and speed estimation is realized under the working condition of low-speed operation and frequent commutation of the steering motor.

Description

Technical Field

[0001] This application relates to the field of motor control technology, and in particular to a method and related equipment for estimating the position and speed of a steering motor. Background Technology

[0002] In the control of steering motors, to ensure that the motor is always in a precise operating state, it is necessary to monitor the position and speed of the motor rotor in real time and apply corresponding control strategies. Especially under the unique conditions of low-speed operation, frequent reversing, and small-angle oscillation of steering motors, or when the motor transitions from low-speed stable operation to high-speed dynamic response, the requirements for the real-time performance and accuracy of position and speed detection are greatly increased.

[0003] Existing methods typically rely on back-EMF models for angle calculation. This involves extracting the back-EMF from the voltage and current using a sliding mode observer, and then combining this with an arctangent function or a phase-locked loop (PLL) to estimate the rotor position. However, the sliding mode observer suffers from inherent chattering due to the switching characteristics of its sign function, severely contaminating the back-EMF estimate. Furthermore, the arctangent function is prone to phase jumps at low speeds, during start-stop cycles, or sudden load changes, leading to amplified estimation errors and unstable convergence. While using a semi-tangent function to construct the error signal can improve the dynamic response to some extent, its nonlinear characteristics cause signal distortion during transient processes with significant estimation deviations, affecting convergence stability and making it difficult to guarantee estimation accuracy across all operating conditions. Consequently, the position and speed estimation accuracy is insufficient, failing to meet the high-precision requirements of steering motor control systems. Summary of the Invention

[0004] The purpose of this application is to at least solve one of the above-mentioned technical defects, in particular the technical defect that the position and speed estimation accuracy in the prior art is insufficient and it is difficult to meet the high precision requirements of the steering motor control system.

[0005] In a first aspect, this application provides a method for estimating the position and speed of a steering motor, the method comprising:

[0006] Acquire the three-phase electrical signals and motor operating parameters of the target steering motor, and convert the three-phase electrical signals into two-phase current signals and two-phase voltage signals in a stationary αβ two-phase coordinate system;

[0007] Substituting the two-phase current signal, two-phase voltage signal, and motor operating parameters into the pre-constructed back EMF extended state observer, we obtain the estimated values ​​of the two-phase back EMF in the stationary αβ coordinate system, and then, through a rotating coordinate transformation, we obtain the estimated values ​​of the two-phase back EMF in the rotating dq coordinate system.

[0008] A half-sine integral type position error signal is constructed using the estimated values ​​of the two rotating opposite electromotive forces.

[0009] The position error signal is input to an integrating filter to obtain the speed estimate of the target steering motor. The speed estimate is then integrated to obtain the position estimate of the target steering motor.

[0010] In one embodiment, the motor operating parameters include the observer gain coefficient, and the back EMF extended state observer includes a stationary two-phase current estimation model and a stationary two-phase back EMF estimation model.

[0011] The steps of substituting the two-phase current signals, two-phase voltage signals, and motor operating parameters into a pre-constructed extended back EMF state observer to obtain the estimated values ​​of the stationary two-phase back EMF in the stationary αβ coordinate system include:

[0012] Based on the two-phase current signal, the two-phase voltage signal, and the first gain coefficient of the observer gain coefficient, the estimated value of the stationary two-phase current is obtained using the stationary two-phase current estimation model.

[0013] Based on the two-phase current signal, the estimated value of the stationary two-phase current, and the second gain coefficient of the observer gain coefficient, the estimated value of the stationary two-phase back electromotive force in the stationary αβ coordinate system is obtained using the stationary two-phase back electromotive force model.

[0014] In one embodiment, the static two-phase current estimation model is as follows:

[0015]

[0016] The estimation model for two stationary back electromotive forces is as follows:

[0017]

[0018] in, Indicates the current iteration step. Indicates the previous iteration step. The time derivative of the estimated α-axis stationary two-phase current. This represents the time derivative of the estimated β-axis stationary two-phase current, where the two-phase current signal includes the actual α-axis stationary two-phase current. and the actual two-phase current at rest along the β axis The two-phase voltage signal includes the actual voltage of the stationary two-phase phases along the α-axis. and the actual voltage of the stationary two-phase voltage along the β axis The motor operating parameters also include stator inductance. and stator resistance The observer gain coefficients include the first gain coefficient along the α-axis. β-axis first gain coefficient α-axis second gain coefficient and β-axis second gain coefficient The stationary two-phase current estimates include the α-axis stationary two-phase current estimates for the current iteration step. and β-axis stationary two-phase current estimates , This represents the estimated values ​​of the two opposite electromotive forces at rest along the α-axis from the previous iteration step. This represents the estimated values ​​of the two opposing electromotive forces at rest along the β-axis from the previous iteration step. This represents the time derivative of the estimated values ​​of the two opposite electromotive forces at rest along the α-axis. This represents the time derivative of the two stationary back EMF estimates along the β-axis, which include the combined... ,right The integral yields the estimated values ​​of the two opposing electromotive forces at rest along the α-axis for the current iteration step, and combines them with... ,right The estimated values ​​of the two opposing electromotive forces at rest along the β axis for the current iteration step are obtained by integration.

[0019] In one embodiment, the step of constructing a half-sine integral position error signal using the estimated values ​​of the two rotating opposite electromotive forces includes:

[0020] Calculate the combined amplitude of the back electromotive force based on the estimated values ​​of the two rotating back electromotive forces;

[0021] Based on the combined amplitude of the back EMF and the d-axis back EMF estimate in the two rotating back EMF estimates, a normalized denominator is constructed.

[0022] By using the q-axis back EMF estimate from the two rotating back EMF estimates and the normalized denominator, a half-sine integral type position error signal is obtained through ratio calculation.

[0023] In one embodiment, the formula for the position error signal is:

[0024]

[0025]

[0026] in, This indicates the position error signal. Indicates the combined amplitude of the back electromotive force. This represents the estimated value of the back electromotive force along the q-axis. This represents the estimated value of the back electromotive force along the d-axis. This indicates the actual position of the target steering motor. This represents the estimated position of the target steering motor.

[0027] In one embodiment, the step of inputting the position error signal to an integrating filter to obtain a speed estimate of the target steering motor includes:

[0028] The speed estimate is calculated using the following formula:

[0029]

[0030] in, This represents the speed estimate. This indicates the position error signal. This represents the proportional gain coefficient of an integrating filter. This represents the integral gain coefficient of the integral filter.

[0031] In one embodiment, the step of integrating the speed estimate to obtain the position estimate of the target steering motor includes:

[0032] The location estimate is calculated using the following formula:

[0033]

[0034] in, This represents the estimated position of the target steering motor. This represents the estimated speed.

[0035] Secondly, this application provides a steering motor position and speed estimation device, the device comprising:

[0036] The two-phase electrical signal determination module is used to acquire the three-phase electrical signals and motor operating parameters of the target steering motor, and convert the three-phase electrical signals into two-phase current signals and two-phase voltage signals in the stationary αβ two-phase coordinate system;

[0037] The rotating two-point back EMF estimation module is used to substitute the two-phase current signal, two-phase voltage signal and motor operating parameters into the pre-built back EMF extended state observer to obtain the estimated values ​​of the stationary two-phase back EMF in the stationary αβ coordinate system, and obtain the estimated values ​​of the rotating two-phase back EMF in the rotating dq two-phase coordinate system through the rotating coordinate transformation.

[0038] The position error signal construction module is used to construct a half-sine integral position error signal using the estimated values ​​of two rotating opposite electromotive forces.

[0039] The position estimation module is used to input the position error signal into an integrating filter to obtain the speed estimate of the target steering motor, and to perform an integration operation on the speed estimate to obtain the position estimate of the target steering motor.

[0040] Thirdly, this application provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of any of the steering motor position and speed estimation methods described in the above embodiments.

[0041] Fourthly, this application provides a computer device, including: one or more processors, and a memory;

[0042] The memory stores computer-readable instructions that, when executed by one or more processors, perform the steps of any of the steering motor position and speed estimation methods described in the above embodiments.

[0043] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0044] The steering motor position and speed estimation method and related equipment provided in this application construct an extended back EMF state observer, substituting two-phase current and voltage signals with motor operating parameters to obtain the back EMF estimate in a stationary two-phase coordinate system. This process uses a continuous state estimation mechanism to replace the sign function switching of the traditional sliding mode observer, effectively avoiding high-frequency chattering caused by switching characteristics. This ensures the purity of the back EMF estimate from the signal source, solving the problem of subsequent estimation inaccuracies caused by chatter contamination. Furthermore, a rotating coordinate transformation is used to obtain the back EMF estimate in a rotating two-phase coordinate system, and this value is used to construct a half-sine integral type position error signal. Compared to the phase jumps and nonlinear distortions that easily occur with arctangent or half-tangent functions under low-speed, commutation conditions, the half-sine error construction method maintains smooth error output characteristics even in transient processes with large estimation deviations, avoiding abrupt changes and amplification of the error signal, and ensuring good continuity and stability of the error signal input to subsequent stages. The smoothed position error signal is then processed by an integral filter to obtain the speed estimate of the target steering motor. This speed estimate is then integrated to obtain the position estimate. Since the preceding stage effectively suppresses chattering and jumps, the integral filter, with its simple structure and convenient parameter tuning, focuses on achieving rapid dynamic response and residual disturbance suppression. Therefore, this application, through the synergistic effect of the back EMF extended state observer and the half-sine integral error construct, solves the problems of input signal contamination and abrupt changes throughout the entire chain from back EMF extraction and error signal generation to speed and position calculation. This achieves high-precision and high-stability position and speed estimation under low-speed operation and frequent reversing conditions of the steering motor, meeting the high-precision and high-stability requirements of the steering motor control system. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application 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 some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 A schematic flowchart illustrating the steering motor position and speed estimation method provided in this application embodiment;

[0047] Figure 2 A schematic diagram of the steering motor position and speed estimation device provided in an embodiment of this application;

[0048] Figure 3 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0049] 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, and 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.

[0050] This application provides a method for estimating the position and speed of a steering motor. The following embodiments illustrate this method using a computer device as an example. It is understood that the computer device can be any device with data processing capabilities, including but not limited to a single server, server cluster, personal laptop, desktop computer, etc. Figure 1 As shown, the method includes:

[0051] S101: Acquire the three-phase electrical signals and motor operating parameters of the target steering motor, and convert the three-phase electrical signals into two-phase current signals and two-phase voltage signals in the stationary αβ two-phase coordinate system.

[0052] The three-phase electrical signals refer to the instantaneous voltage and current values ​​directly collected from the three-phase stator windings of the target steering motor. These signals accurately reflect the electrical state of the motor during operation. Motor operating parameters refer to the inherent physical characteristic constants of the target steering motor, mainly including stator resistance and stator inductance. These parameters are the fundamental data describing the electromagnetic relationships of the motor. The stationary αβ two-phase coordinate system is a two-phase orthogonal reference coordinate system fixed on the stator. The α-axis is aligned with the axis of the motor's a-phase winding, and the β-axis leads the α-axis by 90 electrical degrees in space. This coordinate transformation simplifies the originally complex three-phase time-varying system into a two-phase orthogonal system. The two-phase current and voltage signals refer to the equivalent current and voltage components corresponding to the original three-phase current and voltage values ​​in the stationary αβ two-phase coordinate system after processing with the Clarke transform matrix. These two signals retain all the electromagnetic information of the original three-phase system but are presented in a simpler form.

[0053] In practical implementation, the target steering motor first needs to be equipped with corresponding current and voltage sensors. These sensors are installed on the three-phase input lines of the motor to capture the instantaneous current and voltage values ​​of each phase in real time. When the target steering motor is running at low speed or preparing for a commutation operation, the sensors continuously acquire these analog signals at an extremely high sampling frequency and convert them into digital three-phase current and voltage sample values ​​through an analog-to-digital converter. Simultaneously, the pre-calibrated stator resistance and stator inductance values ​​are read from the motor controller's storage unit. After acquiring this raw data, a coordinate transformation program is immediately started. This program pre-stores the coefficients of the Clarke transform matrix. By substituting the three-phase current sample values ​​into the Clarke transform equation, the current components along the α-axis and β-axis in the stationary αβ two-phase coordinate system are calculated respectively. Similarly, the same coordinate transformation operation is performed on the three-phase voltage sample values ​​to obtain the corresponding α-axis and β-axis voltage components. The entire calculation process is repeated within each sampling period to ensure that the two-phase current and two-phase voltage signals are strictly synchronized with the actual operating state of the motor, providing continuous and accurate input data for subsequent observer processing. During the transition of the steering motor from low-speed stable operation to high-speed dynamic response, this fixed coordinate system transformation-based processing method ensures the real-time nature of signal conversion, preventing additional phase delays or amplitude attenuation due to changes in motor operating state. When the motor performs frequent small-angle oscillations, the coordinate transformation algorithm can still accurately decompose the three-phase time-varying signal into two-phase orthogonal signals, allowing subsequent state observation stages to perform precise calculations based on these stable two-phase signals.

[0054] It should be noted that acquiring the three-phase electrical signals is to obtain the raw electrical quantities for subsequent estimation. This signal completely contains rotor position information and is the fundamental data source for state estimation. Simultaneously acquiring stator resistance and inductance values ​​is to accurately establish the physical relationship between voltage and current, ensuring a strict correspondence between the signals and the motor's electromagnetic model. Converting the three-phase electrical signals into two-phase current and two-phase voltage signals essentially involves using the Clarke transform to orthogonally decouple the original signals. This simplifies the mutually coupled three-phase time-varying system in the natural coordinate system into two spatially orthogonal independent components. This linear transformation completely preserves the original information while eliminating the interference of time-varying coupling terms on subsequent processing. Through this step, the complex three-phase signal is transformed into a standardized two-phase signal with a unified format, providing a directly usable standardized input for constructing a state observer. Furthermore, the linear transformation itself does not introduce additional noise, ensuring high signal fidelity, thus laying a reliable foundation for achieving high-precision position and velocity estimation at the data processing front end.

[0055] In one example, the Clarke transform method is used to perform coordinate transformation on the three-phase electrical signals. The three-phase current and three-phase voltage are expressed as follows: , , and , , ,in, The Clark coordinate transformation matrix is ​​as follows:

[0056]

[0057] With the constraint that the vector magnitude remains unchanged before and after the transformation, the Clarke transform is used to obtain:

[0058]

[0059]

[0060] In the formula, Represents the α-axis current data. Represents β-axis current data. Represents the α-axis voltage data. Represents β-axis voltage data. This represents the current in phase a. This represents the phase b current. This represents the c-phase current. This represents the voltage of phase a. This represents the voltage of phase b. This represents the voltage of phase c. This represents the Clark coordinate transformation matrix.

[0061] It is worth mentioning that coordinate transformation can also be performed using direct calculation method and vector synthesis method to obtain two-phase power data under axis αβ of the stationary two-phase coordinate system.

[0062] S102: Substitute the two-phase current signal, two-phase voltage signal and motor operating parameters into the pre-built back EMF extended state observer to obtain the estimated values ​​of the stationary two-phase back EMF in the stationary αβ coordinate system, and obtain the estimated values ​​of the rotating two-phase back EMF in the rotating dq two-phase coordinate system through a rotating coordinate transformation.

[0063] The back EMF extended state observer is a dynamic estimation model based on modern control theory. This model uses two-phase current signals as input variables and two-phase voltage signals and motor operating parameters as known quantities. By constructing an extended state equation including the back EMF component and configuring the corresponding feedback gain matrix, it achieves continuous observation and real-time estimation of the motor's back EMF. The estimated stationary two-phase back EMF values ​​refer to the back EMF components along the α-axis and β-axis directions, directly output by the observer after calculation. These two components reflect the projection relationship of the motor rotor magnetic field in the stator coordinate system. The rotating coordinate transformation refers to the mathematical operation process of mapping physical quantities in the stationary αβ two-phase coordinate system to the dq two-phase coordinate system that rotates synchronously with the rotor using the Park transformation matrix. This transformation uses the rotor position angle as the conversion basis. The back electromotive force (EMF) estimates of the rotating two-phase coordinate system refer to the back EMF components along the d-axis and q-axis in the rotating dq two-phase coordinate system obtained after Park transformation. These two components are expressed as DC quantities during steady-state operation of the motor, which facilitates subsequent signal processing and error construction.

[0064] In practical implementation, the mathematical model of the back-EMF extended state observer first needs to be pre-built in the memory. This model is based on the voltage equation of the motor in the stationary αβ coordinate system, extending the back-EMF component into the system's state variables. When the motor is running at low speed or preparing to perform a commutation operation, the two-phase current signal and two-phase voltage signal obtained in the previous step are continuously acquired. At the same time, the stator resistance value, stator inductance value, and the pre-set observer gain coefficient are read from the storage unit. In each sampling period, the two-phase current signal at the current moment is compared with the state estimate value at the previous moment, and the current estimation error is calculated. This error is weighted by the gain matrix and used to correct the back-EMF estimate value at the current moment. Through repeated iterations of this closed-loop correction mechanism, the observer can continuously output stable stationary two-phase back-EMF estimates.

[0065] After obtaining the back EMF components in the stationary αβ coordinate system, the rotating coordinate transformation program is immediately started. This program first obtains the rotor position angle value at the current moment from the storage unit of the control system. This angle value can be the position data estimated in the previous cycle. Then, a rotating coordinate transformation matrix is ​​constructed according to the mathematical expression of the Park transform. The α-axis back EMF components and β-axis back EMF components are multiplied by the corresponding sine and cosine coefficients, respectively. After matrix multiplication, the d-axis back EMF components and q-axis back EMF components are obtained.

[0066] During the transition of the motor from low-speed stable operation to high-speed dynamic response, the back EMF extended state observer remains continuously operational. Its internal error correction mechanism adjusts the back EMF output value in real time based on the current estimation error, ensuring that the observation results remain synchronized with the actual electromagnetic state of the motor. When the motor performs frequent small-angle oscillations, the rotating coordinate transformation stage converts the AC quantity in the stationary coordinate system into the DC quantity in the rotating coordinate system based on the real-time updated rotor position angle. This conversion allows subsequent stages to process based on a stable DC signal, avoiding the phase lag problem that may occur in AC signal processing.

[0067] It should be noted that replacing the traditional sliding mode observer with an extended back-EMF state observer essentially involves replacing the sign function switching mechanism with a continuous state estimation mechanism. This continuous observation method fundamentally eliminates the high-frequency chattering phenomenon caused by switching actions, resulting in smooth waveforms and clean spectral characteristics in the output static back-EMF estimates. Substituting the resistance and inductance values ​​from the motor operating parameters into the observer equations ensures a strict match between the observer model and the motor's physical model, avoiding estimation errors caused by model mismatch. After obtaining the static back-EMF estimates, a rotating coordinate transformation is performed to convert the sinusoidal AC back-EMF in the static coordinate system into a constant DC quantity in the rotating coordinate system. This transformation is based on the core concept of field-oriented control, transforming the back-EMF signal from a time-varying form to a time-invariant form. The continuous estimation mechanism of the extended state observer eliminates high-frequency noise pollution in the back-EMF at the signal source, ensuring the purity and smoothness of the static back-EMF estimates. The rotating coordinate transformation converts AC quantities into DC quantities, simplifying the problem of dynamic tracking of sinusoidal signals into a problem of steady-state regulation of DC signals. This transformation significantly reduces the complexity of constructing subsequent error signals and improves the system's adaptability across the entire speed range. Through the synergistic effect of these two steps, clean and continuous back electromotive force observations are obtained, and these observations are converted into a easily processed DC form, laying a solid data foundation for subsequent high-precision and high-stability position and velocity estimation.

[0068] In one example, based on motor operating data, a preset rotating coordinate transformation equation is used to transform the two stationary back electromotive forces (EMFs) to obtain estimated values ​​of the two rotating back EMFs in the rotating two-phase coordinate system. The preset rotating coordinate transformation equation is as follows:

[0069]

[0070] in, This indicates two opposite electromotive forces rotating along the d-axis. This represents two opposite electromotive forces rotating along the q-axis. This represents the location feedback data, which is the target location data estimated in the previous cycle. The initial value is 0. This represents the estimated values ​​of the two opposing electromotive forces at rest along the α-axis. This represents the estimated values ​​of the two opposing electromotive forces at rest along the β axis.

[0071] S103: Construct a half-sine integral type position error signal using the estimated values ​​of the two opposite electromotive forces of rotation.

[0072] Among them, the half-sine integral type position error signal refers to a continuous error variable constructed by introducing a half-sine function and integral operation based on the mathematical relationship between the estimated values ​​of the back electromotive force of the two rotating parts, which is used to characterize the deviation between the actual rotor position and the estimated rotor position. This error signal abandons the traditional direct calculation method of arctangent, and instead uses the orthogonality of the back electromotive force component in the rotating coordinate system and the smooth transition characteristics of the half-sine function to accumulate the instantaneous error through the integral stage, and finally outputs a continuous and stable error indication.

[0073] In the specific implementation process, it is first necessary to read the estimated values ​​of the two back electromotive forces calculated in the previous step from the memory. This data includes the d-axis back electromotive force component and the q-axis back electromotive force component. When the target steering motor is in a low-speed oscillating operation state, these two back electromotive force components are not ideal constant DC quantities in the rotating coordinate system, but will exhibit slow fluctuation characteristics with small changes in the rotor position.

[0074] Within each sampling period, the current d-axis and q-axis back EMF components are acquired in real time, and mathematical constraints that should be satisfied between them are constructed based on the voltage equation of the motor in the rotating coordinate system. Since there is a deviation between the actual rotor position and the estimated rotor position, this deviation is directly reflected in the deviation of the ratio between the d-axis and q-axis back EMF components from the theoretical value. These two components are substituted as input parameters into a pre-constructed half-sine function expression. This expression utilizes the characteristic that the slope of the half-sine function changes continuously near the zero-crossing point to map the deviation between the back EMF components into a preliminary error indication. This preliminary error indication is then fed into an integrator for accumulation. The integrator's function is to accumulate the instantaneous error calculated in each sampling period, eliminating the influence of random noise and instantaneous fluctuations, and finally outputting a continuous and smooth integral position error signal.

[0075] During the transition from low-speed stable operation to high-speed dynamic response, the amplitude and frequency of the back EMF component change rapidly. The half-sine function ensures a continuously changing error value even with large input deviations, avoiding the jump phenomenon that occurs with the traditional arctangent function when the angle crosses boundaries. When the motor performs frequent commutation operations, the constantly changing rotor direction leads to complex changes in the phase relationship of the back EMF component. The accumulation characteristic of the integral term effectively smooths the error fluctuations during commutation, ensuring that the output position error signal remains continuous and stable, providing high-quality input data for subsequent speed estimation.

[0076] It should be noted that a half-sine function is used to construct the position error signal because this function has a smooth characteristic of continuity and continuous derivatives within its domain. This avoids the phase jump problem caused by the traditional arctangent function near the angle boundary, and also overcomes the nonlinear distortion defect of the half-sine function under large estimation deviations. The integration process is introduced to accumulate and smooth the instantaneous error, effectively suppressing high-frequency noise and random disturbances that may remain in the back EMF estimate. The smoothness of the half-sine function ensures that the error signal continues to be output even when the rotor position exceeds the limit or the operating state changes abruptly, fundamentally eliminating the risk of signal jumps. The integration process further improves the stability and signal-to-noise ratio of the error signal. The synergistic effect of these two processes ensures that the constructed position error signal accurately reflects the true deviation and possesses a continuous and smooth shape, providing a high-quality and stable input for the subsequent speed estimation process.

[0077] S104: Input the position error signal to the integrating filter to obtain the speed estimate of the target steering motor, and perform an integration operation on the speed estimate to obtain the position estimate of the target steering motor.

[0078] The integral filter is a signal processing structure based on the principle of integral operation. This structure uses the position error signal as input and accumulates and smooths the input error by setting appropriate integration coefficients or filtering time constants, ultimately outputting a velocity estimate proportional to the integral value of the input error. The velocity estimate refers to the real-time estimation result of the target steering motor rotor angular velocity obtained after processing by the integral filter. Integration operation refers to the mathematical process of continuously accumulating the velocity estimate in the time domain. This operation uses the initial position value as a reference, accumulating the velocity increment in each sampling period to the position value of the previous period, ultimately outputting the target steering motor's position estimate. The position estimate refers to the real-time estimation result of the target steering motor rotor's angular position obtained after integration operation, directly reflecting the rotor's spatial angle at the current moment.

[0079] In the specific implementation process, the position error signal calculated in the previous step is first read from the memory. This signal is a continuously changing value, reflecting the direction and degree of deviation between the current estimated rotor position and the actual rotor position. Within each sampling period, this position error signal is fed as input into a pre-configured integrating filter. This filter can be implemented using a digital first-order low-pass filter structure. Its core is an accumulator with an appropriate attenuation coefficient. By multiplying the current error signal by the integrating coefficient and adding it to the accumulated value from the previous moment, the current speed estimate is obtained. This processing method ensures that the speed estimate gradually changes as the position error signal accumulates, avoiding direct impacts on the speed estimate caused by sudden error changes.

[0080] When the target steering motor is in a low-speed oscillating operation, the amplitude of the position error signal is small and changes slowly. The integrating filter can gradually transform the small error signal into a stable speed output through the accumulation effect, ensuring the smoothness of the speed estimate in the low-speed range. During the transition process of the motor performing frequent commutation operations, the direction of the position error signal changes with the commutation, and the input of the integrating filter is reversed accordingly. Its output speed estimate can also smoothly cross the zero point without speed jumps. After obtaining the speed estimate of the current sampling period, the integration operation program is immediately started. This program first reads the position estimate saved in the previous period from the storage unit as the initial value for integration. Then, it multiplies the current speed estimate by the sampling period time to obtain the angle increment in the current period. This angle increment is added to the position estimate of the previous period to obtain the position estimate of the current period. This newly obtained position estimate is output for the motor control algorithm and written back to the storage unit as the reference value for the integration operation in the next period. As the motor transitions from low-speed, stable operation to high-speed dynamic response, the rate of change of the speed estimate increases accordingly. Integral calculations accurately track this change, converting the speed integral into position change, allowing the position estimate to reflect the rapid movement of the rotor angle in real time. The entire process is executed cyclically within each sampling period, ensuring that the speed and position estimates remain strictly synchronized with the actual operating state of the motor, providing continuous and reliable feedback information for the closed-loop control of the steering motor.

[0081] It should be noted that using an integral filter to process the position error signal essentially utilizes the cumulative characteristic of integral operations to smooth the input error, converting the continuous and stable error quantity into a physically meaningful speed estimate. This process further filters out any minor disturbances that may remain in the error signal, ensuring the stability of the speed output. Integrating the speed estimate to obtain the position estimate is based on the inherent integral relationship between speed and position. This processing method strictly follows the physical laws of motor kinematics, ensuring the continuous and accurate accumulation of the position estimate in the time domain. The integral filter, with its simple structure, achieves a smooth conversion of the error signal into a speed quantity, giving the speed estimate good anti-disturbance capability and avoiding output jitter caused by instantaneous error fluctuations. The process of converting speed to position through integration does not involve any nonlinear distortion, ensuring the cumulative accuracy of the position estimate over time. Because the preceding stage provides a continuous and smooth error signal, the integral filter can achieve fast response and disturbance suppression without complex tuning, ensuring that the final speed and position estimates maintain high accuracy and high stability across the entire operating range.

[0082] In the above embodiments, by constructing a back EMF extended state observer, the two-phase current and voltage signals and motor operating parameters are substituted into it for solution to obtain the back EMF estimate in the stationary two-phase coordinate system. This process uses a continuous state estimation mechanism to replace the sign function switching of the traditional sliding mode observer, suppressing the pollution of back EMF estimation accuracy by high-frequency chattering from the source. Furthermore, the back EMF estimate in the rotating two-phase coordinate system is obtained through rotational coordinate transformation, and this value is used to construct a half-sine integral position error signal. Compared with the phase jump problem that easily occurs in low-speed and commutation conditions by arctangent or half-tangent functions, the half-sine error construction method can still maintain smooth error output characteristics when the angle error is large, avoiding abrupt changes and amplification of the error signal. Subsequently, the smooth position error signal is input into an integral filter for processing to obtain the speed estimate of the target steering motor, and the position estimate is obtained by integrating the speed estimate. The integral filter has a simple structure and convenient parameter tuning, and effectively suppresses residual disturbances in the error signal while achieving fast dynamic response. Therefore, this application improves the stability and anti-disturbance capability of the estimation process through the synergistic effect of the back EMF extended state observer and the half-sine integral error construction, from back EMF extraction and error signal generation to velocity and position calculation. It solves the problem of insufficient position and velocity estimation accuracy of existing methods under low-speed operation and frequent reversing conditions of steering motor, and meets the requirements of steering motor control system for high precision and high stability.

[0083] In one embodiment, the motor operating parameters include the observer gain coefficient, and the back EMF extended state observer includes a stationary two-phase current estimation model and a stationary two-phase back EMF estimation model.

[0084] The steps of substituting the two-phase current signals, two-phase voltage signals, and motor operating parameters into a pre-constructed extended back EMF state observer to obtain the estimated values ​​of the stationary two-phase back EMF in the stationary αβ coordinate system include:

[0085] Based on the two-phase current signal, the two-phase voltage signal, and the first gain coefficient of the observer gain coefficient, the estimated value of the stationary two-phase current is obtained using the stationary two-phase current estimation model.

[0086] Based on the two-phase current signal, the estimated value of the stationary two-phase current, and the second gain coefficient of the observer gain coefficient, the estimated value of the stationary two-phase back electromotive force in the stationary αβ coordinate system is obtained using the stationary two-phase back electromotive force model.

[0087] The observer gain coefficient refers to a set of weighted parameters pre-set in the back-EMF extended state observer to adjust the convergence speed of the estimation error. This set specifically includes a first gain coefficient and a second gain coefficient. The first gain coefficient acts on the error correction channel of the current estimation model, and the second gain coefficient acts on the error correction channel of the back-EMF estimation model. The stationary two-phase current estimation model is a dynamic equation constructed based on the voltage equation of the motor in the stationary αβ coordinate system for real-time calculation of the current estimate. This model uses two-phase voltage signals as input and two-phase current signals as feedback correction basis, and introduces the first gain coefficient to weight and correct the current estimation error. The stationary two-phase current estimate refers to the current estimation component along the α-axis and the current estimation component along the β-axis in the stationary αβ coordinate system output by the current estimation model in each sampling period. The stationary two-phase back EMF estimation model is a state equation constructed based on the dynamic characteristics of the back EMF in the stationary αβ coordinate system for real-time calculation of back EMF estimates. This model uses the deviation between the estimated stationary two-phase currents and the actual two-phase current signals as the driving quantity. A second gain coefficient is introduced to weight and amplify this deviation, driving the back EMF state update. The estimated stationary two-phase back EMF refers to the back EMF estimation components along the α-axis and β-axis directions output by the model in each sampling period in the stationary αβ coordinate system.

[0088] In the specific implementation process, it is first necessary to read the pre-built static two-phase current estimation model and static two-phase back electromotive force estimation model from the memory. These two models are stored in the program storage area of ​​the controller in the form of program code. Each model contains specific mathematical expressions of the voltage equation and back electromotive force dynamic equation of the motor in the static αβ coordinate system.

[0089] When the target steering motor is running at low speed or preparing to perform a commutation operation, the stationary two-phase current signal and stationary two-phase voltage signal obtained from the previous step are continuously acquired in each sampling cycle. At the same time, the pre-set first gain coefficient and second gain coefficient are read from the storage unit. The first step is to calculate the stationary two-phase current estimation model. This model uses the two-phase voltage signal at the current sampling time as input, and combines the estimated values ​​of the stationary two-phase back electromotive force and stator resistance and inductance parameters saved at the previous sampling time. First, the predicted component of the current estimate is calculated. Then, the actual two-phase current signal acquired at the current sampling time is compared with this predicted component to obtain the current estimation error. This error is multiplied by the first gain coefficient and added to the predicted component as a correction. Finally, the estimated value of the stationary two-phase current at the current sampling time is output.

[0090] During the transition from low-speed stable operation to high-speed dynamic response of the motor, the amplitude of the current estimation error will increase accordingly. A reasonable configuration of the first gain coefficient ensures that the correction amount can pull the estimated value back to the true value at an appropriate speed, guaranteeing convergence speed while avoiding overshoot oscillation. Immediately after completing the current estimation, the second calculation step is executed. The estimated static two-phase current value obtained in the first step is compared again with the actual acquired two-phase current signal to obtain another current error. This error is multiplied by the second gain coefficient and used as the driving quantity input to the static two-phase back EMF estimation model. This model, based on the dynamic characteristics of the back EMF in the stationary coordinate system, accumulates the driving quantity to the back EMF estimate from the previous moment, ultimately outputting the estimated static two-phase back EMF value at the current sampling moment. When the motor performs frequent small-angle oscillation movements, the amplitude and direction of the back EMF change frequently. The setting of the second gain coefficient ensures that the back EMF estimate can quickly track these changes while suppressing the interference of measurement noise on the estimation results. The entire process is executed cyclically within each sampling period, with the current estimation model and the back EMF estimation model running alternately and supporting each other to jointly complete the continuous observation of the back EMF.

[0091] It's important to note that the fundamental reason for employing this hierarchical observer structure is that back electromotive force (EMF) itself is not a directly measurable physical quantity; it can only be indirectly deduced from the current signal. By splitting the observer into two cascaded stages—current estimation and back EMF estimation—the current error and back EMF dynamics can be addressed separately. The first gain coefficient is specifically used to adjust the convergence behavior of the current estimation, ensuring that the estimated current value can quickly and accurately track the actual current, providing a reliable error benchmark for subsequent stages. The second gain coefficient is specifically used to adjust the dynamic response of the back EMF estimation, driving the back EMF state update based on the obtained clean current error. This step-by-step approach makes the observer design clearer. The two gain coefficients can be tuned independently without interference. The first gain coefficient mainly affects the speed and stability of current tracking, while the second gain coefficient mainly affects the smoothness and noise immunity of the back EMF estimation. Their collaborative work ensures both the speed of estimation and the smoothness of the output. By obtaining an accurate current estimate in the first step, the second step can update the back EMF based on a reliable error signal. This progressive processing avoids the aliasing and propagation of errors, and ensures that the final estimated back EMF values ​​of the two stationary phases achieve an optimal balance in terms of dynamic response speed and steady-state smoothness, providing high-quality input data for subsequent position and velocity calculations.

[0092] In one embodiment, the static two-phase current estimation model is as follows:

[0093]

[0094] The estimation model for two stationary back electromotive forces is as follows:

[0095]

[0096] in, Indicates the current iteration step. Indicates the previous iteration step. The time derivative of the estimated α-axis stationary two-phase current. This represents the time derivative of the estimated β-axis stationary two-phase current, where the two-phase current signal includes the actual α-axis stationary two-phase current. and the actual two-phase current at rest along the β axis The two-phase voltage signal includes the actual voltage of the stationary two-phase phases along the α-axis. and the actual voltage of the stationary two-phase voltage along the β axis The motor operating parameters also include stator inductance. and stator resistance The observer gain coefficients include the first gain coefficient along the α-axis. β-axis first gain coefficient α-axis second gain coefficient and β-axis second gain coefficient The stationary two-phase current estimates include the α-axis stationary two-phase current estimates for the current iteration step. and β-axis stationary two-phase current estimates , This represents the estimated values ​​of the two opposite electromotive forces at rest along the α-axis from the previous iteration step. This represents the estimated values ​​of the two opposing electromotive forces at rest along the β-axis from the previous iteration step. This represents the time derivative of the estimated values ​​of the two opposite electromotive forces at rest along the α-axis. This represents the time derivative of the two stationary back EMF estimates along the β-axis, which include the combined... ,right The integral yields the estimated values ​​of the two opposing electromotive forces at rest along the α-axis for the current iteration step, and combines them with... ,right The estimated values ​​of the two opposing electromotive forces at rest along the β axis for the current iteration step are obtained by integration.

[0097] Specifically, the back-EMF extended state observer consists of two recursive equations. The first equation is the stationary two-phase current estimation model, whose structure embodies the observer design concept of prediction based on the motor physical model and correction by combining measured errors. The first three terms on the right side of the equation are constructed based on the voltage balance relationship of the motor in the stationary coordinate system. Using the current estimate at the previous sampling time, the voltage sample value at the current time, and the back-EMF estimate at the previous time, the predicted component of the current estimate at the current time is calculated. The fourth term on the right side of the equation is the correction term, which multiplies the deviation between the actual current value collected at the current time and the current estimate at the previous time by the first gain coefficient. This weighted error is used to correct the predicted component online, finally obtaining the current estimate at the current time. The second equation is the stationary two-phase back-EMF estimation model, which has a simpler structure. It directly multiplies the deviation between the actual current value collected at the current time and the just calculated current estimate by the second gain coefficient. This weighted error is used as the update amount of the back-EMF estimate at the current time.

[0098] It should be noted that this hierarchical recursive structure enables the observer to operate continuously in a closed-loop manner. The correction term in the current estimation model ensures that the current estimate can track changes in the actual current in real time, avoiding the cumulative errors that may occur in open-loop prediction. The back EMF estimation model is updated directly based on the current error, using the tracking residual of the current loop as the driving source of the back EMF. This approach ensures that the accuracy of the back EMF estimate is entirely based on the accuracy of the current estimate, with the two forming a mutually supportive progressive relationship. Independent first and second gain coefficients are introduced into the two equations, allowing the dynamic characteristics of current tracking and the smoothness of back EMF estimation to be adjusted independently without interference. The first gain coefficient dominates the convergence speed of the current error, while the second gain coefficient dominates the sensitivity of the back EMF to the current residual. This decoupling design simplifies the complexity of parameter tuning. The entire observer is built entirely on linear operations, without any nonlinear switching links, fundamentally eliminating the high-frequency chattering caused by the switching characteristics of the sign function in traditional methods. This ensures that the output static back EMF estimates have smooth and continuous waveform characteristics across the entire operating range.

[0099] In one embodiment, the step of constructing a half-sine integral position error signal using the estimated values ​​of the two rotating opposite electromotive forces includes:

[0100] Calculate the combined amplitude of the back electromotive force based on the estimated values ​​of the two rotating back electromotive forces;

[0101] Based on the combined amplitude of the back EMF and the d-axis back EMF estimate in the two rotating back EMF estimates, a normalized denominator is constructed.

[0102] By using the q-axis back EMF estimate from the two rotating back EMF estimates and the normalized denominator, a half-sine integral type position error signal is obtained through ratio calculation.

[0103] The composite back EMF amplitude refers to the scalar value reflecting the overall amplitude of the back EMF vector, calculated by taking the square root of the sum of squares of the estimated d-axis and q-axis back EMF values ​​in a rotating dq two-phase coordinate system. The normalized denominator is a divisor factor constructed based on the composite back EMF amplitude and the estimated d-axis back EMF value, used to standardize the estimated q-axis back EMF value. This factor is obtained by performing a specific combination operation on the composite back EMF amplitude and the estimated d-axis back EMF value, and its function is to eliminate the influence of back EMF amplitude fluctuations on the error signal in subsequent ratio calculations. The half-sine integral type position error signal refers to a continuous error variable obtained by calculating the ratio using the estimated q-axis back EMF as the numerator and the normalized denominator as the divisor. This variable reflects the degree of deviation between the actual rotor position and the estimated rotor position. Its mathematical form abandons the traditional arctangent function and adopts a ratio form combined with the smoothing characteristics of the half-sine function.

[0104] In the specific implementation process, the estimated values ​​of the two rotating back electromotive forces calculated in the previous step need to be read from the memory. This data specifically includes the d-axis back electromotive force component and the q-axis back electromotive force component at the current sampling time. When the target steering motor is running at low speed, the amplitudes of these two components may be small, but they still maintain a specific proportional relationship. Within each sampling period, the calculation of the composite amplitude of the back electromotive force is first performed. The square of the d-axis back electromotive force component is added to the square of the q-axis back electromotive force component, and then the square root of the sum is taken to obtain the composite amplitude of the back electromotive force at the current time. This amplitude represents the overall strength of the back electromotive force vector. After the amplitude calculation is completed, the normalization denominator construction step is immediately initiated. The composite amplitude of the back electromotive force obtained in the previous step is calculated with the d-axis back electromotive force component at the current moment according to the pre-set combination rules. These combination rules can be to add the composite amplitude and the d-axis component and take the absolute value, or to select different combination methods according to the sign of the d-axis component. The core purpose is to construct a denominator term that is always positive and associated with the back electromotive force amplitude, so as to avoid abnormal situations where the denominator is zero or negative in subsequent division operations.

[0105] During frequent commutation operations of the motor, the d-axis back EMF component may cross zero. The normalized denominator construction method ensures that the denominator remains within a reasonable positive range, preventing it from approaching zero due to the d-axis component crossing zero. After obtaining the normalized denominator, the final step is to calculate the ratio. The current q-axis back EMF component is used as the numerator, and the constructed normalized denominator is used as the divisor. Dividing the two yields the position error signal at the current sampling time. When the motor transitions from low-speed stable operation to high-speed dynamic response, the amplitude of the back EMF increases accordingly. The synchronous increase of the numerator and denominator keeps the ratio relatively stable. This processing method eliminates the direct impact of back EMF amplitude changes on the error signal amplitude. The entire process is executed cyclically within each sampling period. The final output position error signal is a continuously changing value that directly reflects the direction and degree of rotor position deviation, always remaining within a bounded range, avoiding the infinite jumps that might occur with traditional arctangent functions.

[0106] It should be noted that the normalized denominator is constructed by combining the back EMF synthesized amplitude with the d-axis component to eliminate the influence of amplitude fluctuations on the error signal, ensuring that the subsequent ratio only reflects the proportional relationship between the d-axis and q-axis components and is decoupled from the rotational speed. The construction of the normalized denominator also ensures that the denominator remains positive and stable under all operating conditions, avoiding the risk of division failure. The position error signal obtained by dividing the q-axis component by this denominator has an amplitude that does not change with the rotational speed, and its waveform is continuous, smooth, and without jumps. This simplifies the parameter tuning of subsequent filtering and provides a stable and reliable input for the speed estimation stage.

[0107] In one embodiment, the formula for the position error signal is:

[0108]

[0109]

[0110] in, This indicates the position error signal. Indicates the combined amplitude of the back electromotive force. This represents the estimated value of the back electromotive force along the q-axis. This represents the estimated value of the back electromotive force along the d-axis. This indicates the actual position of the target steering motor. This represents the estimated position of the target steering motor.

[0111] This formula illustrates the specific construction method of the position error signal. The numerator uses the q-axis back electromotive force (EMF) estimate, while the denominator is formed by adding the composite amplitude of the back EMF to the d-axis back EMF estimate. The composite amplitude of the back EMF is obtained by taking the square root of the sum of the squares of the d-axis and q-axis estimates, representing the overall strength of the back EMF vector. The result of the entire fractional operation is approximately equal to the sine of half the difference between the actual and estimated positions.

[0112] It should be noted that this construction method introduces the d-axis component into the denominator, ensuring that the denominator always includes a positive term—the composite amplitude of the back electromotive force (EMF). This guarantees that the denominator will not tend to zero or become negative under all operating conditions, fundamentally avoiding the numerical stability problem of division operations. The numerator uses only the q-axis component instead of the conventional arctangent function, eliminating the risk of phase jumps at angular boundaries. The composite amplitude in the denominator acts as a normalization factor, decoupling the amplitude of the final output error signal from the absolute magnitude of the back EMF. Regardless of whether the motor is operating at low or high speed, the amplitude range of the error signal remains consistent. The result of the fractional operation is directly correlated with the half-angle sine of the position deviation. This relationship ensures that the error signal is approximately linear when the position deviation is small, and maintains a continuous and smooth monotonic change even when the deviation is large, providing a stable and physically meaningful input for subsequent speed estimation.

[0113] In one embodiment, the step of inputting the position error signal to an integrating filter to obtain a speed estimate of the target steering motor includes:

[0114] The speed estimate is calculated using the following formula:

[0115]

[0116] in, This represents the speed estimate. This indicates the position error signal. This represents the proportional gain coefficient of an integrating filter. This represents the integral gain coefficient of the integral filter.

[0117] Specifically, this formula uses a parallel proportional-integral structure to construct an integral filter. The proportional branch amplifies the input position error signal instantly, while the integral branch continuously accumulates and amplifies the error signal. The sum of these two amplifications outputs the velocity estimate. This proportional-integral structure enables the filter to possess both rapid error response and the ability to eliminate accumulated errors.

[0118] It should be noted that the introduction of the proportional term ensures that the speed estimate can respond quickly to instantaneous changes in position error, enabling the system to track quickly during dynamic processes. The introduction of the integral term, through the continuous accumulation of error, effectively eliminates residual deviations that may exist in steady-state conditions, ensuring that the speed estimate stabilizes near the true value when the error approaches zero. The proportional gain coefficient and integral gain coefficient can be adjusted independently without interference, allowing for targeted tuning of the filter's speed and stability. The proportional coefficient dominates the dynamic response speed, while the integral coefficient dominates steady-state accuracy and disturbance suppression capability. The entire formula is concise and physically clear, achieving speed estimation while maintaining low implementation complexity, facilitating real-time operation on digital processors in actual steering motor control systems.

[0119] In one embodiment, the step of integrating the speed estimate to obtain the position estimate of the target steering motor includes:

[0120] The location estimate is calculated using the following formula:

[0121]

[0122] in, This represents the estimated position of the target steering motor. This represents the estimated speed.

[0123] Specifically, the formula uses integration to convert the velocity estimate into a position estimate, a process that strictly adheres to the physical definition of the relationship between angular velocity and angular position.

[0124] It should be noted that the integration operation itself is a linear accumulation process, which does not introduce any nonlinear distortion or additional noise, ensuring that the accumulation process of the position estimate in the time domain is completely faithful to the speed input. Since the preceding stage has already provided a continuous and smooth speed estimate, the integration operation can directly accumulate on this basis, so that the final output position estimate also maintains the continuous and smooth characteristics. The integration process accurately accumulates every instantaneous change in the speed estimate, ensuring that the position estimate can reflect the actual changes in the rotor angle in real time. Whether it is a small angle increment during low-speed crawling or a large angle span during high-speed rotation, the integration operation can accurately track it. The entire processing is simple in structure and has clear physical meaning. While realizing the position estimation function, it maintains extremely low computational complexity, which is convenient for real-time operation on the digital processor in the actual steering motor control system.

[0125] The steering motor position and speed estimation device provided in the embodiments of this application is described below. The steering motor position and speed estimation device described below can be referred to in correspondence with the steering motor position and speed estimation method described above. Figure 2 As shown, this application provides a steering motor position and speed estimation device, the device comprising:

[0126] The two-phase electrical signal determination module 201 is used to acquire the three-phase electrical signals and motor operating parameters of the target steering motor, and convert the three-phase electrical signals into two-phase current signals and two-phase voltage signals in the stationary αβ two-phase coordinate system;

[0127] The rotating two-point back EMF estimation module 202 is used to substitute the two-phase current signal, the two-phase voltage signal and the motor operating parameters into the pre-built back EMF extended state observer to obtain the stationary two-phase back EMF estimation value in the stationary αβ coordinate system, and obtain the rotating two-phase back EMF estimation value in the rotating dq two-phase coordinate system through the rotating coordinate transformation.

[0128] The position error signal construction module 203 is used to construct a half-sine integral type position error signal using the estimated values ​​of two rotating opposite electromotive forces;

[0129] The position estimation determination module 204 is used to input the position error signal to the integrating filter to obtain the speed estimate of the target steering motor, and to perform an integral operation on the speed estimate to obtain the position estimate of the target steering motor.

[0130] In one embodiment, the motor operating parameters include the observer gain coefficient, and the back EMF extended state observer includes a stationary two-phase current estimation model and a stationary two-phase back EMF estimation model.

[0131] The rotating two-point back electromotive force estimation module 202 includes:

[0132] The stationary two-phase current estimation unit is used to obtain the estimated value of the stationary two-phase current based on the two-phase current signal, the two-phase voltage signal and the first gain coefficient of the observer gain coefficient, using the stationary two-phase current estimation model.

[0133] The stationary two-phase back electromotive force estimation unit is used to obtain the estimated values ​​of the stationary two-phase back electromotive force in the stationary αβ coordinate system based on the two-phase current signal, the estimated values ​​of the stationary two-phase current, and the second gain coefficient of the observer gain coefficient, using the stationary two-phase back electromotive force model.

[0134] In one embodiment, the static two-phase current estimation model is as follows:

[0135]

[0136] The estimation model for two stationary back electromotive forces is as follows:

[0137]

[0138] in, Indicates the current iteration step. Indicates the previous iteration step. The time derivative of the estimated α-axis stationary two-phase current. This represents the time derivative of the estimated β-axis stationary two-phase current, where the two-phase current signal includes the actual α-axis stationary two-phase current. and the actual two-phase current at rest along the β axis The two-phase voltage signal includes the actual voltage of the stationary two-phase phases along the α-axis. and the actual voltage of the stationary two-phase voltage along the β axis The motor operating parameters also include stator inductance. and stator resistance The observer gain coefficients include the first gain coefficient along the α-axis. β-axis first gain coefficient α-axis second gain coefficient and β-axis second gain coefficient The stationary two-phase current estimates include the α-axis stationary two-phase current estimates for the current iteration step. and β-axis stationary two-phase current estimates , This represents the estimated values ​​of the two opposite electromotive forces at rest along the α-axis from the previous iteration step. This represents the estimated values ​​of the two opposing electromotive forces at rest along the β-axis from the previous iteration step. This represents the time derivative of the estimated values ​​of the two opposite electromotive forces at rest along the α-axis. This represents the time derivative of the two stationary back EMF estimates along the β-axis, which include the combined... ,right The integral yields the estimated values ​​of the two opposing electromotive forces at rest along the α-axis for the current iteration step, and combines them with... ,right The estimated values ​​of the two opposing electromotive forces at rest along the β axis for the current iteration step are obtained by integration.

[0139] In one embodiment, the position error signal construction module 203 includes:

[0140] The back electromotive force (EMF) composite amplitude calculation unit is used to calculate the composite amplitude of the back EMF based on the estimated values ​​of the back EMFs of the two rotating parts.

[0141] The normalized denominator construction unit is used to construct a normalized denominator based on the synthesized amplitude of the back electromotive force and the d-axis back electromotive force estimate in the two rotated back electromotive force estimates.

[0142] The position error signal construction unit is used to obtain a half-sine integral type position error signal by using the q-axis back electromotive force estimate from the two rotating back electromotive force estimates and the normalized denominator, through ratio calculation.

[0143] In one embodiment, the formula for the position error signal is:

[0144]

[0145]

[0146] in, This indicates the position error signal. Indicates the combined amplitude of the back electromotive force. This represents the estimated value of the back electromotive force along the q-axis. This represents the estimated value of the back electromotive force along the d-axis. This indicates the actual position of the target steering motor. This represents the estimated position of the target steering motor.

[0147] In one embodiment, the location estimate determination module 204 includes:

[0148] The speed estimation calculation unit is used to calculate the speed estimate using the following formula:

[0149]

[0150] in, This represents the speed estimate. This indicates the position error signal. This represents the proportional gain coefficient of an integrating filter. This represents the integral gain coefficient of the integral filter.

[0151] In one embodiment, the location estimate determination module 204 includes:

[0152] The location estimation calculation unit is used to calculate the location estimate using the following formula:

[0153]

[0154] in, This represents the estimated position of the target steering motor. This represents the estimated speed.

[0155] In one embodiment, this application also provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the steering motor position and speed estimation method as described in any of the above embodiments.

[0156] In one embodiment, this application also provides a computer device storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the steering motor position and speed estimation method as described in any of the above embodiments.

[0157] Indicatively, such as Figure 3 As shown, Figure 3 This is a schematic diagram of the internal structure of a computer device 300 provided in an embodiment of this application. The computer device 300 can be provided as a server. (Refer to...) Figure 3 The computer device 300 includes a processing component 302, which further includes one or more processors, and memory resources represented by memory 301 for storing instructions, such as application programs, that can be executed by the processing component 302. The application programs stored in memory 301 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 302 is configured to execute instructions to perform the steering motor position and speed estimation method of any of the above embodiments.

[0158] The computer device 300 may also include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate on an operating system stored in memory 301, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or similar.

[0159] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0160] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, 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 a process, method, article, or apparatus. Unless otherwise specified, 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. In this document, "a," "an," "the," "the," and "its" may also include plural forms unless the context clearly indicates otherwise. "Multiple" refers to at least two, such as 2, 3, 5, or 8, etc. "And / or" includes any and all combinations of the related listed items.

[0161] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0162] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. 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 this application. Therefore, this application 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 estimating the position and speed of a steering motor, characterized in that, The method includes: Acquire the three-phase electrical signals and motor operating parameters of the target steering motor, and convert the three-phase electrical signals into two-phase current signals and two-phase voltage signals in a stationary αβ two-phase coordinate system; Substituting the two-phase current signal, the two-phase voltage signal, and the motor operating parameters into the pre-constructed back EMF extended state observer, we obtain the estimated values ​​of the two-phase back EMF in the stationary αβ coordinate system, and then, through a rotating coordinate transformation, we obtain the estimated values ​​of the two-phase back EMF in the rotating dq coordinate system. Using the estimated values ​​of the two opposing electromotive forces of the rotation, a half-sine integral type position error signal is constructed; The position error signal is input to an integrating filter to obtain the speed estimate of the target steering motor, and the speed estimate is integrated to obtain the position estimate of the target steering motor.

2. The method for estimating the position and speed of the steering motor according to claim 1, characterized in that, The motor operating parameters include the observer gain coefficient, and the back EMF extended state observer includes a stationary two-phase current estimation model and a stationary two-phase back EMF estimation model. The step of substituting the two-phase current signals, the two-phase voltage signals, and the motor operating parameters into a pre-constructed back EMF extended state observer to obtain estimated values ​​of the stationary two-phase back EMF in the stationary αβ coordinate system includes: Based on the two-phase current signal, the two-phase voltage signal, and the first gain coefficient of the observer gain coefficient, the estimated value of the static two-phase current is obtained using the static two-phase current estimation model. Based on the two-phase current signal, the estimated value of the stationary two-phase current, and the second gain coefficient of the observer gain coefficient, the estimated value of the stationary two-phase back electromotive force in the stationary αβ coordinate system is obtained using the stationary two-phase back electromotive force model.

3. The method for estimating the position and speed of the steering motor according to claim 2, characterized in that, The static two-phase current estimation model is as follows: The estimation model for the two static back electromotive forces is as follows: in, Indicates the current iteration step. Indicates the previous iteration step. The time derivative of the estimated α-axis stationary two-phase current. This represents the time derivative of the estimated β-axis stationary two-phase current, where the two-phase current signal includes the actual α-axis stationary two-phase current. and the actual two-phase current at rest along the β axis The two-phase voltage signal includes the actual voltage of the stationary two-phase voltage along the α-axis. and the actual voltage of the stationary two-phase voltage along the β axis The motor operating parameters also include stator inductance. and stator resistance The observer gain coefficient includes the first gain coefficient along the α-axis. β-axis first gain coefficient α-axis second gain coefficient and β-axis second gain coefficient The estimated stationary two-phase current includes the estimated α-axis stationary two-phase current for the current iteration step. and β-axis stationary two-phase current estimates , This represents the estimated values ​​of the two opposite electromotive forces at rest along the α-axis from the previous iteration step. This represents the estimated values ​​of the two opposing electromotive forces at rest along the β-axis from the previous iteration step. This represents the time derivative of the estimated values ​​of the two opposite electromotive forces at rest along the α-axis. This represents the time derivative of the two opposing electromotive forces (EMFs) at rest along the β-axis, wherein the two opposing EMFs at rest include a combination of... ,right The integral yields the estimated values ​​of the two opposing electromotive forces at rest along the α-axis for the current iteration step, and combines them with... ,right The estimated values ​​of the two opposing electromotive forces at rest along the β axis for the current iteration step are obtained by integration.

4. The method for estimating the position and speed of the steering motor according to claim 1, characterized in that, The step of constructing a half-sine integral position error signal using the estimated values ​​of the two rotating opposite electromotive forces includes: Calculate the combined amplitude of the back electromotive force based on the estimated values ​​of the two rotating back electromotive forces; Based on the synthesized amplitude of the back electromotive force and the estimated value of the d-axis back electromotive force in the estimated values ​​of the two rotating back electromotive forces, a normalized denominator is constructed. Using the q-axis back EMF estimate from the two rotating back EMF estimates, and the normalized denominator, a half-sine integral type position error signal is obtained by ratio calculation.

5. The method for estimating the position and speed of the steering motor according to claim 4, characterized in that, The formula for the position error signal is: in, This represents the position error signal. This indicates the combined amplitude of the back electromotive force. This represents the estimated value of the q-axis back electromotive force. This represents the estimated value of the d-axis back electromotive force. This indicates the actual position of the target steering motor. This represents the estimated position of the target steering motor.

6. The method for estimating the position and speed of the steering motor according to claim 1, characterized in that, The step of inputting the position error signal to an integrating filter to obtain the speed estimate of the target steering motor includes: The speed estimate is calculated using the following formula: in, This represents the estimated speed value. This represents the position error signal. This represents the proportional gain coefficient of the integrating filter. This represents the integral gain coefficient of the integral filter.

7. The method for estimating the position and speed of the steering motor according to claim 6, characterized in that, The step of integrating the speed estimate to obtain the position estimate of the target steering motor includes: The estimated location is calculated using the following formula: in, This represents the estimated position of the target steering motor. This represents the estimated speed value.

8. A device for estimating the position and speed of a steering motor, characterized in that, The device includes: The two-phase electrical signal determination module is used to acquire the three-phase electrical signals and motor operating parameters of the target steering motor, and convert the three-phase electrical signals into two-phase current signals and two-phase voltage signals in a stationary αβ two-phase coordinate system; The rotating two-point back EMF estimation module is used to substitute the two-phase current signal, the two-phase voltage signal and the motor operating parameters into the pre-built back EMF extended state observer to obtain the stationary two-phase back EMF estimation value in the stationary αβ coordinate system, and obtain the rotating two-phase back EMF estimation value in the rotating dq two-phase coordinate system through the rotating coordinate transformation. The position error signal construction module is used to construct a half-sine integral position error signal using the estimated values ​​of the two opposing electromotive forces of the rotation. The position estimation module is used to input the position error signal into an integrating filter to obtain the speed estimate of the target steering motor, and to perform an integration operation on the speed estimate to obtain the position estimate of the target steering motor.

9. A storage medium, characterized in that: The storage medium stores computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the steering motor position and speed estimation method as described in any one of claims 1 to 7.

10. A computer device, characterized in that, include: One or more processors, and memory; The memory stores computer-readable instructions that, when executed by the one or more processors, perform the steps of the steering motor position and speed estimation method as described in any one of claims 1 to 7.

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