Inductive and non-inductive combined motor control method applied to brake-by-wire

Abstract

The invention discloses an inductive and non-inductive combined motor control method applied to brake-by-wire. The method comprises the steps of obtaining an inductive position and a signal quality factor of a rotor based on a calibration signal; running an extended Kalman filter (EKF) algorithm to obtain the non-inductive position of the rotor, the rotating speed of the rotor and an estimation error covariance matrix of the non-inductive position; calculating a residual value based on the inductive position and the non-inductive position; if the signal quality factor is greater than a preset quality threshold value and the residual value is smaller than a tolerance threshold value, judging that the inductive position is credible; otherwise, judging that the sensed position is not credible; if the inductive position is credible, setting the observation noise covariance matrix as a first numerical value to obtain a fusion position and a fusion speed; if the inductive position is not credible, setting the observation noise covariance matrix as a second numerical value which is far greater than the first numerical value, and obtaining a fusion position and a fusion speed; a control signal for controlling the motor is generated based on the fusion position and the fusion speed. According to the method, the control accuracy of the brake-by-wire motor is improved.

Description

TECHNICAL FIELD

[0001] The application relates to the fields of motor control and vehicle braking, in particular to a motor control method combining sensing and non-sensing applied to a brake-by-wire system. BACKGROUND

[0002] As a key actuator for realizing advanced driving assistance (ADAS) and automatic driving functions of modern vehicles, the performance of a brake-by-wire system is directly related to vehicle safety and driving experience. A permanent magnet synchronous motor (PMSM) is an ideal actuator for a brake-by-wire system due to its high power density and high efficiency. The performance of the field-oriented control (FOC) algorithm, which is the core control algorithm of the PMSM, is extremely dependent on the accuracy and reliability of rotor position information.

[0003] At present, mature solutions in the industry mostly rely on a single position sensor, such as a rotary transformer or an emerging tunnel magnetoresistance (TMR) sensor. As a high-performance magnetic sensor, the TMR sensor has the advantages of high sensitivity, good temperature stability, and wide frequency response, and can directly output absolute position information, simplifying the signal processing process. However, like all physical sensors, the TMR sensor has inherent potential problems such as susceptibility to external stray magnetic field interference and signal harmonic distortion caused by installation eccentricity, and its instantaneous reliability in complex electromagnetic environments and throughout its life cycle still needs further verification.

[0004] Therefore, to solve the reliability bottleneck of the single sensor solution and fully utilize the high-performance advantages of the TMR sensor, the industry urgently needs an innovative motor control strategy that combines sensing and non-sensing technologies and has online diagnosis and seamless fault-tolerant capabilities to improve the robustness and functional safety level of the brake-by-wire system under all operating conditions. SUMMARY

[0005] To solve the above technical problems, the application provides a motor control method combining sensing and non-sensing applied to a brake-by-wire system.

[0006] The purpose of the application is to overcome the reliability risks and performance bottlenecks of existing brake-by-wire systems that rely on a single position sensor, and to provide a redundant position sensing and fault-tolerant control method for a PMSM based on the deep fusion of a TMR sensor and an extended Kalman filter (EKF) non-sensing algorithm. This method not only builds fault redundancy between two position sources, but also realizes performance stacking in daily operation through an adaptive intelligent fusion mechanism, significantly improving the dynamic response, control accuracy, and functional safety level of the system.

[0007] To achieve the above purpose, the technical solution adopted by the application is to build a closed-loop intelligent processing flow, which specifically includes the following steps: Firstly, the parallel acquisition and preprocessing of dual-channel information is performed. In the TMR sensor channel, the system reads the absolute position raw signal output by the TMR sensor in real time, and performs online harmonic compensation and temperature drift compensation on the absolute position raw signal to obtain a high-precision calibrated inductive position signal, and a signal quality factor representing the signal-to-noise ratio and stability of the signal is generated. In the non-inductive sampling channel, the system synchronously acquires the phase current and DC bus voltage of the motor.

[0008] Secondly, an extended Kalman filter (EKF) non-inductive observer is run. Based on the acquired current and voltage, the EKF algorithm estimates the non-inductive position and speed of the rotor in real time according to the mathematical model of the motor, and outputs the estimation error variance of the non-inductive position, which directly quantifies the uncertainty of the current estimation result of the EKF algorithm.

[0009] Subsequently, a confidence-based dynamic diagnosis of the TMR sensor signal is performed. This step introduces a two-way cross-validation intelligent mechanism: on the one hand, it checks whether the signal quality factor of the TMR is higher than a preset threshold; on the other hand, it calculates the residual error between the calibrated position of the TMR and the non-inductive position of the EKF, and compares the residual error with a dynamic tolerance threshold. One of the key innovations of the present application is that the tolerance threshold is not a fixed value, but is positively correlated with the estimation error variance output by the EKF. This means that when the EKF has a high estimation uncertainty due to the severe system dynamics, the system will intelligently relax the tolerance of the TMR signal; conversely, under stable working conditions, it requires stricter consistency. Only when the signal quality is high and the residual error is within the dynamic threshold, the TMR signal is considered highly reliable.

[0010] Then, adaptive Kalman fusion is performed. This is the core innovation of the present application. The system dynamically adjusts the observation noise covariance matrix of the EKF algorithm according to the diagnosis results. When the TMR signal is reliable, the matrix is set to a small value, so that the EKF fully adopts the absolute position information of the TMR as an observation in the update step, optimally corrects the internal state prediction, and outputs a fused position that combines the absolute accuracy of the TMR and the noise suppression ability of the EKF. When the TMR signal is not reliable, the matrix is set to a maximum value, so that the EKF naturally ignores the unreliable sensor input and degenerates to a pure non-inductive running mode, outputting the non-inductive position as the fused position, achieving seamless fault tolerance.

[0011] Finally, fault-tolerant control based on the fused position is performed. The high-quality fused position signal obtained in the foregoing steps is input into the field-oriented control loop, and through coordinate transformation, current loop regulation and SVPWM modulation, the permanent magnet synchronous motor is finally driven to accurately and stably output the target braking torque. BRIEF DESCRIPTION OF DRAWINGS

[0012] Figure 1A motor control method flow chart of a combined inductive and non-inductive application in line control braking is provided in the embodiments of the present application. Figure 2 A TMR original signal calibration and quality evaluation flow chart is provided in the embodiments of the present application. Figure 3 A signal reliability dynamic diagnosis flow chart is provided in the embodiments of the present application. Figure 4 A dynamic performance enhancement processing flow chart is provided in the embodiments of the present application. DETAILED DESCRIPTION

[0013] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to be limiting on the present application. As used in the specification and the appended claims of the present application, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms used herein are intended to be inclusive of any and all possible combinations of one or more of the listed items.

[0014] Hereinafter, the terms first and second are used only for the purpose of description and cannot be understood as implying or indicating relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with the first and second can explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, the meaning of multiple is two or more, unless otherwise stated.

[0015] The present application provides a motor control method combined inductive and non-inductive application in line control braking, see Figure 1 , comprising: see Figure 2 , first, through the TMR sensor to obtain the absolute position of the rotor real-time signal . The signal usually contains harmonic components and drift caused by sensor installation eccentricity, environmental magnetic field interference and temperature change. In order to improve the reliability of the signal, the system carries out online harmonic compensation and temperature drift compensation, and obtains the calibrated signal, and then obtains the calibrated rotor position , namely inductive position, and the corresponding signal quality factor , which is used to reflect the signal to noise ratio and stability, and its calculation formula is: ; Wherein, the signal intensity represents the amplitude of the calibrated position signal, and the noise level is the fluctuation introduced by external interference or sensor noise in the measurement process. The higher the signal quality factor, the more stable and reliable the TMR sensor output signal is. The calibrated and This will serve as the core input for subsequent fusion estimation. Simultaneously, the system collects the motor phase current. and DC bus voltage It is used for state estimation of the sensorless EKF observer.

[0016] After data acquisition is complete, the system enters the extended Kalman filter (EKF) sensorless observation phase. Based on the acquired current and voltage data, the EKF algorithm predicts the rotor position using the motor's dynamic mathematical model. and rotational speed The system outputs the position estimation error covariance matrix P. The main diagonal element P(1,1) of the covariance matrix directly reflects the reliability of the rotor position estimation, i.e., the uncertainty of the EKF's prediction of the current state. The EKF state update formula is: , ; in, This represents the estimated state value. For Kalman gain, For the observed values, For the measurement matrix, To estimate the error covariance matrix, This represents the estimated state value at the previous moment. Indicates and The corresponding estimation error covariance matrix. EKF achieves seamless estimation through two steps: state prediction and observation update. State prediction is based on the PMSM electrical and mechanical equations, while observation update uses real-time acquired current and voltage signals to correct the predicted values, thus achieving optimal state estimation.

[0017] See Figure 3 After estimating the sensed and non-sense states, the system performs dynamic validity diagnosis on the TMR signal. Specifically, this involves calculating the residual between the sensed and non-sense locations. ; Combined with signal quality factor And the main diagonal element P(1,1) of the covariance matrix, to determine the reliability of the TMR sensor output. The determination logic is as follows: when Higher than the preset threshold and When the TMR signal is less than a tolerance threshold positively correlated with P(1,1), it is considered reliable; otherwise, it is considered unreliable. This dynamic tolerance setting is directly related to the uncertainty of the EKF, meaning the more uncertain the EKF estimation, the larger the tolerable residual. Tolerance Threshold The error covariance matrix can be adaptively calculated based on the diagonal element P(1,1) of the EKF estimation: ;in, , is an empirical coefficient, is generally set to 0.5-2.0; is generally between 0.1°-1.0°; Based on the determination result, a flag signal Flag_TMR_High_Confidence is generated, which is used for mode selection in the subsequent fusion step. At the same time, in the zero-speed start, very low-speed running or emergency braking working condition, the system generates a performance requirement flag Flag_Perf_Req, which is used to enhance the response weight of the EKF to the inductive signal.

[0018] After completing the dynamic validity diagnosis of the TMR signal, the system enters the adaptive Kalman fusion stage. The core goal of this stage is to dynamically fuse the inductive position information estimated by the EKF to achieve high-precision, low-noise fusion position output and fusion speed . The fusion algorithm adjusts the EKF observation noise covariance matrix to realize mode switching, thereby smoothly transitioning between high confidence and low confidence.

[0019] When Flag_TMR_High_Confidence=1, i.e., the TMR signal is determined to be highly reliable, the system enters the high-confidence fusion mode. In this mode, the calibrated is input to the update step of the EKF as an observation. The calculation formula of the Kalman gain is as follows: ; Here is the observation noise covariance matrix, and its value is set to the first value , reflecting the high reliability of the TMR measurement value. The EKF updates the state estimation based on the predicted state combined with the TMR observation: ; The output fusion position and fusion speed after updating. In this mode, the system uses the absolute position information provided by the TMR sensor to realize continuous correction of the EKF state estimation, and uses the noise suppression capability of the EKF to reduce high-frequency fluctuations and improve the smoothness of the position and speed signals, thereby improving the response performance and dynamic bandwidth of the magnetic field oriented control FOC loop.

[0020] ​When Flag_TMR_High_Confidence = 0, i.e. the TMR signal is determined to be untrustworthy, the system enters a low-confidence or fault mode. In this mode, the EKF observer switches to a purely open-loop operation, i.e. the observation is no longer used in the update step . The implementation method is to set the observation noise covariance matrix to a second value much larger than , so that the Kalman gain is almost zero, and the update step almost ignores the observation value, and only relies on the motor dynamic model to make state prediction: ; This design ensures that when the TMR sensor fails or is strongly disturbed, the system can seamlessly degrade to a high-performance EKF open-loop mode, achieving fault redundancy and functional safety.

[0021] By adjusting the coefficient , the dynamic response performance of the system is improved under the premise of ensuring stability. After fusing the position and speed outputs, it directly enters the FOC fault-tolerant control loop. The FOC algorithm first converts the fused mechanical state into dq-axis currents in the synchronous coordinate system: ; wherein the expected current command , is determined by the expected torque command and motor parameters, and both satisfy the torque relationship of PMSM in the dq synchronous coordinate system: ; wherein p is the number of pole pairs, is the permanent magnet flux linkage, , are the d-axis and q-axis inductances respectively; preferably, in the base speed region, i.e. without enabling field weakening control , then , so that the relationship between and is: is fixed at 0, varies in proportion to , and optionally in the high-speed field weakening region, to meet the bus voltage and current constraints, let , and limit to meet ; wherein is the two-phase measured current, and the current control loop generates dq-axis voltage commands according to the expected current command , and then generates inverter switching signals through the SVPWM algorithm to drive the PMSM to track the torque command ​In this process, due to the low fusion signal noise and high dynamic precision, the current loop control response is fast and stable, and at the same time, the control oscillation caused by TMR transient failure is avoided.

[0022] In the mode switching process, the system adjusts the observation noise covariance matrix Smooth transition is realized, and the control quantity mutation caused by traditional hard switching is avoided. The specific implementation is to calculate the smoothing factor According to the signal quality factor And the residual error : , ; Among them, The lower and upper limits of the signal quality factor are The upper limit of the residual error tolerance. Through this continuous adjustment, the Kalman gain of EKF in each control cycle can change smoothly, realizing the disturbance-free switching between sensing and non-sensing.

[0023] The FOC loop of the system generates the control signal of the motor, the mechanical angle Used for coordinate transformation, the fusion speed Used for current loop speed feedforward compensation, so as to realize accurate torque control. In the emergency stop or emergency braking condition, the controller can quickly increase the bandwidth of the current loop, so that the motor can quickly output braking torque, and at the same time, through adaptive fusion, it can ensure continuous and reliable position estimation, prevent braking distortion or braking lag. In the whole implementation process, the hardware advantages of TMR sensor and the dynamic performance of EKF non-sensing algorithm are fully utilized through adaptive Kalman fusion, which not only ensures the absolute position accuracy, but also provides non-sensing fault tolerance capability, forming a kind of motor control method with high reliability and high dynamic response.

[0024] The performance of TMR sensor occupies a core position in the overall control accuracy of the system, and the quality of its output signal directly affects the reliability of EKF non-sensing position estimation and the effect of adaptive Kalman fusion. Therefore, optimizing the structure, installation, signal acquisition and compensation strategy of the sensor is the prerequisite for ensuring the high-performance operation of the whole electric brake-by-wire system.

[0025] First, the TMR sensor employs a differential output structure. Specifically, two output signals with opposite phases are generated on the same sensor chip and processed by a differential amplifier to suppress common-mode magnetic field interference. The design concept of the differential output structure is based on the common-mode nature of electromagnetic interference (EMI) on the two signals: when external stray magnetic fields or conducted interference act on both output signals simultaneously, the differential amplifier automatically cancels the common-mode components of the two signals when calculating the output voltage, retaining only the differential signal portion. This differential signal is the effective position signal caused by the actual change in magnetic flux. This structure not only effectively reduces the impact of external magnetic interference on measurement accuracy but also enhances the system's stability in the complex electromagnetic environment of automobiles and under conditions of multi-source interference.

[0026] Secondly, the installation position of the TMR sensor has been carefully designed and optimized. During mechanical installation, the distance, coaxiality, and parallelism between the sensor and the permanent magnet have a significant impact on the linearity and harmonic characteristics of the output signal. Precise mechanical installation and positioning ensure that the magnetic flux change caused by magnet rotation is as linear as possible, thereby reducing harmonic distortion caused by installation eccentricity, uneven gaps, or magnetic flux nonlinearity. This optimization ensures that the calibrated position signal... It maintains high linearity across the entire angular range, providing a high-precision sensing input signal for subsequent EKF fusion.

[0027] Regarding temperature compensation, the raw signal output by the TMR sensor... The Kalman filter is susceptible to drift due to changes in ambient temperature. Temperature drift can cause output position deviations, thus affecting the state estimation accuracy of the Kalman filter. To address this issue, the system performs real-time monitoring... Temperature compensation is performed. Two approaches can be used: one is to apply linear or nonlinear correction to the output at different temperatures based on the temperature coefficient curve provided by the sensor manufacturer; the other is to obtain the sensor's temperature response model under actual operating conditions through online calibration. The compensation value is calculated in real time during each sampling period to minimize the impact of temperature on position measurement. The calculation formula is as follows: ; Where T is the temperature currently measured by the sensor. The temperature compensation function can be implemented based on curves obtained from experimental calibration or by looking up tables. This temperature compensation process can be completed in real time within the microcontroller, ensuring that the system can still output accurate position signals in low or high temperature environments, thus guaranteeing the dynamic performance of the brake-by-wire system under all temperature conditions.

[0028] Simultaneously, the system also performs harmonic compensation for periodic errors caused by installation eccentricity, cogging effect, and non-ideal characteristics of the magnet. The core idea of ​​harmonic compensation is to decompose the known periodic error into a finite number of Fourier series, and correct the amplitude and phase of each harmonic to eliminate its influence on the output signal. The implementation formula is as follows: ; in, For the first The amplitude of the first harmonic. For the corresponding phase, For mechanical angular velocity, The harmonic order involved in the compensation. By compensating for each harmonic order sequentially, repetitive periodic errors caused by mechanical eccentricity or magnetic field non-ideality can be significantly reduced, further improving the accuracy and smoothness of the calibrated signal. Compensation coefficient. and It can be obtained through sensor calibration experiments and applied to the signal processing module in the microcontroller by means of table lookup or real-time calculation.

[0029] It is worth noting that, to ensure the real-time performance of temperature compensation and harmonic compensation, the system... The processing employs a pipeline-style structure: first, temperature compensation is performed to eliminate the effects of long-term drift; then, harmonic compensation is performed to remove periodic errors; and finally, the output... Used for EKF fusion. This step-by-step processing and prioritization strategy effectively avoids the interaction between temperature compensation and harmonic compensation, ensuring that the output signal is both accurate and smooth.

[0030] Furthermore, to further enhance the system's resistance to transient interference, a signal filtering unit can be added to the microcontroller to perform low-pass filtering or Kalman filtering preprocessing on θtmr_comp, suppressing the superposition of high-frequency noise. This processing is particularly important under zero-speed start-up or emergency braking conditions, as it can prevent EKF state jumps caused by short-term fluctuations in sensor signals, thereby improving the stability and reliability of the control loop.

[0031] During normal operation, the system will adjust the signal quality factor based on the output signal of the TMR sensor. The tolerance threshold is dynamically adjusted using the main diagonal elements P(1,1) of the rotor position error covariance matrix P estimated by the Extended Kalman Filter (EKF). The calculation formula is as follows: ; in, , These are empirical coefficients, used to characterize the amplification effect of covariance on the tolerance threshold and the basic static tolerance, respectively. Specifically, for mapping the uncertainty of the EKF position estimate P(1,1) to a tolerance range, thereby automatically allowing larger residual values when the system state prediction uncertainty is large without prematurely deciding the TMR signal as unreliable; provides a minimum tolerance base to ensure that there is still some tolerance space when the EKF estimate is highly certain to cope with small noise or transient disturbances in the signal.

[0032] tolerance threshold The dynamic adjustment logic of the tolerance threshold plays an important role in practical control. Since the EKF estimation uncertainty varies dynamically with the motor speed, load changes and environmental disturbances, if the tolerance threshold remains fixed, when the EKF uncertainty is high, the residual is very easy to exceed the static threshold, leading to frequent switching to the insensitive mode, increasing the instability of the control loop, and even possibly causing torque output fluctuations. By positively correlating with P(1,1), the system can adaptively amplify the tolerance range according to the current state, so that the TMR signal can still be reasonably utilized when the EKF prediction uncertainty is high, thereby maintaining the continuity and stability of the fused signal.

[0033] At the same time, the dynamic adjustment of the tolerance threshold also takes into account the quality factor of the TMR signal. When is high and the EKF uncertainty P(1,1) is low, will approach its minimum value , so that the system strictly utilizes the TMR signal under high-precision conditions to achieve fine correction of the fused position. Conversely, when is low or the EKF uncertainty rises, increases, allowing to fluctuate in a larger range, thereby preventing false judgment of TMR signal failure and avoiding control oscillation caused by frequent switching. This adaptive mechanism is particularly important in zero-speed starting, very low-speed running and load rapid change conditions, as it ensures that the system neither prematurely switches the mode due to small deviations nor loses effective utilization of the TMR signal in critical transient states.

[0034] In addition, in practical implementation, the values of the experience coefficients , are usually set to 0.5-2.0, adjusted according to the sensitivity of the EKF prediction error to tolerance amplification; The value of is generally between 0.1° and 1.0°, so as to provide a small signal tolerance space, while ensuring full use of the TMR signal at high precision. Through experimental verification, this dynamic tolerance strategy can achieve smooth mode switching under different working conditions, improve the robustness of the system to transient disturbances and external interference, avoid torque fluctuations and control delays caused by misjudgment, and further improve the safety and response performance of the line control braking system. The dynamic adjustment of is not only a key parameter for judging the reliability of the TMR signal, but also an important guarantee for smooth switching and fault-tolerant control of the adaptive Kalman fusion algorithm.

[0035] In the PMSM control system, zero-speed starting and extremely low-speed running belong to the working conditions in which the blind area of the sensorless algorithm is most likely to occur. At this time, due to the fact that the motor back-EMF is close to zero, the traditional sensorless EKF cannot reliably estimate the rotor position and speed, which may lead to unstable or delayed torque output during the starting stage. To solve this problem, the performance enhancement flag Flag_Perf_Req is introduced in the adaptive Kalman fusion module to enhance the response ability of the EKF to the inductive signal under certain working conditions, thereby improving the dynamic performance and starting reliability of the system. See Figure 4 The setting conditions of the performance enhancement flag Flag_Perf_Req include: zero-speed starting condition, actual motor speed wherein is a threshold value less than the lowest measurable speed of the system. Extremely low-speed running, Emergency braking condition, braking request signal is activated, and the expected output torque is greater than the safety braking threshold Tq_emergency. When any of the above conditions is met, Flag_Perf_Req is set to 1, indicating that the system enters the performance enhancement mode. In the performance enhancement mode, the amplitude of the Kalman gain matrix is adjusted to improve the response weight of the EKF to the TMR inductive position signal, so as to realize fast and reliable position signal updating. The enhancement strategy formula is as follows: ; wherein, is the Kalman gain calculated by the standard EKF update step, is the gain adjustment coefficient. When >1, it means that the confidence in the inductive signal is amplified, so as to speed up the state convergence during low-speed or starting stage. The value of should be adjusted on the premise of ensuring system stability and noise suppression. It is generally recommended that 1.2≤ ≤3, and the specific selection is based on the system motor parameters, sampling period, controller bandwidth and noise level. A smaller As 1.2-1.5 is applicable to light load, low noise working condition, it can moderately enhance dynamic response without introducing too much noise. Larger As 2-3 is used for zero speed start or sudden acceleration scene, it gives priority to guaranteeing fast locking of rotor position and accepting the influence of part of high frequency noise.

[0036] By enhancing , the EKF has a larger correction step for the sensed position signal at each sampling period, so that the fused position rapidly approaches the actual rotor position, realizing zero speed start fast locking and extremely low speed stable control. The enhanced gain may amplify the transient noise of the sensor, so the dynamic adjustment combined with the signal quality factor , so that the gain amplification only takes effect under high confidence signals, so as to balance response and noise suppression. The system can quickly lock the rotor position in the zero speed start phase, avoiding the torque fluctuation caused by the initial state uncertainty of the non-inductive algorithm. In the extremely low speed and sudden braking working condition, the fused position signal and the fused speed significantly improve the response speed of the braking command.

[0037] The above embodiments can be realized wholly or partially by software, hardware, firmware or any other combination. When realized by software, the above embodiments can be realized in the form of a computer program product, wholly or partially.

[0038] Those skilled in the art can realize that the modules and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application of the technical solution and the constraints of the invention. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0039] In addition, the functional modules in each embodiment of the present application can be integrated in one processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0040] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any skilled person in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0041] Finally: the above only for the preferred embodiments of the present application, and not for limiting the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application, should be included in the scope of protection of the present application.

Claims

1. A motor control method for brake-by-wire, characterized by, The method comprises the steps of: Online harmonic compensation and temperature drift compensation are performed on the original signal of the TMR sensor output to obtain a calibrated signal, and a sensed position of the rotor and a signal quality factor are obtained based on the calibrated signal; An extended Kalman filter (EKF) algorithm is run based on the obtained phase current and DC bus voltage to obtain a non-sensed position of the rotor, a speed of the rotor, and a covariance matrix of an estimation error of the non-sensed position; and a residual value is calculated based on the sensed position and the non-sensed position; If the signal quality factor is greater than a preset quality threshold and the residual value is less than a tolerance threshold, it is determined that the sensed position is reliable; Otherwise, it is determined that the sensed position is unreliable; wherein the tolerance threshold is positively correlated with the covariance matrix of the estimation error; If the sensed position is reliable, an observation noise covariance matrix is set to a first value, and then the Kalman gain value is increased, so that the EKF algorithm is fused based on the sensed position in the update step to obtain a fused position and a fused speed; If the sensed position is unreliable, the observation noise covariance matrix is set to a second value which is much larger than the first value, and then the Kalman gain value tends to be zero, so that the EKF algorithm ignores the sensed position in the update step to obtain the fused position and the fused speed; Based on the fused position and the fused speed, a control signal for controlling the motor is generated.

2. The motor control method with a combination of inductance and non-inductance applied to brake-by-wire, according to claim 1, wherein The method comprises the steps of: According to the formula determining a tolerance threshold wherein, is the estimated error variance of the non-inductive position, and is a preset coefficient.

3. The motor control method of claim 1, wherein, The method comprises the steps of: By setting the value of the observation noise covariance matrix of the EKF algorithm, the weight of the sensed position in the update step is controlled; When the sensed position is reliable, the observation noise covariance matrix is set to a first value, so that the sensed position participates in the state update of the EKF as an observation, and a fused position is obtained; When the sensed position is unreliable, the observation noise covariance matrix is set to a second value which is much larger than the first value, so that the EKF update step ignores the sensed position, and the non-sensed position is taken as the fused position.

4. The motor control method with a combination of inductance and non-inductance applied to brake-by-wire, according to claim 1, wherein, The method comprises the steps of: The sensed position output by the TMR sensor is read; The state vector of the EKF algorithm is initialized using the sensed position, wherein the rotor position state in the EKF algorithm is assigned the value of the sensed position, and the rotor speed state in the EKF algorithm is assigned a value of zero.

5. The motor control method with a combination of inductance and non-inductance applied to brake-by-wire, according to claim 1, wherein, The method comprises the steps of: Based on the sensing position credible and non credible determination results, the value of the observation noise covariance matrix in the extended Kalman filter algorithm is smoothly changed to realize the non-interference transition ; A smoothing factor is calculated according to the signal quality factor and the residual value and the observation noise covariance matrix is dynamically set according to the formula ; wherein, is the matrix value corresponding to the case that the felt position is reliable, is the matrix value corresponding to the case that the felt position is unreliable, and .

6. The motor control method with a combination of inductance and non-inductance applied to brake-by-wire, according to claim 1, wherein The method comprises the steps of: Based on a pre-calibrated harmonic error map, a Fourier series compensation model is used to modify the original signal; The harmonic compensation model is wherein, , is the compensation coefficient of the th harmonic, is the compensation order.

7. The motor control method with a combination of inductance and non-inductance applied to brake-by-wire, according to claim 1, wherein A performance demand flag is generated when the motor is in any of the following conditions: zero-speed start, extremely low-speed operation, and emergency braking; When the performance demand flag is valid and the sensed position is determined to be reliable, the Kalman gain corresponding to the position state in the EKF algorithm is increased. The TMR sensor adopts a differential output structure, and the differential signal output by the TMR sensor is collected and converted through a differential input interface or a differential amplifier circuit in the motor controller; 8. The motor control method with a combination of inductance and non-inductance applied to brake-by-wire, according to claim 1, wherein, The installation position of the TMR sensor is optimized based on finite element analysis to make the sensed magnetic flux change period have the highest linearity, so as to reduce the harmonic distortion of the original signal from the mechanical structure. The method comprises the steps of: The sampled current is converted to the synchronous rotating coordinate system through coordinate transformation using the fused position to obtain a direct-axis current and a quadrature-axis current; 9. The motor control method with a combination of inductance and non-inductance applied to brake-by-wire, according to claim 1, wherein, The direct-axis current and the quadrature-axis current are closed-loop regulated to generate corresponding voltage instructions; ​ ​ Based on the voltage instruction and the fusion position, a PWM signal for driving an inverter is generated through a space vector pulse width modulation algorithm.

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