A method for suppressing switching noise in a motor controller

Abstract

This invention discloses a method for suppressing switching noise in a motor controller. After the PWM triggers the FOC interrupt function, tasks one and two are executed in parallel. Task one: The traditional FOC algorithm is performed, and the triggering mode of the ADC current sampling is adjusted at the software level to a dual triggering mode of PWM cycle zero and midpoint to achieve dual sampling and dual updating of the three-phase current, generating a PWM modulation wave. Task two: A random triangular carrier frequency is generated, and then the random numbers uniformly distributed in the triangular carrier frequency are processed by a two-state probability number based on a Markov chain to obtain a random array that follows a two-state Markov chain. The PWM switching control signal is generated by combining the PWM modulation wave obtained in task one with the random array that follows a two-state Markov chain obtained in task two. This invention can not only effectively reduce the noise level of the switching frequency and its integer multiples, but also help to reduce the problem of excessive output torque fluctuation.

Description

Technical Field

[0001] This invention relates to a noise suppression method, and more particularly to a method for suppressing switching noise in a motor controller, belonging to the field of motor control technology. Background Technology

[0002] As electrification is increasingly applied in various aspects of people's lives, the inherent high-frequency noise problem urgently needs to be addressed. In the application of motor frequency conversion power supply, the widespread use of pulse width modulation (PWM) technology introduces a large number of current harmonics into the motor's armature windings, resulting in significant high-frequency noise near the motor's switching frequency and its harmonics. For the new energy EV industry, the switching frequency of the main drive motor controller for passenger vehicles is generally set at or around 10kHz. Noise spectrum analysis conducted in a noise laboratory during controller operation reveals significant noise at various speed ranges around the 10kHz frequency. This noise exhibits an umbrella-shaped distribution on the spectrum. Furthermore, during controller operation, Fourier analysis of the phase current of the built-in permanent magnet synchronous motor also reveals significant amplitude spikes at the controller's switching frequency and its harmonics on an oscilloscope.

[0003] There are many methods to suppress this switching noise, among which the most commonly used method is random PWM. Specifically, this includes methods using random carrier waves, random PWM pulse positions, and random PWM frequencies. It also includes some novel composite methods, such as dual random PWM, which superimposes random PWM pulse positions onto random PWM frequencies. Furthermore, it includes improved methods, such as zero-vector-variable-delay random PWM, and research on random numbers, including methods using uniform randomness and Gaussian-normal distribution randomness.

[0004] For example, the paper "Research on Double Random PWM Technology Based on Vector Control System" (see Proceedings of the CSEE, Vol. 30, No. 36, 2010) proposes a zero-vector-variable-delay random PWM method with a fixed sampling period. This method can distribute the current spectrum uniformly over a wide range, reduce the harmonic amplitude of integer multiples of the switching frequency, effectively reduce system noise and suppress electromagnetic interference. However, the random effect of this method deteriorates when the motor has a high modulation ratio, and it only has a suppressive effect on high-frequency vibration of the motor.

[0005] For example, the patent publication number CN114844440, "A Method for Eliminating Electromagnetic Noise at a Specific Frequency of an Electric Motor," proposes a method for suppressing switching noise. It mainly suppresses switching noise by calculating the relationship between the switching position and duty cycle during PWM modulation and thus randomizing the switching position of the PWM. Essentially, this method still belongs to the pulse position randomization strategy in random PWM, but it involves a large amount of computation, posing a significant challenge to software computational load, especially at high switching frequencies, where it consumes substantial computational resources.

[0006] Therefore, it can be seen that the existing motor noise suppression methods have poor random effects and a narrow frequency adaptation range, and also require a large amount of computation and consume a lot of computing resources. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a method for suppressing switching noise of a motor controller, which effectively reduces noise video and solves the problem of excessive output torque fluctuation.

[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0009] A method for suppressing switching noise in a motor controller includes the following steps:

[0010] S1. After the PWM triggers the FOC interrupt function, Task 1 and Task 2 are executed in parallel.

[0011] Task 1: Implement the traditional FOC algorithm and achieve dual sampling and dual updating of the three-phase current by adjusting the triggering mode of ADC current sampling to a dual triggering mode of PWM cycle zero point and midpoint at the software level, thereby generating a PWM modulation wave;

[0012] Task 2: Generate random triangular carrier frequencies, and then sort the uniformly distributed random numbers in the triangular carrier frequencies using a two-state probability number based on a Markov chain to obtain a random array that follows a two-state Markov chain.

[0013] S2. Generate a PWM switching control signal using the PWM modulation wave obtained from Task 1 and the random array following a two-state Markov chain obtained from Task 2.

[0014] Furthermore, the traditional FOC algorithm in step S1 includes current sampling, voltage sampling, rotor angle sampling, coordinate system transformation, PI regulation, and SVPWM space vector modulation algorithm.

[0015] Furthermore, task one in step S1 specifically includes:

[0016] 1.1. The FOC interrupt function is triggered by the PWM channel, and the ADC current sampling of the three-phase current is performed.

[0017] 1.2. Set the carrier mode to Up-Down mode;

[0018] 1.3 Set the ADC sampling trigger mode to trigger ADC sampling at the zero point and the maximum point of the PWM counting cycle respectively;

[0019] 1.4. Set the FOC interrupt triggering mode to trigger the FOC interrupt at the zero point and the maximum point of the PWM counting cycle, respectively.

[0020] Further, the generation of the random triangular carrier frequency in step S1 specifically involves: generating a uniformly distributed random number array G[] in the range of -1 to 1 using a uniform random number generator; then multiplying the uniformly distributed random number array G[] by the random frequency gain value k; and finally adding the minimum value f of the frequency range. min Thus, a random number f that follows a uniform distribution is obtained. s ,

[0021] ;

[0022] in, f min It is the minimum value in the frequency range, f max G[i] is the maximum value in the frequency range, and G[i] is a random number in the uniformly distributed array G[].

[0023] Random numbers f that follow a uniform distribution s The range of values ​​for F is s ±F s / 4, F s It is the original fixed center switching frequency.

[0024] Further, in step S1, the random numbers uniformly distributed in the triangular carrier frequency are processed using a two-state probability number based on a Markov chain to obtain a random array that follows a two-state Markov chain.

[0025] 2.1 Assume that R[i]>0 represents state S1 and R[i]<0 represents state S2; R[i] is a random number f that follows a uniform distribution. s Random numbers in the data;

[0026] 2.2 For any random number f that follows a uniform distribution s Given a random number R[i] (i=0, 1, 2, ...), determine whether R[i] is greater than a preset threshold m. P is the transition probability, and ;

[0027] 2.3 If R[i] is greater than the preset threshold m, and the previous random number R[i-1] of the random number R[i] is in state S1, the current random number M[i] after the transition is in state S2, that is, M[i] = -R[i];

[0028] 2.4 If R[i] is less than or equal to the preset threshold m, and the previous random number R[i-1] of the random number R[i] is in state S1, then the current random number M[i] after the transition is in state S1, that is, M[i] = R[i].

[0029] 2.5. Multiply the transferred random array M[] by the gain G to obtain the random array GMarkov[], that is, GMarkov[] = M[] * G;

[0030] 2.6 The final random array is Rfinal[].

[0031] ;

[0032] Among them, F s It is the original fixed center switching frequency.

[0033] Further, step S2 specifically involves: using the random array obtained from Task 2 that follows a two-state Markov chain, i.e., the final random array Rfinal[], whose frequency is a finite random change, and then converting the frequency of the final random array Rfinal[] into a PWM-compliant period count value. For any random number frequency Rfinal[i] in the final random array Rfinal[] and the PWM-compliant period count value corresponding to Rfinal[i], a random number frequency-PWM period index lookup table is established. The next PWM switching period value is obtained in real time by iteratively looking up the table.

[0034] Further, step S2 specifically involves: dividing the zero vector of the PWM modulation wave obtained from Task 1 into two uniform segments and setting them into three states:

[0035] First state: Both zero vectors appear at the beginning of the PWM modulation wave;

[0036] Second state: Both zero vectors appear at the end of the PWM modulation wave;

[0037] Third state: One segment of zero vector appears at the beginning of the PWM modulation wave, and another segment of zero vector appears at the end of the PWM modulation wave;

[0038] The random array obtained from Task 2, which follows a two-state Markov chain, is used to correspond to the three states of the zero vector of the PWM modulation wave, thereby generating a PWM switching control signal with a random Markov chain.

[0039] Compared with existing technologies, this invention has the following advantages and effects: This invention provides a method for suppressing switching noise in a motor controller. By sampling a random strategy for PWM frequency or pulse position based on a Markov chain, it can not only effectively reduce the noise level of the switching frequency and its integer multiples, but also help reduce the problem of excessive output torque fluctuation. At the same time, by adjusting the control density of the random PWM and increasing the control frequency of the PWM, specifically by adopting a double sampling and double update method for the motor phase current, the distortion rate (THD) of the motor output phase current and the output torque fluctuation are reduced, thereby further reducing the noise vibration level and torque fluctuation, solving the problem of increased output torque fluctuation caused by simply using a random PWM algorithm. Attached Figure Description

[0040] Figure 1 This is a flowchart of a motor controller switching noise suppression method according to the present invention.

[0041] Figure 2 This is a frequency distribution diagram of random numbers that follow a uniform distribution according to the present invention.

[0042] Figure 3 This is a frequency distribution diagram of the random array that follows a two-state Markov chain according to the present invention.

[0043] Figure 4 This is a schematic diagram of the three states of the zero vector in Embodiment 2 of a motor controller switching noise suppression method of the present invention.

[0044] Figure 5 This is the FFT result of the phase current Fourier analysis using the original algorithm.

[0045] Figure 6 This is a graph showing the FFT results of the phase current Fourier analysis of the present invention.

[0046] Figure 7 This is a schematic diagram illustrating the impact of different control strategies on torque fluctuations in embodiments of the present invention.

[0047] Figure 8 This is a schematic diagram illustrating the impact of different control strategies on the phase current THD in embodiments of the present invention. Detailed Implementation

[0048] To illustrate in detail the technical solutions adopted by the present invention to achieve the intended technical objectives, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Furthermore, the technical means or technical features in the embodiments of the present invention can be replaced without creative effort. The present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0049] Example 1: As Figure 1 As shown, a method for suppressing switching noise in a motor controller according to the present invention includes the following steps:

[0050] S1. After the PWM triggers the FOC interrupt function, Task 1 and Task 2 are executed in parallel.

[0051] Task 1: Implement the traditional FOC algorithm and achieve dual sampling and dual updating of the three-phase current by adjusting the triggering mode of ADC current sampling at the software level to a dual triggering mode of PWM cycle zero point and midpoint, thereby generating a PWM modulation wave.

[0052] The traditional FOC algorithm in step S1 includes current sampling, voltage sampling, rotor angle sampling, coordinate system transformation, PI regulation, and SVPWM space vector modulation algorithm.

[0053] Task 1 in step S1 specifically includes:

[0054] 1.1. The FOC interrupt function is triggered by the PWM channel, and the ADC current sampling of the three-phase current is performed.

[0055] 1.2. Set the carrier mode to Up-Down mode;

[0056] 1.3 Set the ADC sampling trigger mode to trigger ADC sampling at the zero point and the maximum point of the PWM counting cycle respectively;

[0057] 1.4. Set the FOC interrupt triggering mode to trigger the FOC interrupt at the zero point and the maximum point of the PWM counting cycle, respectively.

[0058] The specific steps for generating the random triangular carrier frequency in step S1 are as follows: A uniformly distributed random number array G[] is generated within the range of -1 to 1 using a uniform random number generator. Then, the uniformly distributed random number array G[] is multiplied by the random frequency gain value k, and finally, the minimum value f of the frequency range is added. min Thus, we can obtain the following: Figure 2 The random numbers f shown follow a uniform distribution s ,

[0059] ;

[0060] in, f min It is the minimum value in the frequency range, f max G[i] is the maximum value in the frequency range, and G[i] is a random number in the uniformly distributed array G[].

[0061] Random numbers f that follow a uniform distribution sThe range of values ​​for F is s ±F s / 4, F s It is the original fixed center switching frequency.

[0062] In step S1, the random numbers uniformly distributed in the triangular carrier frequency are processed using a two-state probability number based on a Markov chain to obtain a random array that follows a two-state Markov chain.

[0063] 2.1 Assume that R[i]>0 represents state S1 and R[i]<0 represents state S2; R[i] is a random number f that follows a uniform distribution. s Random numbers in the data;

[0064] 2.2 For any random number f that follows a uniform distribution s Given a random number R[i] (i=0, 1, 2, ...), determine whether R[i] is greater than a preset threshold m. P is the transition probability, and ;

[0065] 2.3 If R[i] is greater than the preset threshold m, and the previous random number R[i-1] of the random number R[i] is in state S1, the current random number M[i] after the transition is in state S2, that is, M[i] = -R[i];

[0066] 2.4 If R[i] is less than or equal to the preset threshold m, and the previous random number R[i-1] of the random number R[i] is in state S1, then the current random number M[i] after the transition is in state S1, that is, M[i] = R[i].

[0067] 2.5. Multiply the transferred random array M[] by the gain G to obtain the random array GMarkov[], that is, GMarkov[] = M[] * G;

[0068] 2.6, such as Figure 3 As shown, the final random array is Rfinal[].

[0069] ;

[0070] Among them, F s It is the original fixed center switching frequency.

[0071] Task 2: Generate random triangular carrier frequencies, and then sort the uniformly distributed random numbers in the triangular carrier frequencies using a two-state probability number based on a Markov chain to obtain a random array that follows a two-state Markov chain.

[0072] S2. Generate a PWM switching control signal using the PWM modulation wave obtained from Task 1 and the random array following a two-state Markov chain obtained from Task 2.

[0073] Step S2 specifically involves: using the random array obtained from Task 2 that follows a two-state Markov chain, i.e., the frequency of the final random array Rfinal[] is a finite random change, and then converting the frequency of the final random array Rfinal[] into a PWM-compliant period count value. For any random frequency Rfinal[i] in the final random array Rfinal[] and the corresponding PWM-compliant period count value, a random frequency-PWM period index lookup table is established. The next PWM switching period value is obtained in real time by iteratively looking up the table.

[0074] Example 2: As Figure 1 As shown, a method for suppressing switching noise in a motor controller according to the present invention includes the following steps:

[0075] S1. After the PWM triggers the FOC interrupt function, Task 1 and Task 2 are executed in parallel.

[0076] Task 1: Implement the traditional FOC algorithm and achieve dual sampling and dual updating of the three-phase current by adjusting the triggering mode of ADC current sampling at the software level to a dual triggering mode of PWM cycle zero point and midpoint, thereby generating a PWM modulation wave.

[0077] The traditional FOC algorithm in step S1 includes current sampling, voltage sampling, rotor angle sampling, coordinate system transformation, PI regulation, and SVPWM space vector modulation algorithm.

[0078] Task 1 in step S1 specifically includes:

[0079] 1.1. The FOC interrupt function is triggered by the PWM channel, and the ADC current sampling of the three-phase current is performed.

[0080] 1.2. Set the carrier mode to Up-Down mode;

[0081] 1.3 Set the ADC sampling trigger mode to trigger ADC sampling at the zero point and the maximum point of the PWM counting cycle respectively;

[0082] 1.4. Set the FOC interrupt triggering mode to trigger the FOC interrupt at the zero point and the maximum point of the PWM counting cycle, respectively.

[0083] The specific steps for generating the random triangular carrier frequency in step S1 are as follows: A uniformly distributed random number array G[] is generated within the range of -1 to 1 using a uniform random number generator. Then, the uniformly distributed random number array G[] is multiplied by the random frequency gain value k, and finally, the minimum value f of the frequency range is added. min Thus, we can obtain the following: Figure 2The random numbers f shown follow a uniform distribution s ,

[0084] ;

[0085] in, f min It is the minimum value in the frequency range, f max G[i] is the maximum value in the frequency range, and G[i] is a random number in the uniformly distributed array G[].

[0086] Random numbers f that follow a uniform distribution s The range of values ​​for F is s ±F s / 4, F s It is the original fixed center switching frequency.

[0087] In step S1, the random numbers uniformly distributed in the triangular carrier frequency are processed using a two-state probability number based on a Markov chain to obtain a random array that follows a two-state Markov chain.

[0088] 2.1 Assume that R[i]>0 represents state S1 and R[i]<0 represents state S2; R[i] is a random number f that follows a uniform distribution. s Random numbers in the data;

[0089] 2.2 For any random number f that follows a uniform distribution s Given a random number R[i] (i=0, 1, 2, ...), determine whether R[i] is greater than a preset threshold m. P is the transition probability, and ;

[0090] 2.3 If R[i] is greater than the preset threshold m, and the previous random number R[i-1] of the random number R[i] is in state S1, the current random number M[i] after the transition is in state S2, that is, M[i] = -R[i];

[0091] 2.4 If R[i] is less than or equal to the preset threshold m, and the previous random number R[i-1] of the random number R[i] is in state S1, then the current random number M[i] after the transition is in state S1, that is, M[i] = R[i].

[0092] 2.5. Multiply the transferred random array M[] by the gain G to obtain the random array GMarkov[], that is, GMarkov[] = M[] * G;

[0093] 2.6, such as Figure 3 As shown, the final random array is Rfinal[].

[0094] ;

[0095] Among them, F s It is the original fixed center switching frequency.

[0096] Task 2: Generate random triangular carrier frequencies, and then sort the uniformly distributed random numbers in the triangular carrier frequencies using a two-state probability number based on a Markov chain to obtain a random array that follows a two-state Markov chain.

[0097] S2. Generate a PWM switching control signal using the PWM modulation wave obtained from Task 1 and the random array following a two-state Markov chain obtained from Task 2.

[0098] Step S2 specifically involves: Figure 4 As shown, the zero vector of the PWM modulation wave obtained from Task 1 is divided into two uniform segments and set to three states:

[0099] First state: Both zero vectors appear at the beginning of the PWM modulation wave;

[0100] Second state: Both zero vectors appear at the end of the PWM modulation wave;

[0101] Third state: One segment of zero vector appears at the beginning of the PWM modulation wave, and another segment of zero vector appears at the end of the PWM modulation wave;

[0102] The random array obtained from Task 2, which follows a two-state Markov chain, is used to correspond to the three states of the zero vector of the PWM modulation wave, thereby generating a PWM switching control signal with a random Markov chain.

[0103] In existing technologies, the PWM control signal is compared with a triangular carrier wave by a modulation signal (which is a sine wave, but is microscopically considered a straight line because the actual comparison time is a very small proportion of the sine wave's period). When the amplitude of the triangular carrier wave is greater than that of the modulation signal, the PWM control signal is 0 (i.e., zero vector); when the amplitude of the triangular carrier wave is less than that of the modulation signal, the PWM control signal is 1. The frequency of the traditional triangular carrier wave is fixed, therefore the zero vector always appears at the beginning and end points of the PWM modulation wave. This invention randomizes the triangular carrier wave using a 2-state Markov chain, causing the frequency of the triangular carrier wave to vary according to the 2-state Markov chain. This causes the zero vector of the final PWM modulation wave to shift forward or backward, thus forming the three zero vector distribution states of this invention.

[0104] For Embodiments 1 and 2 above, FFT analysis was performed on the phase current and compared with the original algorithm of traditional uniform random PWM control.

[0105] like Figure 5As shown, the original algorithm exhibits a PSD spike of -26.58 dB near 10 kHz and a PSD spike of -10.86 dB near 20 kHz in the lower motor phase current. Figure 6 As shown, the present invention reduces the PSD near 10kHz to -31.09dB and the PSD near 20kHz to -20.02dB.

[0106] In summary, the present invention reduces PSD by 4.5 dB near 10 kHz and by 10 dB near 20 kHz. Furthermore, it also significantly suppresses noise PSD near even multiples of the switching frequency, demonstrating remarkable effectiveness.

[0107] like Figure 7 and 8 As shown, ordinary single-sampling and double-sampling methods result in the smallest torque fluctuations, but cannot effectively improve switching frequency noise. Adding an ordinary random strategy, while suppressing switching noise, significantly increases output torque fluctuations, such as... Figure 7 The seventh experiment in the paper showed a torque of 10 Nm. After sampling the Markov and double-sampling algorithms claimed in this patent, the torque became 5 Nm, as shown in the figure. Figure 7 As shown in the 3rd and 4th experiments.

[0108] Based on the test results above, it can be seen that the current dual sampling combined with the Markov chain random PWM strategy has the best effect in suppressing torque fluctuations and improving phase current distortion rate. This method has the best practical effect in suppressing 10kHz noise control methods.

[0109] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent substitutions, and improvements made to the above embodiments without departing from the scope of the present invention, based on the technical essence of the present invention and within the spirit and principles of the present invention, shall still fall within the protection scope of the present invention.

Claims

1. A method for suppressing switching noise in a motor controller, characterized in that... Includes the following steps: S1. After the PWM triggers the FOC interrupt function, Task 1 and Task 2 are executed in parallel. Task 1: Implement the traditional FOC algorithm and achieve dual sampling and dual updating of the three-phase current by adjusting the triggering mode of ADC current sampling to a dual triggering mode of PWM cycle zero point and midpoint at the software level, thereby generating a PWM modulation wave; Task 2: Generate random triangular carrier frequencies, and then sort the uniformly distributed random numbers in the triangular carrier frequencies using a two-state probability number based on a Markov chain to obtain a random array that follows a two-state Markov chain. S2. Generate a PWM switching control signal using the PWM modulation wave obtained from Task 1 and the random array following a two-state Markov chain obtained from Task 2.

2. The method for suppressing switching noise of a motor controller according to claim 1, characterized in that: The traditional FOC algorithm in step S1 includes current sampling, voltage sampling, rotor angle sampling, coordinate system transformation, PI regulation, and SVPWM space vector modulation algorithm.

3. The method for suppressing switching noise of a motor controller according to claim 1, characterized in that: Task one in step S1 specifically includes: 1.

1. The FOC interrupt function is triggered by the PWM channel, and the ADC current sampling of the three-phase current is performed. 1.

2. Set the carrier mode to Up-Down mode; 1.3 Set the ADC sampling trigger mode to trigger ADC sampling at the zero point and the maximum point of the PWM counting cycle respectively; 1.

4. Set the FOC interrupt triggering mode to trigger the FOC interrupt at the zero point and the maximum point of the PWM counting cycle, respectively.

4. The method for suppressing switching noise of a motor controller according to claim 1, characterized in that: The specific steps for generating the random triangular carrier frequency in step S1 are as follows: A uniformly distributed random number array G[] within the range of -1 to 1 is generated using a uniform random number generator. Then, the uniformly distributed random number array G[] is multiplied by the random frequency gain value k, and finally, the minimum value f of the frequency range is added. min Thus, a random number f that follows a uniform distribution is obtained. s , ； in, f min It is the minimum value in the frequency range, f max G[i] is the maximum value in the frequency range, and G[i] is a random number in the uniformly distributed array G[]. Random numbers f that follow a uniform distribution s The range of values ​​for F is s ±F s / 4, F s It is the original fixed center switching frequency.

5. The method for suppressing switching noise of a motor controller according to claim 1, characterized in that: In step S1, the random numbers uniformly distributed in the triangular carrier frequency are processed using a two-state probability number based on a Markov chain to obtain a random array that follows a two-state Markov chain. 2.1 Assume that R[i]>0 represents state S1 and R[i]<0 represents state S2; R[i] is a random number f that follows a uniform distribution. s Random numbers in the data; 2.2 For any random number f that follows a uniform distribution s Given a random number R[i] (i=0, 1, 2, ...), determine whether R[i] is greater than a preset threshold m. P is the transition probability, and ; 2.3 If R[i] is greater than the preset threshold m, and the previous random number R[i-1] of the random number R[i] is in state S1, the current random number M[i] after the transition is in state S2, that is, M[i] = -R[i]; 2.4 If R[i] is less than or equal to the preset threshold m, and the previous random number R[i-1] of the random number R[i] is in state S1, then the current random number M[i] after the transition is in state S1, that is, M[i] = R[i]. 2.

5. Multiply the transferred random array M[] by the gain G to obtain the random array GMarkov[], that is, GMarkov[] = M[] * G; 2.6 The final random array is Rfinal[]. ； Among them, F s It is the original fixed center switching frequency.

6. The method for suppressing switching noise of a motor controller according to claim 1, characterized in that: Step S2 specifically involves: obtaining a random array that follows a two-state Markov chain from Task 2, i.e., the final random array Rfinal[], whose frequency is a finite random change. Then, converting the frequency of the final random array Rfinal[] into a PWM-compliant period count value, establishing a random frequency-PWM period index lookup table for any random frequency Rfinal[i] in the final random array Rfinal[] and the corresponding PWM-compliant period count value. The next PWM switching period value is obtained in real time by iteratively looking up the table.

7. The method for suppressing switching noise of a motor controller according to claim 1, characterized in that: Step S2 specifically involves: dividing the zero vector of the PWM modulation wave obtained from Task 1 into two uniform segments and setting them into three states: First state: Both zero vectors appear at the beginning of the PWM modulation wave; Second state: Both zero vectors appear at the end of the PWM modulation wave; Third state: One segment of zero vector appears at the beginning of the PWM modulation wave, and another segment of zero vector appears at the end of the PWM modulation wave; The random array obtained from Task 2, which follows a two-state Markov chain, is used to correspond to the three states of the zero vector of the PWM modulation wave, thereby generating a PWM switching control signal with a random Markov chain.

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