CN111103867B - Motor controller rotation fault positioning and self-adaptive learning method - Google Patents
Motor controller rotation fault positioning and self-adaptive learning method Download PDFInfo
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- CN111103867B CN111103867B CN201911364174.5A CN201911364174A CN111103867B CN 111103867 B CN111103867 B CN 111103867B CN 201911364174 A CN201911364174 A CN 201911364174A CN 111103867 B CN111103867 B CN 111103867B
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0286—Modifications to the monitored process, e.g. stopping operation or adapting control
Abstract
The invention discloses a motor controller resolver fault positioning and self-adaptive learning method, which samples an I/O resolver value transmitted to a digital signal processor by a resolver decoding chip in real time; calculating the difference value of the current moment and the last moment of the rotary change sampling values, and calculating the difference value of the last moment and the last moment of the rotary change sampling values; calculating absolute values of the two difference values, starting calculation from the (n + 2) th bit when the rotary transformer has n-bit random run-out, and determining the output abnormal position of the rotary transformer by judging the absolute values of the two difference values when one bit m of the rotary transformer is abnormal and the absolute values of the two difference values are related to the acceleration of the motor, the sampling time, the n-bit random run-out value of the rotary transformer and the m-bit abnormal run-out value; and negating the current abnormal position to realize the self-adaptive learning function. The method can position a specific abnormal position, and when the abnormal position is positioned, the value of the abnormal position is judged through other normal rotary values, so that self-adaptive learning is achieved, and normal running of the vehicle is ensured.
Description
Technical Field
The invention relates to the technical field of motor control, in particular to a method for positioning and self-adaptive learning of a rotation fault of a motor controller.
Background
The motor rotation value in the new energy automobile influences the control of a motor controller on the motor, and if the rotation value has large amplitude sudden change, current is out of control, so that the whole automobile loses power under the normal running condition.
At present, all the rotary transformer decoding chips have an internal signal fault detection function, the commonly used decoding chips have AD2S1210 of ADI, AU6803 of Morgan and PGA411 of TI, and all the internal fault detection functions are used, and when the analog signals received by the decoding chips are abnormal, the corresponding faults can be reported in real time in the decoding chips.
Generally, the 12bit AD value output by the decoding chip is connected to an I/O port of a digital signal processor, and the digital signal processor converts the received AD value into a corresponding rotation value. When the PCB connection between the decoding chip 12bit and the I/O port of the digital signal processor has an open circuit problem, the motor controller cannot judge whether the rotation value is abnormal or not because the decoding chip does not report a fault, and finally the controller works abnormally when the whole vehicle runs.
If the 12bit low bit output by the decoding chip is abnormal, the whole vehicle end is characterized in that the whole vehicle can shake when the whole vehicle runs at a low speed; if the 12bit high order output by the decoding chip is abnormal, the embodiment of the whole vehicle end is that the motor phase current is out of control, the motor controller reports overload and reduces power to operate, thereby seriously influencing the normal running of the vehicle and having certain potential safety hazard.
Disclosure of Invention
The invention aims to solve the technical problem of providing a motor controller resolver fault positioning and self-adaptive learning method, which solves the problems that the 12bit parallel port output of the traditional resolver decoding chip is abnormal but the specific position cannot be positioned.
In order to solve the technical problem, the method for positioning the rotational variation fault and self-adaptive learning of the motor controller comprises the following steps:
step one, sampling an I/O (input/output) rotary variable value transmitted to a digital signal processor by a rotary variable decoding chip in real time;
step two, calculating the current time t n The value of the sampling and t n-1 Calculating the difference value delta R1 of the sampling values of the time rotation change, and calculating t n-1 Time-rotation sampling value and t n-2 The difference value delta R2 of the sampling values is rotationally changed at the moment;
step three, calculating the absolute value of the difference value between the delta R1 and the delta R2, and when the motor is accelerated to a w Very small and the sampling time Δ t very short, theoretically a w ×Δt 2 The value is close to zero and is smaller than the lowest bit fluctuation value of the rotary transformer;
step four, when the rotary transformer has n-bit random jump, starting calculation from the n + 2bit, and when a certain bit m of the rotary transformer is abnormal, delta R1-delta R2= a w ×Δt 2 ±r 1 ±r 2 ±...±r n +r m When- | a w |×Δt 2 -r 1 -r 2 -...-r n +r m ≤|ΔR1-ΔR2|≤|a w |×Δt 2 +r 1 +r 2 +...+r n +r m When the output is abnormal, the m bit output of the rotary transformer is considered to be abnormal,
wherein n is less than m, r 1 、r 2 …r n Is a rotating n-bit random jitter value, r m A jitter value of a certain bit m of the rotary transformer which is abnormal;
and step five, when the rotation transformation fault is positioned to a certain abnormal position, inverting the current abnormal position to realize the self-adaptive learning function, and even if the rotation transformation fault is positioned to a certain abnormal position, the motor controller can normally run.
The motor controller resolver fault positioning and self-adaptive learning method adopts the technical scheme, namely the method samples the I/O resolver value transmitted to the digital signal processor by the resolver decoding chip in real time; calculating the difference value of the current moment and the last moment of the rotary change sampling values, and calculating the difference value of the last moment and the last moment of the rotary change sampling values; calculating the absolute values of two difference values, starting to calculate from the (n + 2) th bit when the rotary transformer has n-bit random jitter, and determining the output abnormal bit of the rotary transformer by judging the absolute values of the two difference values when a certain bit m of the rotary transformer is abnormal and the absolute values of the two difference values are related to the acceleration of the motor, the sampling time, the n-bit random jitter value of the rotary transformer and the m-bit abnormal jitter value; and negating the current abnormal position to realize the self-adaptive learning function. The method solves the problem that the 12bit parallel port output of the traditional rotary transformer decoding chip is abnormal but cannot be used for positioning specifically, can position specific abnormal bits, can shield the abnormal bits when the abnormal bits are positioned, and judges the value of the abnormal bits through other normal rotary transformer values, thereby achieving self-adaptive learning, ensuring the normal running of vehicles and avoiding potential safety hazards.
Drawings
The invention is described in further detail below with reference to the following figures and embodiments:
FIG. 1 is a schematic block diagram of a method for positioning a rotational fault and adaptive learning of a motor controller according to the present invention;
fig. 2 is a block diagram of the calculation of the rotation-variation sampling difference in the method.
Detailed Description
Embodiment as shown in fig. 1 and fig. 2, the method for locating and adaptively learning a rotational fault of a motor controller according to the present invention includes the following steps:
step one, sampling an I/O (input/output) rotary variable value transmitted to a digital signal processor by a rotary variable decoding chip in real time;
step two, calculating the current time t n The value of the sampling and t n-1 Calculating the difference value delta R1 of the sampling values of the time rotation change, and calculating t n-1 Time-rotation sampling value and t n-2 The difference value delta R2 of the sampling values is rotationally changed at the moment;
step three, calculating the absolute value of the difference value between the delta R1 and the delta R2, and when the motor is accelerated to a w Very small and the sampling time Δ t very short, theoretically a w ×Δt 2 The value is close to zero and is smaller than the lowest fluctuation value of the rotary transformer;
step four, when the rotary transformer has n-bit random jump, starting calculation from the n + 2bit, and when a certain bit m of the rotary transformer is abnormal, delta R1-delta R2= a w ×Δt 2 ±r 1 ±r 2 ±...±r n +r m When- | a w |×Δt 2 -r 1 -r 2 -...-r n +r m ≤|ΔR1-ΔR2|≤|a w |×Δt 2 +r 1 +r 2 +...+r n +r m When the output is abnormal, the m bits of the rotary transformer are considered to be abnormal,
wherein n is less than m, r 1 、r 2 …r n Is a rotating n-bit random jitter value, r m A jitter value of a certain bit m which is subjected to abnormal rotation; n represents the number of random jump bits of the rotary transformer, m represents a certain bit with abnormality in the rotary transformer, the abnormal bit is detected, and if the abnormal bit is smaller than the jump bit of the abnormal bit, the abnormal bit cannot be detected;
and step five, when the rotation transformation fault is positioned to a certain abnormal position, inverting the current abnormal position to realize the self-adaptive learning function, and even if the rotation transformation fault is positioned to a certain abnormal position, the motor controller can normally run.
In the self-adaptive learning, firstly, a level signal of a rotary transformer 12bit '0' or '1' output by a rotary transformer decoding chip is received; secondly, whether the level signal of the 12bit '0' or '1' of the rotational variation is accurate or not is continuously detected through the method, if the detection is accurate, the processing is not carried out, if the detection is wrong, the inversion operation is carried out, and because the level of the normal bit of the rotational variation is not '0' or '1', the error correction is realized through the inversion operation, and the purpose of self-adaptive learning is achieved.
In the process of sampling the rotary transformer, sampling the rotary transformer value in equal delta t sampling time, and calculating to obtain the current time t when the rotary transformer works normally n The value of the sampling and t n-1 Difference value delta R1, t of sampling values of time rotation n-1 Sampling value and t of time rotation n-2 The difference Δ R2 of the sampling values is rotated at the time. t is t n-1 Motor speed S of time n-1 ＝ΔR2/Δt，t n Motor speed S of time n = Δ R1/Δ t, the motor acceleration a w ＝(S n -S n-1 ) At,/Δ t, i.e. a w ×Δt 2 = Δ R1- Δ R2. Due to the precision limit of the rotation change decoding chip and the low-order jumping bit of the rotation change, if the rotation change has n-order random jumping, the delta R1-delta R2= a w ×Δt 2 ±r 1 ±r 2 ±...±r n I.e. | Delta R1-Delta R2| ≦ a w |×Δt 2 +r 1 +r 2 +...+r n When the output is normal, the rotation change output is considered to be normal. The rotary variable decoding chip used at present has the precision of 2LSB at most, namely two random jumps at most.
As shown in fig. 1, at the actual rotational fault location,
1. acquiring a rotation value in real time, and determining a random jumping bit n of the used rotation decoding chip according to an instruction manual of the rotation decoding chip;
2. calculating S 1 ＝|a w |×Δt 2 +r 1 +r 2 +...+r n If | Delta R1-Delta R2| is less than or equal to S 1 Considering the I/O transmission of the resolver decoding output to the digital signal processor to be normal;
3. if | Δ R1- Δ R2| > S 1 Then calculate S m-1 ＝-|α w |×Δt 2 -r 1 -r 2 -...-r n +r m While if | Δ R1- Δ R2| < S m-1 Considering that the I/O transmission of the resolver decoding output to the digital signal processor is abnormal;
4. if | Δ R1- Δ R2| > or more than S m-1 Then calculate S m+1 ＝|α w |×Δt 2 +r 1 +r 2 +...+r n +r m And meanwhile, if the | delta R1-delta R2| is less than or equal to S m+1 If so, the m-th bit is considered to be abnormal;
5. negating the mth bit;
6. if | Δ R1- Δ R2| > S m+1 Then calculate S m+2 ＝-|a w |×Δt 2 -r 1 -r 2 -...-r n +r m+1 While if | Δ R1- Δ R2| < S m+2 If the I/O transmission output by the rotary transformer decoding to the digital signal processor is abnormal;
7. if | Δ R1- Δ R2| ≧ S m+2 Then calculate S m+3 ＝|α w |×Δt 2 +r 1 +r 2 +...+r n +r m+1 And meanwhile, if the | delta R1-delta R2| is less than or equal to S m+3 If so, the m +1 th bit is considered to be abnormal and inverted; the other bits are analogized in turn.
The method can detect the abnormal connection between the 12bit output by the rotary transformer decoding chip and the I/O port of the digital signal processor, can position a specific abnormal point of the rotary transformer, and finally realizes the self-adaptive learning function by negating the current abnormal point.
Claims (1)
1. A motor controller rotation fault positioning and self-adaptive learning method is characterized by comprising the following steps:
step one, sampling an I/O (input/output) rotary variable value transmitted to a digital signal processor by a rotary variable decoding chip in real time;
step two, calculating the current time t n Resolver sampling value and t n-1 Calculating the difference value delta R1 of the sampling values of the time rotation change, and calculating t n-1 Sampling value and t of time rotation n-2 The difference value delta R2 of the sampling values is rotationally changed at the moment;
step three, calculating the absolute value of the difference value between the delta R1 and the delta R2, and when the motor is accelerated to a w Very small and the sampling time deltat very short, theoretically a w ×Δt 2 Is close to zero, less thanThe lowest bit fluctuation value of the rotation;
step four, when the rotary transformer has n-bit random jump, starting calculation from the n + 2bit, and when a certain bit m of the rotary transformer is abnormal, delta R1-delta R2= a w ×Δt 2 ±r 1 ±r 2 ±…±r n ＋r m When- | a w |×Δt 2 －r 1 －r 2 －…－r n ＋r m ≤|ΔR1－ΔR2|≤|a w |×Δt 2 ＋r 1 ＋r 2 ＋…＋r n ＋r m When the output is abnormal, the m bits of the rotary transformer are considered to be abnormal,
wherein n is<m，r 1 、r 2 …r n Is a rotating n-bit random jitter value, r m A jitter value of a certain bit m which is subjected to abnormal rotation;
and step five, when the rotation transformer fault is positioned to a certain abnormal position, inverting the current abnormal position to realize the self-adaptive learning function, and even if the transmission of a certain rotation transformer is abnormal, the motor controller can also normally operate.
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