WO2018159116A1 - 信号処理システム - Google Patents

信号処理システム Download PDF

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Publication number
WO2018159116A1
WO2018159116A1 PCT/JP2018/000830 JP2018000830W WO2018159116A1 WO 2018159116 A1 WO2018159116 A1 WO 2018159116A1 JP 2018000830 W JP2018000830 W JP 2018000830W WO 2018159116 A1 WO2018159116 A1 WO 2018159116A1
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state
signal
algorithm
processing system
phase
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English (en)
French (fr)
Japanese (ja)
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達也 榎並
浩伸 秋田
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Denso Corp
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Denso Corp
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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
    • G05B11/00—Automatic controllers
    • G05B11/01—Automatic controllers electric
    • G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P29/00—Arrangements for regulating or controlling electric motors, appropriate for both AC and DC motors

Definitions

  • This disclosure relates to a signal processing system that reduces noise generated in a control target.
  • a signal processing system in which noise generated in a control target is reduced by superimposing a compensation signal on a control signal for controlling the operation of the control target.
  • a motor control system that reduces torque ripple by generating torque opposite in phase to torque ripple by superimposing a noise correction signal corresponding to a compensation signal on a motor control signal can be mentioned.
  • a secondary path model is required.
  • the error of the secondary path model affects the convergence characteristics of the algorithm. Specifically, it is known that the algorithm diverges when there is a phase error of ⁇ 90 ° or more between the phase rotation of the secondary path model and the actual phase rotation of the secondary path.
  • a method of creating a secondary route model a method of creating by measuring the target system is common.
  • this method cannot be applied to a system whose characteristics change greatly due to an operating state or aging.
  • the above method cannot be applied because the characteristics fluctuate greatly depending on the rotation speed and the total travel distance.
  • An object of the present disclosure is to provide a signal processing system that can stably suppress noise without requiring complicated calculation.
  • the signal processing system is a system that reduces noise generated in a control target by superimposing a compensation signal on a control signal for controlling the operation of the control target, and a signal generation unit, A state detection unit and a phase shift unit are provided.
  • the signal generation unit generates a compensation signal according to a predetermined algorithm.
  • the state detection unit detects the state of the algorithm and detects at least the divergent state.
  • the phase shift unit shifts the phase of the compensation signal by a predetermined angle when the divergence state is detected by the state detection unit.
  • the phase shift unit shifts the phase of the compensation signal by a predetermined angle. If the algorithm shifts to a stable state where the algorithm converges as a result of such a phase shift, a noise reduction effect can be obtained stably thereafter. If the algorithm is still unstable and remains in the divergence state as a result of the phase shift, the divergence detection by the state detection unit and the phase shift by the phase shift unit are executed again.
  • the phase shift is repeatedly performed until the algorithm is stabilized, and eventually the algorithm transitions to a stable state, and as a result, a noise reduction effect can be stably obtained. Furthermore, in the above configuration, mounting with a simple circuit is possible without requiring a complicated operation, so even if the order of suppression noise increases, the mounting cost does not increase significantly. As described above, according to the above configuration, it is possible to obtain an excellent effect that noise can be stably suppressed without requiring a complicated calculation.
  • FIG. 1 is a diagram schematically showing the configuration of the motor control system according to the first embodiment.
  • FIG. 2 is a diagram schematically illustrating the configuration of the noise suppression algorithm according to the first embodiment.
  • FIG. 3 is a diagram for explaining the relationship between the error signal and each threshold value according to the first embodiment.
  • FIG. 4 is a state transition diagram showing state detection and phase shift operation according to the first embodiment.
  • FIG. 5 is a state transition table representing state detection and phase shift operation according to the first embodiment.
  • FIG. 6 is a diagram for explaining a specific example of the state transition according to the first embodiment.
  • FIG. 7 is a state transition diagram showing state detection and phase shift operation according to the second embodiment.
  • FIG. 8 is a state transition table representing state detection and phase shift operation according to the second embodiment.
  • FIG. 9A is a diagram for explaining a specific example 1 of the state transition according to the second embodiment;
  • FIG. 9B is a diagram for explaining a specific example 2 of the state transition according to the second embodiment.
  • FIG. 9C is a diagram for explaining a specific example 3 of the state transition according to the second embodiment.
  • FIG. 10 is a state transition diagram showing state detection and phase shift operation according to the third embodiment.
  • FIG. 11 is a state transition table representing state detection and phase shift operation according to the third embodiment.
  • FIG. 12A is a diagram for explaining a specific example 1 of the state transition according to the third embodiment;
  • FIG. 12B is a diagram for explaining a specific example 2 of the state transition according to the third embodiment.
  • FIG. 12C is a diagram for explaining a specific example 3 of the state transition according to the third embodiment.
  • FIG. 13 is a state transition diagram showing state detection and phase shift operation according to the fourth embodiment.
  • FIG. 14 is a state transition table representing state detection and phase shift operation according to the fourth embodiment.
  • FIG. 15A is a diagram for explaining a specific example 1 of the state transition according to the fourth embodiment;
  • FIG. 15B is a diagram for explaining a specific example 2 of the state transition according to the fourth embodiment;
  • FIG. 15C is a diagram for explaining a specific example 3 of the state transition according to the fourth embodiment.
  • FIG. 16 is a diagram schematically illustrating a configuration of a noise suppression algorithm according to the fifth embodiment.
  • FIG. 17 is a diagram schematically illustrating a configuration of a noise suppression algorithm according to the sixth embodiment.
  • a motor control system 1 shown in FIG. 1 controls a motor 2, and is a signal processing system that reduces noise generated by the motor 2 to be controlled, specifically, torque ripple.
  • the motor control system 1 includes a speed sensor 3, a speed controller 4, a noise suppression algorithm 5, and the like.
  • the actual output of the motor 2 is a torque ripple superimposed on the output of the ideal motor G (s).
  • the speed sensor 3 detects the rotational speed from the output of such a motor 2.
  • a speed detection signal ⁇ representing the rotational speed of the motor 2 output from the speed sensor 3 is given to the speed controller 4 and the noise suppression algorithm 5.
  • the speed sensor 3 corresponds to a detection unit that detects a rotational speed that is a physical quantity that changes in accordance with the state of the motor 2 that is a control target. Further, the speed detection signal ⁇ corresponds to a detection signal.
  • the noise suppression algorithm 5 generates a noise correction signal for reducing torque ripple generated in the motor 2.
  • the control signal output from the speed controller 4 is input to the motor 2 after the noise correction signal output from the noise suppression algorithm 5 is superimposed by the adder 6.
  • the noise correction signal corresponding to the compensation signal is superimposed on the control signal for controlling the operation of the motor 2 to be controlled, so that torque having a phase opposite to that of the torque ripple is obtained. And torque ripple is reduced.
  • the noise suppression algorithm 5 acquires a ripple component of each order from the speed detection signal ⁇ that is information representing the rotation speed of the motor 2 provided from the speed sensor 3.
  • the noise suppression algorithm 5 performs divergence detection by using an error signal Err calculated from the ripple component.
  • the multiplier 7 multiplies the speed detection signal ⁇ by a sine wave of the target order (hereinafter also referred to as the n-th order) that is subject to noise suppression, and the multiplier 8 Multiply ⁇ by an nth-order cosine cosine wave.
  • the outputs of the multipliers 7 and 8 are an nth-order sin component and an nth-order cosine component, respectively, and correspond to an nth-order ripple component.
  • LMSs 9 and 10 are algorithms similar to the feedback control, and calculate the sin component and the cos component of the correction signal to be output so that the n-th order sin component and the cos component obtained in the previous stage become zero. That is, the LMSs 9 and 10 calculate the difference between the sin component and the cos component and the target value of 0, and repeat the same calculation as the integral control in which the difference is multiplied by a constant ( ⁇ ) and integrated to the previous value. , A correction signal such that the sin component and the cos component become 0 is output.
  • the cos component Qout of the output signal is updated based on the following equation (1).
  • Qout (n + 1) Qout (n) + ⁇ (0 ⁇ Qin) (1)
  • the sin component of the output signal is obtained based on the following equation (2).
  • Iout (n + 1) Iout (n) + ⁇ (0 ⁇ Iin) (2) Therefore, the correction signal is expressed by the following equation (3).
  • the multiplier 11 multiplies the sin component of the correction signal that makes the nth-order rotation ripple obtained by the LMS 9 zero, and the multiplier 12 multiplies the cos component of the correction signal by the cos wave.
  • the adder 13 outputs a correction signal obtained by adding the outputs of the multipliers 11 and 12 to the phase shift unit 14.
  • the outputs of the multipliers 7 and 8, that is, the nth-order sin component and the nth-order cosine component are respectively squared and then input to the adder 15.
  • the adder 15 outputs an error signal Err obtained by adding the squares of the outputs of the multipliers 7 and 8.
  • the error signal Err is calculated using the speed detection signal ⁇ . Specifically, the error signal Err is obtained by summing the sine component and the cosine component of the target order extracted from the speed detection signal ⁇ . This is the signal obtained.
  • the state detection unit 16 compares the error signal Err with a plurality of threshold values, and detects the state of the algorithm based on the comparison result. In this case, the state detection unit 16 detects which state is a divergent state, a metastable state, or a stable state (hereinafter also referred to as a converged state). Each of these states (divergence state, metastable state, and stable state) is determined depending on the phase error that is the difference between the phase rotation value of the secondary path model and the phase rotation value of the actual secondary path. Is.
  • the metastable state is a state in which noise (torque ripple) cannot be suppressed stably but is not diverging, that is, a state in which neither the stable state nor the diverging state is present.
  • the state detection unit 16 outputs a signal indicating the state of the detected algorithm.
  • the phase shift unit 14 detects the state of the algorithm based on the output signal of the state detection unit 16 and shifts the phase of the correction signal supplied from the adder 13 according to the detected state.
  • the phase shift unit 14 may not shift the phase of the correction signal depending on the detected state.
  • the phase shift unit 14 outputs a signal obtained by shifting the phase of the correction signal as a noise correction signal.
  • a noise correction signal for reducing torque ripple generated in the motor 2 is generated by the multipliers 7, 8, 11, 12, LMS 9, 10 and the adder 13 according to a predetermined algorithm.
  • a signal generation unit 17 is configured.
  • the state detection unit 16 determines the error signal Err using three threshold values Th_h, Th_m, and Th_l.
  • the relationship between the threshold values Th_h, Th_m, Th_l is as shown in the following equation (4).
  • the threshold Th_l corresponds to a first threshold
  • the threshold Th_m corresponds to a second threshold larger than the first threshold
  • the threshold Th_h corresponds to a third threshold larger than the second threshold.
  • the state detection unit 16 determines that the algorithm is in the convergence state S1 when the level of the error signal Err is lower than the threshold value Th_l.
  • the state detection unit 16 determines that the algorithm is in the metastable state S2 when the level of the error signal Err exceeds the threshold Th_m.
  • the state detection unit 16 determines that the algorithm is the divergence state S3. Note that S0 in FIG. 3 represents an initial state.
  • a hysteresis is provided for each threshold value in order to suppress the influence of harmonics and noise superimposed on the error signal Err.
  • the determination condition is satisfied by exceeding or falling below the threshold value a predetermined number of times within a certain time, the influence of noise and the like can be further suppressed.
  • the threshold values Th_m and Th_h are set to be sufficiently higher than the level of the error signal Err at the time of algorithm convergence.
  • the reason for setting the threshold in this way is as follows. That is, even when the algorithm converges, the level of the error signal Err may fluctuate initially due to the influence of noise or the like. If the error signal Err exceeds the threshold values Th_m and Th_h due to such fluctuations, the phase of the noise correction signal may be shifted although it is not necessary to shift the phase. As described above, if the threshold values Th_m and Th_h are set, the occurrence of such a problem can be prevented.
  • the threshold Th_h when the error signal Err exceeds the threshold Th_h, the threshold Th_h is increased by a certain amount dTh.
  • dTh the threshold value of the error signal Err is increasing. Therefore, when the error signal Err exceeds the threshold value Th_h and divergence is detected and phase shift is performed, but the divergence state is not resolved, if the threshold value Th_h is a constant value, then the threshold value Th_h is again exceeded. There is a possibility that the determination is not made and the state of the algorithm is erroneously determined. As described above, such a problem can be prevented by increasing the threshold value Th_h by a certain amount dTh.
  • the following method (hereinafter referred to as the second method) is used instead of the method of increasing the threshold Th_h by a certain amount dTh (hereinafter referred to as the first method).
  • the first method has an advantage that the divergence can be detected earlier than the second method. Therefore, the first method and the second method may be combined.
  • phase shift unit 14 detects the algorithm state based on the output signal of the state detection unit 16, and shifts the phase of the correction signal according to the detected state. To do. Specifically, when it is detected that the phase is in a divergent state or a metastable state, the phase shift unit 14 shifts the phase of the correction signal by a predetermined angle ⁇ degree.
  • the predetermined angle ⁇ may be any positive or negative angle.
  • the error signal Err changes in either the increasing or decreasing direction. Therefore, even if the phase shift is not performed, the state transits to the diverging state S3 or the convergence state S1. If phase shifting is performed at this time, such a state transition is prevented, and as a result, a situation may occur in which the metastable state S2 is looped. As described above, if the phase shift is not performed, such a problem can be prevented.
  • the algorithm transitions to a converged state by three phase shifts. That is, transition from the range ⁇ 6> to the range ⁇ 5> by the first phase shift, transition from the range ⁇ 5> to the range ⁇ 4> by the second phase shift, and convergence by the third phase shift. It shifts to the range ⁇ 3>.
  • the motor control system 1 of the present embodiment includes a state detection unit 16 that detects the state of the algorithm, and a phase that shifts the phase of the noise correction signal by a predetermined angle when the state detection unit 16 detects a divergence state or a metastable state.
  • a shift unit 14 is provided. According to such a configuration, when the algorithm is diverged or metastable, the phase of the noise correction signal is shifted by the predetermined angle ⁇ . If the algorithm shifts to a stable state where the algorithm converges as a result of such a phase shift, a noise reduction effect can be obtained stably thereafter.
  • the phase shift unit 14 shifts the phase of the noise correction signal to make the algorithm transition to the stable state.
  • the phase shift is repeatedly performed until the algorithm is stabilized. Eventually, the algorithm transitions to a stable state, and as a result, a noise reduction effect can be stably obtained.
  • the above configuration can be implemented with a simple circuit without requiring a complicated operation, even if the order of suppression noise increases, the mounting cost does not increase significantly.
  • the state detection unit 16 compares the error signal Err calculated using the speed detection signal ⁇ output from the speed sensor 3 with a threshold, and detects the state of the algorithm based on the comparison result.
  • the detection accuracy can be satisfactorily maintained by a simple circuit configuration.
  • the error signal Err is a signal obtained by summing the sine component and the cos component of the target order extracted from the speed detection signal ⁇ . In this way, the level of the error signal Err is increased, the detection sensitivity is improved, and the detection accuracy is improved.
  • the phase shift unit 14 of the present embodiment shifts the phase of the correction signal by 180 degrees when it is detected that the state is a divergent state based on the output signal of the state detection unit 16.
  • the state detection operation by the state detection unit 16 and the phase shift operation by the phase shift unit 14 according to the present embodiment can be summarized as a state transition diagram shown in FIG. 7 and a state transition table shown in FIG.
  • FIGS. 9A to 9C a specific example of state transition according to the present embodiment will be described with reference to FIGS. 9A to 9C.
  • 90 degrees has no phase error.
  • FIG. 9A when the initial phase is a value in the range ⁇ 6> and the algorithm is in the divergent state, the phase shifts to the converged state ⁇ 2> by one phase shift.
  • the phase shifts to the range ⁇ 1> in the metastable state by one phase shift.
  • the phase shifts to the range ⁇ 4> in the metastable state by one phase shift.
  • the algorithm transitions to the converged state or metastable state by one phase shift.
  • the metastable state is not necessarily a problem, and there are many signal processing systems that allow the metastable state. If the phase shift method of this embodiment is applied to such a system, it is possible to shift to an always allowed state, that is, a convergence state or a metastable state by one phase shift. Therefore, according to the present embodiment, in a system that allows a metastable state, it is possible to obtain an excellent effect that the system can be stabilized more quickly.
  • FIGS. 10 to 12C a third embodiment will be described with reference to FIGS. 10 to 12C.
  • This embodiment is different from the first embodiment in a specific method of phase shift by the phase shift unit 14.
  • the configuration of this embodiment is the same as that of the first embodiment, and will be described below with reference to FIGS.
  • the phase shift unit 14 of the present embodiment shifts the phase of the correction signal by +90 degrees or ⁇ 90 degrees when it is detected that the state is the divergent state or the metastable state based on the output signal of the state detection unit 16.
  • the state detection operation by the state detection unit 16 and the phase shift operation by the phase shift unit 14 according to the present embodiment can be summarized as a state transition diagram shown in FIG. 10 and a state transition table shown in FIG.
  • FIGS. 12A to 12C a specific example of state transition according to the present embodiment will be described with reference to FIGS. 12A to 12C.
  • 90 degrees has no phase error.
  • the 90 degree phase shift is in the plus direction.
  • FIG. 12A when the initial phase is a value in the range ⁇ 7> and the algorithm is in a divergent state, the phase shifts to the range ⁇ 1> in the metastable state by the first phase shift, The phase shifts to the range ⁇ 3> which is a converged state by the second phase shift.
  • an excellent effect is obtained that noise can be stably suppressed without requiring a complicated calculation.
  • the system can be surely shifted to the converged state by a maximum of three phase shifts, so that an excellent effect that the system can be stabilized quickly is obtained.
  • the phase shift unit 14 of the present embodiment shifts the phase of the correction signal by 180 degrees when it is detected that the state is a divergent state based on the output signal of the state detection unit 16. Further, the phase shift unit 14 of the present embodiment shifts the phase of the correction signal by +90 degrees or ⁇ 90 degrees when the metastable state is detected based on the output signal of the state detection unit 16.
  • the state detection operation by the state detection unit 16 and the phase shift operation by the phase shift unit 14 according to the present embodiment can be summarized as a state transition diagram shown in FIG. 13 and a state transition table shown in FIG.
  • FIGS. 15A to 15C a specific example of state transition according to the present embodiment will be described with reference to FIGS. 15A to 15C.
  • 90 degrees has no phase error.
  • the 90 degree phase shift is in the plus direction.
  • FIG. 15A when the initial phase is a value in the range ⁇ 6> and the algorithm is in a divergent state, a phase shift of 180 degrees is performed to shift to the converged state ⁇ 2>. To do.
  • the phase shift of 180 degrees is performed to the metastable state ⁇ 4>. Then, the phase shift of 90 degrees is performed to shift to the range ⁇ 6> in the divergent state, and then the phase shift of 180 degrees is performed to shift to the range ⁇ 2> in the converged state To do.
  • the algorithm transitions to the converged state by a maximum of three phase shifts.
  • an excellent effect is obtained that noise can be stably suppressed without requiring a complicated calculation.
  • the system can be surely shifted to the converged state by a maximum of three phase shifts, so that an excellent effect that the system can be stabilized quickly is obtained.
  • the noise suppression algorithm 21 of the present embodiment has low-pass filters 22 and 23 (hereinafter abbreviated as LPFs 22 and 23) added to the noise suppression algorithm 5 of the first embodiment. Is different.
  • the outputs of the multipliers 7 and 8, that is, the n-th order sin component and the n-th order cosine component are input to the LPFs 22 and 23, respectively.
  • the LPFs 22 and 23 are designed so that harmonic noise larger than the nth order superimposed on the speed detection signal ⁇ can be removed.
  • the outputs of the LPFs 22 and 23 are respectively squared and then input to the adder 15.
  • the error signal Err is a signal obtained by summing the outputs of the LPFs 22 and 23 that input the sine component and the cosine component of the target order extracted from the speed detection signal ⁇ . ing.
  • the error signal Err is a signal obtained by summing the outputs of the LPFs 22 and 23 that receive the sin component and the cos component of the target order extracted from the speed detection signal ⁇ . In this way, as in the first embodiment, the level of the error signal Err is increased, the detection sensitivity is improved, and the detection accuracy is improved. Furthermore, according to the present embodiment, since the LPFs 22 and 23 are provided, there is also an effect that it is difficult to be affected by noise.
  • the noise suppression algorithm 31 of this embodiment differs from the noise suppression algorithm 21 of the fifth embodiment in the generation method of the error signal Err.
  • the absolute values of the outputs of the LPFs 22 and 23 are obtained, and these absolute values are input to the adder 15.
  • the error signal Err is a signal obtained by summing absolute values of the outputs of the LPFs 22 and 23 that input the sine component and cos component of the target order extracted from the speed detection signal ⁇ . It has become.
  • the error signal Err is a signal obtained by summing absolute values of the outputs of the LPFs 22 and 23 to which the sine component and cos component of the target order extracted from the speed detection signal ⁇ are input. Therefore, according to the present embodiment, although the level of the error signal Err is lower than that of the fifth embodiment, an effect that the circuit scale can be reduced can be obtained.
  • the present disclosure is not limited to the embodiments described above and illustrated in the drawings, and can be arbitrarily modified, combined, or expanded without departing from the scope of the present disclosure.
  • the present disclosure is not limited to the motor control system 1 that controls the motor 2, and a signal processing system that reduces noise generated in a control target by superimposing a compensation signal on a control signal for controlling the operation of the control target. It can be applied in general.
  • the present disclosure can be applied to general uses such as suppressing periodic noise generated in a system controlled by a periodic signal.
  • the state detector 16 only needs to be able to detect the state of the algorithm, and its specific configuration and detection method can be changed as appropriate. For example, the determination is not limited to using three thresholds, and may be determination using one, two, four or more thresholds. Further, after detecting the divergent state, the threshold value Th_h may not be increased by a certain amount dTh. Further, it is not necessary to provide hysteresis for the threshold value. Furthermore, the state detection part 16 should just be the structure which detects the divergence state of an algorithm at least. In that case, the phase shift unit 14 may be configured to shift the phase of the compensation signal by a predetermined angle when a divergence state is detected by the state detection unit 16. Even in such a configuration, the phase can be shifted to a converged state or a metastable state by performing a predetermined number of phase shifts.

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Control Of Electric Motors In General (AREA)
  • Feedback Control In General (AREA)
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