Method for damping fluctuation noise in a steering system and device for managing fluctuation noise in a steering system

Abstract

Method for damping fluctuation noise in a steering system (40), the method comprising: a variety of filter parameters (F p ) is calculated, where each filter parameter (F p ) is based on a corresponding signal in the steering system (40); at least one final filter parameter (f p ) from the multitude of filter parameters (F p ) is determined by choosing between the multitude of filter parameters (F p ) is decided; a filter (240) using at least one final filter parameter (f p ) is dynamically configured; and a motor torque command (T* mc ) is filtered using filter (240), with a filtered final motor torque command (T* m ) is applied to a motor (19) to generate a corresponding amount of torque, characterized by the fact that the filter (240) is a low-pass filter (LPF) or a band-pass filter.

Description

BACKGROUND

[0001] The present application relates to a method for damping fluctuation noise in a steering system according to the preamble of claim 1 and to an apparatus for managing fluctuation noise in a steering system according to claim 7.

[0002] Fluctuating noise refers to low-frequency noise typically found in electric power steering (EPS) systems. Existing evidence suggests that the primary source of fluctuating noise is a handwheel torque sensor used in the EPS system. Due to inherent noise in the acquired analog signals, quantization noise in digital sensors, and A / D converters, the acquired torque signal exhibits noise. This fluctuating noise causes discomfort to an EPS operator. Consequently, it is desirable to reduce, if not completely eliminate, fluctuation noise in EPS.

[0003] The generic EP 2 567 881 A2 discloses a control system for an electric power steering system. The power steering system includes a motor, a steering mechanism, a torque sensor, and a control module that controls the operation of the power steering system. The control module includes a driver speed module, a frequency-dependent damping module, a filter coefficient module, an inertia compensation module, and a filter speed module, which are used to provide a compensation command for the motor. SUMMARY

[0004] The object of the invention is to provide an improved method for damping fluctuation noise in a steering system and an improved apparatus for managing fluctuation noise in a steering system.

[0005] To achieve this object, a method having the features of claim 1 and a device having the features of claim 7 are provided. Advantageous embodiments of the invention can be found in the subclaims, the description, and the drawings.

[0006] In accordance with one or more embodiments, a method for mitigating fluctuation noise in a steering system comprises calculating a plurality of filter parameters, each filter parameter based on a corresponding signal in the steering system. The method further comprises determining at least one final filter parameter from the plurality of filter parameters by deciding between the plurality of filter parameters. The method further comprises dynamically configuring a filter using the at least one final filter parameter. The method also comprises filtering a motor torque command using the filter, wherein a filtered motor torque command is applied to a motor to produce a corresponding amount of torque. The filter is a low-pass filter or a band-pass filter.

[0007] In accordance with one or more embodiments, an apparatus for managing fluctuation noise in a steering system includes a filter module that generates a filtered motor torque command based on an input torque command, wherein the filtered motor torque command is applied to a motor to generate a corresponding amount of torque. The apparatus further includes a filter parameter determination module that calculates a plurality of filter parameters, each filter parameter based on a corresponding signal in the steering system. The apparatus further includes an arbitration module that determines at least one final filter parameter from the plurality of filter parameters by arbitrating between the plurality of filter parameters, and dynamically configures the filter module using the at least one final filter parameter.

[0008] A steering system includes a motor that generates an amount of torque corresponding to a motor torque command. The steering system further includes a filter module that generates a filtered motor torque command based on an input torque command, wherein the filtered motor torque command is applied to the motor to generate the corresponding amount of torque. The steering system further includes a filter parameter determination module that calculates a plurality of filter parameters, each filter parameter based on a corresponding signal in the steering system. The steering system further includes an arbitration module that determines at least one final filter parameter from the plurality of filter parameters by arbitrating between the plurality of filter parameters, and dynamically configures the filter module using the at least one final filter parameter.

[0009] This and other advantages and features will become more apparent from the following description when read in conjunction with the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The subject matter of the present disclosure is particularly disclosed and separately claimed in the claims at the end of the specification. The foregoing and other features and advantages of the present disclosure will become apparent from the following detailed description when read in conjunction with the accompanying drawings, in which: Fig. 1 is an exemplary embodiment of an electric power steering system in accordance with one or more embodiments; Fig. 2 illustrates an EPS control system with fluctuation noise management in accordance with one of several embodiments; Fig. 3 illustrates a dataflow block diagram of fluctuation noise management provided by the torque conditioning block in accordance with one or more embodiments; Fig. 4 illustrates inventive and non-inventive embodiments for filter implementations for fluctuating noise management; Fig. 5 illustrates filter parameter scheduling versus engine speed in accordance with one or more embodiments; Fig. 6 illustrates filter parameter scheduling versus handwheel torque amplitude in accordance with one or more embodiments; Fig. 7 illustrates filter parameter scheduling over handwheel torque frequency content in accordance with one or more embodiments; and Fig. 8 illustrates a flowchart for a method of managing fluctuation noise in an electric power steering system in accordance with one or more embodiments. DETAILED DESCRIPTION

[0011] Referring now to the figures, in which the present disclosure is described with reference to specific embodiments, without being limiting thereof, it is to be understood that the disclosed embodiments are merely illustrative of the present disclosure, which may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be highlighted or minimized to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art how to variously utilize the present disclosure.

[0012] As used herein, the terms module and submodule refer to one or more processing circuits, such as an application-specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) with memory executing one or more software or firmware programs, a combinational logic circuit, and / or other suitable components that provide the described functionality. As will be appreciated, the submodules described below may be combined and / or further subdivided.

[0013] With reference now to the figures, in which the technical solutions are described with reference to specific embodiments without limiting them, Fig. 1 illustrates an exemplary embodiment of an electric power steering (EPS) system 40 suitable for implementing the disclosed embodiments. The steering mechanism 36 is a rack and pinion system and includes a toothed rack (not shown) within a housing 50 and a pinion gear (also not shown) located beneath a gear housing 52. When the operator input, hereinafter referred to as the steering wheel 26 (e.g., a handwheel, etc.), is rotated, the upper steering shaft 29 rotates, and the lower steering shaft 51, which is connected to the upper steering shaft 29 by a pivot 34, rotates the pinion gear. Rotation of the pinion gear moves the rack, which moves tie rods 38 (only one is shown), which in turn move steering knuckles 39 (only one is shown), which turn or steer one or more steerable wheels 44 (only one is shown).

[0014] Electric power steering assistance is provided by the control device, generally designated by reference numeral 24, which includes the controller 16 and an electric machine 19, which may be a synchronous permanent magnet motor, and hereinafter referred to as motor 19. The controller 16 is powered by a line 12 from the vehicle power supply 10. The controller 16 receives a vehicle speed signal 14 representing vehicle speed from a vehicle speed sensor 17. Steering angle is measured by a position sensor 32, which may be an optical encoder sensor, a variable resistance sensor, or any other suitable type of position sensor, and provides a position signal 20 to the controller 16.An engine speed can be measured with a tachometer or any other device and transmitted to the controller 16 as an engine speed signal 21. A as ω. m The motor speed can be measured, calculated, or determined by a combination of these. For example, the motor speed ω m as the change in the motor position θ, measured by the position sensor 32, over a given time interval. The motor speed ω m can be, for example, the derivative of the motor position θ from the equation ω m= Δθ / Δt, where Δt is the sampling time and Δθ is the position change during the sampling interval. Alternatively, the motor speed can be derived from the motor position as the rate of change of position with respect to time. It should be noted that there are numerous well-known methodologies for performing the derivative function.

[0015] When the steering wheel 26 is turned, a torque sensor 28 detects the torque applied to the steering wheel 26 by the vehicle operator. The torque sensor 28 may include a torsion bar (not shown) and a variable resistance sensor (also not shown) that outputs a variable torque signal 18 to the controller 16 in relation to the amount of twist on the torsion bar. Although this is one type of torque sensor, other suitable torque sensing devices using known signal processing techniques will suffice. In response to the various inputs, the controller sends a command 22 to the electric motor 19, which provides torque assist to the steering system through a worm 47 and worm gear 48, thereby providing torque assist to the vehicle's steering.

[0016] Although the disclosed embodiments are described with reference to a motor controller for electric steering applications, it should be understood that these references are for illustrative purposes only and that the disclosed embodiments may be applied to any motor control application that utilizes an electric motor, e.g., steering, valve control, and the like. Furthermore, the references and descriptions herein may be applied to many forms of parameter sensors, including, without limitation, torque, position, speed, and the like. It should also be understood that references herein to electric machines, including, without limitation, motors, are used hereinafter as references to motors without limitation for brevity and convenience.

[0017] In the illustrated control system 24, the controller 16 uses the torque, position, speed, and the like to calculate one or more commands to deliver the required output power. The controller 16 is arranged in communication with the various systems and sensors of the motor control system. The controller 16 receives signals from each of the system sensors, quantifies the received information, and in response provides one or more output command signals, in this case, for example, to the motor 19. The controller 16 is configured to develop the appropriate voltages from an inverter (not shown), which may optionally be integrated with the controller 16 and is referred to herein as the controller 16, so that when applied to the motor 19, the desired torque or position is produced.In one or more examples, the controller 24 is operated in a closed-loop current regulator mode to generate the command 22. Alternatively, in one or more examples, the controller 24 is operated in a feedforward mode to generate the command 22. Since these voltages are related to the position and speed of the motor 19 and the desired torque, the position and / or speed of the rotor and the torque applied by an operator are determined. A position encoder is connected to the steering shaft 51 to detect the angular position θ. The encoder may detect the rotational position based on optical detection, fluctuations in a magnetic field, or other methods. Typical position sensors include potentiometers, resolvers, synchros, encoders, and the like, as well as combinations including at least one of the foregoing.The position encoder outputs a position signal 20 which indicates the angular position of the steering shaft 51 and thus that of the motor 19.

[0018] The desired torque may be determined by one or more torque sensors 28, which transmit torque signals 18 indicative of applied torque. One or more exemplary embodiments include such a torque sensor 28 and the torque signal(s) 18 therefrom, possibly in response to a compliant torsion bar, T-bar, spring, or similar device (not shown) configured to provide a response indicative of applied torque.

[0019] In one or more examples, one or more temperature sensors 23 are disposed on the electric machine 19. The temperature sensor 23 is preferably configured to directly measure the temperature of the sensing portion of the motor 19. The temperature sensor 23 transmits a temperature signal 25 to the controller 16 to enable the processing and compensation described herein. Typical temperature sensors include thermocouples, thermistors, thermostats, and the like, as well as combinations including at least one of the foregoing sensors, which, when appropriately placed, provide a calibratable signal proportional to the specific temperature.

[0020] Among other things, the position signal 20, the speed signal 21, and one or more torque signals 18 are applied to the controller 16. The controller 16 processes all input signals to generate values ​​corresponding to each of the signals, resulting in a rotor position value, a motor speed value, and a torque value being available for processing in the algorithms described herein. Measurement signals such as those mentioned above are also often linearized, compensated, and filtered, if desired, to improve the properties or eliminate undesired characteristics of the acquired signal. For example, the signals may be linearized to improve processing speed or to address a wide dynamic range of the signal. Furthermore, frequency- or time-based compensation and filtering may be used to eliminate noise or avoid undesired spectral characteristics.

[0021] To perform the described functions and the desired processing and computations therefor (e.g., identification of engine parameters, control algorithms, and the like), the controller 16 may include, without limitation, one or more processors, computers, DSPs, memory, mass storage, registers, timers, interrupts, communication interfaces, and input / output signal interfaces, and the like, as well as combinations including at least one of the foregoing. For example, the controller 16 may include input signal processing and filtering to enable accurate sampling and conversion or retrieval of such signals from communication interfaces. Additional features of the controller 16 and certain processes therein will be discussed in detail later.

[0022] As previously described, fluctuation noise refers to low-frequency noise that occurs in an EPS system and is typically caused by the handwheel torque sensor. The sensed torque signal exhibits noise due to inherent noise in the sensed analog signals and quantization noise in digital sensors and A / D converters. The technical solutions described herein address these fluctuation noise technical issues by enabling fluctuation noise reduction. In one or more examples, the technical solutions utilize conditional filtering of the motor torque command signal as a function of multiple system state variables, including motor speed and acceleration, handwheel torque, and vehicle speed, to attenuate fluctuation noise while maintaining the stability and performance of the overall EPS system.Since the motor torque is a translation of the handwheel torque signal, filtering the motor torque signal significantly reduces fluctuation noise. The technical solutions described here strategically utilize filtering between the EPS mechanical control and the electric motor control blocks in such a way that the tuning of both is not significantly affected by the filtering.

[0023] Fig. 2 illustrates an EPS control system with fluctuation noise management in accordance with one or more embodiments. Fig. Figure 2 represents the EPS system 40 using blocks for the motor 19, which provides torque (Te) to the mechanical system 170, which is responsible for transmitting torque to the vehicle's wheels. The mechanical system 170 varies depending on the type of EPS 40. For example, if the EPS 40 is a steer-by-wire system, the mechanical system 170 may include more components that transmit electrical signals compared to a typical power steering system with a driveshaft and other mechanical components.

[0024] The electric power steering control system 100 includes a steering control system 110 that generates a torque command (T*m) for maneuvering the vehicle. In one or more examples, the steering control system 110 may include a handwheel operated by a human operator with handwheel torque T hwAlternatively or additionally, the steering control system 110 includes an automatic driving assistance system (ADAS) that maneuvers the vehicle. The torque command generated by the steering control system is provided to the motor 19 to generate assist torque. The torque command is applied to the motor 19 after being converted into motor voltage commands V by a motor controller 130, which may be a motor control system operating using a feedback and / or feedforward mode of operation. In one or more examples, the EPS control system 100 monitors the torque generated by the mechanical system 170 using one or more sensors 180. Furthermore, the technical solutions herein utilize a torque conditioning block 120 in the EPS control system 100.

[0025] The torque conditioning block 120, which provides fluctuation noise management, includes a filter whose parameters are varied in real time as a function of one or more signals, along with an arbitration module that monitors parameter value fluctuations due to various signal signatures and determines the final value (based on fluctuation noise requirements along with the steering system and vehicle stability conditions). Fluctuation noise is typically audible at lower engine speeds when the vehicle is nearly stationary, when other vehicle noise is low.

[0026] When the engine 19 rotates faster or the vehicle speed is high, the fluctuation noise is masked by the other noises.

[0027] Fig. Figure 3 illustrates a dataflow block diagram of fluctuation noise management provided by the torque conditioning block in accordance with one or more embodiments. The torque conditioning block 120 provides fluctuation noise management using one or more input signals. The torque conditioning block includes, among other components, a filter parameter determination module 210, an arbitration module 220, a filter activation module 230, and a fluctuation noise filter module 240. The components include hardware components and software components.

[0028] As shown in the figure, the filter parameter determination module 210 determines at least four sets of filter parameters F p based on the various input signals. The arbitration module 220 then determines the final filter parameters f p together with a marker F en. The filter activation module 230 uses the flag to enable or disable the filter 240. The filter 240 then uses the final filter parameters and filters the motor torque command Tmc*, to obtain the final motor torque command Tm* to generate.

[0029] Based on the type of filter 240, the arbitration module 220 determines which final filter parameters should be generated. For example, if the filter 240 is a low-pass filter, the generated final filter parameters include the cutoff frequency for the low-pass filter. The filter 240 used to manage fluctuation noise can be a simple low-pass filter or a more complex filter. The ultimate goal is to be able to filter the noise frequencies in the fluctuation range (for example, in the range of 200 to 400 Hz, however, the range may vary for different systems). Depending on stability or performance conditions, the filter 240 can, in some cases, be activated by the filter activation module 230 using the flag F enbe deactivated altogether. Examples of various filters (continuous-time and discrete-time implementations) that can be used to filter fluctuation noise are described here.

[0030] Fig. 4 illustrates inventive and non-inventive examples of filter implementations for fluctuating noise management. For example, plot 410 illustrates examples of continuous-time filters, and plot 420 illustrates examples of discrete-time filters. The transfer functions for continuous-time filters, such as a low-pass filter (LPF) or a band-stop filter (BRF), may be as follows: HLPP(s)=ωcs+ωc HBFF(s)=ωd2ωn2s2+2ζnωns+ωn2s2+2ζdωds+ωd2 where ω c is the cutoff frequency of the LPF, while ω n , ω d are the natural frequencies and ξ n and ξ d the damping ratios of the BRF are.

[0031] It should be noted that the above are examples of filters, and that in other inventive and non-inventive embodiments, other filter designs may be used instead that provide amplitude attenuation in the desired frequency range.

[0032] In one or more examples not according to the invention, for example in the case of the band-stop filter, the filter parameters ω n , ω d , ξ n and ξ d in real time as a function of vehicle speed, handwheel torque, motor speed, and motor acceleration to perform the filtering. Any discretization technique can be used to implement the filter in embedded software, such as the Tustin transform, where the relationship between s-domain and z-domain is as follows. s=2Tsz−1z+1

[0033] As described herein, the torque conditioning block 120 performs filtering only under specific conditions when the fluctuation noise is audible (when no filtering is applied). This ensures that the stability and performance of the EPS system 40 is maintained while simultaneously reducing the fluctuation noise. The criteria for designing the filter parameters as a function of each of the EPS system signals are described further herein. Although the inventive example of a low-pass filter is used for the description, it should be understood that the same idea can be easily applied to a bandpass filter as well.

[0034] Fig. 5 illustrates the scheduling of filter parameters versus engine speed in accordance with one or more embodiments. Fig. Figure 5 illustrates an example scenario where a low-pass filter is used as filter 240. The fluctuation noise is most audible when the motor speed is low (i.e., the handwheel is stationary). As the motor speed increases, the fluctuation noise is masked by other noise, such as that due to the mechanical system 170, and no filtering is performed. Therefore, the filter cutoff frequency can be planned to be low at lower motor speeds ω1, and vice versa. In the recording 520 of Fig. 5 is an example of planning the cutoff frequency f pω above the engine speed ω m shown. The filter parameter determination module 510 may use the received motor speed signal to calculate (510) an absolute value and to determine the filter parameter f based on the absolute value of the motor speed. pDetermine the cutoff frequency f1, f2 of the low-pass filter. The determined filter parameters are used to configure filter 240.

[0035] As in the example of Fig. As shown in Figure 5, the cutoff frequency is maintained at a low value of f1 at motor speeds less than ω1, then increased linearly from ω1 to ω2, and then maintained at a high value of f2 above speeds of ω2. This operation can be represented mathematically as follows: fpω(ωm)={f1,|ωm|<ω1f1+(ωm−ω1)f2−f1ω2−ω1,ω1≤|ωm|<ω2f2,|ωm|≥ω2

[0036] It should be noted that although the function is shown as piecewise linear, any other function can be used instead, as long as the trends are similar. For example, a nonlinear function that uses an exponential function can be used instead, as shown below: fpω(ωm)=max(a,1−e|ωm|a)

[0037] Here, a is a tunable parameter that can be changed to vary the shape of the cutoff frequency function.

[0038] Further, in one or more examples, the filter parameter determination module 210 uses vehicle speed to determine the filter parameters in addition to, or alternatively to, using engine speed. At high vehicle speeds, engine noise masks the fluctuation noise, so the cutoff frequency may be increased under these conditions to improve performance and maintain stability. During stationary or near-stationary conditions (i.e., low vehicle speeds), the fluctuation noise is highly audible, and therefore the cutoff frequency is kept low. Functions similar to those presented above are used for speed and / or vehicle acceleration.

[0039] Furthermore, in one or more examples, the filter parameter determination module 210 may further determine the handwheel torque T hwto determine the filter parameters. Fluctuation noise is modeled as a function of both the amplitude and frequency content of the handwheel torque. Under higher load conditions, the noise is clearly audible. Therefore, filtering is only performed above a specific threshold of the handwheel torque f ph An example function (hard switching) is shown in Fig. 6 shown.

[0040] As in Fig. 6, an absolute value of the handwheel torque input signal is calculated in the filter parameter determination module 610 and used to calibrate the filter parameters f p used for a low-pass filter. In the illustrated example, a step function is used in recording 620, in which the cutoff frequency f1, f2 of the low-pass filter is configured based on the absolute value of the handwheel torque h0.

[0041] Fig. 7 shows a planning of filter parameters f pover a handwheel torque frequency content in accordance with one or more embodiments. During fast maneuvers, the high-frequency content of the handwheel torque signal is high. Therefore, the planning of filter parameters can also be done as a function of the frequency content of the handwheel torque T hw be executed. Fig. Figure 7 shows an example of this planning function. In the example, the handwheel torque input signal T hw first filtered by a high-pass filter 710 and the filtered high-pass content (T' hw ) is used to determine the filter parameters. As described here, an absolute value of the high-pass content (T' hw ) is calculated (at 720) and used (at 730) to determine the cutoff frequency f ph of the low-pass filter used as the fluctuation noise filter 240. For example, the cutoff frequency is set to a predetermined lower value f1 when the high-pass content (T'hw ) is below a predetermined value h0, and a predetermined higher value f2 is used when the high-pass content (T' hw ) is above (or equal to) the predetermined value h0.

[0042] In one or more examples, the filter parameter determination module 210 calculates the filter parameters using the plurality of input signals as described herein. Further, the arbitration module 220 receives the determined plurality of filter parameters and determines the final filter parameters, for example, a filter cutoff frequency, to which the filter 240 is tuned (in real time). Although the arbitration module 220 may determine the final filter parameters from the determined plurality of filter parameters in various ways, the key is to ensure that the stability of the vehicle and the steering system is always maintained, even at the expense of fluctuation noise (if needed). The arbitration module 220 may even deactivate the fluctuation noise filter 240, for example, if the stability of the EPS 40 is threatened.

[0043] An example of the arbitration logic implemented by the arbitration module 220 may include a conditional "minimum" function, i.e., a function that uses the minimum of all cutoff frequencies determined based on the various signals, but transitions to the maximum or default value when stability and / or performance monitoring functions determine that the delay due to the low cutoff frequency due to the "minimum" operation is insufficient to ensure stability and satisfactory performance of the EPS system 40.

[0044] Fig.8 illustrates a flowchart for a method for managing fluctuation noise in an electric power steering system in accordance with one or more embodiments. The method includes, at 810, calculating a plurality of filter parameters, each based on a different signal in the EPS. As described herein, the filter parameters are calculated using, for example, one or more parameters, such as motor speed, acceleration, vehicle speed, and handwheel torque. The method further includes, at 820, determining a final filter parameter by arbitrating between the plurality of filter parameters. Arbitrating may include comparing the filter parameters to one another and / or using predetermined thresholds. In one or more examples, arbitrating may include selecting a lowest (minimum) filter parameter value.Alternatively or additionally, the decision may involve choosing a maximum filter parameter value. Alternatively or additionally, the decision may involve choosing both the lowest and the highest value.

[0045] The selection of the final filter parameter depends on the filter type used. For example, if the filter is a low-pass filter, a single cutoff frequency parameter is selected. Alternatively or additionally, in the case of a bandpass filter, two filter parameter values ​​are selected for the band being filtered. It should be noted that in other non-inventive examples, other filter types may be used, and the selected filter parameters are varied accordingly.

[0046] The method further includes determining at 830 whether to enable or disable filter 240. For example, a filter enable flag is determined based on the final filter parameter(s) compared to one or more predetermined thresholds. Alternatively or additionally, the filter may be enabled / disabled based on a stability of steering system 40, which is determined independently of the filter parameters. If the filter is to be disabled based on the filter enable flag being FALSE / OFF, filter 240 is not used and the motor torque command generated by controller 26 is forwarded for application to motor 19 at 840. Alternatively, if the filter is enabled by the filter enable flag being TRUE / ON, filter 240 is configured with the final filter parameter(s) by arbitration module 220 at 850.At 860, the motor torque command is then filtered using the configured filter 240 to generate a filtered motor torque command for application to the motor 19.

[0047] As described herein, applying a motor torque command may include converting the motor torque command into a voltage / current command that is applied to the motor 19 to produce an amount of torque indicated by the motor torque command.

[0048] The one or more embodiments of the technical solutions described herein address the technical challenge of attenuating fluctuation noise typically present in electric power steering systems. The noise generally includes noise frequency content, where the range of noise frequencies can vary for different systems and is typically around 200 to 250 Hz. In some cases, the frequency range of the noise may coincide with software sweep rates or it may include frequency noise due to PWM and inverter switching. Current technical solutions for attenuating fluctuation noise include reducing the response or bandwidth of the motor controller, oversampling and filtering handwheel torque, directly (unconditionally) low-pass filtering the handwheel torque, and reducing the response of control functions that amplify noise.In one or more examples, a combination of these techniques is used; however, using multiple techniques requires coordinated and laborious tuning due to multiple functions affected and a long tuning time due to the cyclical nature of such a tuning process. Furthermore, these techniques result in degraded steering feel and stability. The technical solutions described here address the technical problem of attenuating fluctuation noise in steering systems by conditionally filtering a motor torque command, where the filtering is based on multiple signals, such as motor speed, acceleration, vehicle speed, and handwheel torque. Therefore, the technical solutions described here reduce fluctuation noise while maintaining steering system performance and stability.Furthermore, the technical solutions provide a single function for complete fluctuation noise management, reducing calibration and tuning time.

Claims

[1] A method for damping fluctuation noise in a steering system (40), the method comprising: a variety of filter parameters (F p ) is calculated, where each filter parameter (F p ) is based on a corresponding signal in the steering system (40); at least one final filter parameter (f p ) from the multitude of filter parameters (F p ) is determined by choosing between the plurality of filter parameters (F p ) is decided; a filter (240) using the at least one final filter parameter (f p ) is dynamically configured; and a motor torque command (T* mc ) is filtered using the filter (240), wherein a filtered final motor torque command (T* m ) is applied to a motor (19) to generate a corresponding amount of torque, characterized by , that the filter (240) is a low-pass filter (LPF) or a band-pass filter. [2] The method of claim 1, wherein the plurality of filter parameters (F p ) based on an engine speed (ω m ), an engine acceleration, a vehicle speed or a handwheel torque (T hw ) is calculated. [3] The method of claim 1, wherein dynamically configuring the filter (240) comprises deactivating the filter (240). [4] The method of claim 1, wherein deciding between the plurality of filter parameters (F p ) includes that a minimum filter parameter (F p ) is selected. [5] The method of claim 1, wherein deciding between the plurality of filter parameters (F p ) includes that a maximum filter parameter (F p ) is selected. [6] The method of claim 1, wherein deciding between the plurality of filter parameters (Fp ) includes that at least two final filter parameters (F p ) can be selected. [7] An apparatus for managing fluctuation noise in a steering system (40), the apparatus comprising: a filter (240) which provides a filtered motor torque command (T* m ) based on an input torque command, where the filtered motor torque command (T* m ) is applied to a motor (19) to generate a corresponding amount of torque; a filter parameter determination module (210) which determines a plurality of filter parameters (F p ) is calculated, where each filter parameter (F p ) is based on a corresponding signal in the steering system (40); and an arbitration module (220) which selects at least one final filter parameter (fp) from the plurality of filter parameters (F p ) by choosing between the multitude of filter parameters (F p), and the filter (240) using the at least one final filter parameter (F p ) configured dynamically. [8] Apparatus according to claim 7, wherein a first filter parameter (F p ) from the multitude of filter parameters (F p ) based on an engine speed (ω m ) is calculated. [9] Apparatus according to claim 7, wherein a first filter parameter (F p ) from the multitude of filter parameters (F p ) is calculated based on engine acceleration. [10] Apparatus according to claim 7, wherein a first filter parameter (F p ) from the multitude of filter parameters (F p ) is calculated based on a vehicle speed. [11] Apparatus according to claim 7, wherein a first filter parameter (F p ) from the multitude of filter parameters (F p ) based on a handwheel torque (T hw) is calculated. [12] The apparatus of claim 7, wherein dynamically configuring the filter (240) comprises deactivating the filter (240). [13] The apparatus of claim 7, wherein the filter (240) is a low-pass filter (LPF).

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