A method for detecting and compensating for road disturbances in a steering system

Abstract

The application discloses a kind of road surface interference detection and compensation control method of steering system, belong to vehicle steering system technical field.The application specifically includes road surface bump detection and steering system road surface interference compensation control, road surface bump detection is using the amplitude difference of steering system high-frequency interference torque, detection signal is obtained by time window integration method, combined with steering torque and vehicle speed and other signals, dynamic threshold is used to detect road surface bump, can adapt to different speed scene, accurate identification is realized.Road surface interference compensation control method establishes the dynamics model of steering system including interference torque, and innovatively designs the extended state observer, actively estimates system state and total disturbance, obtains the state information that traditional sensor cannot obtain, and through the method of feedforward control and compensation control, effective interference torque compensation control is realized, and the robustness of steering control system under bump road condition is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle steering systems, and more particularly to a steering system road disturbance detection and compensation control method. BACKGROUND

[0002] During vehicle driving, road bumps are an important factor affecting vehicle comfort and safety. Bumpy roads usually interfere with the suspension system and steering system of the vehicle, especially under complex road conditions, the steering system of the vehicle is more susceptible to road bumps, which can cause the driver to make steering errors or increase the difficulty of driving. This interference not only affects the driver's control experience, but also may pose a safety hazard.

[0003] The steering system of existing vehicles generally enhances the controllability and steering lightness of the vehicle through the combination of mechanical components and electronic control. However, when the vehicle is driving on a bumpy road, the interference torque of the steering system often cannot be effectively suppressed, especially when the road impact is large, the feedback torque on the steering wheel will fluctuate significantly, making it difficult for the driver to maintain precise steering control. In order to improve the steering stability of the vehicle under complex road conditions and reduce the interference of bumpy roads on the steering system, it is urgent to develop a control method that can effectively detect road bumps and suppress the steering interference torque. Therefore, how to accurately detect road bumps and adjust the torque output in the steering system in real time through control algorithms has become an important technical means to improve the driving comfort and safety of vehicles. SUMMARY

[0004] The present application provides a steering system road disturbance detection and compensation control method to solve the problem that the interference torque of the existing vehicle steering system cannot be effectively suppressed, which makes it difficult for the driver to maintain precise steering control.

[0005] The present application adopts the following technical solutions:

[0006] A steering system road disturbance detection and compensation control method includes road bump detection and steering system road disturbance compensation control.

[0007] The method of road bump detection includes: 1.1 using a high-pass filter to extract high frequencies where the smooth road and bumpy road signals are clearly distinguished, and then taking the absolute value to obtain the amplitude of the high-frequency interference torque; 1.2 integrating the amplitude of the high-frequency torque using a sliding time window integral to obtain a data region with high high-frequency energy, i.e. the road disturbance region; 1.3 using a dynamic threshold method to determine whether to enter a bumpy road;

[0008] The method for road disturbance compensation control of the steering system comprises: 2.1 establishing a steering system dynamics model containing disturbance torque; 2.2 designing an extended state observer to estimate total disturbance; 2.3 configuring observer gain to configure poles to ensure stability of the observer system; and 2.4 calculating a virtual control signal by using feedforward control and performing compensation control on the feedforward control to eliminate the influence of total disturbance f on the control quantity.

[0009] Preferably, the frequency threshold of the high-pass filter is obtained through calibration, and the calibration principle is that the amplitude integral of the torque signal passing through the high-pass filter is maximally 5% of the amplitude integral of the original torque signal, so as to ensure that the torque signal applied by the driver is effectively filtered out.

[0010] The mathematical form of the above step 1.2 is as shown in formula (1):

[0011]

[0012] In the formula, T d represents the torque detected by the torque angle sensor of the steering system, H(T d ) is the high-frequency disturbance torque obtained by passing through the high-pass filter, and E is the result of integrating the amplitude of the high-frequency disturbance torque by using a sliding time window.

[0013] The dynamic threshold method used in the above step 1.3 specifically comprises the following steps: first, the vehicle passes through the same bumpy road at different speeds to obtain the time window integral of the high-frequency disturbance torque amplitude at different speeds; second, an interpolation table of vehicle speed and time window integral of high-frequency disturbance torque amplitude is established, so that the dynamic threshold for bump judgment at different speeds can be obtained; and finally, the data obtained by the window amplitude integral are judged, and a bump / flat state flag is output, thereby providing a state basis for subsequent road disturbance control.

[0014] The dynamics equation of the steering system dynamics model in the above step 2.1 is as shown in formula (2):

[0015]

[0016] In the formula, J s is the system moment of inertia, B u is the system damping coefficient, K s is the system stiffness coefficient, δ d is the steering wheel angle of the steering system, T e is the output torque of the motor after torque increase; and f is total disturbance, including the steering torque fluctuation caused by road bumping, the torque applied by the driver and other unmodeled disturbance terms; and formula (2) is rewritten in the form of a state equation as follows:

[0017]

[0018] y is the output of the steering system; f is also treated as a state variable for the observer to estimate, and the steering system model is transformed into equation (4):

[0019]

[0020] The extended state observer uses a Romberg observer, and constructs the state space equation by adding a correction term based on the output error to the original system, as shown in the following equation (5):

[0021]

[0022] In the formula, State variables The observed values, The output quantity is y = δ d The observed values, The observed values ​​are the output of the extended state observer; A is the state matrix, B is the input matrix, and A and B have the same values ​​as the steering system modeling equations. C is the output matrix, which is set according to the controller requirements; L is the observer gain matrix, which is the weighted sum of the biases, and the observer gain L = [l1 l2 l3)]. T The observer equations are expanded to the following form (6):

[0023]

[0024] Step 2.3 specifically includes the following process:

[0025] First, solve the system's characteristic root λ equation (7):

[0026]

[0027] By placing the observer poles on the negative real axis to ensure that the state estimate converges to the actual value quickly and without oscillation, let the poles be -ω, then we have:

[0028]

[0029] Gain coefficient can be obtained: Once the value of ω is determined, the observer gain L is obtained.

[0030] The above compensation control method is to set T e In It is a compensation term for the total disturbance, u o Given the virtual control quantity calculated for feedforward control, the steering system expression becomes after compensation control: The steering wheel angle δ can then be designed using a feedforward control algorithm in accordance with the control method of a disturbance-free system.d a control law between the virtual input u o a control law between the virtual input u

[0031] From the above description of the present application, compared with the prior art, the present application has the following advantages:

[0032] 1. The present application utilizes the amplitude difference of high-frequency interference torque of the steering system, obtains a detection signal through time window integration method, combines steering torque and vehicle speed and other signals, and adopts a dynamic threshold to detect road bumps, so as to realize accurate identification in adaptive different vehicle speed scenes.

[0033] 2. The present application establishes a steering system dynamics model containing interference torque, and innovatively designs an extended state observer to actively estimate system state and total disturbance, so as to obtain state information that cannot be obtained by a traditional sensor, and realize effective interference torque compensation control through a feedforward control and compensation control method, thereby enhancing the robustness of the steering control system under the bump road condition. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 is a schematic diagram of a steering control system framework of the present application. DETAILED DESCRIPTION

[0035] The specific embodiments of the present application will be described below with reference to the accompanying drawings. In order to fully understand the present application, many details are described below, but the present application can also be implemented without these details for those skilled in the art. For well-known components, methods and processes, the following will not be described in detail.

[0036] The present embodiment provides a steering system road disturbance detection and compensation control method, which is based on a steering control system framework, and refers to Figure 1 The steering control system framework includes a steering system model, an extended state observer (ESO) for real-time estimation of internal state and external disturbance of the system, a compensation control module and a feedforward controller module. The steering system road disturbance detection and compensation control method specifically includes the following steps:

[0037] I. Road bump detection

[0038] The disturbance torque amplitude caused by the road surface to the steering system is collected to determine whether the vehicle is on a bumpy road. Since the torque detected by the steering system torque angle sensor contains the torque applied by the driver, which has a low frequency, a suitable high-pass filter is used to filter the torque applied by the driver, and the high-frequency torque signal passing through the high-pass filter can be used to evaluate the degree of road bumpiness. The frequency threshold of the high-pass filter can be obtained by calibration. The principle of calibration is that the amplitude integral of the torque signal passing through the high-pass filter is 5% of the original torque signal amplitude integral on a smooth road, that is, to ensure that the torque signal applied by the driver is effectively filtered out.

[0039] After the high frequency of the smooth road and bumpy road signal is extracted by high-pass filtering, the absolute value is taken to obtain the amplitude of the high-frequency disturbance torque. Then, the high-frequency data amplitude is integrated using a sliding time window to obtain a data region with high-frequency energy, i.e. a road disturbance region. The mathematical form of the process is as follows:

[0040]

[0041] In the formula, T d represents the torque detected by the steering system torque angle sensor, H(T d ) is the high-frequency disturbance torque obtained by passing through the high-pass filter, and E is the result of integrating the high-frequency disturbance torque amplitude by the sliding time window.

[0042] Since the amplitude of the disturbance torque generated by the bumpy road will generally increase with the increase of the vehicle speed, a fixed threshold cannot be used to determine whether the bumpy road is entered. Therefore, the present application adopts a dynamic threshold method, which is as follows: first, the vehicle passes through the same bumpy road at different speeds to obtain the time window integral of the high-frequency disturbance torque amplitude at different speeds; second, an interpolation table of vehicle speed and time window integral of high-frequency disturbance torque amplitude is established, so that the dynamic threshold for bumpy road judgment at different speeds can be obtained; finally, the data obtained by the window amplitude integral is judged, and the bumpy / flat state flag is output, which provides a state basis for subsequent road disturbance control.

[0043] II. Steering system road disturbance compensation control method

[0044] The main control target of the present application is to suppress the sharp fluctuation of the steering torque caused by the bumpy road, and the steering motor torque needs to be compensated and controlled, so an extended state observer (ESO) is used to estimate the disturbance in real time and suppress the road disturbance through feedforward compensation.

[0045] The main function of the extended state observer (ESO) is to estimate the internal state and external disturbance of the system in real time, so as to effectively offset these uncertain factors in the controller design, thereby realizing accurate compensation control of the system output.

[0046] The basic idea of ESO is to treat the uncertain dynamics and disturbances in the system as an additional "extended state" and incorporate it into the design of the observer. This means that in addition to the regular system states, the extended state observer estimates an extra state vector that contains all the internal dynamics and external disturbances that can affect the system behavior but cannot be directly measured. In this way, the ESO can estimate and compensate for these uncertainties in real time, allowing the control system to achieve precise output tracking or control even in the presence of disturbances. The specific method for the road disturbance supplementary control of the steering system is as follows:

[0047] 2.1 Establishing the steering system dynamics model

[0048] First, the steering system is modeled dynamically. The general expression of the steering system dynamics equation can be written as equation (2):

[0049]

[0050] In the formula, J s is the system moment of inertia, B u is the system damping coefficient, K s is the system stiffness coefficient, δ d is the steering wheel angle of the steering system, T e is the output torque of the motor after torque increase. The total disturbance is set as f, including the steering torque fluctuation caused by road bumps, the torque applied by the driver, and other unmodeled disturbance terms.

[0051] Rewrite equation (2) as a state equation:

[0052]

[0053] y is the output of the steering system.

[0054] f is also taken as a state variable for the observer to estimate. The steering system model can be converted to equation (4):

[0055]

[0056] 2.2 Establishing the extended state observer

[0057] Since the torque angle sensor can only measure δ d , it cannot measure the total disturbance f, so an extended state observer needs to be constructed to estimate f. Here, the commonly used Luenberger observer is used. By adding a correction term based on the output error to the original system, the state space equation is constructed, and its general form is equation (5):

[0058]

[0059] wherein, is the state variable is the observation value of the output variable y = δ is the observation value of the output variable y = δ d is the observation value of the output variable y = δ is the observation variable of the extended state observer output. A is a state matrix, B is an input matrix, A and B are the same as the values of the steering system modeling equation, i.e. C is an output matrix, which can be set according to the needs of the controller, and in this scheme, three state variables are output, C = I (I is an identity matrix). L is an observer gain matrix, i.e. the weighting of the deviation, and the observer gain L = [l1 l2 l3] T .

[0060] The above observer equation is expanded as formula (6):

[0061]

[0062] 2.3 Selecting the observer gain

[0063] In order to make the estimated value of the observer converge to the actual value, the stability of the observer system needs to be satisfied. The observer gain is configured in the present application to configure the poles to ensure the stability of the observer system.

[0064] First, solve the characteristic root λ equation (7) of the system:

[0065]

[0066] By configuring the observer poles on the negative real axis, the state estimation value is quickly and non-oscillatory converged to the actual value. Let the pole be -ω, then:

[0067]

[0068] The gain coefficient can be obtained:

[0069] The larger the ω in the observer, the faster the estimated value converges to the actual value, but at this time the observer gain L is also larger, and the influence of the sensor noise is larger, so the more reasonable approach is to make the bandwidth ω of the observer slightly higher than the frequency of the state variable, and much lower than the frequency of the noise. The frequency threshold of the high-pass filter calibrated in step one filters most of the state signals and passes the high-frequency sensor noise, and this threshold can meet the requirements of the observer ω. After the value of ω is determined, the observer gain L is obtained.

[0070] 2.4 Compensation control strategy

[0071] The overall framework of the steering control system is shown in the accompanying Figure 1 illustration, wherein The target steering wheel angle for vehicle control demand. The control is divided into two parts, the first part is feedforward control, according to the error between the target steering wheel angle and the current angle observed by the extended state observer, a virtual control signal u o is outputted; the other part is compensation control, which aims to eliminate the influence of total disturbance f on the control quantity, so that the control object of feedback control (the sum of compensation control and steering system) becomes a disturbance-free control object.

[0072] The compensation control method is: T e , wherein is the compensation term for the total disturbance, u o is the virtual control quantity calculated by the feedforward control. After compensation control, the expression of the steering system becomes:

[0073]

[0074] That is, the feedforward control algorithm can be used to design the control law between the steering wheel angle δ d and the virtual input u o . This application takes PD control as an example, as shown in Figure 1 , the controller input is the steering angle error value and the derivative of the error value , then K p , K d are the proportional coefficient and the differential coefficient respectively, and appropriate coefficients can be obtained through experience debugging to obtain better control effect.

[0075] The road bump detection and disturbance compensation control method of the application is not limited to the steering system used above, and can be applied to any system using motor steering assistance, including electric power steering system, electro-hydraulic coupling power steering system. In addition, the application object can be a passenger car or a commercial vehicle.

[0076] The above is only a specific embodiment of the application, but the design concept of the application is not limited to this, and any non-essential modification of the application using this concept shall be regarded as an infringement of the protection scope of the application.

Claims

1. A method for detecting and compensating for road surface disturbances in a steering system, characterized in that, This includes road bump detection and road disturbance compensation control for the steering system; The method for detecting road bumps includes: 1.1 Using a high-pass filter to extract the high-frequency signals that clearly distinguish between smooth and bumpy road surfaces, and taking the absolute value to obtain the amplitude of the high-frequency interference torque; 1.2 Integrating the amplitude of the high-frequency torque using a sliding time window to obtain the data region with higher high-frequency energy, i.e., the road interference region; 1.3 Using a dynamic threshold method to determine whether the road surface is bumpy. The dynamic threshold method specifically includes: First, the vehicle passes through the same bumpy road surface at different constant speeds to obtain the time window integral of the high-frequency interference torque amplitude at different speeds; Second, establishing an interpolation table of the time window integral of vehicle speed and high-frequency interference torque amplitude to obtain the dynamic threshold for bump judgment at different speeds; Finally, judging the data obtained by the window amplitude integral and outputting a bumpy / smooth state flag to provide a state basis for subsequent road interference control. The method for road surface disturbance compensation control of the steering system includes: 2.1 establishing a dynamic model of the steering system containing disturbance torque; 2.2 designing an extended state observer to estimate the total disturbance; 2.3 configuring the observer gain to configure the poles to ensure the stability of the observer system; 2.4 using feedforward control to calculate the virtual control signal and performing compensation control on the feedforward control to eliminate the influence of the total disturbance f on the control quantity.

2. The method for detecting and compensating for road surface interference in a steering system as described in claim 1, characterized in that: The frequency threshold of the high-pass filter is obtained through calibration. The calibration principle is that the maximum integral of the torque signal amplitude passing through the high-pass filter on a smooth road surface is 5% of the integral of the original torque signal amplitude, ensuring that the torque signal applied by the driver is effectively filtered out.

3. The method for detecting and compensating for road surface interference in a steering system as described in claim 1, characterized in that: The mathematical form of step 1.2 is as shown in equation (1): In the formula, T d H(T) represents the torque detected by the torque angle sensor of the steering system. d ) represents the high-frequency interference torque obtained after passing through a high-pass filter, and E is the result of integrating the amplitude of the high-frequency interference torque using the sliding time window integral.

4. The method for detecting and compensating for road surface interference in a steering system as described in claim 1, characterized in that, The dynamic equation of the steering system dynamic model in step 2.1 is as follows (2): In the formula, J s Let B be the system's moment of inertia. u K is the system damping coefficient. s Let δ be the system stiffness coefficient. d T is the steering wheel angle of the steering system. e is the output torque after the motor torque is increased; f is the total disturbance, including the violent fluctuation of steering torque caused by road bumps and the torque applied by the driver; Equation (2) can be rewritten as a state equation as follows: y is the output of the steering system; f is also treated as a state variable for the observer to estimate, and the steering system model is transformed into equation (4):

5. The method for detecting and compensating for road surface interference in a steering system as described in claim 4, characterized in that: The extended state observer uses a Romberg observer, and constructs the state space equation by adding a correction term based on the output error to the original system, as shown in the following equation (5): In the formula, State variables The observed values, The output quantity is y = δ d The observed values, The observations output by the extended state observer; A is the state matrix, B is the input matrix, and A and B have the same values ​​as the steering system modeling equations. C is the output matrix, which is set according to the controller requirements; L is the observer gain matrix, which is the weighted sum of the biases, and the observer gain L = [l1 l2 l3)]. T The observer equations are expanded to the following form (6):

6. The method for detecting and compensating for road surface interference in a steering system as described in claim 5, characterized in that, Step 2.3 specifically includes the following process: First, solve the system's characteristic root λ equation (7): By placing the observer poles on the negative real axis to ensure that the state estimate converges to the actual value quickly and without oscillation, let the poles be -ω, then we have: Gain coefficient can be obtained: Once the value of ω is determined, the observer gain L is obtained.

7. The method for detecting and compensating for road surface interference in a steering system as described in claim 4, characterized in that: The compensation control method is to set T e In It is a compensation term for the total disturbance, u o Given the virtual control quantity calculated for feedforward control, the steering system expression becomes after compensation control: The steering wheel angle δ can then be designed using a feedforward control algorithm in accordance with the control method of a disturbance-free system. d With virtual input u o The control law between them.

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