A dynamic force feedback closed-loop control method and system in a simulation driving

Abstract

The application provides a dynamic force feedback closed-loop control method and system in a simulation driving, vehicle model parameters, steering wheel rotation angle measurement values and virtual sensor data such as vehicle speed and lateral acceleration output by a vehicle solver are acquired, state space sub-models suitable for low-speed, medium-speed and high-speed working conditions are constructed, and volume Kalman filters are configured for each sub-model; and the confidence weight of each sub-model is calculated in combination with the vehicle speed and the lateral acceleration, and then the globally optimal state estimation and an equivalent linear system model are obtained through weighted fusion; high-frequency energy of the steering wheel is extracted as a driver input feature through short-time Fourier transform, a cost function weight matrix of a linear quadratic regulator is determined by using a lookup table, optimal feedback gain is obtained by solving an algebraic Riccati equation, and then the target output torque of a force feedback steering wheel motor is calculated.

Description

Technical Field

[0001] The present application relates to the field of control, and in particular to a dynamic force feedback closed-loop control method and system in simulated driving. Background Art

[0002] The force feedback steering wheel in a car driving simulator accurately transmits the steering system torque to the driver in real time based on the driving state of the virtual vehicle, thereby simulating real-world road feel and steering feel. The quality of the force feedback control method determines the realism and effectiveness of the simulated driving. There are various ways to implement force feedback. One approach is based on empirical formulas or lookup tables. These methods directly map a few parameters, such as vehicle speed and steering wheel angle, to output torque using a pre-set mapping relationship. This approach is simple to implement and requires minimal computation, but it is overly simplistic and cannot accurately reproduce the complex dynamic characteristics of the vehicle under different operating conditions. In particular, when the vehicle enters a nonlinear region, the feedback torque becomes significantly disconnected from the actual dynamic response of the virtual vehicle, resulting in a distorted driving experience and a failure to provide the driver with accurate vehicle status information. Another approach incorporates a vehicle dynamics model and combines it with modern control theory to generate the feedback torque, such as by using PID control or a linear quadratic regulator (LQR). While theoretically more advanced, these methods are typically nonlinear and time-varying systems, with dynamic characteristics that vary significantly under low, medium, and high speed conditions. Using a single linear model for full-condition control will inevitably lead to model mismatch problems and reduce control accuracy. In the state estimation link, in order to obtain the state variables required by the controller, such as the center of mass sideslip angle, observers such as Kalman filtering are generally used. However, traditional filtering algorithms mostly use a fixed process and measurement noise covariance matrix, which cannot adapt to changes in operating conditions such as tire state changes, resulting in low state estimation accuracy. Moreover, the weighting matrix in the performance indicators of existing optimal control methods is usually a fixed value set offline, and cannot be adjusted online according to the vehicle state and the driver's driving intention, making the control strategy relatively rigid and difficult to always provide better force feedback in changing driving scenarios. Summary of the Invention

[0003] In a first aspect of the present invention, a dynamic force feedback closed-loop control method in simulated driving is provided, comprising the following steps:

[0004] Obtaining vehicle model parameters, real-time steering wheel angle measurements, and virtual sensor data output by a vehicle solver, including vehicle speed and lateral acceleration; establishing state-space submodels corresponding to low-speed, medium-speed, and high-speed driving conditions based on the vehicle model parameters, configuring a corresponding cubic Kalman filter for each submodel, and determining the process and measurement noise covariance matrix of the cubic Kalman filter using tire state and steering wheel angular velocity;

[0005] Calculating confidence weights for each sub-model based on the vehicle speed and lateral acceleration in the virtual sensor data, and using the confidence weights to perform weighted fusion on the state estimation vectors output in parallel by each filter and the system matrix of each sub-model to obtain a global optimal state estimation vector and an equivalent linear system model;

[0006] Performing a short-time Fourier transform on the real-time steering wheel angle measurement value, and using the high-frequency energy ratio as a driver input characteristic parameter; constructing a linear quadratic regulator, and determining a state weighting matrix and a control weighting matrix in a cost function of the linear quadratic regulator based on a three-dimensional lookup table, wherein the inputs of the lookup table are the vehicle speed and center of mass sideslip angle components in the global optimal state estimation vector, and the driver input characteristic parameter;

[0007] The algebraic Riccati equation is solved based on the equivalent linear system model, the state weighting matrix and the control weighting matrix to obtain the optimal feedback gain matrix, and the gain matrix is ​​multiplied by the global optimal state estimation vector to obtain the target output torque of the force feedback steering wheel actuator motor.

[0008] In a second aspect of the present invention, a dynamic force feedback closed-loop control system for simulated driving is proposed, comprising the following modules:

[0009] A configuration module is configured to obtain vehicle model parameters, real-time steering wheel angle measurements, and virtual sensor data output by a vehicle solver, including vehicle speed and lateral acceleration; based on the vehicle model parameters, establish state-space submodels corresponding to low-speed, medium-speed, and high-speed driving conditions, configure a corresponding cubic Kalman filter for each submodel, and determine the process and measurement noise covariance matrix of the cubic Kalman filter using tire state and steering wheel angular velocity;

[0010] a model building module for calculating confidence weights for each sub-model based on the vehicle speed and lateral acceleration in the virtual sensor data, and using the confidence weights to perform weighted fusion on the state estimation vectors output in parallel by each filter and the system matrix of each sub-model to obtain a global optimal state estimation vector and an equivalent linear system model for controller design;

[0011] a transformation module for performing a short-time Fourier transform on the real-time steering wheel angle measurement value, using the high-frequency energy ratio as a driver input characteristic parameter; constructing a linear quadratic regulator, and determining a state weighting matrix and a control weighting matrix in a cost function of the linear quadratic regulator based on a three-dimensional lookup table, wherein the inputs of the lookup table are the vehicle speed and center of mass sideslip angle components in the global optimal state estimation vector, and the driver input characteristic parameter;

[0012] A control module is used to solve the algebraic Riccati equation based on the equivalent linear system model and the state weighting matrix and the control weighting matrix to obtain an optimal feedback gain matrix, and multiply the gain matrix with the global optimal state estimation vector to obtain a target output torque of the force feedback steering wheel actuator motor.

[0013] The present invention establishes and weightedly fuses multiple vehicle state space sub-models covering low, medium and high speeds, which can more effectively cover the equivalent system model of the vehicle's nonlinear characteristics under the entire operating condition range; a volumetric Kalman filter with noise parameters that vary with the operating condition is configured for each sub-model, thereby improving the estimation accuracy of key state variables such as the center of mass sideslip angle; the extracted driver input characteristic parameters are used together with the vehicle state as the query basis for the weighted matrix of the linear quadratic regulator cost function, so that the solution of the optimal control law can take into account the vehicle's stability and the driver's manipulation intention. The generated feedback torque not only matches the motion state of the virtual vehicle, but also responds to differences in driving style, thereby improving the road feel authenticity of the simulated driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 Flowchart for the calculation of process noise covariance matrix and measurement noise covariance matrix;

[0015] Figure 2 Schematic diagram of model confidence calculation;

[0016] Figure 3 Schematic diagram of steering wheel angle signal and corresponding spectrum and frequency band;

[0017] Figure 4 Schematic diagram for weighting matrix determination. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0019] In a first embodiment of the present invention, a dynamic force feedback closed-loop control method in simulated driving is provided, such as Figure 1 As shown, the following steps are included:

[0020] S1, obtaining vehicle model parameters, real-time steering wheel angle measurements, and virtual sensor data including vehicle speed and lateral acceleration output by a vehicle solver; based on the vehicle model parameters, establishing state-space submodels corresponding to low-speed, medium-speed, and high-speed driving conditions, respectively, and configuring a corresponding cubic Kalman filter for each submodel; and determining the process and measurement noise covariance matrix of the cubic Kalman filter using tire state and steering wheel angular velocity;

[0021] Different vehicles have different driving experiences. For example, the actual driving experience differs between SUVs and compact cars, and these differences need to be reflected in driving simulations. Vehicle model parameters such as vehicle mass, moment of inertia, wheelbase, and front and rear wheel cornering stiffness are pre-set by consulting the vehicle design manual. Real-time steering wheel angle measurements are obtained by sampling a photoelectric encoder mounted on the simulator's steering shaft at a frequency of 1000 Hz. Virtual sensor data is calculated in real time using vehicle dynamics simulation software such as CarSim or Adams, outputting the current longitudinal vehicle speed and lateral acceleration at the center of mass at each simulation step.

[0022] After the vehicle model is established, a two-degree-of-freedom vehicle dynamics bicycle model is used as the basis, and three state space sub-models are obtained by linearizing at different vehicle speeds and tire side slip angles. For example, the low-speed model corresponds to 0 to 40 kilometers per hour, the medium-speed model corresponds to 40 to 120 kilometers per hour and a smaller tire side slip angle, and the high-speed nonlinear model corresponds to more than 120 kilometers per hour or a larger tire side slip angle. An independent volumetric Kalman filter is configured for each sub-model for state estimation. In one embodiment, the adjustment of the process noise covariance matrix Q and the measurement noise covariance matrix R is achieved through a fuzzy logic inference system. The fuzzy logic inference system uses the steering wheel angular velocity and the estimated tire side slip degree as input. When the steering wheel angular velocity is large or the tire enters the nonlinear region, the value of the Q matrix is ​​increased and the credibility of the model is reduced, and vice versa. Figure 1 In one embodiment, the linearization described above is specifically that low speed and medium speed are linear regions, and high speed is nonlinear. In another embodiment, low speed and high speed are nonlinear, and medium speed is linear. Alternatively, both speeds may be linearized, and the present invention does not make specific limitations on this.

[0023] S2, calculating the confidence weight of each sub-model based on the vehicle speed and lateral acceleration in the virtual sensor data, and using the confidence weight to perform weighted fusion on the state estimation vectors output in parallel by each filter and the system matrix of each sub-model to obtain the global optimal state estimation vector and the equivalent linear system model for controller design;

[0024] The confidence weight of each sub-model is calculated using the Gaussian membership function. For example, three Gaussian functions are established for vehicle speed, with 20, 80, and 140 kilometers per hour as the center, respectively. Figure 2 As shown in the figure, the weight of each model in the vehicle speed dimension is calculated according to the current vehicle speed. Similarly, a membership function is established for the lateral acceleration. The weights in the two dimensions are multiplied and normalized to obtain the final weight coefficients w1, w2, and w3. The global optimal state estimation vector is obtained by the formula Calculated, where x1, x2, x3 are the outputs of the Kalman filters of each volume. The system matrix of the equivalent linear system model is preferably obtained by as well as Obtained by weighted average.

[0025] S3, performing a short-time Fourier transform on the real-time steering wheel angle measurement value, and using the high-frequency energy ratio as the driver input characteristic parameter; constructing a linear quadratic regulator, and determining a state weighting matrix and a control weighting matrix in a cost function of the linear quadratic regulator based on a three-dimensional lookup table, wherein the inputs of the lookup table are the vehicle speed and center of mass sideslip angle components in the global optimal state estimation vector, and the driver input characteristic parameter;

[0026] The steering wheel angle signal sequence within the past second is intercepted, and a short-time Fourier transform is performed after applying a window function to obtain the spectrum distribution of the signal. The frequency band from 0 to 10 Hz is divided into a low frequency band from 0 to 2 Hz and a high frequency band from 2 to 10 Hz, as shown in the following example: Figure 3 As shown, the energy within the two frequency bands, or the sum of the squares of the spectrum amplitudes, is calculated. The high-frequency energy is divided by the total energy. The resulting high-frequency energy ratio is the driver input characteristic parameter, which ranges from 0 to 1. A larger value indicates more aggressive driver control.

[0027] State weighting matrix in the linear quadratic regulator LQR cost function J and control weight matrix The element values ​​of are obtained by online interpolation from a pre-calibrated 3D lookup table. The three input dimensions of the lookup table are vehicle speed, center of mass side slip angle, and driver input characteristic parameters, such as Figure 4 For example, when the vehicle speed is high and the sideslip angle is large, the lookup table will output a state with a large penalty term for the sideslip angle. matrix to prioritize vehicle stability; when the driver inputs a larger characteristic parameter, the lookup table will output a smaller one matrix to reduce the constraints on the amount of control, allowing the system to respond faster.

[0028] S4, solving the algebraic Riccati equation based on the equivalent linear system model and the state weighting matrix and the control weighting matrix to obtain the optimal feedback gain matrix, and multiplying the gain matrix with the global optimal state estimation vector to obtain the target output torque of the force feedback steering wheel actuator motor.

[0029] In each control cycle, the equivalent system matrix obtained by weighted fusion is 、 The weighted matrix obtained by table lookup 、 Substituting into the algebraic Riccati equation , use numerical iterative algorithms such as Kleinman algorithm to solve the positive definite matrix P. According to the formula Calculate the optimal feedback gain matrix K at the current moment. Combine the gain matrix K with the global optimal state estimation vector Multiply, that is The obtained result is appropriately scaled and sent as a command to the servo motor of the force feedback steering wheel to drive it to generate the target output torque.

[0030] The operating conditions of the vehicle are constantly changing, which also interferes with the accuracy of the Kalman filter. To improve the accuracy of the Kalman filter, in a more specific embodiment, the process of determining the cubature Kalman filter and the measurement noise covariance matrix using the tire state and the steering wheel angular velocity includes:

[0031] Calculating a tire saturation factor and an absolute value of a steering wheel angular velocity by combining the virtual sensor data, vehicle model parameters, and the global optimal state estimation vector;

[0032] Taking the tire saturation factor and the absolute value of the steering wheel angular velocity as input, respectively calculating scaling coefficients of the process noise covariance matrix and the measurement noise covariance matrix through two nonlinear mapping functions;

[0033] The scaling factor is multiplied by a preset reference process noise covariance matrix and a reference measurement noise covariance matrix respectively to obtain an adjusted process and measurement noise covariance matrix.

[0034] The process noise covariance matrix reflects the uncertainty of the vehicle model prediction, while the measurement noise covariance matrix reflects the uncertainty of the sensor measurement. When the vehicle is driven aggressively, for example, the tires are close to the adhesion limit or the driver turns the steering wheel rapidly, the nonlinear characteristics of the vehicle are enhanced, and the prediction error based on the linear model will increase significantly. The process noise covariance matrix is increased to reduce the trust of the filter on the model prediction.

[0035] In one embodiment, the side slip angle and yaw rate of the centroid provided in the globally optimal state estimation vector are used in combination with the vehicle speed and the real-time steering wheel angle in the virtual sensor data to calculate the side slip angles of the front and rear tires respectively through the vehicle kinematic model. Based on these side slip angles and the tire model preset in the vehicle model parameters, the lateral forces currently acting on the front and rear tires are estimated. The estimated lateral forces are compared with the maximum lateral adhesion that the tires can provide under the current vertical load, and the ratio is the tire saturation factor. The maximum lateral adhesion is calculated by, for example, the road adhesion coefficient and the vertical load. Preferably, the absolute value of the steering wheel angular velocity is obtained by taking the absolute value of the time difference of the real-time measurement value of the steering wheel angle.

[0036] For example, when the calculated tire saturation factor increases from 0.2 to 0.8, and the absolute value of the steering wheel angular velocity increases from 10 degrees per second to 200 degrees per second, it indicates that the vehicle has entered the nonlinear region. The preset nonlinear mapping function, such as an S-shaped function, will map these two inputs to a larger process noise scaling coefficient, such as 5.0, and may also adjust the scaling coefficient of the measurement noise to 1.5. Multiply these two coefficients by the preset reference noise covariance matrix, for example, a diagonal matrix, to obtain the dynamically adjusted covariance matrix. This allows the filter to maintain accurate state estimation performance under different working conditions.

[0037] In a more specific embodiment, the confidence weight of each sub-model is calculated according to the vehicle speed and lateral acceleration in the virtual sensor data, including:

[0038] The center point coordinates of the vehicle speed and lateral acceleration are set for low speed, medium speed, and high speed working conditions respectively;

[0039] The Euclidean distance between the current vehicle speed and lateral acceleration obtained from the virtual sensor data and the center point coordinates of each working condition is calculated;

[0040] The Euclidean distance is substituted into the Gaussian membership function to calculate the confidence corresponding to the three sub-models respectively;

[0041] The three confidences are normalized so that the sum of the three confidences is 1, and the normalized confidence weight is obtained.

[0042] A single linear vehicle model cannot accurately describe all driving scenarios, from low speed to high speed and from linear to nonlinear. Therefore, this embodiment employs a multi-model switching strategy. By establishing independent sub-models for different typical operating conditions, the vehicle characteristics under each condition can be more accurately captured. To achieve a smooth transition between models, the applicability of each sub-model, i.e., the confidence weight, is calculated based on the current vehicle state. For example, assume that the coordinates of the center points for the three operating conditions are: 20 kilometers per hour and a lateral acceleration of 0.5 seconds per square meter for the low-speed zone; 70 kilometers per hour and a lateral acceleration of 2 seconds per square meter for the medium-speed-linear zone; and 120 kilometers per hour and a lateral acceleration of 6 seconds per square meter for the high-speed-nonlinear zone. When the virtual sensor measures a current vehicle speed of 75 kilometers per hour and a lateral acceleration of 2.5 seconds per square meter, the Euclidean distance from that point to the three center points is calculated. This point is closest to the center point of the medium-speed-linear zone and farthest from the low-speed zone. Using the Gaussian membership function, closer distances indicate higher membership. The initial confidence scores might be 0.1 for low speed, 0.8 for medium speed, and 0.3 for high speed. They are then normalized to 0.08 for low speed, 0.67 for medium speed, and 0.25 for high speed, with the sum of the three being 1.

[0043] In a more specific embodiment, the state estimation vectors output in parallel by each filter and the system matrix of each sub-model are weightedly fused using the confidence weights to obtain the global optimal state estimation vector and the equivalent linear system model for controller design, including:

[0044] Extract the state matrices A1, A2, A3 and control matrices B1, B2, B3 from the low-speed, medium-speed, and high-speed state space submodels respectively;

[0045] Using the calculated confidence weights w1, w2, and w3 corresponding to each sub-model, the state matrix of the equivalent linear system model is calculated using the following linear weighted summation formula: and control matrix :

[0046] ;

[0047] .

[0048] After obtaining the confidence weight of each sub-model under the current working condition, in order to provide an accurate system model for the subsequent model predictive controller, the three discrete sub-models are fused into an equivalent global model. The fusion process adopts the fuzzy logic method, which integrates the dynamic characteristics of the vehicle under different working conditions through weighted averaging, avoiding the control mutation that may occur when the model is switched. Taking the weights calculated above as an example, w1 is equal to 0.08, w2 is equal to 0.67, and w3 is equal to 0.25. The state matrices A1, A2, and A3 and the control matrices B1, B2, and B3 are retrieved from the pre-established three sub-model libraries, respectively. The equivalent state matrix is A1 multiplied by 0.08, plus A2 multiplied by 0.67, and plus A3 multiplied by 0.25. Similarly, the equivalent control matrix is also weighted and summed in the same way. The obtained and matrix integrates the characteristics of each sub-model under the current working condition, obtaining an equivalent linear time-varying model that can accurately reflect the real-time response of the vehicle.

[0049] In a more specific embodiment, the real-time measurement of the steering wheel angle is subjected to short-time Fourier transform, and the proportion of high-frequency energy is taken as the driver input characteristic parameter, including:

[0050] The collected steering wheel angle signal sequence is subjected to frame processing by setting the time window and the overlap rate;

[0051] A window function is applied to each frame signal, and fast Fourier transform is performed to obtain the frequency spectrum of the frame;

[0052] The frequency range of the high-frequency band and the full frequency band is set, and the total energy sum of the high-frequency band and the total energy sum of the full frequency band are calculated;

[0053] The ratio of the total high-frequency energy to the total full-band energy is taken as the driver input characteristic parameter.

[0054] Different drivers have different driving styles, such as smooth versus aggressive. Drivers' steering habits are reflected in the steering wheel angle signal. Smooth steering maneuvers primarily contain low-frequency components, while rapid, abrupt steering movements generate a large number of high-frequency components. By analyzing the spectral characteristics of the steering wheel angle signal, the driver's instantaneous steering intensity can be identified. In practice, steering wheel angle data is continuously collected. For example, a 1-second window is set, sliding forward with a 50% overlap, meaning calculations are performed every 0.5 seconds. The data within each window, such as 256 samples, is first processed using a Hanning window to reduce spectral leakage, followed by a fast Fourier transform. Assume that frequencies above 2 Hz are defined as the high-frequency band, and 0 to 25 Hz as the full-frequency band. The sum of the energy in the 2 to 25 Hz range of the spectrum is calculated, followed by the total energy in the 0 to 25 Hz range. If the driver is making an emergency evasive maneuver, the calculated high-frequency energy ratio may be as high as 0.8; during steady cruising, this ratio may be less than 0.2.

[0055] In a more specific embodiment, determining the state weighting matrix and the control weighting matrix in the cost function of the linear quadratic regulator according to the three-dimensional lookup table includes:

[0056] Construct a three-dimensional lookup table whose three dimensional axes correspond to vehicle speed, center of mass sideslip angle, and driver input characteristic parameters, and store pre-calibrated element values ​​of the state weight matrix and control weight matrix at discrete grid points within the three-dimensional lookup table;

[0057] The vehicle speed component, the center of mass sideslip angle component and the calculated driver input characteristic parameters in the global optimal state estimation vector at the current moment are used as input coordinates;

[0058] The trilinear interpolation algorithm is used to calculate the state weighting matrix and control weighting matrix under the current working condition according to the values ​​of the input coordinates at eight adjacent grid points in the lookup table.

[0059] The performance of a model predictive controller depends on the setting of the weighting matrices in its cost function. These matrices determine the balance between the controller's pursuit of path tracking accuracy, driving stability, and reduction of control energy consumption. Using fixed weighting matrices cannot adapt to varying driving conditions and driver intent. For example, when avoiding obstacles at high speed, more emphasis should be placed on vehicle stability, i.e. increasing the penalty weight on the center of mass side slip angle; while parking at low speed, more attention should be paid to the smoothness of control, i.e. increasing the penalty weight on the control amount. To achieve real-time adjustment, a three-dimensional lookup table is constructed. For example, the speed axis ranges from 0 to 150 km / h, the center of mass side slip angle axis ranges from -4 to 4 degrees, and the driver input characteristic parameter axis ranges from 0 to 1. At the intersection of these axes, the values of the weighting matrices optimized through a large number of simulations and real vehicle tests are stored. When the vehicle is running in a condition of 95 km / h, 1.5 degrees of center of mass side slip angle, and 0.75 of driver input characteristic parameter, the coordinate point is located in the lookup table, and the nearest eight points around it are found. Through a three-linear interpolation algorithm, a state and control weighting matrix corresponding to the specific working condition is calculated according to the relative distance between the current coordinate point and the eight points, thereby realizing online optimization of the controller performance.

[0060] In a second embodiment of the present application, a dynamic force feedback closed-loop control system in a simulation driving is provided, comprising the following modules:

[0061] A configuration module is configured to obtain vehicle model parameters, real-time measurement of steering wheel angle, and virtual sensor data containing vehicle speed and lateral acceleration output by a vehicle solver; based on the vehicle model parameters, state space sub-models corresponding to low speed, medium speed, and high speed driving conditions are established, and corresponding cubature Kalman filters are configured for each sub-model, and process and measurement noise covariance matrices of the cubature Kalman filters are determined by using tire state and steering wheel angular velocity;

[0062] A model establishment module is configured to calculate confidence weight of each sub-model according to vehicle speed and lateral acceleration in the virtual sensor data, and to weight and fuse state estimation vectors output in parallel by each filter and system matrices of each sub-model by using the confidence weight, so as to obtain a globally optimal state estimation vector and an equivalent linear system model for controller design;

[0063] A transformation module is configured to perform short-time Fourier transform on the real-time measurement of steering wheel angle, and to take high-frequency energy proportion as a driver input characteristic parameter; a linear quadratic regulator is constructed, and state weighting matrices and control weighting matrices in a cost function of the linear quadratic regulator are determined according to a three-dimensional lookup table, and the input of the lookup table is vehicle speed and center of mass side slip angle components in the globally optimal state estimation vector and the driver input characteristic parameter;

[0064] A control module is used to solve the algebraic Riccati equation based on the equivalent linear system model and the state weighting matrix and the control weighting matrix to obtain an optimal feedback gain matrix, and multiply the gain matrix with the global optimal state estimation vector to obtain a target output torque of the force feedback steering wheel actuator motor.

[0065] It should be noted that for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of this specification are not limited by the order of the actions described, because according to the embodiments of this specification, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of this specification.

[0066] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0067] The preferred embodiments disclosed above are intended only to help illustrate this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made based on the content of the embodiments of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of this specification, so that those skilled in the art can better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A dynamic force feedback closed-loop control method in simulated driving, characterized in that: The following steps are involved: Obtain vehicle model parameters, real-time steering wheel angle measurements, and virtual sensor data including vehicle speed and lateral acceleration output by the vehicle solver; Based on the vehicle model parameters, state space sub-models corresponding to low-speed, medium-speed, and high-speed driving conditions are established, and a corresponding cubic Kalman filter is configured for each sub-model. The process and measurement noise covariance matrix of the cubic Kalman filter is determined using tire state and steering wheel angular velocity; Calculating confidence weights for each sub-model based on the vehicle speed and lateral acceleration in the virtual sensor data, and using the confidence weights to perform weighted fusion on the state estimation vectors output in parallel by each filter and the system matrix of each sub-model to obtain a global optimal state estimation vector and an equivalent linear system model; Perform short-time Fourier transform on the real-time steering wheel angle measurement and use the high-frequency energy ratio as the driver input feature parameter; Constructing a linear quadratic regulator and determining a state weighting matrix and a control weighting matrix in a cost function of the linear quadratic regulator based on a three-dimensional lookup table, wherein the inputs of the lookup table are the vehicle speed and center of mass sideslip angle components in the global optimal state estimation vector and the driver input characteristic parameter; The algebraic Riccati equation is solved based on the equivalent linear system model, the state weighting matrix and the control weighting matrix to obtain the optimal feedback gain matrix, and the gain matrix is ​​multiplied by the global optimal state estimation vector to obtain the target output torque of the force feedback steering wheel actuator motor.

2. The method according to claim 1, characterized in that The process of determining the cubature Kalman filter and the measurement noise covariance matrix using the tire state and the steering wheel angular velocity includes: Calculating a tire saturation factor and an absolute value of a steering wheel angular velocity by combining the virtual sensor data, vehicle model parameters, and the global optimal state estimation vector; Taking the tire saturation factor and the absolute value of the steering wheel angular velocity as input, respectively calculating scaling coefficients of the process noise covariance matrix and the measurement noise covariance matrix through two nonlinear mapping functions; The scaling factor is multiplied by a preset reference process noise covariance matrix and a reference measurement noise covariance matrix respectively to obtain an adjusted process and measurement noise covariance matrix.

3. The method according to claim 1, characterized in that The calculating of the confidence weight of each sub-model according to the vehicle speed and lateral acceleration in the virtual sensor data includes: Set the center point coordinates of vehicle speed and lateral acceleration for low speed, medium speed and high speed conditions respectively; Calculate the Euclidean distance between the vehicle speed and lateral acceleration obtained by the virtual sensor data at the current moment and the center point coordinates of each working condition; Substituting the Euclidean distance into the Gaussian membership function, the confidence scores corresponding to the three sub-models are calculated; The three confidence levels are normalized so that the sum of the three confidence levels is 1, thereby obtaining a normalized confidence weight.

4. The method according to claim 1, wherein The method uses the confidence weights to perform weighted fusion on the state estimation vectors output in parallel by each filter and the system matrix of each sub-model to obtain a global optimal state estimation vector and an equivalent linear system model for controller design, including: Extract the state matrices A1, A2, A3 and control matrices B1, B2, B3 from the low-speed, medium-speed, and high-speed state space submodels respectively; Using the calculated confidence weights w1, w2, and w3 corresponding to each sub-model, the state matrix of the equivalent linear system model is calculated using the following linear weighted summation formula: and control matrix : ； 。 5. The method according to claim 1, wherein The method of performing a short-time Fourier transform on the real-time steering wheel angle measurement value and using the high-frequency energy ratio as the driver input characteristic parameter includes: Set the time window and overlap rate to perform frame processing on the acquired steering wheel angle signal sequence; Apply a window function to each frame signal and perform a fast Fourier transform to obtain the frequency spectrum of the frame; Setting the frequency range of the high-frequency band and the full-frequency band, and calculating the sum of the spectrum energy in the high-frequency band and the sum of the energy in the full-frequency band; The ratio of the high-frequency energy sum to the full-band energy sum is used as the driver input characteristic parameter.

6. The method according to claim 1, characterized in that The determining of the state weighting matrix and the control weighting matrix in the cost function of the linear quadratic regulator according to the three-dimensional lookup table includes: Construct a three-dimensional lookup table whose three dimensional axes correspond to vehicle speed, center of mass sideslip angle, and driver input characteristic parameters, and store pre-calibrated element values ​​of the state weight matrix and control weight matrix at discrete grid points within the three-dimensional lookup table; The vehicle speed component, the center of mass sideslip angle component and the calculated driver input characteristic parameters in the global optimal state estimation vector at the current moment are used as input coordinates; The trilinear interpolation algorithm is used to calculate the state weighting matrix and control weighting matrix under the current working condition according to the values ​​of the input coordinates at eight adjacent grid points in the lookup table.

7. A dynamic force feedback closed-loop control system for simulated driving, characterized in that: Includes the following modules: A configuration module is configured to obtain vehicle model parameters, real-time steering wheel angle measurements, and virtual sensor data output by a vehicle solver, including vehicle speed and lateral acceleration; based on the vehicle model parameters, establish state-space submodels corresponding to low-speed, medium-speed, and high-speed driving conditions, configure a corresponding cubic Kalman filter for each submodel, and determine the process and measurement noise covariance matrix of the cubic Kalman filter using tire state and steering wheel angular velocity; a model building module for calculating confidence weights for each sub-model based on the vehicle speed and lateral acceleration in the virtual sensor data, and using the confidence weights to perform weighted fusion on the state estimation vectors output in parallel by each filter and the system matrix of each sub-model to obtain a global optimal state estimation vector and an equivalent linear system model; A transformation module is used to perform short-time Fourier transform on the real-time steering wheel angle measurement value and use the high-frequency energy ratio as the driver input feature parameter; Constructing a linear quadratic regulator and determining a state weighting matrix and a control weighting matrix in a cost function of the linear quadratic regulator based on a three-dimensional lookup table, wherein the inputs of the lookup table are the vehicle speed and center of mass sideslip angle components in the global optimal state estimation vector and the driver input characteristic parameter; A control module is used to solve the algebraic Riccati equation based on the equivalent linear system model and the state weighting matrix and the control weighting matrix to obtain an optimal feedback gain matrix, and multiply the gain matrix with the global optimal state estimation vector to obtain a target output torque of the force feedback steering wheel actuator motor.

8. The system according to claim 7, characterized in that The process of determining the cubature Kalman filter and the measurement noise covariance matrix using the tire state and the steering wheel angular velocity includes: Calculating a tire saturation factor and an absolute value of a steering wheel angular velocity by combining the virtual sensor data, vehicle model parameters, and the global optimal state estimation vector; Taking the tire saturation factor and the absolute value of the steering wheel angular velocity as input, respectively calculating scaling coefficients of the process noise covariance matrix and the measurement noise covariance matrix through two nonlinear mapping functions; The scaling factor is multiplied by a preset reference process noise covariance matrix and a reference measurement noise covariance matrix respectively to obtain an adjusted process and measurement noise covariance matrix.

9. The system according to claim 7, wherein: The calculating of the confidence weight of each sub-model according to the vehicle speed and lateral acceleration in the virtual sensor data includes: Set the center point coordinates of vehicle speed and lateral acceleration for low speed, medium speed and high speed respectively; Calculate the Euclidean distance between the vehicle speed and lateral acceleration obtained by the virtual sensor data at the current moment and the center point coordinates of each working condition; Substituting the Euclidean distance into the Gaussian membership function, the confidence scores corresponding to the three sub-models are calculated; The three confidence levels are normalized so that the sum of the three confidence levels is 1, thereby obtaining a normalized confidence weight.

10. The system according to claim 7, wherein: The method uses the confidence weights to perform weighted fusion on the state estimation vectors output in parallel by each filter and the system matrix of each sub-model to obtain a global optimal state estimation vector and an equivalent linear system model for controller design, including: Extract the state matrices A1, A2, A3 and control matrices B1, B2, B3 from the low-speed, medium-speed, and high-speed state space submodels respectively; Using the calculated confidence weights w1, w2, and w3 corresponding to each sub-model, the state matrix of the equivalent linear system model is calculated using the following linear weighted summation formula: and control matrix : ； 。

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