Active suspension control method, system and product

Abstract

The invention provides an active suspension control method, system and product, and the method comprises the steps: determining a posterior augmented state vector of a vehicle according to a prior augmented state vector of the vehicle, the augmented state vector being composed of various state parameters of the vehicle; according to the posterior augmented state vector, determining the system state of the vehicle and front wheel road surface input information; according to the system state of the vehicle and the front wheel road surface input information, the optimal suspension control output quantity in the prediction time domain is determined; and controlling the vehicle suspension through the optimal suspension control output quantity. The invention aims to control an active suspension of a vehicle based on road surface input information of road surface unevenness and a vehicle system state so as to improve the driving comfort of the vehicle.

Description

Technical Field

[0001] The present application relates to the field of suspension control technology, and in particular to an active suspension control method, system and product. Background Art

[0002] With the rapid development of the automobile industry, people have higher and higher requirements for the driving comfort of automobiles. As the connecting component between the vehicle body and the wheels, the vehicle suspension system plays a very critical role in alleviating the impact load during driving and maintaining good riding comfort. However, due to its inherent transfer characteristics, the performance of the traditional passive suspension has bottlenecks. In order to obtain better vertical dynamics performance of the vehicle, a variety of active control strategies (such as LQR control, sliding mode control, H norm control, etc.) have been proposed. However, most of the above active control algorithms realize feedback control by extracting the state of the vehicle during driving, but do not consider the influence of road roughness, and only achieve the best control in the average statistical sense. Road roughness is the main excitation during vehicle driving. If the current road waveform profile is obtained in real time and accurately during vehicle driving, and the road input information is added to the formulation of the active suspension control strategy, the vertical dynamics performance of the vehicle will be greatly improved. Summary of the invention

[0003] In view of this, the present application provides an active suspension control method, system and product, which are intended to control the active suspension of a vehicle based on road surface roughness input information and vehicle system status to improve vehicle driving comfort.

[0004] A first aspect of the present application provides an active suspension control method, the method comprising:

[0005] Determining a posterior augmented state vector of the vehicle according to the prior augmented state vector of the vehicle, wherein the augmented state vector is composed of various state parameters of the vehicle;

[0006] Determining the system state of the vehicle and the front wheel road surface input information according to the posterior augmented state vector;

[0007] Determining an optimal suspension control output in a prediction time domain according to a system state of the vehicle and front wheel road surface input information;

[0008] The vehicle suspension is controlled by the optimal suspension control output.

[0009] Optionally, determining a posterior augmented state vector of the vehicle according to the prior augmented state vector of the vehicle includes:

[0010] Construct the differential equations for the half-car model:

[0011] Among them, m His the sprung mass of the half-car model; I y is the moment of inertia about the y-axis; m f 、m r are the masses of the front and rear wheels respectively; k f , k r are the front and rear suspension stiffness respectively; k tf , k tr are the front and rear tire stiffnesses respectively; c f 、c r are the front and rear suspension damping coefficients respectively; x Hf 、x Hr are the vertical displacements of the front and rear suspension spring masses respectively; x H is the vertical displacement at the center of mass; x f 、x r are the vertical displacements of the front and rear wheels respectively; θ is the pitch angle of the center of mass; L f , L r are the distances from the center of mass to the front and rear axles respectively; q f ,q r are the road surface input information of the front and rear wheels respectively; f 、u r are the forces acting on the front and rear active suspensions respectively; within the set pitch angle range, the displacement of the front and rear axle bodies is

[0012] Based on the vehicle state vector and the differential equation of the half-vehicle model, the state space equation is constructed: The vehicle state vector is I4 is the identity matrix with 1 on the diagonal and 0 1×4 is a 1×4 zero matrix, 0 1×6 is a 1×6 zero matrix, u=[u f u r ] T , q=[q f q r ] T ,

[0013] Based on the state-space equation and the noise error between the real vehicle and the half-vehicle model, a random linear state-space equation is constructed: in, is the observation vector; w(t) and v(t) are the process noise and measurement noise respectively satisfying the zero-mean Gaussian distribution; u(t) is the force of the front and rear suspensions; q(t) is the road surface input information of the front and rear wheels,

[0014]

[0015] Determine the road surface input information of the front and rear wheels as a state vector, and construct the corresponding augmented state vector:

[0016] According to the first target setting and the augmented state vector, an augmented state space equation corresponding to the random linear state space equation is determined: The first goal is to set the front and rear road inputs to satisfy the random walk process. H a =[H 0 2×2 ], D a =D,ζ(t)=[w(t) η(t)] T ;

[0017] The augmented state space equation is discretized to obtain a discrete augmented state space equation: in, H ad =H a , D ad =D a , ΔT is the sensor sampling time interval, ζ k =[w k η k ] T , w k 、v k , η k They are Gaussian white noises that are independent of each other and follow normal distribution;

[0018] The a priori augmented state vector is determined by calculating a first preset algorithm constructed based on the discrete augmented state space equation, and the expression of the first preset algorithm is:

[0019] The prior state error covariance matrix is ​​updated by a first updating algorithm, and the expression of the first updating algorithm is:

[0020] Based on the updated prior state error covariance matrix, the Kalman gain is calculated and determined by a second preset algorithm, and the expression of the second preset algorithm is:

[0021] Based on the a priori augmented state vector and the calculated Kalman gain, the a posteriori augmented state vector is determined by a third preset algorithm, and the expression of the third preset algorithm is:

[0022] Optionally, determining the optimal suspension control output in the prediction time domain according to the system state of the vehicle and the front wheel road surface input information includes:

[0023] Pre-construct a road surface input discrete space function, and simplify the road surface input discrete space function to obtain a simplified road surface input discrete space function: qq (k+1)=A q q q (k)+F q q f (k); where the road surface input discrete space function is: q0(k) represents the rear wheel road input information at time k, Nl is the discretized time interval of the front and rear wheels experiencing the same road input, Nl = L / u / ΔT, L represents the vehicle wheelbase, u represents the vehicle speed, q0(k) to q Nl-1 (k) represents the road surface input information within the wheelbase range between the front and rear wheels at time k, q f (k) represents the front wheel road surface input information at time k, q0(k+1) represents the rear wheel road surface input information at time k+1, and q0(k+1) to q Nl-1 (k+1) represents the road surface input information within the wheelbase range between the front and rear wheels at time k+1, q q =[q0 q1…q Nl-2 q Nl-1 ] T ;

[0024] Based on the sampling time interval and the state space equation, the discrete state space equation is constructed: Among them, A d =e AΔT ; F d =[F d1 F d2 ] is the road surface input matrix, F d1 and F d2 Represent the input matrices of the front wheel road surface and the rear wheel road surface respectively;

[0025] Based on the simplified road surface input discrete space function and the discrete state space equation, the target state space equation is constructed: * (k+1)=A s x * (k)+B su u(k)+F sq q f (k); where

[0026] Based on the performance output vector and the target state space equation, determine the output equation: y(k)=C s x * (k)+D s u(k);

[0027] in, The performance output vector is

[0028] Under the target constraint condition, based on the output equation, the output prediction equation in the corresponding prediction time domain is constructed: Y = A p x * (k)+B p U;

[0029] in, The target constraint is N c ≤N p ≤Nl,N c To control the time domain, N p For the prediction time domain;

[0030] According to the system state of the vehicle and the front wheel road surface input information, the optimal suspension control output in the prediction time domain is determined through a pre-constructed output prediction equation.

[0031] Optionally, the method further includes:

[0032] Based on the updated prior state error covariance matrix and the calculated Kalman gain, the posterior state error covariance matrix is ​​updated by a second updating algorithm, and the expression of the second updating algorithm is: k|k =(IK k H ad ) k|k-1 ;

[0033] The updated posterior state error covariance matrix is ​​used to update the subsequent prior state error covariance matrix.

[0034] Optionally, the method further includes: defining the initial value of the state error covariance matrix as The initial value of the vehicle state vector is

[0035] Optionally, according to the system state of the vehicle and the front wheel road surface input information, the optimal suspension control output in the prediction time domain is determined by a pre-constructed output prediction equation, including:

[0036] Determine the initial objective function in the prediction time domain as:

[0037] Among them, Q c Represents the weighted matrix of the system output, R c is the weighting matrix of the control quantity;

[0038] Substitute the output prediction equation into the initial objective function to determine the objective function:

[0039] in, is the weighting matrix of the output in the prediction time domain, is the weighting matrix of the control quantity in the control time domain;

[0040] According to the determined vehicle system state and front wheel road input information, the output in the prediction time domain is solved by the objective function to determine the optimal open-loop control sequence U=[u(k)u(k+1)...u(k+N c -1)] T ;

[0041] The optimal open-loop control sequence U = [u(k)u(k+1)...u(k+N c -1)] T The optimal suspension control output is determined within the prediction time domain.

[0042] Optionally, controlling the vehicle suspension by using the optimal suspension control output comprises:

[0043] Extract the optimal open-loop control sequence U=[u(k)u(k+1)...u(k+N c -1)] T The first term u(k) in ;

[0044] The first term u(k) is sent to the vehicle control system for suspension control.

[0045] A second aspect of the present application provides an active suspension control system, the system comprising:

[0046] A posterior augmented state vector determination module is used to determine the posterior augmented state vector of the vehicle according to the prior augmented state vector of the vehicle, wherein the augmented state vector is composed of various state parameters of the vehicle;

[0047] A state information determination module, used to determine the system state of the vehicle and the front wheel road surface input information according to the posterior augmented state vector;

[0048] An optimal suspension control output determination module, used to determine the optimal suspension control output in a prediction time domain according to the system state of the vehicle and the front wheel road surface input information;

[0049] A control module is used to control the vehicle suspension through the optimal suspension control output.

[0050] The third aspect of the present application provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and running on the processor, wherein when the computer program is executed by the processor, the steps in an active suspension control method as described in the first aspect of the present application are implemented.

[0051] A fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps in an active suspension control method as described in the first aspect of the present application are implemented.

[0052] The active suspension control method provided by the present application has the following advantages:

[0053] An active suspension control method provided in an embodiment of the present application first determines the vehicle's posterior augmented state vector based on the vehicle's prior augmented state vector, wherein the augmented state vector is composed of various state parameters of the vehicle; determines the vehicle's system state and front wheel road surface input information based on the posterior augmented state vector; determines the optimal suspension control output in the prediction time domain based on the vehicle's system state and front wheel road surface input information; and controls the vehicle suspension through the optimal suspension control output. Therefore, when determining the suspension control output, the present application not only considers the vehicle system state, but also the road surface input information of road surface roughness, so that the suspension control effect is better and the comfort of the vehicle driving process can be effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor.

[0055] Figure 1 A flowchart of an active suspension control method shown in one embodiment of the present application;

[0056] Figure 2 A schematic diagram of a half-vehicle model in an active suspension control method according to an embodiment of the present application;

[0057] Figure 3 A schematic diagram of the correlation between road surface input information within the wheelbase range between front and rear wheels in an active suspension control method according to an embodiment of the present application;

[0058] Figure 4 Another flow chart of an active suspension control method shown in one embodiment of the present application;

[0059] Figure 5 A schematic diagram of an active suspension control system according to an embodiment of the present application. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0061] refer to Figure 1 , Figure 1 FIG. 1 is a schematic diagram of an active suspension control method according to an embodiment of the present application. Figure 1 As shown, the method includes:

[0062] Step S1: Determine the a posteriori augmented state vector of the vehicle according to the a priori augmented state vector of the vehicle, wherein the augmented state vector is composed of various state parameters of the vehicle.

[0063] In this embodiment, the vehicle's prior augmented state vector is the vehicle's augmented state vector at the previous moment (e.g., the previous moment is the k-1 moment), and the vehicle's posterior augmented state vector is the vehicle's augmented state vector at the next moment after the previous moment (i.e., the next moment after the previous moment is the k moment). The augmented state vector is composed of various state parameters of the vehicle. A preferred implementation is: the augmented state vector is Among them, x Hf 、x Hr are the vertical displacements of the front and rear suspension spring masses respectively; x f 、x r are the vertical displacements of the front and rear wheels respectively; within the set pitch angle range, the displacements of the front and rear axle bodies are x H is the vertical displacement at the center of mass; θ is the pitch angle of the center of mass; L f , L r are the distances from the center of mass to the front and rear axles respectively; q f ,q r The road surface input information of the front and rear wheels respectively. Based on the vehicle's prior augmented state vector, the vehicle's posterior augmented state vector is determined by prediction. Among them, the augmented state vector at least needs to include the vehicle's front wheel road surface input information. In this application, the road surface input information refers to the road surface roughness information.

[0064] Step S2: Determine the system state of the vehicle and the front wheel road surface input information according to the posterior augmented state vector.

[0065] In this embodiment, after the a posteriori augmented state vector of the vehicle is obtained by prediction in step S1, the system state of the vehicle and the front wheel road surface input information are determined based on the a posteriori augmented state vector. In the case of , the corresponding posterior augmented state vector of the vehicle can be determined based on the input information q of the front wheel road surface f The value of the vehicle system state determined includes the parameter x Hf 、x Hr 、x f 、x r , The value of .

[0066] Step S3: Determine the optimal suspension control output in the prediction time domain according to the system state of the vehicle and the front wheel road surface input information.

[0067] In this embodiment, based on the system state of the vehicle determined in step S2 and the front wheel road surface input information, the optimal suspension control output within a preset time domain under the state is determined.

[0068] Step S4: Controlling the vehicle suspension by means of the optimal suspension control output.

[0069] In this embodiment, the vehicle suspension is then controlled based on the optimal suspension control output determined in step S3.

[0070] An active suspension control method provided in an embodiment of the present application first determines the vehicle's posterior augmented state vector based on the vehicle's prior augmented state vector, wherein the augmented state vector is composed of various state parameters of the vehicle; determines the vehicle's system state and front wheel road surface input information based on the posterior augmented state vector; determines the optimal suspension control output in the prediction time domain based on the vehicle's system state and front wheel road surface input information; and controls the vehicle suspension through the optimal suspension control output. Therefore, when determining the suspension control output, the present application not only considers the vehicle system state, but also the road surface input information of road surface roughness, so that the suspension control effect is better and the comfort of the vehicle driving process can be effectively improved.

[0071] In combination with the above embodiments, in one implementation, the present application embodiment further provides an active suspension control method. In the active suspension control method, step S1 may include:

[0072] Construct the differential equations for the half-car model: Among them, m H is the sprung mass of the half-car model; I y is the moment of inertia about the y-axis; m f 、m r are the masses of the front and rear wheels respectively; k f , k r are the front and rear suspension stiffness respectively; k tf , k tr are the front and rear tire stiffnesses respectively; cf 、c r are the front and rear suspension damping coefficients respectively; x Hf 、x Hr are the vertical displacements of the front and rear suspension spring masses respectively; x H is the vertical displacement at the center of mass; x f 、x r are the vertical displacements of the front and rear wheels respectively; θ is the pitch angle of the center of mass; L f , L r are the distances from the center of mass to the front and rear axles respectively; q f ,q r are the road surface input information of the front and rear wheels respectively; f 、u r are the forces acting on the front and rear active suspensions respectively; within the set pitch angle range, the displacement of the front and rear axle bodies is

[0073] Based on the vehicle state vector and the differential equation of the half-vehicle model, the state space equation is constructed: The vehicle state vector is I4 is the identity matrix with 1 on the diagonal and 0 1×4 is a 1×4 zero matrix, 0 1×6 is a 1×6 zero matrix, u=[u f u r ] T , q=[q f q r ] T ,

[0074]

[0075] Based on the state-space equation and the noise error between the real vehicle and the half-vehicle model, a random linear state-space equation is constructed: in, is the observation vector; w(t) and v(t) are the process noise and measurement noise respectively satisfying the zero-mean Gaussian distribution; u(t) is the force of the front and rear suspensions; q(t) is the road surface input information of the front and rear wheels,

[0076]

[0077] Determine the road surface input information of the front and rear wheels as a state vector, and construct the corresponding augmented state vector:

[0078] According to the first target setting and the augmented state vector, an augmented state space equation corresponding to the random linear state space equation is determined: The first goal is to set the front and rear road inputs to satisfy the random walk process. H a =[H 0 2×2 ], D a =D,ζ(t)=[w(t) η(t)] T .

[0079] The augmented state space equation is discretized to obtain a discrete augmented state space equation: in, H ad =H a , D ad =D a , ΔT is the sensor sampling time interval, ζ k =[w k η k ] T , w k 、v k , η k They are independent of each other and obey the normal distribution of Gaussian white noise.

[0080] The a priori augmented state vector is determined by calculating a first preset algorithm constructed based on the discrete augmented state space equation, and the expression of the first preset algorithm is:

[0081] The prior state error covariance matrix is ​​updated by a first updating algorithm, and the expression of the first updating algorithm is:

[0082] Based on the updated prior state error covariance matrix, the Kalman gain is calculated and determined by a second preset algorithm, and the expression of the second preset algorithm is:

[0083] Based on the a priori augmented state vector and the calculated Kalman gain, the a posteriori augmented state vector is determined by a third preset algorithm, and the expression of the third preset algorithm is:

[0084] In this embodiment, for step S1 of the present application, the present application provides an optional implementation method. When describing the optional implementation method, the expressions of the related equations and vectors involved have been described in the content that can be included in the above step S1. The following will only describe them by name without repeating the existing expressions. This optional implementation method is: using the augmented Kalman filter (AKF-u) algorithm of the control item, combined with the sensor measurement information, the vertical acceleration of the center of mass, the vertical displacement, the pitch angular velocity and the pitch angle are used as observation vectors, and the system state of the vehicle and the front wheel road input information are estimated based on the half-vehicle model. Specifically: The half-vehicle model can more completely reflect the pitch vibration and vertical vibration of the vehicle. Therefore, if Figure 2 As shown, the present application first establishes a half-vehicle linear model of the vehicle, and based on the half-vehicle linear model, constructs a half-vehicle model differential equation of the vehicle. Then, based on the pre-set vehicle state vector And the constructed half-car model differential equations, construct the state space equations.

[0085] Taking into account the process noise error between the real vehicle and the half-vehicle model and the measurement noise error of the sensor, this application constructs the corresponding random linear state-space equation based on the constructed state-space equation and the noise error between the real vehicle and the half-vehicle model (including the process noise error and the measurement noise error of the sensor).

[0086] Next, the road surface information of the front and rear wheels is input as a state vector and combined with the vehicle state vector Combine to form an augmented state vector: It is assumed that the front and rear road surface inputs all satisfy the random walk process. Under this setting, based on the constructed augmented state vector and the constructed random linear state space equation, the corresponding augmented state space equation is further constructed, and then the constructed augmented state space equation is discretized to obtain the discrete augmented state space equation.

[0087] Next, three algorithms are constructed based on a series of equations constructed in this implementation method, namely, the first preset algorithm Second preset algorithm The third preset algorithm and the first update algorithm

[0088] Finally, the corresponding calculation is performed through the augmented Kalman filter method, that is, the corresponding calculation is performed based on the three constructed algorithms, specifically: the vehicle's a priori augmented state vector is calculated through the constructed first preset algorithm, and the a priori state error covariance matrix is ​​updated through the constructed first update algorithm to obtain the updated a priori state error covariance matrix. After obtaining the updated a priori state error covariance matrix, the updated a priori state error covariance matrix is ​​calculated through the second preset algorithm to obtain the Kalman gain. Finally, the obtained a priori augmented state vector and Kalman gain are calculated through the third preset algorithm to obtain the vehicle's posterior augmented state vector.

[0089] In combination with the above embodiments, in one implementation, the present application embodiment further provides an active suspension control method. In the active suspension control method, the method further includes: defining the initial value of the state error covariance matrix as The initial value of the vehicle state vector is

[0090] In this embodiment, when estimating the system state of the vehicle and the front wheel road input information by the augmented Kalman filter method, it is first necessary to define the initial value of the state error covariance matrix and the initial value of the vehicle state vector. The initial value of the state error covariance matrix is ​​defined as The initial value of the vehicle state vector is

[0091] In combination with the above embodiments, in one implementation, the present application embodiment further provides an active suspension control method. In the active suspension control method, step S3 may include:

[0092] Pre-construct a road surface input discrete space function, and simplify the road surface input discrete space function to obtain a simplified road surface input discrete space function: q q (k+1)=A q q q (k)+F q q f (k); where the road surface input discrete space function is: q0(k) represents the rear wheel road input information at time k, Nl is the discretized time interval of the front and rear wheels experiencing the same road input, Nl = L / u / ΔT, L represents the vehicle wheelbase, u represents the vehicle speed, q0(k) to q Nl-1 (k) represents the road surface input information within the wheelbase range between the front and rear wheels at time k, q f (k) represents the front wheel road surface input information at time k, q0(k+1) represents the rear wheel road surface input information at time k+1, and q0(k+1) to q Nl-1 (k+1) represents the road surface input information within the wheelbase range between the front and rear wheels at time k+1, q q =[q0 q1…q Nl-2 q Nl-1 ] T .

[0093] Based on the sampling time interval and the state space equation, the discrete state space equation is constructed: Among them, A d =e AΔT ; F d =[F d1 F d2 ] is the road surface input matrix, F d1 and F d2 Represent the input matrices of the front wheel road surface and the rear wheel road surface respectively.

[0094] Based on the simplified road surface input discrete space function and the discrete state space equation, the target state space equation is constructed: *(k+1)=A s x * (k)+B su u(k)+F sq q f (k); where

[0095] Based on the performance output vector and the target state space equation, determine the output equation: y(k)=C s x * (k)+D s u(k);

[0096] in, The performance output vector is

[0097] Under the target constraint condition, based on the output equation, the output prediction equation in the corresponding prediction time domain is constructed: Y = A p x * (k)+B p U;

[0098] in, The target constraint is N c ≤N p ≤Nl,N c To control the time domain, N p For the prediction time domain.

[0099] According to the system state of the vehicle and the front wheel road surface input information, the optimal suspension control output in the prediction time domain is determined through a pre-constructed output prediction equation.

[0100] In this embodiment, for step S3 of the present application, the present application provides an optional implementation method. When describing the optional implementation method, the expressions of the related equations and vectors involved have been described in the content that can be included in the above step S3. The following will only describe them by name without repeating the existing expressions. The optional implementation method will be calculated in the wheelbase preview MPC controller. Specifically, for a vehicle traveling in a straight line, the front and rear wheels will experience the same road surface input information, but the rear wheel road surface input information is delayed by a time difference than the front wheel road surface input information. Therefore, the rear wheel road surface input information can be derived from the estimated front wheel road surface input information. The front and rear wheel road surface input information has q r (k+Nl)=q f(k) is a discrete delay relationship, where Nl is the discrete time interval of the front and rear wheels experiencing the same road surface input, Nl = L / u / ΔT. The present application uses a shift register to transfer the estimated front wheel road surface input information to the rear wheel through the shift register digital storage method, which can achieve more accurate rear wheel road surface preview, that is, obtain more accurate rear wheel road surface input information, which is determined by constructing a road surface input discrete space function, such as Figure 3 As shown, Figure 3 The relationship between the road surface input information within the wheelbase range between the front and rear wheels is shown. The present application pre-constructs a road surface input discrete space function, and simplifies the constructed road surface input discrete space function to obtain a simplified road surface input discrete space function. According to the sampling time interval of the road surface input information and the constructed state space equation, a discrete state space equation is constructed, and then, based on the constructed simplified road surface input discrete space function and the constructed discrete state space equation, a target state space equation is constructed.

[0101] Next, based on the defined performance output vector And the target state equation constructed, construct the output equation. This application defines the target constraint condition as the discrete interval time domain needs to satisfy N c ≤N p ≤Nl, then under the target constraint, based on the constructed output equation, further construct the corresponding prediction time domain N p The output prediction equation within.

[0102] Finally, based on the constructed output prediction equation, the system state of the vehicle and the front wheel road surface input information estimated in step S2 are solved to determine the optimal suspension control output in the prediction time domain.

[0103] In combination with the above embodiments, in one implementation, the embodiment of the present application further provides an active suspension control method. In the active suspension control method, in step S2, the method further includes: based on the updated prior state error covariance matrix and the calculated Kalman gain, updating the posterior state error covariance matrix by a second updating algorithm, wherein the second updating algorithm expression is: P k|k =(IK k H ad ) k|k-1 ; The updated posterior state error covariance matrix is ​​used to update the subsequent prior state error covariance matrix.

[0104] In this embodiment, since the suspension control process is a continuous time series process, the estimation of the vehicle system state and the front wheel road input information by the augmented Kalman filter method is a time series process. In this time series estimation process, for the posterior state error covariance matrix of the next moment (if the next moment is moment k), the present application updates the posterior state error covariance matrix of the next moment (i.e. moment k) with the updated prior state error covariance matrix of the previous moment (i.e. moment k-1) of the next moment. The specific updating method is based on the updated prior state error covariance matrix P of the previous moment (i.e. moment k-1) of the next moment. k|k-1 And the calculated Kalman gain K k , through the second update algorithm P k|k =(IK k H ad ) k|k-1 Update the posterior state error covariance matrix of the next moment (i.e., moment k) to obtain the updated posterior state error covariance matrix P of the next moment (i.e., moment k) k|k After the update, the posterior state error covariance matrix P at the next moment (i.e., moment k) is obtained k|k , the posterior state error covariance matrix P at the next moment (i.e., moment k) k|k The prior state error covariance matrix at the next moment (i.e., moment k+1) will be involved in the update of the subsequent prior state error covariance matrix. For example, at moment k+1, the posterior state error covariance matrix P at moment k is k|k It is called the prior state error covariance matrix P k|k , and then the prior state error covariance matrix P is updated by the first update algorithm k|k Update, and then the updated prior state error covariance matrix P k|k The Kalman gain at time k+1 is calculated by the second preset algorithm, and the cycle continues.

[0105] In combination with the above embodiments, in one implementation, the present application embodiment further provides an active suspension control method. In the active suspension control method, according to the system state of the vehicle and the front wheel road surface input information, the optimal suspension control output in the prediction time domain is determined by a pre-constructed output prediction equation, which may include:

[0106] Determine the initial objective function in the prediction time domain as: Among them, Q c Represents the weighted matrix of the system output, R c is the weighting matrix of the control quantity.

[0107] Substitute the output prediction equation into the initial objective function to determine the objective function: in, is the weighting matrix of the output in the prediction time domain, is the weighting matrix of the control quantity in the control time domain.

[0108] According to the determined vehicle system state and front wheel road input information, the output in the prediction time domain is solved by the objective function to determine the optimal open-loop control sequence U=[u(k)u(k+1)...u(k+N c -1)] T .

[0109] The optimal open-loop control sequence U = [u(k)u(k+1)...u(k+N c -1)] T The optimal suspension control output is determined within the prediction time domain.

[0110] In this embodiment, for the application, according to the system state of the vehicle and the front wheel road input information, the optimal suspension control output in the prediction time domain is determined by a pre-constructed output prediction equation. This application provides an optional implementation method. When describing this optional implementation method, the expressions of the relevant equations and vectors involved have been described in the content that can be included in the application, according to the system state of the vehicle and the front wheel road input information, the optimal suspension control output in the prediction time domain is determined by a pre-constructed output prediction equation. The existing expressions will be described only by name later without repeating them. This optional implementation method is: first determine the initial objective function in the preset time domain, and then substitute the output prediction equation into the determined initial objective function to obtain the objective function. Then substitute the determined system state of the vehicle and the front wheel road input information into the objective function for optimization and solution, and calculate the optimal open-loop control sequence U=[u(k)u(k+1)...u(k+N c -1)] T Then the optimal open-loop control sequence U=[u(k)u(k+1)...u(k+N c -1)] T The optimal suspension control output in the prediction time domain is determined and used for suspension control of the vehicle.

[0111] In combination with the above embodiments, in one implementation, the embodiment of the present application further provides an active suspension control method. In the active suspension control method, the vehicle suspension is controlled by the optimal suspension control output, including: extracting the optimal open-loop control sequence U=[u(k)u(k+1)...u(k+N c -1)] TThe first item u(k) in is sent to the vehicle control system for suspension control.

[0112] In this embodiment, the optimal open-loop control sequence U=[u(k)u(k+1)...u(k+N c -1)] T After that, the first item u(k) is extracted and then sent to the vehicle control system for the current vehicle suspension control.

[0113] In this embodiment, if Figure 4 As shown in the figure, the current observation vector and the current road input information of the vehicle are first determined by various monitoring sensors of the vehicle, and then the system state X(k) of the vehicle and the front wheel road input information q are estimated by the augmented Kalman filter (AKF-u) algorithm of the control item. f (k). Then based on the front wheel road surface input information q f (k) Determine the road surface input information q within the wheelbase range between the front and rear wheels through the shift register q (k), then based on the estimated vehicle system state X(k) and the road surface input information q within the wheelbase range between the front and rear wheels q (k), construct vector Then the vector The input is sent to the wheelbase preview MPC controller for calculation and processing to obtain u(k) for controlling the vehicle's suspension. At the same time, this u(k) will participate in the calculation of the parameters for controlling the vehicle's suspension at the next moment.

[0114] Based on the same inventive concept, an embodiment of the present application provides an active suspension control system, such as Figure 5 As shown, the system 500 includes:

[0115] A posterior augmented state vector determination module 501 is used to determine the posterior augmented state vector of the vehicle according to the prior augmented state vector of the vehicle, wherein the augmented state vector is composed of various state parameters of the vehicle;

[0116] A state information determination module 502, for determining the system state of the vehicle and the front wheel road surface input information according to the posterior augmented state vector;

[0117] The optimal suspension control output determination module 503 is used to determine the optimal suspension control output in the prediction time domain according to the system state of the vehicle and the front wheel road surface input information;

[0118] The control module 504 is used to control the vehicle suspension through the optimal suspension control output.

[0119] Optionally, the posterior augmented state vector determination module 501 includes:

[0120] The first equation building block is used to build the differential equations for the half-car model:

[0121] Among them, m H is the sprung mass of the half-car model; I y is the moment of inertia about the y-axis; m f 、m r are the masses of the front and rear wheels respectively; k f , k r are the front and rear suspension stiffness respectively; k tf , k tr are the front and rear tire stiffnesses respectively; c f 、c r are the front and rear suspension damping coefficients respectively; x Hf 、x Hr are the vertical displacements of the front and rear suspension spring masses respectively; x H is the vertical displacement at the center of mass; x f 、x r are the vertical displacements of the front and rear wheels respectively; θ is the pitch angle of the center of mass; L f , L r are the distances from the center of mass to the front and rear axles respectively; q f ,q r are the road surface input information of the front and rear wheels respectively; f 、u r are the forces acting on the front and rear active suspensions respectively; within the set pitch angle range, the displacement of the front and rear axle bodies is

[0122] And the state space equations are constructed based on the vehicle state vector and the half-car model differential equations: The vehicle state vector is I4 is the identity matrix with 1 on the diagonal and 0 1×4 is a 1×4 zero matrix, 0 1×6 is a 1×6 zero matrix, u=[u f u r ] T , q=[q f q r ] T ,

[0123] And for constructing a random linear state space equation based on the state space equation and the noise error between the real vehicle and the half vehicle model: in, is the observation vector; w(t) and v(t) are the process noise and measurement noise respectively satisfying the zero-mean Gaussian distribution; u(t) is the force of the front and rear suspensions; q(t) is the road surface input information of the front and rear wheels,

[0124] And for determining the road surface input information of the front and rear wheels as a state vector, constructing the corresponding augmented state vector:

[0125] And for determining an augmented state space equation corresponding to the random linear state space equation according to the first target setting and the augmented state vector: The first goal is to set the front and rear road inputs to satisfy the random walk process. H a =[H 0 2×2 ], D a =D,ζ(t)=[w(t) η(t)] T ;

[0126] And for discretizing the augmented state space equation to obtain a discrete augmented state space equation: in, H ad =H a , D ad =D a , ΔT is the sensor sampling time interval, ζ k =[w k η k ] T , w k 、v k , η k They are Gaussian white noises that are independent of each other and follow normal distribution;

[0127] The a posteriori augmented state vector determination submodule is used to calculate and determine the a priori augmented state vector by using a first preset algorithm constructed based on the discrete augmented state space equation, wherein the expression of the first preset algorithm is:

[0128] And used to update the prior state error covariance matrix through a first update algorithm, wherein the first update algorithm expression is:

[0129] And used to calculate and determine the Kalman gain based on the updated priori state error covariance matrix through a second preset algorithm, wherein the second preset algorithm expression is:

[0130] And for determining the a posteriori augmented state vector by a third preset algorithm based on the a priori augmented state vector and the calculated Kalman gain, wherein the expression of the third preset algorithm is:

[0131] Optionally, the optimal suspension control output determination module 503 includes:

[0132] The second equation building module is used to pre-build a road surface input discrete space function and simplify the road surface input discrete space function to obtain a simplified road surface input discrete space function: q q (k+1)=A q q q (k)+F q q f (k); where the road surface input discrete space function is: q0(k) represents the rear wheel road input information at time k, Nl is the discretized time interval of the front and rear wheels experiencing the same road input, Nl = L / u / ΔT, L represents the vehicle wheelbase, u represents the vehicle speed, q0(k) to q Nl-1 (k) represents the road surface input information within the wheelbase range between the front and rear wheels at time k, q f (k) represents the front wheel road surface input information at time k, q0(k+1) represents the rear wheel road surface input information at time k+1, and q0(k+1) to q Nl-1 (k+1) represents the road surface input information within the wheelbase range between the front and rear wheels at time k+1, q q =[q0 q1…q Nl-2 q Nl-1 ] T ;

[0133] And for constructing discrete state space equations based on sampling time intervals and state space equations: Among them, A d =e AΔT ; F d =[F d1 F d2 ] is the road surface input matrix, F d1 and F d2 Represent the input matrices of the front wheel road surface and the rear wheel road surface respectively;

[0134] and for constructing a target state space equation based on the simplified road surface input discrete space function and the discrete state space equation: * (k+1)=A s x * (k)+B su u(k)+F sq qf (k); where

[0135] and for determining the output equation based on the performance output vector and the target state space equation: y(k)=C s x * (k)+D s u(k);

[0136] in, The performance output vector is

[0137] And for constructing an output quantity prediction equation in the corresponding prediction time domain based on the output equation under the target constraint condition: Y = A p x * (k)+B p U;

[0138] in, The target constraint is N c ≤N p ≤Nl,N c To control the time domain, N p For the prediction time domain;

[0139] The optimal suspension control output determination submodule is used to determine the optimal suspension control output in the prediction time domain through a pre-constructed output prediction equation according to the system state of the vehicle and the front wheel road input information.

[0140] Optionally, the system 500 further includes:

[0141] The first updating module is used to update the posterior state error covariance matrix through a second updating algorithm based on the updated prior state error covariance matrix and the calculated Kalman gain, and the second updating algorithm expression is: k|k =(IK k H ad ) k|k-1 ;

[0142] And for using the updated posterior state error covariance matrix for subsequent updating of the prior state error covariance matrix.

[0143] Optionally, the a posteriori augmented state vector determination module in the system 500 defines the initial value of the state error covariance matrix as The initial value of the vehicle state vector is

[0144] Optionally, the optimal suspension control output determination submodule includes:

[0145] The initial objective function determination module is used to determine the initial objective function in the prediction time domain:

[0146]

[0147] Among them, Q c Represents the weighted matrix of the system output, R c is the weighting matrix of the control quantity;

[0148] The objective function determination module is used to substitute the output prediction equation into the initial objective function to determine the objective function:

[0149]

[0150] in, is the weighting matrix of the output in the prediction time domain, is the weighting matrix of the control quantity in the control time domain;

[0151] The optimal open-loop control sequence determination module is used to solve the output in the prediction time domain through the objective function according to the determined vehicle system state and front wheel road input information, and determine the optimal open-loop control sequence U=[u(k)u(k+1)...u(k+N c -1)] T ;

[0152] The first optimal suspension control output determination module is used to convert the optimal open-loop control sequence U = [u(k)u(k+1)...u(k+N c -1)] T The optimal suspension control output is determined within the prediction time domain.

[0153] Optionally, the control module 504 includes:

[0154] The extraction module is used to extract the optimal open-loop control sequence U = [u(k)u(k+1)...u(k+N c -1)] T The first term u(k) in ;

[0155] The control submodule is used to send the first item u(k) to the vehicle control system for suspension control.

[0156] Based on the same inventive concept, an embodiment of the present application provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and running on the processor. When the computer program is executed by the processor, it implements the steps in an active suspension control method as described in the first aspect of the present application.

[0157] Based on the same inventive concept, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in an active suspension control method as described in the first aspect of the present application are implemented.

[0158] As for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0159] It should be noted that, for the method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present application are not limited by the described order of actions, because according to the embodiments of the present application, 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 the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present application.

[0160] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0161] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the embodiments of the present application may adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0162] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that specifies the functions of a block or multiple blocks.

[0163] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0164] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable terminal device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0165] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present application.

[0166] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0167] The above is a detailed introduction to an active suspension control method, system and product provided by the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. An active suspension control method, characterized in that: The method comprises: Determining a posterior augmented state vector of the vehicle according to the prior augmented state vector of the vehicle, wherein the augmented state vector is composed of various state parameters of the vehicle; Determining the system state of the vehicle and the front wheel road surface input information according to the posterior augmented state vector; Determining an optimal suspension control output in a prediction time domain according to a system state of the vehicle and front wheel road surface input information; The vehicle suspension is controlled by the optimal suspension control output.

2. The active suspension control method according to claim 1, characterized in that: According to the prior augmented state vector of the vehicle, the posterior augmented state vector of the vehicle is determined, including: Construct the differential equations for the half-car model: Among them, m H is the sprung mass of the half-car model; I y is the moment of inertia about the y-axis; m f 、m r are the masses of the front and rear wheels respectively; k f , k r are the front and rear suspension stiffness respectively; k tf , k tr are the front and rear tire stiffness respectively; c f 、c r are the front and rear suspension damping coefficients respectively; x Hf 、x Hr are the vertical displacements of the front and rear suspension spring masses respectively; x H is the vertical displacement at the center of mass; x f 、x r are the vertical displacements of the front and rear wheels respectively; θ is the pitch angle of the center of mass; L f , L r are the distances from the center of mass to the front and rear axles respectively; q f ,q r are the road surface input information of the front and rear wheels respectively; f 、u r are the forces acting on the front and rear active suspensions respectively; within the set pitch angle range, the displacement of the front and rear axle bodies is Based on the vehicle state vector and the differential equation of the half-vehicle model, the state space equation is constructed: The vehicle state vector is I4 is the identity matrix with 1 on the diagonal and 0 1×4 is a 1×4 zero matrix, 0 1×6 is a 1×6 zero matrix, u=[u f u r ] T , q=[q f q r ] T , Based on the state-space equation and the noise error between the real vehicle and the half-vehicle model, a random linear state-space equation is constructed: in, is the observation vector; w(t) and v(t) are the process noise and measurement noise respectively satisfying the zero-mean Gaussian distribution; u(t) is the force of the front and rear suspensions; q(t) is the road surface input information of the front and rear wheels, Determine the road surface input information of the front and rear wheels as a state vector, and construct the corresponding augmented state vector: According to the first target setting and the augmented state vector, an augmented state space equation corresponding to the random linear state space equation is determined: The first goal is to set the front and rear road inputs to satisfy the random walk process. H a =[H 0 2×2 ], D a =D,ζ(t)=[w(t)η(t)] T ; The augmented state space equation is discretized to obtain a discrete augmented state space equation: in, H ad =H a , D ad =D a , ΔT is the sensor sampling time interval, ζ k =[w k η k ] T , w k 、v k , η k They are Gaussian white noises that are independent of each other and follow normal distribution; The a priori augmented state vector is determined by calculating a first preset algorithm constructed based on the discrete augmented state space equation, and the expression of the first preset algorithm is: The prior state error covariance matrix is ​​updated by a first updating algorithm, and the expression of the first updating algorithm is: Based on the updated prior state error covariance matrix, the Kalman gain is calculated and determined by a second preset algorithm, and the expression of the second preset algorithm is: Based on the a priori augmented state vector and the calculated Kalman gain, the a posteriori augmented state vector is determined by a third preset algorithm, and the expression of the third preset algorithm is:

3. The active suspension control method according to claim 2, characterized in that: According to the system state of the vehicle and the front wheel road surface input information, determining the optimal suspension control output in the prediction time domain includes: Pre-construct a road surface input discrete space function, and simplify the road surface input discrete space function to obtain a simplified road surface input discrete space function: q q (k+1)=A q q q (k)+F q q f (k); where the road surface input discrete space function is: q0(k) represents the rear wheel road input information at time k, Nl is the discretized time interval of the front and rear wheels experiencing the same road input, Nl = L / u / ΔT, L represents the vehicle wheelbase, u represents the vehicle speed, q0(k) to q Nl-1 (k) represents the road surface input information within the wheelbase range between the front and rear wheels at time k, q f (k) represents the front wheel road surface input information at time k, q0(k+1) represents the rear wheel road surface input information at time k+1, and q0(k+1) to q Nl-1 (k+1) represents the road surface input information within the wheelbase range between the front and rear wheels at time k+1, q q =[q0 q1…q Nl-2 q Nl-1 ] T ; Based on the sampling time interval and the state space equation, the discrete state space equation is constructed: Among them, A d =e AΔT ; F d =[F d1 F d2 ] is the road surface input matrix, F d1 and F d2 Represent the input matrices of the front wheel road surface and the rear wheel road surface respectively; Based on the simplified road surface input discrete space function and the discrete state space equation, the target state space equation is constructed: * (k+1)=A s x * (k)+B su u(k)+F sq q f (k); where Based on the performance output vector and the target state space equation, determine the output equation: y(k)=C s x * (k)+D s u(k); in, The performance output vector is Under the target constraint condition, based on the output equation, the output prediction equation in the corresponding prediction time domain is constructed: Y = A p x * (k)+B p U; in, The target constraint is N c ≤N p ≤Nl,N c To control the time domain, N p For the prediction time domain; According to the system state of the vehicle and the front wheel road surface input information, the optimal suspension control output in the prediction time domain is determined through a pre-constructed output prediction equation.

4. The active suspension control method according to claim 3, characterized in that: The method further comprises: Based on the updated prior state error covariance matrix and the calculated Kalman gain, the posterior state error covariance matrix is ​​updated by a second updating algorithm, and the expression of the second updating algorithm is: k|k =(IK k H ad ) k|k-1 ; The updated posterior state error covariance matrix is ​​used to update the subsequent prior state error covariance matrix.

5. The active suspension control method according to claim 3, characterized in that: The method further includes: defining the initial value of the state error covariance matrix as The initial value of the vehicle state vector is 6. The active suspension control method according to claim 3, characterized in that: According to the system state of the vehicle and the front wheel road surface input information, the optimal suspension control output in the prediction time domain is determined by using a pre-constructed output prediction equation, including: Determine the initial objective function in the prediction time domain as: Among them, Q c Represents the weighted matrix of the system output, R c is the weighting matrix of the control quantity; Substitute the output prediction equation into the initial objective function to determine the objective function: in, is the weighting matrix of the output in the prediction time domain, is the weighting matrix of the control quantity in the control time domain; According to the determined vehicle system state and front wheel road input information, the output in the prediction time domain is solved by the objective function to determine the optimal open-loop control sequence U=[u(k)u(k+1)...u(k+N c -1)] T ; The optimal open-loop control sequence U = [u(k)u(k+1)...u(k+N c -1)] T The optimal suspension control output is determined within the prediction time domain.

7. The active suspension control method according to claim 6, characterized in that: The vehicle suspension is controlled by the optimal suspension control output, including: Extract the optimal open-loop control sequence U=[u(k)u(k+1)...u(k+N c -1)] T The first term u(k) in ; The first term u(k) is sent to the vehicle control system for suspension control.

8. An active suspension control system, characterized in that: The system comprises: A posterior augmented state vector determination module is used to determine the posterior augmented state vector of the vehicle according to the prior augmented state vector of the vehicle, wherein the augmented state vector is composed of various state parameters of the vehicle; A state information determination module, used to determine the system state of the vehicle and the front wheel road surface input information according to the posterior augmented state vector; An optimal suspension control output determination module, used to determine the optimal suspension control output in a prediction time domain according to the system state of the vehicle and the front wheel road surface input information; A control module is used to control the vehicle suspension through the optimal suspension control output.

9. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and running on the processor, wherein the computer program, when executed by the processor, implements the steps in an active suspension control method as claimed in claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps in the active suspension control method according to claims 1 to 7 are implemented.

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