Active suspension control method, system and product

Abstract

The application provides an active suspension control method, system and product. The method comprises: determining a posterior augmented state vector of a vehicle according to an a priori augmented state vector of the vehicle, wherein the augmented state vector is composed of various state parameters of the vehicle; determining a system state of the vehicle and front wheel road input information according to the posterior augmented state vector; determining an optimal suspension control output in a prediction time domain according to the system state of the vehicle and the front wheel road input information; and controlling the vehicle suspension through the optimal suspension control output. The purpose is to control the active suspension of the vehicle based on the road input information of road roughness and the system state of the vehicle to improve the driving comfort of the vehicle.

Description

TECHNICAL FIELD

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

[0002] With the rapid development of the automobile industry, people's requirements for the driving comfort of automobiles are getting higher and higher. The vehicle suspension system, as a connecting component of the vehicle body and the wheels, plays a very key role in relieving impact load during driving and maintaining good ride comfort. However, the traditional passive suspension has a bottleneck in performance due to its inherent transmission characteristics. In order to obtain better vertical dynamic performance of the automobile, various 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 achieve 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 accurately obtained in real time during vehicle driving, and the road input information is added to the formulation of the active suspension control strategy, the vertical dynamic performance of the vehicle will be greatly improved. SUMMARY

[0003] Therefore, the present application provides an active suspension control method, system and product. The purpose is to control the active suspension of the vehicle based on the road roughness as the road input information and the state of the vehicle system to improve the driving comfort of the vehicle.

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

[0005] determining 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;

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

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

[0008] controlling the vehicle suspension through the optimal suspension control output.

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

[0010] constructing a half-car model differential equation:

[0011] wherein, m His the sprung mass of the half car model; I y is the moment of inertia around the y-axis; m f , m r are the front and rear wheel masses, respectively; k f , k r are the front and rear suspension stiffnesses, 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 front and rear suspension sprung mass vertical displacements; x H is the vertical displacement at the center of mass; x f , x r are the front and rear wheel vertical displacements; θ is the center of mass pitch angle; 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 front and rear wheel road inputs; u f , u r are the front and rear active suspension forces; the displacements at the front and rear axles within a set pitch angle range are

[0012] Based on the vehicle state vector and the half car model differential equation, a state space equation is constructed: wherein the vehicle state vector is I4 is a unit matrix with a diagonal line of 1, 0 1×4 is a zero matrix of 1x4, 0 1×6 is a zero matrix of 1x6, 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 car and the half car model, a stochastic linear state space equation is constructed: wherein, is an observation vector; w(t), v(t) are process noise and measurement noise satisfying zero mean Gaussian distribution, respectively; u(t) is the front and rear suspension force; q(t) is the front and rear wheel road input,

[0014]

[0015] The road input information of the front and rear wheels is determined as the state vector, and the corresponding augmented state vector is constructed:

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

[0017] Discretize the augmented state space equation to obtain a discrete augmented state space equation: Wherein, H ad =H a , D ad =D a , ΔT is a sensor sampling time interval, ζ k =[w k η k ] T , w k , v k , η k are mutually independent Gaussian white noise obeying normal distribution;

[0018] Determine the prior augmented state vector by a first preset algorithm based on the discrete augmented state space equation, and an expression of the first preset algorithm is:

[0019] Update the prior state error covariance matrix by a first update algorithm, and an expression of the first update algorithm is:

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

[0021] Determine the posterior augmented state vector based on the prior augmented state vector and the calculated Kalman gain by a third preset algorithm, and an expression of the third preset algorithm is:

[0022] 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, comprising:

[0023] A road surface input discrete space function is constructed in advance, and the road surface input discrete space function is simplified to obtain a simplified road surface input discrete space function: qq (k+1) = A q q q (k) + F q q f (k); wherein the road input discrete space function is: q0(k) represents the rear wheel road input information at time k, N1 is the discretization time interval of the front and rear wheels experiencing the same road input, N1 = L / u / ΔT, L represents the wheelbase of the vehicle, u represents the vehicle speed, q0(k) to q Nl-1 (k) represents the road input information within the wheelbase range between the front and rear wheels at time k, q f (k) represents the front wheel road input information at time k, q0(k+1) represents the rear wheel road input information at time k+1, q0(k+1) to q Nl-1 (k+1) represents the road 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, a discrete state space equation is constructed: wherein A d = e AΔT ; F d = [F d1 F d2 ] is a road input matrix, F d1 and F d2 represent the input matrixes of the front wheel road and the rear wheel road respectively;

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

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

[0027] wherein, the performance output vector is

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

[0029] wherein, the target constraint is N c ≤ N p ≤ Nl, N c is a control time domain, N p is a 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 quantity in the prediction time domain is determined through the pre-constructed output quantity prediction equation.

[0031] Optionally, the method further comprises:

[0032] Based on the updated prior state error covariance matrix and the obtained Kalman gain, the posterior state error covariance matrix is updated through a second update algorithm, and the second update algorithm is expressed as: P k|k = (I - K k H ad ) P k|k-1 ;

[0033] The updated posterior state error covariance matrix is used for updating the subsequent prior state error covariance matrix.

[0034] Optionally, the method further comprises: 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 quantity in the prediction time domain is determined through the pre-constructed output quantity prediction equation, comprising:

[0036] The initial target function in the prediction time domain is determined as:

[0037] wherein, Q c represents a weighted matrix of system output, and R c is a weighted matrix of control quantity;

[0038] The output quantity prediction equation is substituted into the initial target function to determine the target function:

[0039] wherein, a weighting matrix for the output quantity in the prediction horizon, a weighting matrix for the control quantity in the control horizon;

[0040] solving the output quantity in the prediction horizon through the target function according to the determined system state of the vehicle and the front wheel road input information, determining an optimal open-loop control sequence U = [u(k) u(k+1) … u(k+N c -1)] T

[0041] determining the first term u(k) in the optimal open-loop control sequence U = [u(k) u(k+1) … u(k+N c -1)] T

[0042] Optionally, controlling the vehicle suspension through the optimal suspension control output quantity, comprising:

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

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

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

[0046] a posterior augmented state vector determination module configured to determine a posterior augmented state vector of the vehicle according to a 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 configured to determine the system state of the vehicle and the front wheel road input information according to the posterior augmented state vector;

[0048] an optimal suspension control output quantity determination module configured to determine an optimal suspension control output quantity in a prediction horizon according to the system state of the vehicle and the front wheel road input information;

[0049] a control module configured to control the vehicle suspension through the optimal suspension control output quantity.

[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 the computer program, when executed by the processor, implements the steps in the active suspension control method according to the first aspect of the present application.​​​

[0051] The fourth aspect of the present application provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps in the active suspension control method according to the first aspect of the present application.

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

[0053] The active suspension control method provided by the embodiment of the present application first determines 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; determines the system state of the vehicle and the front wheel road input information according to the posterior augmented state vector; determines the optimal suspension control output in the prediction time domain according to the system state of the vehicle and the front wheel road input information; and controls the vehicle suspension through the optimal suspension control output. Thus, when determining the suspension control output, the present application not only considers the state of the vehicle system, but also considers the road roughness as road input information, so that the control effect of the suspension control is better, and the comfort during the driving of the vehicle can be effectively improved. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments of the present application will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0055] Figure 1 A flow chart of an active suspension control method according to an embodiment of the present application is shown;

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

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

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

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

[0060] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only some of the embodiments of the present application, but not all of the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of the present application.

[0061] Reference Figure 1 , Figure 1 is a schematic diagram of an active suspension control method shown in an embodiment of the present application. As shown in the figure, the method comprises: Figure 1

[0062] Step S1: determining a posterior augmented state vector of a vehicle according to a prior augmented state vector of the vehicle, wherein the augmented state vector is composed of various state parameters of the vehicle.

[0063] In the embodiment, the prior augmented state vector of the vehicle is the augmented state vector of the vehicle at a previous time (for example, the previous time is k-1), and the posterior augmented state vector of the vehicle is the augmented state vector of the next time of the previous time (for example, the next time of the previous time is k). The augmented state vector is composed of various state parameters of the vehicle. In a preferred embodiment, the augmented state vector is wherein x Hf and x Hr are vertical displacements of front and rear suspension sprung masses respectively; x f and x r are vertical displacements of front and rear wheels respectively; within a set pitch angle range, displacements of the vehicle body at the front and rear axles are x H is a vertical displacement at the center of mass; θ is a pitch angle of the center of mass; L f and L r are distances from the center of mass to the front and rear axles respectively; q f and q r are road input information of the front and rear wheels respectively. Based on the prior augmented state vector of the vehicle, the posterior augmented state vector of the vehicle is determined through prediction. At least the front wheel road input information of the vehicle needs to be included in the augmented state vector. In the present application, the road input information refers to the unevenness information of the road.

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

[0065] In the embodiment, after the posterior augmented state vector of the vehicle is obtained through step S1, the system state of the vehicle and the front wheel road input information are determined based on the posterior augmented state vector. In the embodiment, the augmented state vector is ​In the case, the value of the front wheel road input information q f can be determined based on the obtained vehicle posterior augmented state vector, and the determined system state of the vehicle includes the values of parameters x Hf , x Hr , x f , x r , .

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

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

[0068] Step S4: controlling the vehicle suspension through the optimal suspension control output.

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

[0070] The active suspension control method provided by the embodiment of the application first determines a vehicle posterior augmented state vector according to a vehicle prior augmented state vector, wherein the augmented state vector is composed of various state parameters of the vehicle; determines a system state of the vehicle and front wheel road input information according to the posterior augmented state vector; determines an optimal suspension control output in a prediction time domain according to the system state of the vehicle and the front wheel road input information; and controls the vehicle suspension through the optimal suspension control output. Thus, when determining the suspension control output, the application not only considers the state of the vehicle system, but also considers the road unevenness as road input information, so that the control effect of the suspension control is better, and the comfort in the driving process of the vehicle can be effectively improved.

[0071] In combination with the above embodiment, in an implementation manner, the embodiment of the application further provides an active suspension control method. In the active suspension control method, step S1 can include:

[0072] constructing a half-car model differential equation: wherein m H is the sprung mass of the half-car model; I y is the moment of inertia around the y-axis; m f and m r are the front and rear wheel masses, respectively; k f and k r are the front and rear suspension stiffnesses, respectively; k tf and k tr are the front and rear tire stiffnesses, respectively; cf , c r are front and rear suspension damping coefficients respectively; x Hf , x Hr are front and rear suspension sprung mass vertical displacements respectively; x H is the vertical displacement at the center of mass; x f , x r are front and rear wheel vertical displacements 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 input information of the front and rear wheels respectively; u f , u r are the forces of the front and rear active suspensions respectively; within the set pitch angle range, the displacements at the front and rear axles of the vehicle body are

[0073] Based on the vehicle state vector and the half-vehicle model differential equation, a state space equation is constructed: wherein the vehicle state vector is I4 is a unit matrix with a diagonal line of 1, 0 1×4 is a zero matrix of 1x4, 0 1×6 is a zero matrix of 1x6, 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 stochastic linear state space equation is constructed: wherein, is an observation vector; w(t), v(t) are process noise and measurement noise respectively satisfying zero mean Gaussian distribution; u(t) is the force of the front and rear suspensions; q(t) is the road input information of the front and rear wheels,

[0076]

[0077] The road input information of the front and rear wheels is determined as the state vector, and a corresponding augmented state vector is constructed:

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

[0079] Discretize the augmented state space equation to obtain a discrete augmented state space equation: wherein, H ad = H a , D ad = D a , ΔT is a sensor sampling time interval, ζ k = [w k η k ] T , w k , v k , η k are Gaussian white noises independent of each other and subject to normal distribution.

[0080] Determine the prior augmented state vector by a first preset algorithm based on the discrete augmented state space equation, and an expression of the first preset algorithm is:

[0081] Update the prior state error covariance matrix by a first update algorithm, and an expression of the first update algorithm is:

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

[0083] Determine the posterior augmented state vector based on the prior augmented state vector and the Kalman gain obtained by calculation by a third preset algorithm, and an expression of the third preset algorithm is:

[0084] In this embodiment, for step S1 of the application, an optional implementation is provided, and in the description of the optional implementation, the expressions of related equations and vectors involved therein have been described in the content that can be included in step S1 above. In the following, only the name will be mentioned without repeating the existing expressions. The optional implementation is: an augmented Kalman filter (AKF-u) algorithm of a control item is used, the center of mass vertical acceleration, the vertical displacement, the pitch angle velocity and the pitch angle are taken as the observation vector in combination with the sensor measurement information, 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 the vertical vibration of the vehicle, therefore, for example,Figure 2 As shown, the application first establishes a half-car linear model of the vehicle, and based on the half-car linear model, a half-car model differential equation of the vehicle is constructed. Then, based on a pre-set vehicle state vector and the constructed half-car model differential equation, a state space equation is constructed.

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

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

[0087] Next, based on the series of equations constructed in this embodiment, three algorithms are constructed, which are a first preset algorithm a second preset algorithm a third preset algorithm and a first update algorithm

[0088] Finally, corresponding calculations are performed through an augmented Kalman filtering method, that is, corresponding calculations are performed based on the three constructed algorithms. Specifically, the prior augmented state vector of the vehicle is calculated through the constructed first preset algorithm, and the prior state error covariance matrix is updated through the constructed first update algorithm to obtain an updated prior state error covariance matrix. After obtaining the updated prior state error covariance matrix, the updated prior state error covariance matrix is calculated through the second preset algorithm to obtain a Kalman gain. Finally, the prior augmented state vector and the Kalman gain are calculated through the third preset algorithm to obtain the posterior augmented state vector of the vehicle.

[0089] In combination with the above embodiments, in an embodiment, the application also 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 and the initial value of the vehicle state vector as

[0090] In the embodiment, when the system state of the vehicle and the front wheel road input information are estimated by the augmented Kalman filtering method, the initial value of the state error covariance matrix and the initial value of the vehicle state vector also need to be defined first, and 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 an implementation, the embodiment of the application further provides an active suspension control method. In the active suspension control method, step S3 can include:

[0092] A road input discrete space function is constructed in advance and simplified to obtain a simplified road input discrete space function: q q (k+1) = A q q q (k) + F q q f (k); wherein the road input discrete space function is: q0(k) represents the rear wheel road input information at time k, Nl is the discretization time interval of the front and rear wheels experiencing the same road input, Nl = L / u / ΔT, L represents the wheelbase of the vehicle, u represents the vehicle speed, q0(k) to q Nl-1 (k) represent the road input information between the front and rear wheels within the wheelbase range at time k, q f (k) represents the front wheel road input information at time k, q0(k+1) represents the rear wheel road input information at time k+1, q0(k+1) to q Nl-1 (k+1) represent the road input information between the front and rear wheels within the wheelbase range 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, a discrete state space equation is constructed: wherein A d = e AΔT ; F d = [F d1 F d2 ] is a road input matrix, F d1 and F d2 represent the input matrices of the front wheel road and the rear wheel road respectively.

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

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

[0096] wherein, The performance output vector is

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

[0098] wherein, The target constraint is N c ≤ N p ≤ Nl, N c is a control time domain, N p is a 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 quantity in the prediction time domain is determined through the pre-constructed output quantity prediction equation.

[0100] In this embodiment, for step S3 of the application, an alternative embodiment is provided, and in the description of the alternative embodiment, the expressions of related equations and vectors involved have been described in the content that can be included in step S3 above. In the following, only the name will be mentioned without repeating the existing expressions. The alternative embodiment will be calculated in the wheelbase preview MPC controller, specifically: for a straight driving vehicle, the front and rear wheels will experience the same road surface input information, only the rear wheel road surface input information is delayed by a time difference compared with the front wheel road surface input information, so 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 exists q r (k+Nl) = q f(k) the time delay relationship of discrete form, wherein Nl is the discretization time interval of the front and rear wheels experiencing the same road input, Nl = L / u / ΔT. The application can realize more accurate rear wheel road preview by transferring the estimated front wheel road input information to the rear wheel through the shift register, that is, obtaining more accurate rear wheel road input information, which is determined by the constructed road input discrete space function, as shown in Figure 3 Figure 3 The relationship between the road input information in the wheelbase range between the front and rear wheels is shown. The application pre-constructs the road input discrete space function, and simplifies the constructed road input discrete space function to obtain a simplified road input discrete space function. According to the sampling time interval of the road input information and the constructed state space equation, a discrete state space equation is constructed, and then based on the constructed simplified road input discrete space function and the constructed discrete state space equation, a target state space equation is constructed.

[0101] Then, based on the defined performance output vector and the constructed target state equation, an output equation is constructed. The application defines the target constraint condition as N c ≤N p ≤Nl, and then based on the constructed output equation, an output quantity prediction equation within the prediction time domain N p is further constructed under the target constraint condition.

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

[0103] In combination with the above embodiments, in an implementation, the application also provides an active suspension control method. In the active suspension control method, in step S2, the method further includes: updating the posterior state error covariance matrix based on the updated prior state error covariance matrix and the calculated Kalman gain, and the second update algorithm is expressed as: P k|k =(I-K k H ad )P k|k-1 ; the updated posterior state error covariance matrix is used for updating the subsequent prior state error covariance matrix.

[0104] ​In this embodiment, since the suspension control process is a continuous temporal process, the estimation of the vehicle's system state and front wheel road surface input information using the augmented Kalman filter method is also a temporal process. During this temporal estimation process, for the posterior state error covariance matrix of the next time step (where the next time step is k), this application updates the posterior state error covariance matrix of the next time step (where k-1 is the previous time step) using the updated prior state error covariance matrix P. Specifically, the update method is based on the updated prior state error covariance matrix P of the previous time step (where k-1 is the previous time step). k|k-1 and the calculated Kalman gain K k Through the second update algorithm P k|k =(IK k H ad )P k|k-1 Update the posterior state error covariance matrix for the next time step (i.e., time k) to obtain the updated posterior state error covariance matrix P for the next time step (i.e., time k). k|k After obtaining the updated posterior state error covariance matrix P at the next time step (i.e., time k), k|k The posterior state error covariance matrix P at the next time step (i.e., time k) k|k The prior state error covariance matrix at the next time step (i.e., time k+1) will participate in the subsequent update of the prior state error covariance matrix. For example, at time k+1, the posterior state error covariance matrix P at time k... k|k This is called the prior state error covariance matrix P. k|k Then, the prior state error covariance matrix P is updated using the first update algorithm. k|k The prior state error covariance matrix P is then updated. k|k The Kalman gain at time k+1 will be calculated using a second preset algorithm, and this process will continue in a loop.

[0105] In conjunction with the above embodiments, in one implementation, this application also provides an active suspension control method. In this active suspension control method, based on the vehicle's system state and 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, which may include:

[0106] The initial objective function in the prediction time domain is determined as follows: Among them, Q c R represents the weighting matrix of the system output. c This is the weighting matrix for the control quantities.

[0107] Substituting the output prediction equation into the initial objective function, the objective function is determined as follows: wherein, is an output quantity weighting matrix in a prediction time domain, is a control quantity weighting matrix in a control time domain.

[0108] According to the determined system state of the vehicle and the front wheel road surface input information, the output quantity in the prediction time domain is solved through the target function, and an optimal open-loop control sequence U=[u(k) u(k+1)...u(k+N c -1)] T is determined, which makes the value of the target function minimum.

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

[0110] In the embodiment, for the optimal suspension control output quantity in the prediction time domain according to the system state of the vehicle and the front wheel road surface input information, the output quantity prediction equation constructed in advance is used, and an optional embodiment is provided in the application. In the description of the optional embodiment, the expressions of related equations and vectors involved in the description have been described in the content that can be included in the optimal suspension control output quantity in the prediction time domain according to the system state of the vehicle and the front wheel road surface input information, and the existing expressions will only be described by name without further description. The optional embodiment is as follows: first, an initial target function in a preset time domain is determined, then the output quantity prediction equation is substituted into the determined initial target function to obtain a target function. Then, the determined system state of the vehicle and the front wheel road surface input information are substituted into the target function for optimization and solving, and the value of the optimal open-loop control sequence U=[u(k) u(k+1)...u(k+N c -1)] T which makes the value of the target function minimum is calculated. Then, the optimal open-loop control sequence U=[u(k) u(k+1)...u(k+N c -1)] T is determined as the optimal suspension control output quantity in the prediction time domain, which is used for suspension control of the vehicle.

[0111] In combination with the above embodiment, in an embodiment, the application also provides an active suspension control method. In the active suspension control method, the vehicle suspension is controlled through the optimal suspension control output quantity, including: extracting the optimal open-loop control sequence U=[u(k) u(k+1)...u(k+N c -1)] Tthe first term u(k) in the first term u(k) 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 The first term u(k) is extracted and sent to the vehicle control system for current vehicle suspension control.

[0113] In this embodiment, as shown in Figure 4 , the current observation vector of the vehicle and the current road input information 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 f (k) are estimated by an augmented Kalman filter (AKF-u) algorithm of the control term. Then, the road input information q f (k) in the wheelbase range between the front and rear wheels is determined by a shift register based on the front wheel road input information q q (k), and then the system state X(k) of the vehicle and the road input information q q (k) in the wheelbase range between the front and rear wheels are estimated based on the system state X(k) of the vehicle and the road input information q The vector is then input to the wheelbase preview MPC controller for calculation and processing to obtain u(k) for controlling the suspension of the vehicle, which will participate in the calculation of the parameters for controlling the suspension of the vehicle at the next time.

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

[0115] A posterior augmented state vector determination module 501 is configured to determine a posterior augmented state vector of the vehicle based on a 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 is configured to determine the system state of the vehicle and the front wheel road input information based on the posterior augmented state vector.

[0117] An optimal suspension control output quantity determination module 503 is configured to determine the optimal suspension control output quantity in the prediction time domain based on the system state of the vehicle and the front wheel road input information.

[0118] A control module 504 is configured to control the suspension of the vehicle by the optimal suspension control output quantity.

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

[0120] a first equation construction module, configured to construct a half-car model differential equation:

[0121] wherein, m H is the sprung mass of the half-car model; I y is the moment of inertia around the y-axis; m f , m r are the front and rear wheel masses respectively; k f , k r are the front and rear suspension stiffnesses 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 front and rear suspension sprung mass vertical displacements respectively; x H is the vertical displacement at the center of mass; x f , x r are the front and rear wheel vertical displacements respectively; θ is the center of mass pitch angle; 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 front and rear wheel road input information respectively; u f , u r are the front and rear active suspension forces respectively; within the set pitch angle range, the displacements at the front and rear axles of the vehicle body are

[0122] and a state space equation construction module, configured to construct a state space equation based on a vehicle state vector and the half-car model differential equation: wherein the vehicle state vector is I4 is a unit matrix with a diagonal line of 1, 0 1×4 is a zero matrix of 1x4, 0 1×6 is a zero matrix of 1x6, u = [u f u r ] T , q = [q f q r ] T ,

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

[0124] and the road input information of the front and rear wheels is a state vector, and a corresponding augmented state vector is constructed:

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

[0126] and the augmented state space equation is discretized to obtain a discrete augmented state space equation: wherein, H ad = H a , D ad = D a , ΔT is a sensor sampling time interval, ζ k = [w k η k ] T , w k , v k , η k are mutually independent Gaussian white noises satisfying normal distribution respectively;

[0127] The posterior augmented state vector determination submodule is configured to determine the prior augmented state vector by a first preset algorithm constructed based on the discrete augmented state space equation, and an expression of the first preset algorithm is:

[0128] and the prior state error covariance matrix is updated by a first update algorithm, and an expression of the first update algorithm is:

[0129] and the Kalman gain is determined by a second preset algorithm based on the updated prior state error covariance matrix, and an expression of the second preset algorithm is:

[0130] and determining a posterior augmented state vector based on the prior augmented state vector and the Kalman gain obtained by a third preset algorithm, an expression of the third preset algorithm being:

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

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

[0133] and constructing a discrete state space equation based on the sampling time interval and the state space equation: wherein A d = e AΔT ; F d = [F d1 F d2 ] is a road input matrix, F d1 and F d2 represent the input matrices of the front wheel road and the rear wheel road respectively;

[0134] and constructing a target state space equation x * (k+1) = A s x * (k) + B su u(k) + F sq qf (k) ; wherein,

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

[0136] wherein, the performance output vector is

[0137] and for constructing an output quantity prediction equation in a corresponding prediction horizon based on the output equation under a target constraint: Y = A p x * (k) + B p U.

[0138] wherein, the target constraint is N c ≤ N p ≤ Nl, N c is a control horizon, N p is a prediction horizon.

[0139] An optimal suspension control output quantity determination sub-module is configured to determine an optimal suspension control output quantity in the prediction horizon according to the system state of the vehicle and the front wheel road surface input information through the pre-constructed output quantity prediction equation.

[0140] Optionally, the system 500 further comprises:

[0141] A first updating module is configured to update the posterior state error covariance matrix based on the updated prior state error covariance matrix and the obtained Kalman gain through a second updating algorithm, and an expression of the second updating algorithm is P k|k = (I - K k H ad ) P k|k-1 .

[0142] and the updated posterior state error covariance matrix is used for updating of a subsequent prior state error covariance matrix.

[0143] Optionally, the posterior augmented state vector determination module in the system 500 defines an initial value of the state error covariance matrix as during the posterior augmented state vector determination process.

[0144] Optionally, the optimal suspension control output quantity determination sub-module comprises:

[0145] An initial target function determination module is configured to determine an initial target function in a prediction time domain as:

[0146]

[0147] wherein Q c represents a weighting matrix of system output, R c represents a weighting matrix of control quantity;

[0148] A target function determination module is configured to substitute the output quantity prediction equation into the initial target function to determine a target function as:

[0149]

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

[0151] An optimal open-loop control sequence determination module is configured to solve the output quantity in the prediction time domain through the target function according to the determined system state of the vehicle and the front wheel road surface input information, and determine an optimal open-loop control sequence U=[u(k)u(k+1)...u(k+N c -1)] T that makes the target function take the minimum value.

[0152] A first optimal suspension control output quantity determination module is configured to determine the optimal open-loop control sequence U=[u(k)u(k+1)...u(k+N c -1)] T as the optimal suspension control output quantity in the prediction time domain.

[0153] Optionally, the control module 504 comprises:

[0154] An extraction module is configured to extract a first term u(k) in the optimal open-loop control sequence U=[u(k)u(k+1)...u(k+N c -1)] T corresponding to the optimal suspension control output quantity in the prediction time domain.

[0155] A control sub-module is configured to send the first term u(k) to a 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, wherein the computer program, when executed by the processor, implements the steps in the active suspension control method according to 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, wherein the computer readable storage medium stores a computer program, and the computer program, when executed by a processor, implements the steps in the active suspension control method according to the first aspect of the present application.

[0158] 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 part of the description of the method embodiment.

[0159] It should be noted that, for the method embodiment, in order to simply describe, the method embodiment is described as a series of action combinations, but those skilled in the art should know that the method embodiment is not limited to the described action sequence, because according to the method embodiment, some steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions involved are not necessarily required by the method embodiment.

[0160] Each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0161] Those skilled in the art should know that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the embodiments of the present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0162] The embodiments of the present application are described with reference to the flowchart illustrations and / or block diagrams of the methods, terminal devices (systems) and computer program products according to the embodiments of the present application. It is understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal devices to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal devices, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0163] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal devices to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0164] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal devices, such that a series of operational steps are performed on the computer or other programmable terminal devices to create a computer implemented process so that the instructions executed on the computer or other programmable terminal devices provide steps for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0165] Although preferred embodiments of the present application have been described, those skilled in the art will be able to make additional modifications and variations to these embodiments once they have the benefit of the foregoing description. Accordingly, the appended claims are intended to cover all modifications and variations of the preferred embodiments that fall within the scope of the present application.

[0166] Finally, it is to be understood that the phraseology or terminology such as "first" and "second" etc. used herein is merely intended to differentiate one entity or operation from another entity or operation, without necessarily requiring or implying any actual such relationship or order between such entities or operations. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0167] The above provides a kind of active suspension control method, system and product provided by the present application, have carried out detailed introduction, the principle and implementation mode of the present application are described in this paper, the above example is only for helping to understand the method and its core idea of the present application;For the general technical personnel of the field, according to the idea of the present application, there will be changes in specific implementation mode and application range, as described above, the content of the specification should not be understood as the limitation of the present application.

Claims

1. An active suspension control method, characterized by, The method comprises: determining a posterior augmented state vector of the vehicle according to a prior augmented state vector of the vehicle, wherein the augmented state vector is composed of various state parameters of the vehicle; determining a system state of the vehicle and front wheel road input information according to the posterior augmented state vector; determining an optimal suspension control output in a prediction time domain according to the system state of the vehicle and the front wheel road input information; controlling the vehicle suspension through the optimal suspension control output; wherein determining the optimal suspension control output in the prediction time domain according to the system state of the vehicle and the front wheel road input information comprises: A road input discrete space function is pre-constructed and simplified to obtain a simplified road input discrete space function: ; wherein the road input discrete space function is: , represents the rear wheel road input information at time k, is a discretization time interval of the front and rear wheels experiencing the same road input, , L represents the wheelbase of the vehicle, and u represents the driving speed of the vehicle, to represents the road input information within the wheelbase range between the front and rear wheels at time k, represents the front wheel road input information at time k, represents the rear wheel road input information at time k+1, to represents the road input information within the wheelbase range between the front and rear wheels at time k+1, ; Based on the sampling time interval and the state space equation, a discrete state space equation is constructed: ; wherein, ; ; ; is a road input matrix, , and respectively represent the front wheel road and the rear wheel road input matrix; Based on the simplified road input discrete space function and the discrete state space equation, a target state space equation is constructed: ; wherein, , , , ; determining an output equation based on a performance output vector and the target state space equation: ; wherein , , the performance output vector is ; Under the target constraint condition, based on the output equation, an output quantity prediction equation in a corresponding prediction time domain is constructed: ; wherein, , , , , the target constraints are , is the control horizon, is the prediction horizon; determining the optimal suspension control output in the prediction time domain according to the system state of the vehicle and the front wheel road input information through a pre-constructed output prediction equation.

2. The active suspension control method according to claim 1, characterized by, determining a posterior augmented state vector of the vehicle according to a prior augmented state vector of the vehicle comprises: constructing a half-car model differential equation: ; where, is the sprung mass of the half-car model; is the moment of inertia about axis; , are the front and rear wheel masses, respectively; , are the front and rear suspension stiffnesses, respectively; , are the front and rear tire stiffnesses, respectively; , are the front and rear suspension damping coefficients, respectively; , are the front and rear suspension sprung mass vertical displacements, respectively; is the vertical displacement at the center of mass; , are the front and rear wheel vertical displacements, respectively; is the pitch angle at the center of mass; , are the distances from the center of mass to the front and rear axles, respectively; , are the road input information for the front and rear wheels, respectively; , are the front and rear active suspension forces, respectively; the displacement at the body of the front and rear axles is , within a set pitch angle range. Based on the vehicle state vector and the half-car model differential equation, a state space equation is constructed: ; wherein the vehicle state vector is , , , , , , , is a unit matrix with a diagonal of 1, is a zero matrix of 1x4, is a zero matrix of 1x6, , , ; 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: ; wherein, is an observation vector; , are process noise and measurement noise satisfying zero mean Gaussian distribution respectively; is the force of the front and rear suspensions; is the road input information of the front and rear wheels, ; ; The road surface input information of the front and rear wheels is determined as a state vector, and a corresponding augmented state vector is constructed: ; According to the first target setting and the augmented state vector, an augmented state space equation corresponding to a random linear state space equation is determined: wherein the first target setting is that the front and rear road surface inputs both satisfy a random walk process, , , , , ; discretizing the augmented state space equation to obtain a discrete augmented state space equation: ; wherein, , , , , is a sensor sampling time interval, , , , are mutually independent Gaussian white noises subject to normal distribution, respectively. The prior augmented state vector is determined by a first preset algorithm based on a discrete augmented state space equation, and an expression of the first preset algorithm is: ; The prior state error covariance matrix is updated by a first update algorithm, which is expressed as: ; Based on the updated prior state error covariance matrix, the Kalman gain is determined by a second preset algorithm, and the expression of the second preset algorithm is: ; Based on the prior augmented state vector and the Kalman gain obtained by calculation, a posterior augmented state vector is determined by a third preset algorithm, and an expression of the third preset algorithm is: .

3. The active suspension control method according to claim 2, characterized by, The method further comprises: The posterior state error covariance matrix is updated by a second updating algorithm based on the updated prior state error covariance matrix and the obtained Kalman gain, and the second updating algorithm is expressed as: ; using the updated posterior state error covariance matrix for updating a subsequent prior state error covariance matrix.

4. The active suspension control method according to claim 2, characterized by, The method further comprises defining an initial value of the state error covariance matrix as and an initial value of the vehicle state vector as .

5. The active suspension control method according to claim 2, wherein determining the optimal suspension control output in the prediction time domain according to the system state of the vehicle and the front wheel road input information through a pre-constructed output prediction equation comprises: The initial objective function within the prediction horizon is determined as: ; wherein a weighting matrix representing the system output, a weighting matrix for the control quantity; Substituting the output quantity prediction equation into the initial objective function, the objective function is determined: wherein is an output quantity weighting matrix in the prediction horizon, is a control quantity weighting matrix in the control horizon; According to the determined system state of the vehicle and the front wheel road input information, the output quantity in the prediction time domain is solved through the target function, and an optimal open-loop control sequence that makes the value of the target function minimum is determined ; determining the optimal open-loop control sequence determining an optimal suspension control output quantity for the prediction horizon.

6. The active suspension control method according to claim 5, wherein controlling the vehicle suspension through the optimal suspension control output comprises: extracting an optimal open-loop control sequence corresponding to an optimal suspension control output in a prediction horizon the first term in ; The first item to the vehicle control system for suspension control.

7. An active suspension control system characterised in that, The system comprises: a posterior augmented state vector determination module configured to determine a posterior augmented state vector of the vehicle according to a 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 configured to determine a system state of the vehicle and front wheel road input information according to the posterior augmented state vector; an optimal suspension control output determination module configured to determine an optimal suspension control output in a prediction time domain according to the system state of the vehicle and the front wheel road input information; a control module configured to control the vehicle suspension through the optimal suspension control output; wherein the optimal suspension control output determination module comprises: a second equation construction module, configured to pre-construct a road input discrete space function, and simplify the road input discrete space function to obtain a simplified road input discrete space function: ; wherein the road input discrete space function is: , represents rear wheel road input information at time k, is a discretization time interval of the front and rear wheels experiencing the same road input, , L represents a wheelbase of the vehicle, and u represents a vehicle driving speed, to represents road input information within the wheelbase range between the front and rear wheels at time k, represents front wheel road input information at time k, represents rear wheel road input information at time k+1, to represents road input information within the wheelbase range between the front and rear wheels at time k+1, ; and for constructing a discrete state space equation based on the sampling time interval and the state space equation: ; wherein, ; ; ; is a road input matrix, , and denote the front wheel road and the rear wheel road input matrices, respectively. and constructing a target state space equation based on the simplified road input discrete space function and the discrete state space equation: ; wherein, , , , ; and for determining an output equation based on the performance output vector and the target state space equation: ; wherein , , the performance output vector is ; and for constructing, under target constraint conditions, an output quantity prediction equation in a corresponding prediction time domain based on the output equation: ; wherein, , , , , the target constraints are , is the control horizon, is the prediction horizon; an optimal suspension control output determination sub-module configured 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 input information through a pre-constructed output prediction equation.

8. An electronic device, comprising: comprise: a processor, a memory, and a computer program stored on the memory and running on the processor, wherein the computer program is executed by the processor to implement the steps in the active suspension control method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer program is stored on the computer readable storage medium and is executed by the processor to implement the steps in the active suspension control method according to any one of claims 1 to 6.

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