Automobile active suspension state feedback control method based on self-adaptive event triggering

Abstract

The invention discloses an automobile active suspension state feedback control method based on self-adaptive event triggering. The method comprises the steps that a state space model is built based on state variables and constraint output of an active suspension; determining effective state data based on a self-adaptive threshold according to state data output by the state space model; and the effective state data is converted into control input through a control law. According to the method, an event triggering mechanism is introduced into the field of active suspension control, and the data transmission quantity and the calculation complexity are remarkably reduced on the premise of ensuring the dynamic performance of the suspension system by establishing the quantitative relation between the triggering threshold parameter and the system control performance index.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile active suspension control, and more particularly to an automobile active suspension state feedback control method based on adaptive event triggering. Background Art

[0002] Suspension is becoming increasingly important in modern high-end automotive design. To improve suspension performance, numerous researchers are dedicated to developing various active control strategies. These strategies, with the core goal of enhancing ride comfort and operational safety, utilize methods such as optimal control, robust control, and sliding mode control to enable active suspension systems to dynamically adjust damping force based on road conditions, vehicle motion, and external excitation, effectively suppressing excessive suspension vibration and maintaining optimal damping.

[0003] The operation of the active controller depends on the data collected by the sensor, and the controller calculates the required damping force through an algorithm based on this data.

[0004] However, existing vehicle active suspension feedback controllers typically use a periodic sampling and control mode, meaning that sensors collect data at a fixed frequency, and the controller also performs calculations at the same frequency, regardless of the vehicle's road conditions. This fixed-frequency operation easily leads to unnecessary redundant calculations in the controller, increasing actuator losses, wasting communication and computing resources, and significantly increasing CPU usage. Summary of the Invention

[0005] In view of this, in order to at least partially solve the above technical problems, the present invention provides a vehicle active suspension state feedback control method based on adaptive event triggering.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A state feedback control method for an automobile active suspension based on adaptive event triggering, comprising:

[0008] Construct a state space model based on the state variables and constraint outputs of the active suspension;

[0009] determining effective state data based on an adaptive threshold according to state data output by the state space model;

[0010] The effective state data is converted into control input through the control law.

[0011] Preferably, the state variable x(t) comprises,

[0012] Suspension disturbance: x1(t)=z s (t)-z u (t)

[0013] Tire disturbance: x2(t)=z u (t)-z r (t)

[0014] Velocity of sprung mass:

[0015] Velocity of unsprung mass:

[0016] Where z s (t) represents the displacement of the sprung mass at time t, z u (t) represents the displacement of the unsprung mass at time t; z r (t) represents the displacement excitation of the road disturbance at time t.

[0017] Preferably, the constraint output is:

[0018]

[0019]

[0020] Where z1(t) represents the first constraint output, z2(t) represents the second constraint output, and z max Indicates the maximum allowable dynamic deflection, m s Represents the sprung mass, m u represents the unsprung mass, g represents the acceleration due to gravity, and T represents the matrix transpose.

[0021] Preferably, the state space model expression is:

[0022]

[0023] Where, represents the state data output by the state space model, x(t) represents the state variable, u(t) represents the control input of the active suspension system, w(t) represents the road disturbance, A, B, B ω Represents the system-related matrix, C1 represents the output matrix of the first constraint, and C2 represents the output matrix of the second constraint.

[0024] Preferably, determining effective state data based on an adaptive threshold according to the state data output by the state space model comprises:

[0025] When the error between the state data output by the state space model and the state data when the previous trigger feedback control takes effect is greater than the weighted modulus value at the previous trigger moment after the adaptive threshold is applied, the state data output by the current state space model is used as the latest valid state data.

[0026] Preferably, the updating mechanism of the adaptive threshold is:

[0027]

[0028] σ(t k+1 h)=max{σ m ,σ(t k+1 h)}

[0029] Where, Δ=||x(t k+1 h)||-κ||x(t k h)||,l k and t k The sequence is a natural number sequence, l k is the sampling time sequence, which is used to store the state value at each sampling time; t k It is used to store the moment of successful triggering. The sampled data will be stored in the trigger sequence only when and only when the sampled data meets the triggering condition. k+1 h) represents the status data at time k+1, and this time is the latest triggering effective time, x(t k h) represents the state data at time k, which is the last trigger effective time, h is the sampling period, κ>0 is an adjustable constant used to adapt to different working states, making the designed control method more universal, σ m The minimum constraint value for the set initial threshold and adaptive threshold is used to avoid excessive trigger data.

[0030] Preferably, the control law is expressed as follows:

[0031] u(t)=Kx(t k h)

[0032] Where u(t) represents the control input of the active suspension system, and K is the controller gain matrix to be solved, which is obtained by solving the linear matrix inequality so that the closed-loop system meets the H∞ performance index.

[0033] Preferably, the transmission delay of the control input of the active suspension system is considered, which is defined as:

[0034] τ(t)=tl k h

[0035] Where τ(t) is the transmission delay, t is the real time, l k h is the sampling time;

[0036] Define the state error variable:

[0037] e(l k h)=x(t k h)-x(l k h)

[0038] Then the state at the triggering moment can be expressed by transformation into a form that does not depend on the triggering sequence:

[0039] x(t k h)=x(t-τ(t))+e(l k h)

[0040] The variables in the formula all depend on the sampling time, which is convenient for the processor to handle;

[0041] At this point, the state equation in the state space model is replaced by:

[0042]

[0043] represents the state data output by the state space model, x(t) represents the state variable, w(t) represents the road disturbance, A, B, B ω represents the system correlation matrix, and K represents the gain matrix.

[0044] The present invention also provides an automobile active suspension state feedback control system based on adaptive event triggering, which uses any of the above-mentioned automobile active suspension state feedback control methods based on adaptive event triggering, including:

[0045] A state data acquisition module is used to build a state space model based on the state variables and constraint outputs of the active suspension and obtain state data using the state space model;

[0046] An event triggering processor, configured to determine valid state data based on an adaptive threshold according to state data output by the state space model;

[0047] A control law calculation unit, used for converting effective state data into control input through control law;

[0048] The action execution module is used to convert the control input into the suspension adjustment force.

[0049] The present application also provides a computer-readable storage medium storing a computer program, wherein when the program is executed by a processor, the method for controlling the state feedback of an active suspension of an automobile based on adaptive event triggering as described in any one of the above items is implemented.

[0050] The present invention discloses a method for controlling the state feedback of an automobile active suspension based on adaptive event triggering, which has the following advantages:

[0051] An event-triggered information transmission mechanism is introduced into the feedback control of the active suspension system, and a correlation is established between threshold condition parameters and system control performance indicators. In this way, the present invention can significantly reduce the transmission and computation of redundant sampled data while ensuring system performance.

[0052] Compared to traditional periodic sampling control methods, the present invention significantly shortens the average execution period of control tasks, effectively reducing the amount of information transmitted by the suspension system and conserving communication and computing resources. Therefore, compared to existing technologies, the present invention significantly reduces redundant sampling data transmission and calculations, conserving communication and computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0054] Figure 1 Schematic diagram of the active suspension system model of the present invention;

[0055] Figure 2 Outputting a response graph for the present invention;

[0056] Figure 3 It is the state response diagram of the present invention;

[0057] Figure 4 This is the event trigger interval diagram of the present invention. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0059] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0060] The present invention relates to an event-triggered automobile active suspension state feedback control method, which aims to solve the problems of waste of computing resources, excessive actuator loss and low communication efficiency in traditional active control methods.

[0061] The method includes three core steps: (1) defining the state variables and constraint outputs of the active suspension and constructing a state space mathematical model of the vehicle active suspension system; (2) designing an intelligent information transmission mechanism based on adaptive event triggering to determine the effective state data; (3) deriving an event-triggered H∞ controller to convert the effective state data into control input.

[0062] In one embodiment, in step 1:

[0063] For a 1 / 4 vehicle body suspension model with two degrees of freedom, such as Figure 1 As shown, its dynamic equation is:

[0064]

[0065] where m s Represents the sprung mass, m u represents the unsprung mass; c s represents the suspension damping, c t represents tire damping; k s Represents the suspension spring stiffness, k t represents the tire stiffness; z s represents the displacement of the sprung mass, z u represents the displacement of the unsprung mass; z r represents the displacement excitation of the roadside; u(t) represents the control input of the active suspension system.

[0066] Therefore, the suspension disturbance, tire disturbance, sprung mass velocity and unsprung mass velocity are selected as the state variable x(t), that is, the state variable x(t) includes,

[0067] Suspension disturbance: x1(t)=z s (t)-z u (t)

[0068] Tire disturbance: x2(t)=z u (t)-z r (t)

[0069] Velocity of sprung mass:

[0070] Velocity of unsprung mass:

[0071] represents the velocity of the sprung mass, Velocity of the unsprung mass.

[0072] Furthermore, the road disturbance input is defined as The state equation of the system is obtained as:

[0073]

[0074] in:

[0075]

[0076] In this embodiment, the constraints include:

[0077] (1) The transfer function from road disturbance input to vehicle body vertical acceleration satisfies the given H∞ performance index;

[0078] (2) The dynamic deflection of the suspension satisfies |z s (t)-z u (t)|≤z max , z max is the maximum allowable dynamic deflection;

[0079] (3) The dynamic load of the tire must meet k t |z u (t)-z r (t)|≤(m u +m s )g, g is the acceleration due to gravity.

[0080] The constraint output determined according to the constraint conditions is:

[0081]

[0082]

[0083] Where z1(t) represents the first constraint output, z2(t) represents the second constraint output, and z max Indicates the maximum allowable dynamic deflection, m s Represents the sprung mass, m u represents the unsprung mass, g represents the acceleration due to gravity, and T represents the matrix transpose.

[0084] Finally, the state space model expression of the active suspension system constructed in this application is:

[0085]

[0086] Where, represents the state data output by the state space model, x(t) represents the state variable, u(t) represents the control input of the active suspension system, w(t) represents the road disturbance, A, B, B w represents the system correlation matrix, C1 represents the output matrix of the first constraint, and C2 represents the output matrix of the second constraint.

[0087] in,

[0088]

[0089] In one embodiment, step 2, establishing an information transmission mechanism based on event triggering;

[0090] In this embodiment, when the error between the state data output by the state space model and the state data at the previous trigger feedback control effective moment is greater than the weighted modulus value at the previous trigger moment when the adaptive threshold is applied, the state data output by the current state space model is used as the valid state data.

[0091] In an exemplary embodiment, let h be the sampling period, and the state x(t) is periodically sampled as x(l k h), where l k h is the sequence of sampling signals, let x(t k h) is the transmitted data, t k h is the trigger time sequence. Since the trigger time sequence is a subset of the sampling time sequence, we can define t k h=l k h+Ih, is a constant that depends on the event triggering scheme selected. The sampling data for feedback control is selected using the following formula, that is, if and only if the following formula holds, x(l k , h) is used for feedback control:

[0092]

[0093] Where Φ>0, it means the matrix is ​​positive definite, e(l k h)=x(t k h)-x(t k h+lh) represents the error between the current sampling moment data and the previous trigger data.

[0094] σ(t k h) is an adaptive threshold. In a preferred embodiment, the update mechanism is:

[0095]

[0096] σ(t k+1 h)=max{σ m ,σ(t k+1 h)}

[0097] Where, Δ=|x(t k+1 h)-κ|x(t k h)||,x(t k+1 h) represents the latest trigger time data, x(t k h) represents the data at the last trigger moment, h is the sampling period, κ>0 represents a constant that can be freely selected according to the situation to match different systems, σm The minimum constraint for setting the initial threshold and the adaptive threshold is to avoid generating too much trigger data.

[0098] In one embodiment, step three includes:

[0099] The following state feedback controller is used to convert the effective state data into control input,

[0100] u(t)=Kx(t k h)

[0101] Where u(t) represents the control input of the active suspension system, and K is the controller gain matrix to be solved, which is obtained by solving the linear matrix inequality so that the closed-loop system meets the H∞ performance index.

[0102] To further optimize the above technical solution, the transmission delay of the control input of the active suspension system is considered and defined as:

[0103] τ(t)=tl k h

[0104] Where t is the real time, l k h is the sampling time, and τ(t) is the transmission delay.

[0105] Define the state error variable:

[0106] e(l k h)=x(t k h)-x(l k h)

[0107] Then the state at the triggering moment can be expressed by transformation into a form that does not depend on the triggering sequence:

[0108] x(t k h)=x(t-τ(t))+e(l k h)

[0109] The variables in the formula all depend on the sampling time, which is convenient for the processor to handle. At this time, the state equation in the state space model is replaced by:

[0110]

[0111] represents the state data output by the state space model, x(t) represents the state variable, w(t) represents the road disturbance, A, B, B ω represents the system correlation matrix, and K represents the gain matrix.

[0112] In an optional embodiment, establishing a stability condition for H∞ control based on adaptive event triggering includes:

[0113] For the active suspension system and the given adaptive event triggering mechanism, the stability conditions of the system are given:

[0114] For a given τ M , d m , d M , and 0<σ m <1, if there exists a positive definite matrix P,Q x (x=1,2,3,4,5), R1, R2, Φ, and matrices S1 and S2 of appropriate dimensions satisfy the following linear matrix inequality:

[0115] ①

[0116] ②

[0117] in,

[0118]

[0119] (8,1)=(1-α)PBK,(9,1)=PBw

[0120] (2,2)=-Q1-R2, (3,2)=S2, (4,2)=(4,3)=R2-S2, (3,3)=-Q2-R2,

[0121]

[0122]

[0123] (6,5)=R1-S1,(7,5)=2σ M Φ, (6, 6) = -Q5-R1,

[0124] (7,7)=σ M Φ-Φ, (8,8)=-I, (9,9)=-γ 2 I

[0125]

[0126]

[0127]

[0128]

[0129] Wherein the matrix R<0 means the matrix is ​​negative, * means the symmetric term in the matrix, R -1 represents the inverse of the matrix R, then the original system is asymptotically stable and satisfies the H∞ performance with parameter γ.

[0130] In this embodiment, the state feedback gain matrix K and event trigger matrix Φ of the system are derived based on the stability condition of H∞ control to achieve specific control of the system and reduce the amount of data transmission. Specifically,

[0131] The above linear matrix inequality is transformed as follows:

[0132]

[0133]

[0134] Multiply inequality ① by diag{X,X,X,X,X,X,X,X,I,I,I,I,I,I,I,I} on the left and its transpose on the right. Do the same for inequality ② with diag{X,X}, taking scaling into account. Substitute the scaling result into the above current linear matrix inequality, then The asymptotic stability conditions of are as follows:

[0135] For a given τ M , d m , d M , and 0<σ m <1, if there is a positive definite matrix And a matrix of appropriate dimension Satisfying the above current matrix inequality, the controller gain is K = YX -1 .

[0136] Furthermore, a Lyapunov functional is constructed to prove that the stability conditions of the adaptive event-triggered control mentioned above are valid, including:

[0137] The following Lyapunov-Krasovskii functional depending on the time delay and adaptive event triggering parameters is constructed:

[0138]

[0139] in,

[0140] V1(t)=x T (t)Px(t)+(τ M -τ(t))x T (t-τ(t))Q4x(t-τ(t))

[0141]

[0142]

[0143]

[0144] By proving that under the stable conditions of H∞ control, the Lyapunov functional is positive definite and the derivative of the Lyapunov function is negative definite, it is proved according to the Lyapunov stability theory that the system can achieve asymptotic stability under the above conditions and through linear transformation, the system feedback matrix and event trigger matrix can be collaboratively designed.

[0145] In another embodiment, the present application discloses an adaptive event-triggered vehicle active suspension state feedback control system, which applies any of the above-described adaptive event-triggered vehicle active suspension state feedback control methods, including:

[0146] A state data acquisition module is used to build a state space model based on the state variables and constraint outputs of the active suspension and obtain state data using the state space model;

[0147] An event triggering processor, configured to determine valid state data based on an adaptive threshold according to state data output by the state space model;

[0148] A control law calculation unit, used for converting effective state data into control input through control law;

[0149] The action execution module is used to convert the control input into the suspension adjustment force.

[0150] In addition, the present application also provides a computer-readable storage medium storing a computer program, which implements the steps of any of the above methods when executed by a processor.

[0151] The adaptive event-triggered state feedback control method for an automotive active suspension disclosed in the present invention can intelligently adjust the sampling frequency according to the actual system state, compared with traditional periodic sampling control, thereby extending the service life of the actuator and providing a more efficient communication and computing resource utilization solution for the automotive intelligent suspension system.

[0152] In a specific application, the parameter values ​​of the vehicle active suspension model are as shown in Table 1.

[0153] Table 1

[0154] Mass (kg) Stiffness (N / m) Damping (Ns / m) 320 18000 1000 50 200000 10

[0155] Select z max =0.3,d m =0.01,d M =0.35,γ=50,τ M =0.2,h=0.05,initial state x0=[0.60.24 0.4 0.15] T , solving the inequality:

[0156] K=[-2.35 -10.151 -116.4987 -4.7647]

[0157]

[0158] The system runs for 30 seconds, and the system output and status response are as follows: Figure 2 , Figure 3 As shown in the figure, it can be seen that under the designed controller, the system can quickly reach stability from the initial state and meet the constraints to ensure ride comfort. The event trigger interval, that is, the sampling data points are as follows Figure 4 As shown, it can be seen that under the premise of ensuring system performance, the amount of data required to be transmitted is greatly reduced, which has obvious advantages in saving resources.

[0159] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0160] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A vehicle active suspension state feedback control method based on adaptive event triggering, characterized in that: Construct a state space model based on the state variables and constraint outputs of the active suspension; determining effective state data based on an adaptive threshold according to the state data output by the state space model; The effective state data is converted into control inputs by a control law.

2. The control method according to claim 1, characterized in that: The state variable x(t) includes, Suspension disturbance: x1(t)=z s (t)-z u (t) Tire disturbance: x2(t)=z u (t)-z r (t) Velocity of sprung mass: Velocity of unsprung mass: Where z s (t) represents the displacement of the sprung mass at time t, z u (t) represents the displacement of the unsprung mass at time t; z r (t) represents the displacement excitation of the road disturbance at time t.

3. The control method according to claim 2, characterized in that: The constraint output is: Where z1(t) represents the first constraint output, z2(t) represents the second constraint output, and z max Indicates the maximum allowable dynamic deflection, m s Represents the sprung mass, m u represents the unsprung mass, g represents the acceleration due to gravity, and T represents the matrix transpose.

4. The control method according to claim 3, characterized in that: The state space model expression is: Where, represents the state data output by the state space model, x(t) represents the state variable, u(t) represents the control input of the active suspension system, w(t) represents the road disturbance, A, B, B ω Represents the system-related matrix, C1 represents the output matrix of the first constraint, and C2 represents the output matrix of the second constraint.

5. The control method according to claim 1, characterized in that: Determining effective state data based on the state data output by the state space model and an adaptive threshold, including: When the error between the state data output by the state space model and the state data when the previous trigger feedback control takes effect is greater than the weighted modulus value at the previous trigger moment after the adaptive threshold is applied, the state data output by the current state space model is used as the latest valid state data.

6. The control method according to claim 1, characterized in that: The updating mechanism of the adaptive threshold is: σ(t k+1 h)=max{σ m ,σ(t k+1 h)} Where, Δ=||x(t k+1 h)||-κ||x(t k h)||,l k and t k The sequence is a natural number sequence, l k is the sampling time sequence, which is used to store the state value at each sampling time; t k It is used to store the moment of successful triggering. The sampled data will be stored in the trigger sequence only when and only when the sampled data meets the triggering condition. k+1 h) represents the status data at time k+1, and this time is the latest triggering effective time, x(t k h) represents the status data at time k, which is the last trigger effective time, h is the sampling period, κ>0 is an adjustable constant, σ m The initial threshold and the minimum constraint value of the adaptive threshold are set.

7. The control method according to claim 1, characterized in that: The control law is expressed as follows: u(t)=Kx(t k h) Where u(t) represents the control input of the active suspension system, and K is the controller gain matrix to be solved, which is obtained by solving the linear matrix inequality so that the closed-loop system meets the H∞ performance index.

8. The control method according to claim 1, characterized in that: The state-space model takes into account the transmission delay of the active suspension system control input, which is defined as: τ(t)=t-l k h Where τ(t) is the transmission delay, t is the real time, l k h is the sampling time; The status at the trigger moment is: x(t k h)=x(t-τ(t))+e(l k h) e(l k h) is the state error variable, and e(l k h)=x(t k h)-x(l k h); At this time, the state equation in the state space model is: represents the state data output by the state space model, x(t) represents the state variable, w(t) represents the road disturbance, A, B, B ω represents the system correlation matrix, and K represents the gain matrix.

9. An automobile active suspension state feedback control system based on adaptive event triggering, characterized in that: The method for controlling an active suspension state of an automobile based on adaptive event triggering according to any one of claims 1 to 8 comprises: A state data acquisition module is used to build a state space model based on the state variables and constraint outputs of the active suspension and obtain state data using the state space model; An event triggering processor, configured to determine valid state data based on an adaptive threshold according to state data output by the state space model; A control law calculation unit, used for converting effective state data into control input through control law; The action execution module is used to convert the control input into the suspension adjustment force.

10. A computer-readable storage medium storing a computer program, characterized in that: When the program is executed by a processor, the vehicle active suspension state feedback control method based on adaptive event triggering as described in any one of claims 1 to 8 is implemented.

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