Self-adaptive control method, device and system for active suspension, vehicle and medium
By constructing a system observer based on state space equations in an active suspension system, adaptive control is realized, solving the problem of low control accuracy in traditional systems, and improving the control accuracy of the system and the comfort of the vehicle.
Patent Information
- Application Number
- CN202311191804.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-09-14
- Publication Date
- 2025-03-21
AI Technical Summary
There are a large number of uncertainties and unknown quantities in traditional active suspension systems, resulting in low control accuracy.
A system observer based on the state space equation is adopted to build an adaptive control system by estimating the state quantity and uncertainty factors of the active suspension system, relying solely on the system output data, control target values and external incentives to eliminate the influence of unknown factors in the traditional model.
The control accuracy of the active suspension system is improved, the vibration filtering performance of the system is enhanced, and the NVH performance and ride comfort of the vehicle are improved.
Smart Images
Figure CN119659298A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of vehicle control technology, and in particular to an adaptive control method, device, system, vehicle and medium for active suspension. Background Art
[0002] According to the control method, the automotive powertrain mounts are divided into passive mounts, semi-active mounts, and active mounts. Active mounts are based on passive mounts and add active force devices, combined with control algorithms, to compensate or suppress the vibrations of the powertrain and road surface to the vehicle interior under various working conditions.
[0003] Traditional solutions generally control the active suspension system by building a data model corresponding to the system, such as a PID control model. However, it is difficult to create an accurate mathematical model to complete the design of the control algorithm. There are a large number of uncertain factors and unknown quantities in the active suspension system. These unknown factors and uncertain variables will affect the control of the active suspension, thereby reducing the control accuracy of the active suspension. Summary of the invention
[0004] The present application provides an adaptive control method, device, system, vehicle and medium for active suspension to solve the technical problem of low active suspension control accuracy in traditional solutions.
[0005] An adaptive control method for an active suspension, the method comprising:
[0006] acquiring an estimated value of a system observer of an active mount, an output value of the active mount, and a control target value;
[0007] acquiring a control value of the active mount according to an estimated value of the system observer, an output value of the active mount, and a control target value;
[0008] performing adaptive control on the active mount according to the control value of the active mount;
[0009] The system observer is constructed based on a state space expression of the state space equation of the active mount.
[0010] Furthermore, the state space expression of the active mount is constructed as follows:
[0011] The system state quantities of the active suspension system are set as follows:
[0012]
[0013] make Then the state space expression of the active suspension is obtained:
[0014]
[0015] Among them, x1, x2, and x3 are the system state quantities of the active suspension, respectively. m and x s They represent the vertical displacement of the powertrain and the unsprung mass, M p represents the powertrain mass, k m 、c m are the stiffness and damping of the active mount respectively, is the state quantity of the uncertainty estimate of the active mount, b0 is the actuator force parameter, and u represents the control value of the active mount.
[0016] Furthermore, the system observer is constructed in the following manner:
[0017] Based on the state space expression, the state space equation of the active mount is constructed:
[0018]
[0019] Let Z=[z1 z2 z3] T The system state quantity X of the active suspension is [x1 x2 x3] T The estimated value of L = [β1 β2 β3] T is the gain vector of the system observer, then the following system observer is constructed based on the state space equation:
[0020]
[0021] in, C=[1 0 0],D=[0], is the external excitation of the active mount, and x3=f is the state quantity of the uncertainty estimate of the active mount.
[0022] Further, acquiring the control value of the active mount according to the estimated value of the system observer, the output value of the active mount and the control target value includes:
[0023] The estimated value of the system observer is set to include a reference state trajectory under the control target value, including a reference state trajectory as x 1d and the reference state trajectory x 2d , the output value of the active mount includes the actual state trajectory corresponding to the control target value, including the actual state trajectory x1 and the actual state trajectory x2;
[0024] Based on the reference state trajectory x 1d , reference state trajectory x 2d, actual state trajectory x1 and actual state trajectory x2, construct the control value of the active suspension.
[0025] Furthermore, the reference state trajectory is x 1d , reference state trajectory x 2d , the actual state trajectory x1 and the actual state trajectory x2, construct the control value of the active mount, including:
[0026] Determine the actual state trajectory x1 and the reference state trajectory x 1d The error between them is e1 = x1-x 1d , and expressed as:
[0027] Set the reference state trajectory x 2d for: Determine the actual state trajectory x2 and the reference state trajectory x 2d The error between And expressed as:
[0028]
[0029] make To construct the actuator force of the active suspension system as follows:
[0030]
[0031] An adaptive control device for active suspension, comprising:
[0032] an acquisition module, used for acquiring an estimated value of a system observer of an active mount, an output value of the active mount, and a control target value;
[0033] a control module, configured to obtain an actuator force of the active mount according to an estimated value of the system observer and an output value of the active mount; and to adaptively control the active mount according to the actuator force and an external excitation;
[0034] The system observer is constructed based on a state-space expression of the state-space equation of the active mount.
[0035] Furthermore, the state space expression of the active mount is constructed as follows:
[0036] The system state quantities of the active suspension system are set as follows:
[0037]
[0038] make Then the state space expression of the active suspension is obtained:
[0039]
[0040] Among them, x1, x2, and x3 are the system state quantities of the active suspension, respectively. m and x s They represent the vertical displacement of the powertrain and the unsprung mass, M p represents the powertrain mass, k m 、c m are the stiffness and damping of the active mount respectively, is the state quantity of the uncertainty estimate of the active mount, b0 is the actuator force parameter, and u represents the actuator force.
[0041] An adaptive control system for an active suspension comprises an active suspension and an adaptive control device, wherein the adaptive control device is connected to the active suspension and is used to implement the steps of the adaptive control method for the active suspension as described in any one of the above items.
[0042] A vehicle comprises an adaptive control system for an active suspension as described in any one of the above.
[0043] A computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the adaptive control method for active suspension as described in any one of the above items.
[0044] In one of the solutions provided above, an adaptive control method for active suspension is provided, in which a system observer is used to replace the general discrete nonlinear system when a series of changing dynamic linears are applied to the controlled active suspension, and then the vector of the entire active suspension closed-loop system is estimated based on the I / O data output by the active suspension, thereby completing the model-free adaptive control of the nonlinear system. For the active suspension, it only requires the system output data, control target value and external excitation, and does not require other system model information, thereby eliminating the influence of unknown factors and determined variables in the traditional control model on the active suspension system, and improving the control accuracy of the active suspension. BRIEF DESCRIPTION OF THE DRAWINGS
[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.
[0046] Figure 1 is a system schematic diagram of an adaptive control system of an active suspension in an embodiment of the present application;
[0047] Figure 2 is a flow chart of an adaptive control method of an active suspension in an embodiment of the present application;
[0048] Figure 3 is a schematic diagram of a dynamic model of an active suspension in an embodiment of the present application;
[0049] Figure 4 is a structural schematic diagram of an adaptive control device for active suspension in an embodiment of the present application;
[0050] Figure 5 It is a structural schematic diagram of an actuator in an embodiment of the present application. DETAILED DESCRIPTION
[0051] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0052] Since traditional solutions generally control the active suspension system (also called active suspension system) by constructing a corresponding data model, such as a PID control model, it is difficult to create an accurate mathematical model to complete the design of the control algorithm. There are a large number of uncertain factors and unknown quantities in the active suspension system. These unknown factors and uncertain variables will affect the control of the active suspension system, thereby reducing the control accuracy of the active suspension system. In addition, control methods such as PID control models have time-varying, nonlinear and PID parameter coupling phenomena, and cannot complete adaptive control.
[0053] The embodiment of the present application mainly provides an adaptive control method of an active suspension, which is used in the active suspension control logic of various vehicles including active suspensions. The adaptive control method can be specifically configured to be implemented by a main controller of the vehicle or an independent suspension controller, and the specifics are not limited. The embodiment of the present application provides a new adaptive control method of an active suspension, which can improve the control accuracy of the active suspension. The adaptive control method of the active suspension provided by the present application is described in detail below.
[0054] See also Figure 1 As shown, in one embodiment, an adaptive control method for active suspension is provided, which mainly includes the following steps:
[0055] S10: Acquire an estimated value of a system observer of an active mount, an output value of the active mount, and a control target value;
[0056] S20: acquiring a control value of the active mount according to an estimated value of the system observer, an output value of the active mount and a control target value;
[0057] S30: performing adaptive control on the active mount according to the control value of the active mount.
[0058] like Figure 2 As shown, Figure 2 A schematic diagram of an adaptive control system framework of an adaptive control method for an active mount provided in an embodiment of the present application includes an actuator, an active mount and a system observer, wherein a control target value refers to a target value that a vehicle initially needs to control the active mount, and the system observer is constructed based on a state space expression of a state space equation of the active mount. In an adaptive control process, a control value of the active mount is obtained based on an estimated value of the system observer, an output value of the active mount and a control target value, and is characterized as an actuator force. Based on the control value of the active mount, the active mount is adaptively controlled.
[0059] In this embodiment, since the state-space expression of the state-space equation of the active suspension is used to construct the control system, the problem of introducing unknown factors and uncertain variables in the adopted model is eliminated. By constructing a system observer, the uncertainty factors and external excitations of the active suspension can be estimated with only a small amount of active suspension, and finally compensation is performed through the actuator to obtain the final control value, which greatly improves the vibration filtering performance of the suspension system, thereby improving the NVH performance and ride comfort of the vehicle, and a control method for the influence of the active suspension system.
[0060] As an example, the state space expression of the active mount is constructed as follows:
[0061] The system state of the active suspension is set as follows:
[0062]
[0063] make Then the state space expression of the active suspension is obtained:
[0064]
[0065] Among them, x1, x2, and x3 are the system state quantities of the active suspension, respectively. m and x s They represent the vertical displacement of the powertrain and the unsprung mass, M p represents the powertrain mass, k m 、c mare the stiffness and damping of the active mount respectively, is the state quantity of the uncertainty estimate of the active mount, b0 is the actuator force parameter, and u represents the actuator force.
[0066] In this embodiment, before performing adaptive control, the system dynamics equation of the active suspension needs to be established first, such as Figure 3 As shown, Figure 3 is a schematic diagram of the system dynamics model of the active suspension, where M p 、M s Represent the powertrain mass and sprung mass of the vehicle, F kM 、F CM ,u,F kS and F CS , respectively represent the elastic force, damping force, actuator force of the active suspension, and the spring force and damping force of the unsprung elastic member, x m and x s represent the vertical displacement of the powertrain and the unsprung mass respectively.
[0067] based on Figure 2 The schematic diagram of the system dynamics model of the active suspension shown in the figure can construct the system dynamics equation of the active suspension, which is expressed as:
[0068]
[0069]
[0070] After obtaining the system dynamics equation of the active mount, the uncertainty estimation in the active mount is first set based on the system dynamics equation, and the state space equation of the active mount is established through the uncertainty estimation of the active mount.
[0071] Specifically, according to the above formula (1), the vertical displacement x of the powertrain is m Take the derivative and express it as follows:
[0072]
[0073] In the above formula (3), k m is the stiffness of the active mount, c m is the damping of the active mount, b0 is the actuator force parameter, Δf is the uncertainty estimate of the active mount, and f is the state quantity of the uncertainty estimate of the active mount. Let:
[0074]
[0075] Based on equations (3) and (4), the system state quantities of the active mount are set as follows:
[0076]
[0077] Among them, x1, x2, and x3 are the system state quantities of the active suspension respectively.
[0078] make The state space expression can be obtained as shown in the following formula (6):
[0079]
[0080] After converting the state space expression shown in equation (6) into the state space equation, it can be expressed as:
[0081]
[0082] in:
[0083] C = [1 0 0], D = [0]; is the velocity excitation of the unsprung mass, which can be regarded as the external excitation of the active mount, x3=f is the additional state, which is the state quantity of the uncertainty estimate of the active mount, is the rate of change of uncertainty, and it is assumed to be an unknown bounded function.
[0084] After establishing the state space equation of the active mount, the estimated value of the system state quantity of the active mount is set to construct the system observer of the active mount.
[0085] In one embodiment, specifically, let Z = [z1 z2 z3] T X = [x1 x2 x3] T The estimated value of is L = [β1β2 β3] T is the gain vector of the system observer. According to the state space expression of formula (6), the state equation corresponding to the system observer of the active mount is constructed as follows:
[0086]
[0087] Expand formula (8):
[0088]
[0089] Then the error estimation equation of the system observer is expressed as:
[0090]
[0091] in, If the eigenvalue is set to be less than 0, the error will converge.
[0092] z1, z2, z3 are the estimated values of the system state quantities x1, x2, x3 respectively; β1, β2, β3 are the gain factors of the system observer, is the velocity excitation of the unsprung mass, which serves as the external excitation of the active mount.
[0093] Furthermore, an actuator force u of the active mount is constructed as a control value of the active mount.
[0094] Specifically, obtaining the control value of the active mount according to the estimated value of the system observer, the output value of the active mount and the control target value includes:
[0095] The estimated value of the system observer is set to include a reference state trajectory under the control target value, including a reference state trajectory as x 1d and the reference state trajectory x 2d , the output value of the active mount includes the actual state trajectory corresponding to the control target value, including the actual state trajectory x1 and the actual state trajectory x2;
[0096] Based on the reference state trajectory x 1d , reference state trajectory x 2d , actual state trajectory x1 and actual state trajectory x2, construct the control value of the active suspension.
[0097] Furthermore, the reference state trajectory is x 1d , reference state trajectory x 2d , the actual state trajectory x1 and the actual state trajectory x2, construct the control value of the active mount, including:
[0098] Determine the actual state trajectory x1 and the reference state trajectory x 1d The error between them is e1 = x1-x 1d , and expressed as:
[0099] Set the reference state trajectory x 2d for: Determine the actual state trajectory x2 and the reference state trajectory x 2d The error between And expressed as:
[0100]
[0101] make To construct the actuator force of the active suspension system as follows:
[0102]
[0103] In this embodiment, the active suspension is first set as the reference state trajectory of the control system as x1d , then the actual state trajectory x1 and the reference state trajectory x 1d The error between them can be expressed as:
[0104] e1=x1-x 1d (11)
[0105]
[0106] Set the reference state trajectory of the actual state trajectory x2:
[0107]
[0108] Then the actual state trajectory x2 and the reference state trajectory x 2d The error between them can be expressed as
[0109]
[0110]
[0111] make but:
[0112]
[0113] therefore:
[0114]
[0115] The actuator force of the active mount, that is, the control value of the active mount, is expressed as:
[0116]
[0117] From the above embodiments, it can be seen that in this embodiment, an adaptive control method for an active suspension is provided, in which a system observer is used to replace a general discrete nonlinear system when a series of changing dynamic linear systems are applied to the controlled active suspension, and then the vector of the entire active suspension closed-loop system is estimated based on the I / O data output by the active suspension, thereby completing the model-free adaptive control of the nonlinear system. For the active suspension, it only requires the system output data, control target value and external excitation, and does not require other system model information, thereby eliminating the influence of unknown factors and determined variables in the traditional control model on the active suspension system, and improving the control accuracy of the active suspension.
[0118] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
[0119] In one embodiment, an adaptive control device for active suspension is provided, and the adaptive control device corresponds one-to-one with an adaptive control method in the above embodiment. Figure 4 As shown, the adaptive control device includes an acquisition module 101 and a control module 102. Each functional module is described in detail as follows:
[0120] An acquisition module 101 is used to acquire an estimated value of a system observer of an active mount, an output value of the active mount, and a control target value;
[0121] A control module 102, configured to obtain an actuator force of the active mount according to an estimated value of the system observer and an output value of the active mount; and to perform adaptive control on the active mount according to the actuator force and an external excitation;
[0122] The system observer is constructed based on a state-space expression of the state-space equation of the active mount.
[0123] Furthermore, the state space expression of the active mount is constructed as follows:
[0124] The system state quantities of the active suspension system are set as follows:
[0125]
[0126] make Then the state space expression of the active suspension is obtained:
[0127]
[0128] Among them, x1, x2, and x3 are the system state quantities of the active suspension, respectively. m and x s They represent the vertical displacement of the powertrain and the unsprung mass, M p represents the powertrain mass, k m 、c m are the stiffness and damping of the active mount respectively, is the state quantity of the uncertainty estimate of the active mount, b0 is the actuator force parameter, and u represents the actuator force.
[0129] Furthermore, the system observer is constructed in the following manner:
[0130] Based on the state space expression, the state space equation of the active mount is constructed:
[0131]
[0132] Let Z=[z1 z2 z3] TThe system state quantity X of the active suspension is [x1 x2 x3] T The estimated value of L = [β1 β2 β3] T is the gain vector of the system observer, then the following system observer is constructed based on the state space equation:
[0133]
[0134] in, C=[1 0 0],D=[0], is the external excitation of the active mount, and x3=f is the state quantity of the uncertainty estimate of the active mount.
[0135] Further, acquiring the control value of the active mount according to the estimated value of the system observer, the output value of the active mount and the control target value includes:
[0136] The estimated value of the system observer is set to include a reference state trajectory under the control target value, including a reference state trajectory as x 1d and the reference state trajectory x 2d , the output value of the active mount includes the actual state trajectory corresponding to the control target value, including the actual state trajectory x1 and the actual state trajectory x2;
[0137] Based on the reference state trajectory x 1d , reference state trajectory x 2d , actual state trajectory x1 and actual state trajectory x2, construct the control value of the active suspension.
[0138] Furthermore, the reference state trajectory is x 1d , reference state trajectory x 2d , the actual state trajectory x1 and the actual state trajectory x2, construct the control value of the active mount, including:
[0139] Determine the actual state trajectory x1 and the reference state trajectory x 1d The error between them is e1 = x1-x 1d , and expressed as:
[0140] Set the reference state trajectory x 2d for: Determine the actual state trajectory x2 and the reference state trajectory x 2d The error between And expressed as:
[0141]
[0142] make To construct the actuator force of the active suspension system as follows:
[0143]
[0144] In one of the solutions provided above, an adaptive control device for an active suspension is provided, which replaces the general discrete nonlinear system with a system observer when a series of changing dynamic linears are applied to the controlled active suspension, and then estimates the vector of the entire active suspension closed-loop system based on the I / O data output by the active suspension, thereby completing the model-free adaptive control of the nonlinear system. For the active suspension, it only requires the system output data, control target value and external excitation, and does not require other system model information, thereby eliminating the influence of unknown factors and determined variables in the traditional control model on the active suspension system, and improving the control accuracy of the active suspension.
[0145] For the specific definition of the adaptive control device, please refer to the definition of the adaptive control method above, which will not be repeated here. Each module in the above-mentioned adaptive control device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.
[0146] In one embodiment, an adaptive control system for an active suspension is provided, comprising an active suspension and an adaptive control device, wherein the adaptive control device is connected to the active suspension, and the adaptive control device is used to implement the steps of the adaptive control method for the active suspension as described in any of the above items.
[0147] In one embodiment, a vehicle is provided, comprising the adaptive control system of the active suspension as described in the above embodiments.
[0148] It should be noted that for more information about the adaptive control system and the vehicle, please refer to the description of the aforementioned embodiment, which will not be repeated here.
[0149] In one embodiment, an actuator is provided, and its internal structure diagram can be shown as follows: Figure 5 As shown. The actuator includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the actuator is used to provide computing and control capabilities. The memory of the actuator includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The actuator is used to execute an adaptive control method.
[0150] In one embodiment, an executor is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:
[0151] acquiring an estimated value of a system observer of an active mount, an output value of the active mount, and a control target value;
[0152] acquiring a control value of the active mount according to an estimated value of the system observer, an output value of the active mount, and a control target value;
[0153] performing adaptive control on the active mount according to the control value of the active mount;
[0154] The system observer is constructed based on a state-space expression of the state-space equation of the active mount.
[0155] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the vehicle actuator control method mentioned in any of the above embodiments is implemented, which will not be repeated here.
[0156] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
[0157] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.
[0158] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.
Claims
1. An adaptive control method for active suspension, characterized in that: The method comprises: acquiring an estimated value of a system observer of an active mount, an output value of the active mount, and a control target value; acquiring a control value of the active mount according to an estimated value of the system observer, an output value of the active mount, and a control target value; performing adaptive control on the active mount according to the control value of the active mount; The system observer is constructed based on a state-space expression of the state-space equation of the active mount.
2. The adaptive control method of active suspension according to claim 1, characterized in that: The state space expression of the active mount is constructed as follows: The system state quantities of the active suspension system are set as follows: make Then the state space expression of the active suspension is obtained: Among them, x1, x2, and x3 are the system state quantities of the active suspension, respectively. m and x s They represent the vertical displacement of the powertrain and the unsprung mass, M p represents the powertrain mass, k m 、c m are the stiffness and damping of the active mount respectively, is the state quantity of the uncertainty estimate of the active mount, b0 is the actuator force parameter, and u represents the control value of the active mount.
3. The adaptive control method of active suspension according to claim 2, characterized in that: The system observer is constructed as follows: Based on the state-space expression, the state-space equation of the active mount is constructed: Let Z=[z1 z2 z3] T The system state quantity X of the active suspension is [x1 x2 x3] T The estimated value of is, let L = [β1β2β3] T is the gain vector of the system observer, then the following system observer is constructed based on the state space equation: in, C = [100], D = [0], is the external excitation of the active mount, and x3=f is the state quantity of the uncertainty estimate of the active mount.
4. The adaptive control method of active suspension according to claim 3, characterized in that: The step of obtaining the control value of the active mount according to the estimated value of the system observer, the output value of the active mount and the control target value comprises: The estimated value of the system observer is set to include a reference state trajectory under the control target value, including a reference state trajectory as x 1d and the reference state trajectory x 2d , the output value of the active mount includes the actual state trajectory corresponding to the control target value, including the actual state trajectory x1 and the actual state trajectory x2; Based on the reference state trajectory x 1d , reference state trajectory x 2d , actual state trajectory x1 and actual state trajectory x2, construct the control value of the active suspension.
5. The adaptive control method of active suspension according to claim 4, characterized in that: The reference state trajectory is x 1d , reference state trajectory x 2d , the actual state trajectory x1 and the actual state trajectory x2, construct the control value of the active mount, including: Determine the actual state trajectory x1 and the reference state trajectory x 1d The error between them is e1 = x1-x 1d , and expressed as: Set the reference state trajectory x 2d for: Determine the actual state trajectory x2 and the reference state trajectory x 2d The error between And expressed as: make To construct the actuator force of the active suspension system as follows:
6. An adaptive control device for active suspension, characterized in that: include: An acquisition module, used for acquiring an estimated value of a system observer of an active mount, an output value of the active mount, and a control target value; a control module, configured to obtain an actuator force of the active mount according to an estimated value of the system observer and an output value of the active mount; Adaptively controlling the active suspension according to the actuator force and external excitation; The system observer is constructed based on a state space expression of the state space equation of the active mount.
7. The adaptive control device for active suspension according to claim 6, characterized in that: The state space expression of the active mount is constructed as follows: The system state quantities of the active suspension system are set as follows: make Then the state space expression of the active suspension is obtained: Among them, x1, x2, and x3 are the system state quantities of the active suspension, respectively. m and x s They represent the vertical displacement of the powertrain and the unsprung mass, M p represents the powertrain mass, k m 、c m are the stiffness and damping of the active mount respectively, is the state quantity of the uncertainty estimate of the active mount, b0 is the actuator force parameter, and u represents the actuator force.
8. An adaptive control system for active suspension, characterized in that: The invention comprises an active suspension and an adaptive control device, wherein the adaptive control device is connected to the active suspension, and the adaptive control device is used to implement the steps of the adaptive control method of the active suspension according to any one of claims 1 to 5.
9. A vehicle, characterized in that: An adaptive control system comprising an active mount as claimed in claim 8.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the adaptive control method of the active mount according to any one of claims 1 to 5 are implemented.