A method and system for active identification and control collaborative optimization of key parameters of a drive-by-wire chassis

Abstract

The present application relates to a kind of drive-by-wire chassis key parameter active identification and control collaborative optimization method and system, for the low precision of parameter identification caused by insufficient regular driving excitation, and active excitation exists strong intervention, the problem of vehicle stability decline, establish the mapping relationship of target motion state and generalized force, construct the control distribution constraint of redundant actuator, realize the whole vehicle multi-objective motion control in the main space of constraint model, in the zero space, design the estimator of active enhancement of excitation condition, and using the orthogonality of main space and zero space, realize the collaborative optimization of control and estimation task through completely decoupled hierarchical control framework.For model uncertainty and zero space projection error, construct lumped disturbance compensation mechanism, improve the robustness of system under complex environment.The present application can quickly and accurately identify the key parameters of vehicle-road under the premise of not changing the trajectory and attitude of vehicle, ensure vehicle stability and trajectory tracking performance.

Description

Technical Field

[0001] This invention relates to the field of redundant chassis control, and in particular to a method and system for active identification and coordinated optimization of key parameters of a drive-by-wire chassis. Background Technology

[0002] As a core component of intelligent vehicles, the stability and reliability of the drive-by-wire chassis system are crucial to vehicle safety. With the evolution of chassis architecture towards fully decoupled configurations, the resulting redundant degrees of freedom offer new opportunities for actively enhancing perception and control performance. The drive-by-wire chassis system integrates actuators such as steer-by-wire, brake-by-wire, drive-by-wire, and suspension-by-wire through an electronic control system, significantly improving vehicle handling and active safety. However, under normal driving and low-excitation conditions, the accuracy of chassis dynamic parameters and actuator state recognition is limited, making it difficult to achieve continuous and accurate online identification of key parameters, thus affecting overall vehicle performance.

[0003] Existing vehicle control-estimation frameworks struggle to provide sufficient excitation conditions under weak excitation or small sideslip angles, leading to decreased accuracy in estimating internal actuator states and external environmental parameters, thus impacting overall vehicle performance and safety. This problem is particularly pronounced in autonomous vehicles, thus necessitating an algorithmic framework that can fully utilize chassis redundancy degrees of freedom to achieve active online estimation without compromising overall vehicle handling performance.

[0004] Chinese invention patent CN121232608B discloses a robust control method for a decoupled chassis oriented to road adhesion disturbances. The method includes: based on the dynamic characteristics of a decoupled chassis system where each wheel's drive, braking, and steering can be independently controlled, analyzing the attainability of different control action combinations to achieve the target force through demand force reachability analysis, and constructing a feasible domain for the control action decomposition of the target force under ideal conditions; calculating the impact of different longitudinal and lateral control actions on the target force and the control accuracy of the vehicle's motion state under road adhesion disturbances, and constructing a model of the impact of adhesion coefficient disturbances on the overall vehicle state under disturbed conditions; designing a robust control algorithm for the decoupled chassis based on a multi-objective optimization problem framework, utilizing the redundant tire force control mechanism of the decoupled steering chassis to generate control actions that minimize the impact of adhesion disturbances on the overall vehicle state. This invention suppresses uncertainty propagation through a multi-objective optimization control framework, adapts to various complex road disturbance conditions, and achieves high-precision dynamic control. However, there are still problems such as insufficient excitation in the working conditions leading to limited accuracy of chassis key parameter identification, low sensitivity of the existing vehicle control estimation framework under weak excitation conditions, and inability to simultaneously take into account the output of the main control task and the accuracy of parameter identification.

[0005] In summary, there is currently a lack of a method and system for the active identification and coordinated optimization of key parameters of drive-by-wire chassis to solve or partially solve the above problems. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art by providing a method and system for active identification and coordinated optimization of key parameters of drive-by-wire chassis, so as to solve or partially solve the problems of insufficient excitation in the working conditions leading to limited identification accuracy of key chassis parameters, low sensitivity of existing vehicle control estimation framework under weak excitation conditions, and inability to simultaneously take into account the output of the main control task and the accuracy of parameter identification.

[0007] The objective of this invention can be achieved through the following technical solutions: According to one aspect of the present invention, a method for active identification and coordinated optimization of key parameters of a drive-by-wire chassis is provided, comprising: S1. Establish a control mapping relationship between the actuators of the drive-by-wire chassis system and the vehicle motion state, wherein the vehicle motion state is the main control task. Perform null space decomposition on the control matrix corresponding to the control mapping relationship to obtain a null space projection matrix that is orthogonal to the control subspace of the main control task. S2. The dynamic deviations caused by system model errors and null space leakage are uniformly represented as lumped disturbances. A lumped disturbance observer is constructed to estimate the lumped disturbances online to obtain the disturbance estimate. S3. Based on the null space projection matrix, a null space hierarchical control framework is constructed, including an upper-level controller and a lower-level active excitation controller. The upper-level controller generates the main control input and introduces the disturbance estimate as a model compensation term into the upper-level controller. The lower-level active excitation controller constructs a Fisher information matrix to adjust the main control input. S4. Based on the analysis of the road surface adhesion coefficient parameters, the influence of the slip angle on the Fisher information matrix in the tire force model is determined, the slip angle sensitive interval that maximizes the Fisher information matrix is ​​determined, and a recursive state space model of actuator state and parameter sensitivity is constructed based on the first-order actuator dynamics model. S5. Based on the side slip angle sensitive interval and the recursive state space model of the actuator state and parameter sensitivity, an active loading control optimization model is constructed. The optimization index for maximizing the actuator parameter estimation sensitivity and the optimization index for tracking the desired tire side slip angle are solved. The adjusted main control input is constrained to achieve control of the motion of the redundant chassis vehicle.

[0008] As a preferred technical solution, the construction process of the Fisher information matrix includes: Based on the relationship between the vehicle's parameters to be estimated and the system output, a Fisher information matrix is ​​established to characterize the estimation accuracy of the parameters to be estimated. The Fisher information matrix is ​​then calculated for the parameters to be estimated, and the Fisher information matrix is ​​maximized under null space constraints by adjusting the control input.

[0009] As a preferred technical solution, the step of determining the side slip angle sensitive range includes: Establish a restoring torque model that characterizes the relationship between tire mechanical properties and road adhesion coefficient; Based on the aforementioned self-aligning torque model, the influence of sideslip angle variation on the Fisher information matrix in the tire force model is analyzed, and the sideslip angle sensitive interval that maximizes the Fisher information matrix is ​​determined.

[0010] As a preferred technical solution, the parameter sensitivity of the road surface adhesion coefficient is analyzed based on the self-aligning moment model, and an offline sensitivity mapping diagram corresponding to the road surface adhesion coefficient and the slip angle is constructed. The maximum sensitivity slip angle corresponding to the current road surface adhesion coefficient is selected from the mapping diagram as a reference value for the slip angle sensitivity range. .

[0011] As a preferred technical solution, the construction of the recursive state-space model of the actuator state and parameter sensitivity includes: Define an actuator state model, define the parameter sensitivity that characterizes the actuator model state to the actuator time constant, and transform the influence of the actuator state on the Fisher information matrix into a linear sensitivity expression. The parameter sensitivity is expressed as a linear recursive state-space form, resulting in a recursive state-space model of the actuator state and parameter sensitivity.

[0012] As a preferred technical solution, the active loading control optimization model is as follows: in, This represents the optimization metric that maximizes the sensitivity of actuator parameter estimation. This represents the optimization index for tracking the desired tire slip angle. Optimizing both is equivalent to maximizing the Fisher information matrix. To predict the length of the time domain, To characterize the effect of actuator model state on actuator time constant The parameter sensitivity, Sampling time, To predict the time-domain step size index, This is the weight matrix. For the first The slip angle of each wheel, This is a reference value for the side slip angle sensitive range.

[0013] As a preferred technical solution, the lower-level active excitation controller adjusts the main control input, and the constructed optimization function is expressed as follows: in, For the weighting coefficients corresponding to the estimation task, In order to be with the first A cost function related to the active estimation subtask. This is a null subspace orthogonal to the main control task. To actually implement the corner increment, For time step index, For the current optimization of the initial time, For the actual execution of the turn, The augmented state variables for vehicle and actuator states. To augment the system state transition matrix, To augment the system input matrix, Input the increment to the main task controller. Input the increment to the secondary task controller. To predict the length of the time domain, To estimate the number of subtasks for the active stimulus controller, For the control quantity weight matrix, To control the incremental weight matrix, The predicted time domain length is used for the active excitation controller.

[0014] As a preferred technical solution, the cost function Represented as: in, For parameters The relevant first A Fisher information matrix, It is a weighted matrix.

[0015] According to another aspect of the present invention, a system for active identification and coordinated optimization of key parameters of a drive-by-wire chassis is provided. The system is used to execute the method for active identification and coordinated optimization of key parameters of a drive-by-wire chassis as described above. The system includes multiple actuators, a main task control module, a parameter excitation control module, a zero-space decoupling module, an external environment parameter estimation module, and an internal state parameter estimation module.

[0016] As a preferred technical solution, the multiple actuators are electrically connected to the vehicle motion state of the drive-by-wire chassis through a control mapping relationship; The main task control module is connected to the multi-actuator signal and is used to generate the main control input that meets the requirements of vehicle stability control and motion control. The parameter excitation control module is connected to the multi-actuator signal and is used to generate secondary control inputs that enhance parameter identifiability. The null space decoupling module is signal-connected to the main task control module and the parameter excitation control module, and is used to perform null space decomposition on the control matrix corresponding to the control mapping relationship to obtain the null space projection matrix. The external environment parameter estimation module is connected to the signals of the multi-actuator and vehicle state sensor, and is used to estimate the road surface adhesion coefficient online based on the vehicle motion response. The internal state parameter estimation module is connected to the multi-actuator signal and is used to estimate the actuator time constant online based on the actuator response.

[0017] Compared with the prior art, the present invention has at least one of the following beneficial effects: (1) This invention utilizes the redundancy characteristics of the drive-by-wire chassis to construct a primary and secondary task decoupling mechanism based on zero-space control theory, and establishes a zero-space hierarchical control framework for estimation tasks to achieve deep integration of control and parameter identification functions. Without interfering with the main task of the vehicle, the active excitation signal is enhanced by multi-actuator collaborative control based on zero-space control, which effectively solves the problem of insufficient excitation in the working condition leading to limited accuracy of chassis key parameter identification. This achieves the goal of improving the accuracy of key parameter identification while ensuring that the vehicle stability control and trajectory tracking performance are not affected.

[0018] (2) This invention determines the sensitive interval of the slip angle and the recursive state space model of the actuator state and parameter sensitivity by estimating the road surface adhesion coefficient and the actuator characteristic parameters, respectively, and constructs an active loading control optimization model. This realizes the equivalent linearization of the original nonlinear optimization objective, solves the problem of low sensitivity of the existing vehicle control estimation framework under weak excitation conditions, and achieves the technical effect of enhancing the excitation signal of key parameters and improving the Fisher information matrix value while ensuring the main task performance of the vehicle. This enables the fast, accurate and online estimation of actuator parameters and road surface adhesion coefficient.

[0019] (3) The present invention calculates the Fisher information matrix of the parameter to be estimated by constraining the operation of the lower active excitation controller in the upper controller, and maximizes the Fisher information matrix under the zero space constraint by adjusting the control input. This solves the problem that it is impossible to simultaneously take into account the output of the main control task and the accuracy of parameter identification. It achieves the technical effect of improving the identifiability and estimation accuracy of the parameter to be estimated without interfering with the output of the upper main control task. Attached Figure Description

[0020] Figure 1 This is a schematic flowchart of the method of the present invention; Figure 2 A zero-space diagram illustrating constraint allocation for decoupled chassis actuator control; Figure 3 A null-space hierarchical control framework diagram for estimation tasks; Figure 4 This is a schematic diagram illustrating the sensitivity of tire lateral force and self-aligning torque to road surface adhesion. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] Example 1 To address the problems existing in the prior art, this embodiment provides a method for active identification and coordinated optimization of key parameters of a drive-by-wire chassis, such as... Figure 1 As shown, it specifically includes: S1. Utilizing the actuator redundancy characteristics of the drive-by-wire chassis system, and based on zero-space control theory, vehicle motion control is defined as the main control task, and a control matrix is ​​established between the main control task and the chassis actuator input. By performing zero-space decomposition on the control matrix, a zero-space projection matrix orthogonal to the control subspace of the main control task is constructed, so that the control input corresponding to the additional control task does not affect the main control task after being mapped by the zero-space projection matrix. Thus, at the structural level, decoupling and coordinated control between the main and secondary tasks of the chassis system are achieved.

[0023] First, establish the control mapping relationship between actuator input and vehicle motion state in the drive-by-wire chassis system, take vehicle motion control as the main control task, and establish the control matrix between the main control task and chassis actuator input.

[0024] in, The system dynamic equations are as follows: For vehicle status quantities. To control the input, The state matrix, This is the control matrix.

[0025] Secondly, the control mapping matrix corresponding to the main control task is decomposed into null space to obtain a null space subspace that is orthogonal to the main control task.

[0026] in, The null projection matrix satisfies , It is an identity matrix.

[0027] Redundant control degrees of freedom for additional control tasks are retained in the zero-space subspace to form redundant control channels that do not affect the output of the main control task, thereby achieving decoupling between multiple control tasks of the chassis system.

[0028] in, Control actions for the main task. This is a secondary task control action, and the secondary task control action satisfies... .

[0029] like Figure 2 The diagram shown is a zero-space schematic of the control allocation constraints for a decoupled chassis actuator. This schematic visually represents the equivalence of different actuator combinations in the zero space, meaning that multiple actuator inputs, under the condition of satisfying the control allocation constraints, do not affect the generalized forces of the vehicle, and therefore do not change the vehicle's dynamic response.

[0030] S2. In the zero-space control framework, the zero-space projection matrix is ​​calculated from the nominal model of the system. Due to model uncertainties and modeling errors between the actual vehicle system and the nominal model, these errors are propagated to the zero-space projection matrix, thus affecting the theoretical decoupling conditions. It is difficult to strictly meet the requirements, thus leading to secondary task control input. The leakage channel can affect the dynamics of the main mission vehicle. To mitigate this impact, this embodiment constructs a lumped disturbance observer to uniformly estimate and compensate for model uncertainties and null-space coupling errors.

[0031] Let the matrix of the real system be: in, The true state matrix of the system, The actual input matrix of the system, These represent the perturbations in the state and input matrices caused by model uncertainty, parameter estimation errors, and unmodeled dynamics, respectively.

[0032] Simultaneously considering the model error of the null space projection matrix, it is expressed as: in, This refers to the null space projection error caused by model uncertainty.

[0033] Substitute it into the system dynamics model and utilize the decoupling conditions satisfied by the nominal model. The dynamic equations of the actual system can be obtained as follows: Among them, system lumped disturbance It can be represented as: The disturbance terms are as follows: , which is the leakage disturbance term caused by zero-space projection error, has a large amplitude when the model error is large; , which is the dynamic error term caused by the uncertainty of the main task model; This is a higher-order term caused by the subtask control channel model error, and it is usually relatively small.

[0034] Therefore, the null projection error and model uncertainty will jointly affect the main task dynamics model, thus impacting the main task control performance. Therefore, the above disturbances are uniformly represented as lumped disturbances. And construct a perturbation observer for estimation.

[0035] The vehicle dynamics model is represented as: in, For system state variables, and This is the nominal system matrix.

[0036] Furthermore, the disturbance Consider it a slowly changing signal By augmenting the state of the system, we obtain an augmented system, which is represented as: Based on the above augmented system, a perturbation observer is constructed: in, , This is the disturbance estimate. and The observer gain matrix is... For the system output matrix, This is the system measurement output.

[0037] After obtaining the disturbance estimate, the disturbance estimate is introduced into the main task controller as a model compensation term to correct the nominal vehicle dynamics model online, thereby compensating for the dynamic deviation caused by model uncertainty and null projection error, and improving the prediction accuracy and robustness of the main task control.

[0038] S3. Construct a zero-space hierarchical control framework, including an upper-level controller and a lower-level active excitation controller. The upper-level controller is used to realize the main task of vehicle motion control of the chassis system and is responsible for generating the main control input that meets the requirements of vehicle stability control and motion control. The lower-level active excitation controller aims to improve the information content of the parameters to be estimated. It constructs an information matrix related to the parameters to be estimated and adjusts the main control input under the constraints of the upper-level controller to enhance the system's ability to identify the parameters.

[0039] Figure 3 The overall architecture and data flow relationships of the proposed control method are presented. The driver's steering input first passes through a reference two-degree-of-freedom vehicle model to generate a reference yaw rate and a reference sideslip angle. A parameter estimator, based on the vehicle state and control input, estimates key parameters such as actuator characteristic parameters and road adhesion coefficient in real time; simultaneously, it uses the estimated parameters to calculate a Fisher information matrix to assess the observability of the parameters. Based on the aforementioned reference state, the actual vehicle state, and the lumped disturbance output by the disturbance observer, the handling and stability controller calculates the main task control quantity to ensure vehicle handling stability. Subsequently, the active excitation controller generates an excitation signal by maximizing the Fisher information matrix of the parameters to be estimated. This excitation signal is embedded into the null space of the main task control through null projection, enhancing parameter identifiability without affecting the performance of the main task. Finally, the main task control quantity and the null space excitation control quantity are superimposed and applied to the steering actuator, driving the vehicle system to form a closed-loop control.

[0040] The optimization function for the main task of vehicle motion control in the chassis system is designed as follows: in, The main task controls the incremental sequence. To predict the length of the time domain, Output for the main task. For predicting the step sequence index, At the current sampling time, Reference quantity for the main task The weight matrix for the main task's output error. It is an orthogonal matrix of the null space projection matrix. To actually implement the corner increment, The main task control weight matrix, The zero-space control weight matrix, The main task controls the incremental weight matrix. This represents the state quantity after augmenting the vehicle state and actuator state. For the actual execution of the turn, To augment the system state transition matrix, To augment the system input matrix, To control the length of the time domain, These are constraints.

[0041] Based on the relationship between the parameters to be estimated and the system output, a Fisher information matrix characterizing the estimation accuracy of the parameters is established. The Fisher information matrix is ​​then calculated online, and an active excitation controller is established with its maximization as the objective. The Fisher information matrix is ​​expressed as: in, For the corresponding number The total amount of information about the parameters accumulated within each sampling time period. For the parameters to be estimated, To measure the noise variance, For measurement data Relative to the parameter to be estimated The sensitivity matrix.

[0042] Under the constraints of the upper-level controller, the lower-level active excitation controller calculates the Fisher Information Matrix (FIM) for the parameters to be estimated. By adjusting the control input, it maximizes the FIM under zero-space constraints, thereby improving the identifiability and estimation accuracy of the parameters without interfering with the output of the upper-level main control task. The optimization function of the active excitation controller is designed as follows: in, For the weighting coefficients corresponding to the estimation task, In order to be with the first A cost function related to the active estimation subtask. The main task control quantity. For secondary task control variables. To estimate the number of subtasks for the active stimulus controller, The predicted time domain length is used for the active excitation controller.

[0043] in, For parameters The relevant first A Fisher information matrix, It is a weighted matrix.

[0044] S4. For the nonlinear optimization problem with the goal of maximizing the road surface adhesion coefficient and the Fisher information matrix of actuator characteristic parameters, a linear active loading control optimization model is designed through equivalent linearization and constraint reconstruction.

[0045] For the road surface adhesion coefficient parameter, the influence of the sideslip angle on the Fisher information matrix in the tire force model is analyzed, and the sideslip angle sensitive interval that maximizes the Fisher information matrix is ​​determined. The specific steps are as follows: A tire lateral force model and a self-aligning torque model based on the Magic Tire model are established to describe the relationship between tire mechanical properties and road adhesion coefficient. Specifically, the established tire lateral force model and self-aligning torque model of the Magic Tire model are as follows: in, This refers to the lateral force of the tire. The road surface adhesion coefficient, This is the standard reference value (usually 1.0). , , and For the corresponding model fitting parameters, This refers to the tire slip angle. This is the tire return torque. , , and These are the parameters for fitting the corresponding model.

[0046] The tire model was normalized, and the parameter sensitivity of the normalized tire lateral force model and the self-aligning moment model to the road adhesion coefficient was analyzed. The sensitivity characteristics of the two were compared, and it was determined that the self-aligning moment model has a higher information gain in characterizing the road adhesion coefficient.

[0047] Normalized Magic Tire Lateral Force Model With the normalizing torque model as follows: The parameter sensitivity of the normalized tire lateral force model and the restoring moment model to the road adhesion coefficient is as follows: in, For partial differential operators, This represents the normalized partial derivative of the lateral force with respect to the adhesion coefficient. This is the partial derivative of the normalized restoring torque with respect to the adhesion coefficient.

[0048] Figure 4 This paper presents the normalized road adhesion sensitivity curves of tire lateral force and self-aligning torque as a function of sideslip angle under different road adhesion coefficients. Figure 4It can be seen that within the small sideslip angle range, the normalized sensitivity of the self-aligning torque is significantly higher than that of the tire lateral force, and this sensitivity advantage further increases as the road adhesion coefficient decreases. Conversely, the sensitivity of the tire lateral force approaches zero within this range, only demonstrating effective adhesion sensitivity under larger sideslip angle excitation conditions. These results indicate that the tire self-aligning torque has a stronger parameter identification capability for the road adhesion coefficient under small sideslip angle and weak excitation conditions. Therefore, the tire self-aligning torque is selected as the core observation for the active estimation subtask in active estimation control.

[0049] Based on the self-aligning moment model, the influence of sideslip angle changes on the Fisher information matrix in the tire force model is analyzed, thereby determining the sideslip angle sensitivity interval that maximizes the Fisher information matrix. The sensitivity of the tire self-aligning moment model to the road adhesion coefficient is analyzed, and an offline sensitivity mapping diagram between the road adhesion coefficient and the sideslip angle is constructed. During actual operation, the maximum sensitivity sideslip angle corresponding to the current road adhesion coefficient in the mapping diagram is selected as a reference value for the sideslip angle sensitivity interval, with a set value of [value missing]. .

[0050] For the dynamic model of a first-order actuator, a recursive state-space model of actuator state and parameter sensitivity is constructed, and the influence of actuator state on Fisher information matrix is ​​transformed into a linear sensitivity expression.

[0051] The executor model is defined as follows: in, For the first The actual response status of each actuator , , which are the parameters of the discretized actuator model. The sampling period of the controller, The time constant of the actuator, For the first The instruction input for each executor.

[0052] definition To characterize the actuator model state with respect to the actuator time constant The parameter sensitivity is expressed in the following linear recursive state-space form: in, The parameter sensitivity state transition matrix is... This is the input matrix for the parameter sensitivity.

[0053] S5. Based on the side slip angle sensitive interval and parameter sensitivity recursive model, a linear active loading control optimization model is constructed, which is expressed as follows: in, This represents the optimization metric that maximizes the sensitivity of actuator parameter estimation. This represents the optimization index for tracking the desired tire slip angle. Optimizing both is equivalent to maximizing the Fisher information matrix. To predict the time-domain step size index, To optimize the indicator weight matrix, For the first The actual slip angle of each tire.

[0054] By solving the active loading control optimization model, we obtain the optimization indexes for maximizing the sensitivity of actuator parameter estimation and the optimization indexes for tracking the desired tire slip angle. We then constrain the adjusted main control input to achieve control of the motion of the redundant chassis vehicle.

[0055] Example 2 In view of the above-mentioned method for active identification and coordinated optimization of key parameters of a drive-by-wire chassis, this embodiment provides a system for active identification and coordinated optimization of key parameters of a drive-by-wire chassis, which is used to execute the above-mentioned method for active identification and coordinated optimization of key parameters of a drive-by-wire chassis. Specifically, it includes multiple actuators, a main task control module, a parameter excitation control module, a zero-space decoupling module, an external environment parameter estimation module, and an internal state parameter estimation module.

[0056] Among them, the multi-actuator and the vehicle motion state of the drive-by-wire chassis are electrically connected through a control mapping relationship; The main task control module is connected to the multi-actuator signal and is used to generate the main control input that meets the requirements of vehicle stability control and motion control. The parameter excitation control module is connected to the multi-actuator signal to generate secondary control inputs that enhance parameter identifiability; The null space decoupling module is signal-connected to the main task control module and the parameter excitation control module, and is used to perform null space decomposition on the control matrix corresponding to the control mapping relationship to obtain the null space projection matrix. The external environment parameter estimation module is connected to the signals of multiple actuators and vehicle state sensors to estimate the road adhesion coefficient online based on the vehicle motion response; The internal state parameter estimation module is connected to the multi-actuator signal and is used to estimate the actuator time constant online based on the actuator response.

[0057] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for active identification and coordinated optimization of key parameters of a drive-by-wire chassis, characterized in that, The method specifically includes: S1. Establish a control mapping relationship between the actuators of the drive-by-wire chassis system and the vehicle motion state, wherein the vehicle motion state is the main control task. Perform null space decomposition on the control matrix corresponding to the control mapping relationship to obtain a null space projection matrix that is orthogonal to the control subspace of the main control task. S2. The dynamic deviations caused by system model errors and null space leakage are uniformly represented as lumped disturbances. A lumped disturbance observer is constructed to estimate the lumped disturbances online to obtain the disturbance estimate. S3. Based on the null space projection matrix, a null space hierarchical control framework is constructed, including an upper-level controller and a lower-level active excitation controller. The upper-level controller generates the main control input and introduces the disturbance estimate as a model compensation term into the upper-level controller. The lower-level active excitation controller constructs a Fisher information matrix to adjust the main control input. S4. Based on the analysis of the road surface adhesion coefficient parameters, the influence of the slip angle on the Fisher information matrix in the tire force model is determined, the slip angle sensitive interval that maximizes the Fisher information matrix is ​​determined, and a recursive state space model of actuator state and parameter sensitivity is constructed based on the first-order actuator dynamics model. S5. Based on the side slip angle sensitive interval and the recursive state space model of the actuator state and parameter sensitivity, an active loading control optimization model is constructed. The optimization index for maximizing the actuator parameter estimation sensitivity and the optimization index for tracking the desired tire side slip angle are solved. The adjusted main control input is constrained to achieve control of the motion of the redundant chassis vehicle.

2. The method for active identification and coordinated optimization of key parameters of a drive-by-wire chassis according to claim 1, characterized in that, The construction process of the Fisher information matrix includes: Based on the relationship between the vehicle's parameters to be estimated and the system output, a Fisher information matrix is ​​established to characterize the estimation accuracy of the parameters to be estimated. The Fisher information matrix is ​​then calculated for the parameters to be estimated, and the Fisher information matrix is ​​maximized under null space constraints by adjusting the control input.

3. The method for active identification and coordinated optimization of key parameters of a drive-by-wire chassis according to claim 1, characterized in that, The steps for determining the side slip angle sensitive range include: Establish a restoring torque model that characterizes the relationship between tire mechanical properties and road adhesion coefficient; Based on the aforementioned self-aligning torque model, the influence of sideslip angle variation on the Fisher information matrix in the tire force model is analyzed, and the sideslip angle sensitive interval that maximizes the Fisher information matrix is ​​determined.

4. The method for active identification and coordinated optimization of key parameters of a drive-by-wire chassis according to claim 3, characterized in that, The sensitivity of the road surface adhesion coefficient parameters is analyzed based on the aforementioned aligning moment model, and an offline sensitivity mapping diagram between the road surface adhesion coefficient and the slip angle is constructed. The maximum sensitivity slip angle corresponding to the current road surface adhesion coefficient is selected from the mapping diagram as a reference value for the slip angle sensitivity range. .

5. The method for active identification and coordinated optimization of key parameters of a drive-by-wire chassis according to claim 1, characterized in that, The construction of the recursive state-space model of the actuator state and parameter sensitivity includes: Define an actuator state model, define the parameter sensitivity that characterizes the actuator model state to the actuator time constant, and transform the influence of the actuator state on the Fisher information matrix into a linear sensitivity expression. The parameter sensitivity is expressed as a linear recursive state-space form, resulting in a recursive state-space model of the actuator state and parameter sensitivity.

6. The method for active identification and coordinated optimization of key parameters of a drive-by-wire chassis according to claim 1, characterized in that, The active loading control optimization model is characterized as follows: in, This represents the optimization metric that maximizes the sensitivity of actuator parameter estimation. This represents the optimization index for tracking the desired tire slip angle. Optimizing both is equivalent to maximizing the Fisher information matrix. To predict the length of the time domain, To characterize the effect of actuator model state on actuator time constant The parameter sensitivity, Sampling time, To predict the time-domain step size index, This is the weight matrix. For the first The slip angle of each wheel, This is a reference value for the side slip angle sensitive range.

7. The method for active identification and coordinated optimization of key parameters of a drive-by-wire chassis according to claim 1, characterized in that, The lower-level active excitation controller adjusts the main control input, and the constructed optimization function is expressed as follows: in, For the weighting coefficients corresponding to the estimation task, In order to be with the first A cost function related to the active estimation subtask. This is a null subspace orthogonal to the main control task. To actually implement the corner increment, For time step index, For the current optimization of the initial time, For the actual execution of the turn, The augmented state variables for vehicle and actuator states. To augment the system state transition matrix, To augment the system input matrix, Input the increment to the main task controller. Input the increment to the secondary task controller. To predict the length of the time domain, To estimate the number of subtasks for the active stimulus controller, For the control quantity weight matrix, To control the incremental weight matrix, The predicted time domain length is used for the active excitation controller.

8. The method for active identification and coordinated optimization of key parameters of a drive-by-wire chassis according to claim 7, characterized in that, The cost function Represented as: in, For parameters The relevant first A Fisher information matrix, It is a weighted matrix.

9. A system for active identification and coordinated optimization of key parameters of a drive-by-wire chassis, characterized in that, The system is used to execute the active identification and control collaborative optimization method for key parameters of the drive-by-wire chassis as described in any one of claims 1-8. The system includes multiple actuators, a main task control module, a parameter excitation control module, a zero-space decoupling module, an external environment parameter estimation module, and an internal state parameter estimation module.

10. The drive-by-wire chassis key parameter active identification and control collaborative optimization system according to claim 9, characterized in that, The multi-actuator is electrically connected to the vehicle motion state of the drive-by-wire chassis through a control mapping relationship; The main task control module is connected to the multi-actuator signal and is used to generate the main control input that meets the requirements of vehicle stability control and motion control. The parameter excitation control module is connected to the multi-actuator signal and is used to generate secondary control inputs that enhance parameter identifiability. The null space decoupling module is signal-connected to the main task control module and the parameter excitation control module, and is used to perform null space decomposition on the control matrix corresponding to the control mapping relationship to obtain the null space projection matrix. The external environment parameter estimation module is connected to the signals of the multi-actuator and vehicle state sensor, and is used to estimate the road surface adhesion coefficient online based on the vehicle motion response. The internal state parameter estimation module is connected to the multi-actuator signal and is used to estimate the actuator time constant online based on the actuator response.

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