Vehicle robust control method and device based on online estimation of self-vehicle state noise

Abstract

The invention discloses a vehicle robust control method and device based on online estimation of self-vehicle state noise, and belongs to the field of automatic drive.The method comprises the steps that noise in the running process of an intelligent vehicle is estimated through a real-time auto-covariance least square method, and vehicle state estimation errors are dynamically updated; determining an estimation error set of a vehicle state uncertainty range according to a preset confidence interval of the vehicle state estimation error; constructing a linear programming problem by using the estimation error set, and calculating a minimum robust positive invariant set; and the minimum robust positive invariant set obtained through calculation is applied to a tube-based model prediction control algorithm, and the vehicle is controlled. According to the method, a vehicle state estimation error is dynamically updated based on a real-time auto-covariance least square method, an error boundary is defined according to distribution of the vehicle state estimation error, an uncertainty range of a vehicle state is described, an error system is restrained in a minimum robust positive invariant set, and safety accidents caused by external disturbance or model uncertainty are avoided.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving, and in particular to a vehicle robust control method and device based on online estimation of vehicle state noise. Background Art

[0002] Autonomous driving technology can improve road traffic safety and also has far-reaching significance for promoting the innovation and development of intelligent transportation systems and related industries. During the driving process of intelligent vehicles, the complexity of the driving environment and the uncertainty of roads and sensors require the vehicle control system to have high robustness to cope with the ever-changing driving environment and ensure that the vehicle can drive safely under any circumstances. Vehicle state estimation is the basis for realizing robust control of intelligent vehicles. Based on accurate vehicle state estimation information, the control system can make effective decisions.

[0003] Currently, many vehicle state estimation methods are implemented based on Kalman filters, which recursively update the vehicle state by using sensor information and predictions from the system dynamic model. However, Kalman filters rely on the setting of a priori noise covariance matrices, which are often difficult to obtain or cannot be accurately modeled in practical applications. When the noise characteristics are unknown or change, traditional Kalman filtering methods will face the problem of reduced estimation accuracy or filter divergence. Summary of the invention

[0004] The main purpose of the present invention is to avoid safety accidents caused by external disturbances or model uncertainties.

[0005] The present invention provides a vehicle robust control method based on online estimation of vehicle state noise, comprising: using real-time autocovariance least squares method to estimate the noise during the operation of an intelligent vehicle, and dynamically updating the vehicle state estimation error; determining an estimation error set of the vehicle state uncertainty range according to a preset confidence interval of the vehicle state estimation error; using the estimation error set to construct a linear programming problem and calculate a minimum robust positive invariant set; applying the calculated minimum robust positive invariant set to a tube-based model predictive control algorithm to control the vehicle.

[0006] In the vehicle robust control method based on online estimation of vehicle state noise of the present invention, the noise in the operation process of the intelligent vehicle is estimated by using the real-time autocovariance least squares method, including:

[0007] Convert the state space equation of the vehicle system into the state space equation based on the new information;

[0008] For each time window, if the current window is the first window, the estimated error covariance matrix, process noise covariance matrix and observation noise covariance matrix initialized according to the empirical information are assigned to the current window; otherwise, the process noise covariance matrix and observation noise covariance matrix at the initial moment of the current window are determined according to the estimated value of the previous window;

[0009] In each time window, the Kalman filter is used to update the vehicle state and the estimated error covariance matrix at each moment until the window ends. The autocovariance sequence is calculated based on the new information obtained from the two state-space equations in the current window, and a least squares problem is constructed to estimate the process noise covariance matrix and the observation noise covariance matrix.

[0010] In the vehicle robust control method based on online estimation of vehicle state noise of the present invention, the state space equation includes:

[0011]

[0012] The state space equation based on the new information includes:

[0013]

[0014] Among them, k represents the kth moment, x is the state vector of the system, u is the control input of the system, and y is the system observation in the state space. is the estimation error, is the estimated value of the state quantity at the kth moment, is the new information, A, B, C are the coefficient matrices of the system, w and v are the process noise and observation noise respectively, L is the steady-state Kalman gain, I is the identity matrix,

[0015] In the vehicle robust control method based on online estimation of vehicle state noise of the present invention, the preset confidence interval includes 99.7%.

[0016] In the vehicle robust control method based on online estimation of vehicle state noise of the present invention, the innovation calculation autocovariance sequence of the innovation calculation autocovariance sequence obtained based on the current window based on two state space equations includes:

[0017] The autocovariance sequence Ξ(N) of length N in the nth window is calculated according to the following formula: n :

[0018]

[0019] Among them, α i is the forgetting factor, and the calculation formula includes:

[0020]

[0021] Among them, m is a parameter, and different values ​​are selected according to the characteristics of different systems. The larger m is, the smaller the weight of early new information in the window is.

[0022] In the vehicle robust control method based on online estimation of vehicle state noise of the present invention, the least squares problem is constructed to estimate the process noise covariance matrix and the observation noise covariance matrix, including: Compute the covariance matrix of process noise and observation noise, where:

[0023]

[0024] in, is A in the nth window LS , W n With V n is the estimated value of the process noise covariance matrix and the observation noise covariance matrix in the nth window, S n-1 is the weight matrix. When n=1, S n-1 is the unit matrix. When n>1, S n-1 The calculation formula is:

[0025]

[0026] The parameter matrices are:

[0027]

[0028] Among them, the process noise covariance matrix W = E(ww T ), the observation noise covariance matrix V = E(vv T ).

[0029] In the vehicle robust control method based on online estimation of vehicle state noise of the present invention, the estimated error set is used to construct a linear programming problem and calculate the minimum robust positive invariant set, including:

[0030] Utilization control rate The actual vehicle system is divided into a nominal system and an error system, and the state space equation of the error system is obtained:

[0031] e k+1 =(A+BK)e k +w k

[0032] in, x k and Represent the state quantities of the actual system and the nominal system respectively. The state quantities of the system include the lateral velocity v y , yaw rate Tracking Error d and heading error

[0033] Calculate F according to the calculation formula of the minimum robust positive invariant set ∞ :

[0034]

[0035] Among them, F ∞ is the minimum robust positive invariant set, Θ is the step estimation error set, ⊕ is the Minkowski sum between the sets, A K =A+BK, K is the state feedback gain matrix.

[0036] In the vehicle robust control method based on online estimation of vehicle state noise of the present invention, the minimum robust positive invariant set calculated is applied to the tube-based model predictive control algorithm to control the vehicle, including:

[0037] The model predictive control algorithm based on the tube divides the actual vehicle system into a nominal system and an error system, and transforms the control of the actual system into the control of the nominal system. When controlling the nominal system, the objective function includes:

[0038]

[0039] Among them, Q and R are weight matrices, and M is the prediction domain of the model predictive control algorithm.

[0040] When solving the nominal control quantity, the state constraints and control constraints of the nominal system are strengthened according to the constraints of the actual system. The constraints are expressed as:

[0041]

[0042] After solving the nominal control quantity After that, use Calculate the amount of control that needs to be applied to the actual vehicle system.

[0043] The present invention also provides a vehicle robust control device based on online estimation of vehicle state noise, comprising: an online estimation module for vehicle state noise, which estimates the noise during the operation of the intelligent vehicle using the real-time autocovariance least squares method, and dynamically updates the vehicle state estimation error, and determines the estimated error set of the vehicle state uncertainty range based on the preset confidence interval of the vehicle state estimation error; a minimum robust positive invariant set calculation module, which is used to construct a linear programming problem using the estimated error set and calculate the minimum robust positive invariant set; and a vehicle robust control module, which is used to apply the calculated minimum robust positive invariant set to a tube-based model predictive control algorithm to control the vehicle.

[0044] The beneficial effects of the present invention are as follows: aiming at the problem of robust control of intelligent vehicles in complex driving environments, a method of estimating vehicle state noise online is adopted, and the vehicle state estimation error is dynamically updated based on the real-time autocovariance least squares method; according to the distribution of the vehicle state estimation error, a confidence interval such as 99.7% is calculated to define the error boundary and describe the uncertainty range of the vehicle state; the error boundary information is combined, and the error system is constrained within a minimum robust positive invariant set to avoid safety accidents caused by external disturbances or model uncertainty. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] 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 some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 is a flow chart of a vehicle robust control method based on online estimation of vehicle state noise according to an embodiment of the present invention;

[0047] Figure 2 is a schematic diagram of a process of online estimation of vehicle state noise according to an embodiment of the present invention;

[0048] Figure 3 It is a structural schematic diagram of a vehicle robust control device based on online estimation of vehicle state noise according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0050] In complex driving environments, intelligent vehicles need to deal with multiple uncertainties, such as uncertainty in vehicle dynamics models, changes in sensor noise, and external disturbances. Existing vehicle control methods often find it difficult to maintain the safety of the system under disturbance conditions when facing these challenges. In particular, when the vehicle state estimation error is large or the external disturbance is strong, the system is prone to instability or reduced control accuracy. Traditional control methods rely on state estimation based on prior information and lack sufficient flexibility to cope with real-time changes in the environment and system state, resulting in unsatisfactory control effects in complex driving environments.

[0051] The present invention proposes a robust control method and system based on online estimation of vehicle state noise, which uses real-time autocovariance least squares method to estimate the vehicle state estimation error online, and dynamically adjusts the control strategy by calculating the 99.7% confidence interval of the error, responding to the change of the vehicle state estimation error in real time. In addition, the present invention also combines the method based on support function to efficiently calculate the minimum robust positive invariant set, and applies it to the tube-based model predictive control algorithm. By dividing the actual system into a nominal system and an error system, the control of the actual system is converted into the control of the nominal system, and the error system is constrained within a safe range, thereby avoiding safety accidents caused by external disturbances or model uncertainty.

[0052] Combine the following Figures 1 to 3 The vehicle robust control method and device based on online estimation of vehicle state noise of the present invention are described. Figure 1 is a flow chart of a vehicle robust control method based on online estimation of vehicle state noise according to an embodiment of the present invention, such as Figure 1 As shown, the present invention provides a vehicle robust control method based on online estimation of vehicle state noise, comprising:

[0053] S1. Use the real-time autocovariance least squares method to estimate the noise during the operation of the intelligent vehicle and dynamically update the vehicle state estimation error;

[0054] S2. Determine a set of estimated errors of a vehicle state uncertainty range according to a preset confidence interval of the vehicle state estimated error; in some embodiments, the preset confidence interval includes 99.7%;

[0055] S3, constructing a linear programming problem using the estimated error set, and calculating a minimum robust positive invariant set;

[0056] S4. Apply the calculated minimum robust positive invariant set to the tube-based model predictive control algorithm to control the vehicle.

[0057] At each moment, according to the estimated error covariance matrix obtained in step S1, the confidence interval of the estimated error, such as the 99.7% confidence interval, is calculated, and the error set is defined accordingly, and its boundary is set as the boundary of the confidence interval. The error set fully considers the distribution characteristics of the error and covers the vast majority of state estimation errors, thereby ensuring that the vehicle can still maintain safe driving under disturbance conditions. Even in extreme cases, the actual state estimation error may exceed the range of the error set, but through the rolling update characteristics of the model predictive control algorithm, the system can dynamically adjust and continuously optimize the control strategy to still ensure the driving safety of the vehicle.

[0058] The vehicle robust control method based on online estimation of vehicle state noise of the present invention aims at the robust control problem of intelligent vehicles in complex driving environments, adopts a method of online estimation of vehicle state noise, and dynamically updates the vehicle state estimation error based on the real-time autocovariance least squares method; according to the distribution of the vehicle state estimation error, a confidence interval such as 99.7% is calculated to define the error boundary and describe the uncertainty range of the vehicle state; the error boundary information is combined, and the error system is constrained within a minimum robust positive invariant set to avoid safety accidents caused by external disturbances or model uncertainty.

[0059] In some embodiments, the method of estimating noise during operation of the intelligent vehicle using the real-time autocovariance least squares method includes:

[0060] S11, converting the state space equation of the vehicle system into a state space equation based on new information;

[0061] Wherein, the state space equation includes:

[0062]

[0063] The state space equation based on the new information includes:

[0064]

[0065] Among them, k represents the kth moment, x is the state vector of the system, u is the control input of the system, and y is the system observation in the state space. is the estimation error, is the estimated value of the state quantity at the kth moment, is the new information, A, B, C are the coefficient matrices of the system, w and v are the process noise and observation noise respectively, L is the steady-state Kalman gain, I is the identity matrix,

[0066] Among them, new information refers to the error between the best predicted value predicted based on existing information and the measured value.

[0067] S12, for each time window, if the current window is the first window, the estimated error covariance matrix, process noise covariance matrix and observation noise covariance matrix initialized according to the empirical information are assigned to the current window, otherwise the process noise covariance matrix and observation noise covariance matrix at the initial moment of the current window are determined according to the estimated value of the previous window;

[0068] like Figure 2 As shown, the initial estimation error covariance P0, process noise covariance matrix W0 and observation noise covariance matrix V0 are given according to empirical information. The process noise covariance matrix W = E(ww T ), the observation noise covariance matrix V = E(vv T ).

[0069] Determine whether the current window is the first window. If it is the first window, directly assign the parameters initialized in S12 to the current window. Otherwise, use the estimated value of the previous window to update the process noise covariance and observation noise covariance matrix at the initial moment of the current window, that is, W n =W n-1 , V n =V n-1 .

[0070] S13. In each time window, the vehicle state x is updated using the Kalman filter at each moment. k The error covariance matrix is ​​estimated until the end of the window. The autocovariance sequence is calculated according to the new information obtained in the current window based on the two state-space equations, and a least squares problem is constructed to estimate the process noise covariance matrix and the observation noise covariance matrix.

[0071] At each moment, the vehicle state x is updated using the Kalman filter according to the two state space equations of S12. k and the estimated error covariance matrix P k . The estimated error covariance matrix will be used to calculate the minimum robust positive invariant set.

[0072] Determine whether the current window is finished. If not, proceed to S13. Otherwise, calculate the autocovariance sequence based on the new information observed in the current window. Determine whether the entire estimation process is finished. If not, apply the estimation result of the current window to the next window.

[0073] In some embodiments, the step of calculating the autocovariance sequence based on the new information obtained from the current window based on the two state space equations includes: calculating the autocovariance sequence Ξ(N) of length N in the nth window according to the following formula: n :

[0074]

[0075] Among them, α i is the forgetting factor, and the calculation formula includes:

[0076]

[0077] Among them, m is a parameter, and different values ​​are selected according to the characteristics of different systems. The larger m is, the smaller the weight of early new information in the window is.

[0078] In some embodiments, constructing the least squares problem to estimate the process noise covariance matrix and the observation noise covariance matrix includes: Compute the covariance matrix of process noise and observation noise, where:

[0079]

[0080] in, is A in the nth window LS , W n With V n is the estimated value of the process noise covariance matrix and the observation noise covariance matrix in the nth window, S n-1 is the weight matrix. When n=1, S n-1 is the unit matrix. When n>1, S n-1 The calculation formula is:

[0081]

[0082] The parameter matrices are:

[0083]

[0084] Among them, the process noise covariance matrix W = E(ww T ), the observation noise covariance matrix V = E(vv T ).

[0085] In some embodiments, using the estimated error set, constructing a linear programming problem and calculating a minimum robust positive invariant set includes:

[0086] Utilization control rate The actual vehicle system is divided into a nominal system and an error system, and the state space equation of the error system is obtained:

[0087] e k+1 =(A+BK)e k +w k

[0088] in, xk and Represent the state quantities of the actual system and the nominal system respectively. The state quantities of the system include the lateral velocity v y , yaw rate Tracking Error d and heading error

[0089] Calculate F according to the calculation formula of the minimum robust positive invariant set ∞ :

[0090]

[0091] Among them, F ∞ is the minimum robust positive invariant set, Θ is the state estimation error set obtained in step S2, ⊕ is the Minkowski sum between the sets, A K =A+BK, K is the state feedback gain matrix, which can usually be calculated using the LQR (Linear Quadratic Regulator) method.

[0092] Since the calculation method of the minimum robust positive invariant set is an infinite iterative process, in practical applications, an approximate accuracy can be set, and the approximate solution of the minimum robust positive invariant set can be calculated based on this approximate accuracy. According to the approximate accuracy, select the γ and β values ​​that meet the following conditions:

[0093]

[0094] Among them, γ is a positive integer, β∈[0,1).

[0095] Construct an auxiliary calculation set C to facilitate the subsequent calculation of the approximate solution of the minimum robust positive invariant set:

[0096]

[0097] Among them, H x and H u They are the constraints describing the actual system state X={x|H x x≤b x} and the control constraint U = {x|H u x≤b u}, Represents Θ for The support function of .

[0098] Through recursive calculation, the approximate solution Z of the minimum robust positive invariant set is obtained, and the calculation formula is:

[0099]

[0100] Among them, H p,0 =Hc , b p,0 =b c .

[0101] In some embodiments, applying the calculated minimum robust positive invariant set to a tube-based model predictive control algorithm to control the vehicle includes:

[0102] The model predictive control algorithm based on the tube divides the actual vehicle system into a nominal system and an error system, and transforms the control of the actual system into the control of the nominal system. When controlling the nominal system, the objective function includes:

[0103]

[0104] Among them, Q and R are weight matrices, and M is the prediction domain of the model predictive control algorithm.

[0105] When solving the nominal control quantity, the state constraints and control constraints of the nominal system are strengthened according to the constraints of the actual system. The constraints are expressed as:

[0106]

[0107] After solving the nominal control quantity After that, use Calculate the amount of control that needs to be applied to the actual vehicle system.

[0108] See also Figure 3 The present invention also provides a vehicle robust control device based on online estimation of vehicle state noise, including: a vehicle state noise online estimation module, which estimates the noise during the operation of the intelligent vehicle by using the real-time autocovariance least squares method, and dynamically updates the vehicle state estimation error, and determines the estimated error set of the vehicle state uncertainty range according to the preset confidence interval of the vehicle state estimation error; a minimum robust positive invariant set calculation module, which is used to construct a linear programming problem using the estimated error set and calculate the minimum robust positive invariant set; a vehicle robust control module, which is used to apply the calculated minimum robust positive invariant set to the tube-based model predictive control algorithm to control the vehicle.

[0109] The vehicle robust control device based on online estimation of vehicle state noise provided in the embodiment of the present invention has the same implementation principle and technical effects as those in the aforementioned vehicle robust control method embodiment based on online estimation of vehicle state noise. For the sake of brief description, for parts not mentioned in the embodiment of the vehicle robust control device based on online estimation of vehicle state noise, reference may be made to the corresponding contents in the aforementioned vehicle robust control method embodiment based on online estimation of vehicle state noise.

[0110] The present application also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, and when the program is executed by a processor, a corresponding function is realized. When the computer-readable storage medium of this embodiment is executed by a processor, a method embodiment is implemented based on a vehicle robust control method for online estimation of vehicle state noise.

[0111] It should be pointed out that, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, and two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0112] The order of execution of each step in the above embodiment does not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0113] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all these improvements and changes should fall within the scope of protection of the appended claims of the present invention.

Claims

1. A vehicle robust control method based on online estimation of vehicle state noise, characterized in that: include: The real-time autocovariance least squares method is used to estimate the noise during the operation of intelligent vehicles and dynamically update the vehicle state estimation error; Determining a set of estimated errors of a vehicle state uncertainty range according to a preset confidence interval of the vehicle state estimated error; Using the estimated error set, construct a linear programming problem and calculate a minimum robust positive invariant set; The calculated minimum robust positive invariant set is applied to the tube-based model predictive control algorithm to control the vehicle.

2. The vehicle robust control method based on online estimation of vehicle state noise according to claim 1, characterized in that: The method of estimating noise during operation of an intelligent vehicle by using the real-time autocovariance least squares method comprises: Convert the state space equation of the vehicle system into the state space equation based on the new information; For each time window, if the current window is the first window, the estimated error covariance matrix, process noise covariance matrix and observation noise covariance matrix initialized according to the empirical information are assigned to the current window; otherwise, the process noise covariance matrix and observation noise covariance matrix at the initial moment of the current window are determined according to the estimated value of the previous window; In each time window, the Kalman filter is used to update the vehicle state and the estimated error covariance matrix at each moment until the window ends. The autocovariance sequence is calculated based on the new information obtained from the two state-space equations in the current window, and a least squares problem is constructed to estimate the process noise covariance matrix and the observation noise covariance matrix.

3. The vehicle robust control method based on online estimation of vehicle state noise according to claim 2, characterized in that: The state space equations include: The state space equation based on the new information includes: Among them, k represents the kth moment, x is the state vector of the system, u is the control input of the system, and y is the system observation in the state space. is the estimation error, is the estimated value of the state quantity at the kth moment, is the new information, A, B, C are the coefficient matrices of the system, w and v are the process noise and observation noise respectively, L is the steady-state Kalman gain, I is the identity matrix, 4. The vehicle robust control method based on online estimation of vehicle state noise according to claim 1, characterized in that: The preset confidence interval includes 99.7%.

5. The vehicle robust control method based on online estimation of vehicle state noise according to claim 2, characterized in that: The calculating of the autocovariance sequence according to the new information obtained from the current window based on the two state space equations includes: The autocovariance sequence Ξ(N) of length N in the nth window is calculated according to the following formula: n : Among them, α i is the forgetting factor, and the calculation formula includes: Among them, m is a parameter, and different values ​​are selected according to the characteristics of different systems. The larger m is, the smaller the weight of early new information in the window is.

6. The vehicle robust control method based on online estimation of vehicle state noise according to claim 5, characterized in that: The least squares problem is constructed to estimate the process noise covariance matrix and the observation noise covariance matrix, including Compute the covariance matrix of process noise and observation noise, where: in, is A in the nth window LS , W n With V n is the estimated value of the process noise covariance matrix and the observation noise covariance matrix in the nth window, S n-1 is the weight matrix. When n=1, S n-1 is the unit matrix. When n>1, S n-1 The calculation formula is: The parameter matrices are: Among them, the process noise covariance matrix W = E(ww T ), the observation noise covariance matrix V = E(vv T ).

7. The vehicle robust control method based on online estimation of vehicle state noise according to claim 1, characterized in that: Using the estimated error set, a linear programming problem is constructed to calculate the minimum robust positive invariant set, including: Utilization control rate The actual vehicle system is divided into a nominal system and an error system, and the state space equation of the error system is obtained: have been k+1 =(A+BK)e k +w k in, x k and Represent the state quantities of the actual system and the nominal system respectively. The state quantities of the system include the lateral velocity v y , yaw rate Tracking Error d and heading error Calculate F according to the calculation formula of the minimum robust positive invariant set ∞ : Among them, F ∞ is the minimum robust positive invariant set, Θ is the estimation error set, is the Minkowski sum between sets, A K =A+BK, K is the state feedback gain matrix.

8. The vehicle robust control method based on online estimation of vehicle state noise according to claim 1, characterized in that: The method of applying the calculated minimum robust positive invariant set to a tube-based model predictive control algorithm to control the vehicle includes: The model predictive control algorithm based on the tube divides the actual vehicle system into a nominal system and an error system, and transforms the control of the actual system into the control of the nominal system. When controlling the nominal system, the objective function includes: Among them, Q and R are weight matrices, and M is the prediction time domain of the model predictive control algorithm; When solving the nominal control quantity, the state constraints and control constraints of the nominal system are strengthened according to the constraints of the actual system. The constraints are expressed as: After solving the nominal control quantity After that, use Calculate the amount of control that needs to be applied to the actual vehicle system.

9. A vehicle robust control device based on online estimation of vehicle state noise, characterized in that: include: The ego vehicle state noise online estimation module estimates the noise during the operation of the intelligent vehicle using the real-time autocovariance least squares method, and dynamically updates the vehicle state estimation error. Based on the preset confidence interval of the vehicle state estimation error, the estimation error set of the vehicle state uncertainty range is determined; A minimum robust positive invariant set calculation module, used to construct a linear programming problem using the estimated error set and calculate the minimum robust positive invariant set; The vehicle robust control module is used to apply the calculated minimum robust positive invariant set to the tube-based model predictive control algorithm to control the vehicle.

10. A computer storage medium, characterized in that: A computer program executable by a processor is stored therein, and the computer program executes the vehicle robust control method based on online estimation of vehicle state noise as described in any one of claims 1 to 8.

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