Finite-time integral sliding mode control method for intelligent vehicles based on disturbance observer

Abstract

The present invention discloses a finite-time integral sliding mode control method for an intelligent vehicle based on a disturbance observer, wherein a composite disturbance is input into a disturbance observer model to obtain a disturbance observer estimation value, and the expected input linear velocity information and the expected input angular velocity information are input, and the linear velocity integral sliding mode controller and the angular velocity integral sliding mode controller are used to obtain the position information and the vehicle running trajectory, and the traditional sliding mode controller is used to control the position information and the running trajectory under the traditional sliding mode; the running trajectory obtained by the linear velocity integral sliding mode controller and the angular velocity integral sliding mode controller is compared with the tracking of the reference trajectory and the running trajectory under the traditional sliding mode. This scheme effectively alleviates chattering, enhances system robustness, improves control accuracy, and improves the convergence speed of the system, has better intelligence and adaptability, as well as better control performance and system stability, and is suitable for precise path tracking and dynamic control of intelligent vehicles.

Description

Technical Field

[0001] The invention relates to the field of automatic control technology, and in particular to a finite time integral sliding mode control method for an intelligent vehicle based on a disturbance observer. Background Art

[0002] In the field of intelligent vehicle control, the research and optimization of motion control systems has always been one of the core directions of technological development. With the rapid development of intelligent technology, intelligent vehicles are increasingly used in industrial manufacturing, logistics and transportation, national defense and military, and daily life, which puts forward higher requirements on the stability, rapid response capability and anti-disturbance performance of control systems. However, external disturbances and uncertainty of system parameters in complex environments have brought severe technical challenges to the motion control of intelligent vehicles. With the development of autonomous driving technology, intelligent vehicle control systems not only require efficient dynamic response, but also must have strong anti-disturbance capabilities to cope with changing road conditions, climate and emergencies.

[0003] Although traditional vehicle control methods such as PID control are widely used, they are highly dependent on system models and have difficulty coping with the impact of nonlinearity and external disturbances on system performance. In this context, modern control methods, such as adaptive control and robust control, are gradually being introduced into intelligent vehicle control to enhance the adaptability and stability of the system. As a typical nonlinear control method, sliding mode control has shown good control effects in the face of system parameter uncertainty and external disturbances due to its strong robustness. However, sliding mode control often faces the problem of "jittering" in practical applications, that is, high-frequency oscillation, which affects the stability and control accuracy of the system, especially in a fast and dynamic environment.

[0004] To address these issues, disturbance observer technology has been introduced into vehicle control systems to estimate and compensate for external disturbances in real time. Disturbance observers can effectively improve the system's ability to resist disturbances, especially when the vehicle encounters complex environmental changes during driving, and can reduce system errors by estimating and compensating for disturbances. However, the design of disturbance observers depends on the accuracy of the system model, and when the model is uncertain or has strong nonlinearity, the performance of the observer may deteriorate. Therefore, how to design a robust and accurate disturbance observer in a complex dynamic environment has become the key to improving system performance.

[0005] In addition, finite-time control theory also provides new ideas for solving vehicle control problems. Traditional asymptotic convergence control methods often require infinite time to complete state convergence, but in practical applications, the system usually needs to achieve fast response within a finite time. Finite-time control designs specific control laws to enable the system to quickly reach the target state within a finite time, thereby improving the system's response speed and control accuracy. Summary of the invention

[0006] In view of the shortcomings of traditional sliding mode control technology, the present invention provides a finite-time integral sliding mode control method for intelligent vehicles based on a disturbance observer. Compared with the traditional sliding mode control method, the control method proposed in the present invention can more effectively ensure vehicle stability when dealing with external disturbances and internal uncertainties, and improve the vehicle's convergence speed and anti-disturbance ability, so that the vehicle's running trajectory can accurately keep up with the reference trajectory.

[0007] The present invention is implemented by adopting the following technical scheme: a finite time integral sliding mode control method for an intelligent vehicle based on a disturbance observer comprises the following steps:

[0008] Step S1: Starting from a stationary state, a dynamic and kinematic model of the vehicle in motion is established;

[0009] Step S2: Considering external disturbance and internal uncertainty as composite disturbance to design disturbance observer model;

[0010] Step S3: designing a linear velocity integral sliding mode controller and an angular velocity integral sliding mode controller, obtaining a linear velocity error, an angular velocity error and position information, and then obtaining a final vehicle running trajectory;

[0011] Step S4: designing a traditional sliding surface and a traditional sliding mode controller according to the linear velocity error and the angular velocity error in step S3;

[0012] Step S5: inputting the composite disturbance into the disturbance observer model to obtain the disturbance observer estimation value, inputting the expected input linear velocity information and the expected input angular velocity information, obtaining the position information and the vehicle running trajectory by the linear velocity integral sliding mode controller and the angular velocity integral sliding mode controller, and obtaining the position information and the running trajectory under the traditional sliding mode by the traditional sliding mode controller;

[0013] Step S6: Compare the running trajectory obtained by the linear velocity integral sliding mode controller and the angular velocity integral sliding mode controller with the tracking conditions of the reference trajectory and the running trajectory under the traditional sliding mode.

[0014] Specifically, the dynamic and kinematic model expressions of the vehicle in step S1 when in motion are:

[0015] ;

[0016] in, The vehicle The horizontal and vertical axis coordinates and direction angles of the center point of the vehicle, express The derivative of is the rotation matrix; It is Linear speed of the vehicle and angular velocity information, express The derivative of is the mass matrix, expressed as: ,in Indicates the quality of the car, represents the inertia moment of the vehicle; is the linear damping experienced by the smart vehicle and nonlinear damping The damping matrix is ​​formed by: = ; represents the control input of the model, represents the composite disturbance consisting of model parameter uncertainty and external disturbance, where and They represent the composite disturbance of linear velocity disturbance and angular velocity disturbance respectively.

[0017] Specifically, the step S2 specifically includes:

[0018] Step S21: Design the composite disturbance expression as:

[0019] ;

[0020] and represent the composite disturbance of linear velocity disturbance and angular velocity disturbance respectively;

[0021] Step S22: The composite disturbance is estimated by using a disturbance observer, and the linear velocity disturbance observer model and the angular velocity disturbance observer model are designed to be expressed as:

[0022] ;

[0023] in, is the adjustment parameter of the nonlinear function and 0< <1; , , , , , are greater than zero; , = , and is an auxiliary variable, its derivative and satisfy: = + + - ; The disturbance observation errors are: , .

[0024] Specifically, the step S22 further includes: setting the disturbance observer parameters , , , , , and , and are the parameters related to the linear perturbation term, and is the parameter related to the nonlinear disturbance term, and It is a parameter used to suppress the influence of sudden changes or strong interference on disturbance estimation, and is specifically set to: 0< <1, , , , , and Both are greater than zero.

[0025] Specifically, the step S3 specifically includes:

[0026] Step S31: Calculate the linear velocity error and angular velocity error , the calculation formulas are:

[0027] ;

[0028] in, and represent the expected input linear velocity signal and the expected input angular velocity signal respectively;

[0029] Step S32: According to the linear velocity error and angular velocity error Design of Linear Velocity Integral Sliding Surface Controller Sliding Surface Controller with Angular Velocity Integration for:

[0030] ;

[0031] in, and The sliding mode control gain parameters are used to adjust the system's sensitivity to errors and are all greater than zero; and = is a nonlinear function defined as is a symbolic function; and is the sliding surface parameter used to adjust the dynamic performance of the sliding surface and , ; and Respectively and The derivative of

[0032] Step S33: Sliding surface controller based on linear velocity integration , Angular velocity integral sliding surface controller The linear velocity integral sliding mode controller model is designed based on the dynamic and kinematic models. And angular velocity integral sliding mode controller model , specifically:

[0033] ;

[0034] in, and The compensation gains used to suppress unknown disturbances and errors in the system are all greater than zero. is the hyperbolic tangent function;

[0035] Step S34: Linear velocity integral sliding surface controller Sliding Surface Controller with Angular Velocity Integration After the action, according to the dynamics and kinematics model formula:

[0036] ;

[0037] The linear velocity error, angular velocity error and position information are obtained, and then the final vehicle trajectory is obtained.

[0038] Specifically, the step S33 also includes: setting the linear velocity integral sliding mode controller model And angular velocity integral sliding mode controller model Parameters , , , , and , the specific settings are: , , , and Both are greater than zero.

[0039] Specifically, step S4 includes:

[0040] Step S41: define the same linear velocity error and angular velocity error as:

[0041] ;

[0042] Step S42: Designing a traditional linear velocity sliding surface according to the error and the traditional angular velocity sliding surface for:

[0043] ;

[0044] in, and are the gain parameters of the error terms and are all greater than zero;

[0045] Step S43: According to the conventional linear velocity sliding surface , Traditional angular velocity sliding surface With the dynamic and kinematic model formula:

[0046] ;

[0047] Design of a traditional linear speed sliding mode controller and the traditional angular velocity sliding mode controller for:

[0048] ;

[0049] in, and are nonlinear gain parameters and are all greater than zero.

[0050] Specifically, the step S43 also includes: setting a conventional linear speed sliding mode controller and the traditional angular velocity sliding mode controller Parameters , , and , the specific settings are: , , and Both are greater than zero.

[0051] The beneficial effect of the present invention is that the present invention proposes a finite-time integral sliding mode control method based on a disturbance observer. This method combines the real-time disturbance estimation of the disturbance observer with the robustness of the integral sliding mode control to ensure that the vehicle can converge stably within a finite time when affected by external disturbances and internal uncertainties, and make the state error tend to zero. In this way, not only can the jitter problem in traditional sliding mode control be effectively alleviated, but also the stability and control accuracy of the system can be significantly improved.

[0052] The finite-time integral sliding mode control method for intelligent vehicles based on disturbance observers can improve the robustness, accuracy and dynamic response capability of the system. By introducing disturbance observers, external disturbances (such as uneven roads, wind changes, etc.) and internal uncertainties (such as changes in vehicle mass and fluctuations in dynamic parameters) to which the vehicle is subjected during driving can be estimated and compensated in real time. The disturbance observer provides accurate disturbance information and feeds back to the controller to output the corresponding control quantity, thereby ensuring the stability and accuracy of the vehicle under complex working conditions.

[0053] The present invention adopts a finite-time integral sliding mode control strategy to ensure that the system quickly converges to the desired state within a finite time, significantly improving the response speed and dynamic performance. Compared with the traditional sliding mode control method, the finite-time integral sliding mode controller eliminates the steady-state error by introducing an integral term, ensuring that the vehicle can accurately reach the target state during long-term operation, allowing the vehicle to perfectly track the reference trajectory.

[0054] In addition, the present invention also effectively alleviates the jitter phenomenon in traditional sliding mode control, especially the high-frequency oscillation near the sliding surface, and improves the stability and comfort of the vehicle in a dynamic environment. By designing the integral sliding surface, the influence of external disturbances and system model uncertainty on the control accuracy is further reduced, and the overall performance of the control system is improved. Secondly, the method proposed in the present invention has a high practical application value, especially in complex environments, and is of great significance for improving the response speed, accuracy and robustness of intelligent vehicle control systems. It also has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0056] Figure 1 It is an implementation flow chart of the finite time integral sliding mode control method of an intelligent vehicle based on a disturbance observer of the present invention;

[0057] Figure 2 It is a schematic diagram of the simulation results of the performance of the linear velocity disturbance observer in an embodiment of the present invention;

[0058] Figure 3 It is a schematic diagram of the simulation results of the performance of the angular velocity disturbance observer in an embodiment of the present invention;

[0059] Figure 4 It is a schematic diagram of the simulation results of the linear velocity error convergence effect in an embodiment of the present invention;

[0060] Figure 5 Schematic diagram of the simulation result of the angular velocity error convergence effect in an embodiment of the present invention;

[0061] Figure 6 It is a schematic diagram of the simulation results comparing the X-direction position error of the embodiment of the present invention with that of the traditional sliding mode;

[0062] Figure 7 It is a schematic diagram of the simulation results comparing the Y-direction position error of the embodiment of the present invention with that of the traditional sliding mode;

[0063] Figure 8 Schematic diagram of simulation results comparing the trajectory, reference trajectory and traditional sliding mode trajectory in an embodiment of the present invention. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0065] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0066] The following is combined with Figures 1 to 8 , some embodiments of the present invention are described in detail. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0067] The present invention proposes a finite-time integral sliding mode control method for an intelligent vehicle based on a disturbance observer. Compared with the traditional sliding mode control method, the control method proposed in the present invention can more effectively ensure vehicle stability when dealing with external disturbances and internal uncertainties, and improve the vehicle's convergence speed and anti-disturbance ability, so that the vehicle's running trajectory can accurately keep up with the reference trajectory.

[0068] The finite time integral sliding mode control method for intelligent vehicles based on disturbance observer is implemented as follows:

[0069] (1) The kinematic and dynamic models of the intelligent vehicle are established as follows:

[0070] ;

[0071] in, The vehicle The horizontal and vertical axis coordinates and direction angles of the center point of the vehicle, express The derivative of is the rotation matrix; It is Linear speed of the vehicle and angular velocity information, express The derivative of is the mass matrix, expressed as: ,in Indicates the quality of the car, represents the inertia moment of the vehicle; is the linear damping experienced by the smart vehicle and nonlinear damping The damping matrix is ​​formed by: = ; represents the control input of the model, represents the composite disturbance consisting of model parameter uncertainty and external disturbance, where and They represent the composite disturbance of linear velocity disturbance and angular velocity disturbance respectively.

[0072] (2) Establish linear velocity and angular velocity disturbance observers to accurately estimate the vehicle motion state. The disturbance observer expressions are:

[0073] ; ;

[0074] Where: 0< <1; , , , , , are greater than zero; , = , and is an auxiliary variable, its derivative and satisfy: = + + - ; The disturbance observation errors are: , .

[0075] (3) Define the dynamic errors of vehicle linear velocity and angular velocity as: , .in , They are represented as the expected input linear velocity signal and the expected input angular velocity signal respectively.

[0076] (4) The designed integral sliding surface is:

[0077] ;

[0078] in, and are greater than zero; , .

[0079] (5) The integral sliding mode surface is introduced into the controller to obtain an integral sliding mode controller to accurately adjust the linear velocity and angular velocity and improve the response speed of the vehicle control system. The expressions of the integral sliding mode controller are:

[0080] ;

[0081] in, and Both are greater than zero.

[0082] like Figures 1 to 8 As shown, the intelligent vehicle finite time integral sliding mode control method based on disturbance observer of the present invention comprises the following steps:

[0083] (1) The present invention provides an implementation process of a finite-time integral sliding mode control method for an intelligent vehicle based on a disturbance observer. Figure 1 ;

[0084] In this embodiment, a vehicle control system is considered, in which the dynamic and kinematic models of the vehicle when in motion are as follows:

[0085] .

[0086] The vehicle starts from a stationary state, so the initial position, linear velocity information and angular velocity information are all zero. is the vehicle's location information, is the linear velocity and angular velocity information of the vehicle, is the mass matrix of the vehicle, and the parameter value is the mass parameter =24kg, moment of inertia = , damping coefficient parameter , = , Represents the control input to the model.

[0087] (2) Design a disturbance observer;

[0088] Vehicles are affected by external disturbances (such as uneven roads, wind changes, etc.) and internal uncertainties (such as changes in vehicle mass and fluctuations in dynamic parameters). These disturbances are considered as compound disturbances. , represents the composite disturbance consisting of model parameter uncertainty and external disturbance, and is designed as:

[0089] .

[0090] These disturbances are estimated by disturbance observers. The linear velocity disturbance observer and angular velocity disturbance observer are designed as follows:

[0091] .

[0092] In this embodiment, the parameters of the disturbance observer are selected as =0.5, =8, =4, =5.4, =4, =12, =8.6. Figure 2 , Figure 3 It can be obtained that the convergence time of the disturbance observer is about 0.8s and about 0.4s respectively, and the estimated value of the disturbance almost completely coincides with the actual composite disturbance. This shows that the two disturbance observers designed by the present invention can quickly respond to and accurately track the composite disturbance in a very short time, thereby ensuring the stable operation of the system in a complex environment, proving the effectiveness of the disturbance observer designed by the present invention.

[0093] (3) Designing the linear velocity controller and angular velocity controller of the present invention;

[0094] The linear velocity error and angular velocity error are:

[0095] .

[0096] Input the expected linear velocity information and angular velocity information as follows: , , the integral sliding surface is:

[0097] ;

[0098] The integral sliding mode parameter =12.5, =0.7, =70, =0.6. By introducing the integral term, the steady-state error caused by disturbance or uncertainty can be effectively eliminated. The linear velocity controller and angular velocity controller of the present invention are designed according to the integral sliding surface and the dynamic and kinematic models as follows:

[0099] ;

[0100] in =22, =18, the controller acts according to the model formula , The linear velocity error, angular velocity error and position information can be obtained, and then the final vehicle running trajectory can be obtained. Figure 4 , Figure 5 It can be concluded that under the action of the integral sliding mode controller of the present invention, the linear velocity information responds quickly and converges to zero within 0.8s, and the angular velocity information responds quickly and converges to zero within 0.4s. Figure 6 , Figure 7 The solid line in the figure shows that the position errors in the X and Y directions of the vehicle converge quickly to zero under the controller designed by the present invention, and remain stable at zero without oscillation. Figure 8 By comparing the solid line and the dotted line in the figure, it can be seen that the trajectory generated by the controller designed by the present invention is more accurate and quickly keeps up with the reference trajectory. This shows that the system can quickly converge to the desired state within a limited time, and the system can quickly and stably eliminate errors and finally accurately track the target.

[0101] (4) Design of traditional sliding surface and controller (comparative experiment);

[0102] Define the linear velocity error and angular velocity error as:

[0103] .

[0104] Enter the expected linear velocity information and angular velocity information as follows: , =0.2 (rad / s) The traditional sliding surface is designed as:

[0105] .

[0106] Sliding mode gain parameters =4.5, =3.2. According to the sliding surface and the model formula of the present invention , + = The designed sliding mode control law is:

[0107] .

[0108] Controller gain =8, =5. Figure 6 , Figure 7 Comparing the solid line (the position error in the X and Y directions ultimately generated by the present invention) with the dotted line (the position error in the X and Y directions of the traditional sliding mode control), it can be seen that the amplitude of the error of the traditional sliding mode control on the x-axis varies greatly, with the maximum error reaching nearly 3 meters. However, the amplitude of the position error of the control method of the present invention is very small, close to zero, showing a higher control accuracy. In addition, the error curve of the traditional sliding mode control method oscillates at zero and has a large amplitude, showing a large dynamic error of the system. In contrast, the error of the present invention varies less and almost remains near zero, indicating that the present invention can more effectively suppress error oscillations and improve the stability of the system. Figure 8 By comparing the solid line (the trajectory generated by the control method of the present invention), the dotted line (the reference trajectory), and the dash-dotted line (the trajectory generated by the traditional sliding mode control method), it can be seen that the trajectory generated by the control method of the present invention achieves complete tracking of the reference trajectory, especially in the part close to the center, showing a very high trajectory tracking accuracy. In contrast, the trajectory of the traditional sliding mode control has a large deviation from the reference trajectory. In addition, the control method of the present invention exhibits smoother and more accurate trajectory tracking, while the traditional sliding mode control method, although basically able to track the trajectory, has deviations and oscillations from the reference trajectory. Ultimately, it can be concluded that the present invention performs better than the traditional sliding mode control in trajectory tracking tasks, especially in scenarios with high precision and complex paths.

[0109] In this embodiment, the composite disturbance is input into the disturbance observer model, the system is operated to obtain the disturbance observer estimation value, the expected input linear velocity information and the expected input angular velocity information are input into the system model, and after the controller acts, the position information and vehicle running trajectory under the action of the present invention (intelligent vehicle finite time integral sliding mode control based on disturbance observer) are obtained from the system model. Similarly, the same linear velocity and angular velocity information are input into the same system model, and then the position information and running trajectory under the traditional sliding mode are obtained after the controller designed by the traditional sliding mode action. Finally, the running trajectory of the present invention is compared with the tracking of the reference trajectory and the trajectory under the traditional sliding mode action.

[0110] When affected by external disturbances and internal uncertainties, the disturbance observer ensures the high accuracy and stability of the system by real-time monitoring and estimation of external disturbances, which significantly improves the robustness and adaptability of the control system in complex environments.

[0111] The integral sliding mode control method is adopted; in step (3), the introduction of this method can effectively suppress the chattering phenomenon in the sliding mode control. By introducing the integral term, the system can compensate for the accumulated error, thereby improving the tracking accuracy and anti-disturbance ability of the system. In addition, the integral sliding mode design also improves the stability of the system in the face of external disturbances and internal uncertainties, making the overall control smoother and more stable.

[0112] In this embodiment, step (2) also includes the step of setting disturbance observer parameters, specifically setting disturbance observer parameters: setting disturbance observer parameters , , , , , and , where 0< <1, , , , , and are greater than zero. Steps (3) and (4) also include the step of setting control parameters, specifically setting the integral sliding mode controller parameters: , , , , and ,in , , , and are greater than zero. Set the traditional sliding mode controller parameters , , and ,in , , and Both are greater than zero.

[0113] The disturbance observer designed by the present invention introduces auxiliary systems and multiple nonlinear functions (including power functions, hyperbolic tangent functions, etc.), and focuses on estimating disturbances through dynamic changes in errors to adjust the response of the system. The system response mainly focuses on the precise control of linear velocity and angular velocity; the integral sliding mode controller designed adds an integral term to improve the performance of the system on the basis of traditional sliding mode control, and can better cope with the long-term error or cumulative error of the system. The control input of the integral sliding mode controller designed by the present invention not only involves the dynamic response of the system, but also introduces the estimation and compensation of disturbances and adjusts the control strategy through compensation terms (including sign functions and error terms). Multiple new adjustment parameters are introduced in the controller to cope with different control targets including the sensitivity of the sliding surface, nonlinear feedback response, disturbance estimation accuracy, control gain, etc.; focusing on the precise path tracking of intelligent vehicles, especially in the scenario of autonomous driving, the focus is on trajectory tracking and system stability in uncertain and complex environments. By estimating and compensating for compound disturbances by the disturbance observer, the trajectory accuracy and stability of intelligent vehicles are improved, which is particularly suitable for coping with complex external disturbances in dynamic environments.

[0114] The present invention combines integral sliding mode control with disturbance observer, with particular emphasis on rapid convergence and robustness in complex environments. By establishing the kinematic and dynamic models of the vehicle and combining the disturbance observer to compensate for complex disturbances, the stability of the system and the path tracking accuracy are improved. It is characterized by being able to effectively alleviate control jitter and enhance system robustness. It is particularly suitable for precise path tracking of intelligent vehicles, focusing on the error between linear velocity, angular velocity and expected linear velocity and expected angular velocity. By introducing the integral term of the target velocity, the control strategy can be adjusted according to the reference trajectory, and the response of the system can be adjusted according to the velocity error.

[0115] For the aforementioned embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily required by the present application.

[0116] The above embodiments describe the basic principles and main features of the present invention and the advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the changes and modifications made by those skilled in the art shall be within the scope of protection of the appended claims of the present invention without departing from the spirit and scope of the present invention.

Claims

1. A finite-time integral sliding mode control method for intelligent vehicles based on disturbance observer, characterized in that: The following steps are involved: Step S1: Starting from a stationary state, a dynamic and kinematic model of the vehicle in motion is established; Step S2: Considering external disturbance and internal uncertainty as composite disturbance to design disturbance observer model; Step S3: Design a linear velocity integral sliding mode controller and an angular velocity integral sliding mode controller to obtain linear velocity error, angular velocity error and position information, and then obtain the final vehicle running trajectory, specifically including: Step S31: Calculate the linear velocity error e vi and angular velocity error e wi , the calculation formulas are: in, and They represent the expected input linear velocity signal and the expected input angular velocity signal, respectively, v i and w i Respectively represent the linear velocity and angular velocity information of the i-th vehicle; Step S32: According to the linear velocity error e vi and angular velocity error e wi Design the linear velocity integral sliding surface controller s1 and the angular velocity integral sliding surface controller s2 as: Among them, ρ and σ are sliding mode control gain parameters that adjust the system's sensitivity to errors and are both greater than zero; sig λ (e vi )=sign(e vi ) vi λ and sig μ (e wi )=sign(e wi ) wi λ is a defined nonlinear function, where sign(·) is a sign function; λ and μ are sliding surface parameters used to adjust the dynamic performance of the sliding surface and λ∈(0,1),μ∈(0,1); and Respectively and The derivative of Step S33: Design a linear velocity integral sliding mode controller model u according to the linear velocity integral sliding mode controller s1, the angular velocity integral sliding mode controller s2 and the dynamics and kinematics model 1i And the angular velocity integral sliding mode controller model u 2i , specifically: in, and The compensation gain used to suppress unknown disturbances and errors in the system is greater than zero. Tanh(·) is the hyperbolic tangent function. 1i is the linear damping, D 2i is the nonlinear damping, m i represents the mass of the car, I i represents the inertia moment of the vehicle, f 1i (t) and f 2i (t) represent the composite disturbance of linear velocity disturbance and angular velocity disturbance, and They represent the linear velocity disturbance observer model and the angular velocity disturbance observer model respectively; and They represent the disturbance observation errors corresponding to the linear velocity disturbance observer model and the angular velocity disturbance observer model respectively; Step S34: After the linear velocity integral sliding surface controller s1 and the angular velocity integral sliding surface controller s2 act, according to the dynamics and kinematics model formula: Obtain linear velocity error, angular velocity error and position information, and then obtain the final vehicle trajectory; Among them, K i =(x i ,y i ,θ i ) T are the horizontal and vertical axis coordinates and direction angles of the center point of the i-th vehicle, K i The derivative of S(θ i ) is the rotation matrix; ξ i =(v i , w i ) T is the linear velocity v of the ith vehicle i and the angular velocity w i information, Represents ξ i The derivative of H i is the mass matrix, expressed as: Where m i represents the mass of the car, I i Indicates the inertia moment of the vehicle; D i is the linear damping D experienced by the smart vehicle 1i and nonlinear damping D 2i The damping matrix is ​​expressed as: u i =(u 1i ,u i2 ) T represents the control input of the model, represents the composite disturbance composed of model parameter uncertainty and external disturbance, where f 1i (t) and f 2i (t) represent the composite disturbance of linear velocity disturbance and angular velocity disturbance respectively; Step S4: designing a traditional sliding surface and a traditional sliding mode controller according to the linear velocity error and the angular velocity error in step S3; Step S5: inputting the composite disturbance into the disturbance observer model to obtain the disturbance observer estimation value, inputting the expected input linear velocity information and the expected input angular velocity information, and obtaining the position information and the vehicle running trajectory by the linear velocity integral sliding mode controller and the angular velocity integral sliding mode controller described in step S3, and obtaining the position information and the running trajectory under the traditional sliding mode by the traditional sliding mode controller described in step S4; Step S6: Compare the running trajectory obtained by the linear velocity integral sliding mode controller and the angular velocity integral sliding mode controller in step S5 with the tracking condition of the reference trajectory and the running trajectory under the traditional sliding mode.

2. The finite-time integral sliding mode control method for an intelligent vehicle based on a disturbance observer as claimed in claim 1, characterized in that: The step S2 specifically includes: Step S21: Design the composite disturbance expression as: f 1i (t) and f 2i (t) represent the composite disturbance of linear velocity disturbance and angular velocity disturbance respectively; Step S22: The composite disturbance is estimated by using a disturbance observer, and the linear velocity disturbance observer model and the angular velocity disturbance observer model are designed to be expressed as: Where α is the adjustment parameter of the nonlinear function and 0<α<1; n 1i , n 2i , n 3i , n 4i , n 5i , n 6i are all greater than zero; Γ 1i =v i -γ 1i , Γ 2i =w i -γ 2i , γ 1i and γ 2i is an auxiliary variable, its derivative and satisfy: The disturbance observation errors are:

3. The finite-time integral sliding mode control method for an intelligent vehicle based on a disturbance observer as claimed in claim 2, characterized in that: The step S22 also includes: setting disturbance observer parameters α, n 1i 、n 2i 、n 3i 、n 4i 、n 5i and n 6i , n 1i and n 4i is the parameter related to the linear perturbation term, n 2i and n 5i is the parameter related to the nonlinear disturbance term, n 3i and n 6i is a parameter used to suppress the influence of sudden changes or strong interference on disturbance estimation, which is specifically set to: 0<α<1, n 1i 、n 2i 、n 3i 、n 4i 、n 5i and n 6i Both are greater than zero.

4. The finite-time integral sliding mode control method for an intelligent vehicle based on a disturbance observer as claimed in claim 1, characterized in that: The step S33 also includes: setting the linear velocity integral sliding mode controller model u 1i And the angular velocity integral sliding mode controller model u 2i The parameters ρ, σ, λ, μ, and The specific settings are: λ∈(0,1),μ∈(0,1),ρσ, and Both are greater than zero.

5. The finite-time integral sliding mode control method for an intelligent vehicle based on a disturbance observer as claimed in claim 4, characterized in that: The step S4 comprises: Step S41: define the same linear velocity error and angular velocity error as: Step S42: Design the traditional linear velocity sliding surface S3 and the traditional angular velocity sliding surface S4 according to the error: Among them, c1 and c2 are the gain parameters of the error term and are both greater than zero; Step S43: According to the traditional linear velocity sliding surface s3, the traditional angular velocity sliding surface s4 and the dynamics and kinematics model formula: Design of traditional linear speed sliding mode controller u 3i and the traditional angular velocity sliding mode controller u 4i for: in, and are nonlinear gain parameters and are all greater than zero.

6. The finite-time integral sliding mode control method for an intelligent vehicle based on a disturbance observer as claimed in claim 5, characterized in that: The step S43 also includes: setting a conventional linear speed sliding mode controller u 3i and the traditional angular velocity sliding mode controller u 4i The parameters c1, c2, and The specific settings are: c1, c2, and Both are greater than zero.

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