Heavy-load vehicle trajectory tracking control method, device, equipment and medium

Abstract

The embodiment of the invention provides a heavy-load vehicle trajectory tracking control method and device, equipment and a medium, and the method comprises the steps: building a vehicle transverse dynamics model which can describe the dynamic characteristics of a vehicle and a trajectory tracking error model which considers the uncertainty according to vehicle state data and reference trajectory data; the state quantity of the target heavy-load vehicle containing the error information is output; obtaining a closed-loop control system feedback gain and a minimum robust invariant set of the target heavy-load vehicle obtained through offline calculation, and a state quantity tolerance set and a control quantity tolerance set of a nominal system; and under the constraint of the minimum robust invariant set, the state quantity tolerance set and the control quantity tolerance set, outputting the nominal control quantity of the target heavy-load vehicle according to the state quantity and the reference trajectory data, and generating the actual control quantity of the target heavy-load vehicle based on the feedback gain and the nominal control quantity so as to control the target heavy-load vehicle. Therefore, the target heavy-load vehicle runs along the reference trajectory data, and the running reliability of the heavy-load vehicle on the unstructured road is improved.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle control and processing technology, and in particular to a method, device, equipment and medium for tracking and controlling the trajectory of heavy-duty vehicles. Background Technology

[0002] For intelligent heavy-duty vehicles traveling on unstructured roads, terrain such as potholes and soft soil reduces the vehicle's grip and stability. In addition, the carrying requirements of heavy-duty vehicles cause the vehicle load to change constantly during operation, and the vehicle's dynamic characteristics will also change accordingly. The difficulty in accurately obtaining vehicle state parameters has become a key factor restricting the formation of safe and reliable control strategies.

[0003] In related technologies, due to the complex and variable curvature and surface adhesion of unstructured roads, there are more and more uncertain disturbance factors. Vehicles are at risk of instability when following curves and avoiding obstacles. This makes it difficult for heavy-duty vehicles to drive effectively based on preset reference trajectories on unstructured roads, thereby reducing the driving reliability of heavy-duty vehicles on unstructured roads. Summary of the Invention

[0004] The main objective of this disclosure is to provide a method, apparatus, device, and medium for tracking and controlling the trajectory of heavy-duty vehicles, which can improve the driving reliability of heavy-duty vehicles on unstructured roads.

[0005] To achieve the above objectives, a first aspect of this disclosure provides a method for trajectory tracking and control of heavy-duty vehicles, comprising:

[0006] Obtain vehicle status data of the target heavy-load vehicle, as well as reference trajectory data of the target heavy-load vehicle;

[0007] Based on the vehicle state data and the reference trajectory data, a vehicle lateral dynamics model that can describe the dynamic characteristics of the vehicle and a trajectory tracking error model that considers uncertainties are established. The state variables of the target heavy-load vehicle containing error information are output through the vehicle lateral dynamics model and the trajectory tracking error model.

[0008] Obtain the feedback gain, minimum robust invariant set, and nominal system state and control allowable sets of the closed-loop control system of the target heavy-duty vehicle obtained through offline calculation;

[0009] Under the constraints of the minimum robust invariant set, the state quantity allowable set, and the control quantity allowable set, the nominal control quantity of the target heavy-load vehicle is output based on the state quantity and the reference trajectory data, and the actual control quantity of the target heavy-load vehicle is generated based on the feedback gain and the nominal control quantity.

[0010] The target heavy-duty vehicle is controlled based on the actual control quantity so that it travels along the reference trajectory data.

[0011] In some embodiments, the step of establishing a vehicle lateral dynamics model capable of describing the vehicle's dynamic characteristics and a trajectory tracking error model considering uncertainties based on the vehicle state data and the reference trajectory data, and outputting the state variables of the target heavy-load vehicle containing error information through the vehicle lateral dynamics model and the trajectory tracking error model, includes:

[0012] Based on the vehicle state data, a vehicle lateral dynamics model is established that can describe the dynamic characteristics of the vehicle, and the center of gravity sideslip angle and yaw angle are output through the vehicle lateral dynamics model.

[0013] Based on the centroid sideslip angle, the yaw angle, and the reference trajectory data, a trajectory tracking error model considering uncertainties is established, and the state variables of the target heavy-load vehicle containing error information are output through the trajectory tracking error model.

[0014] In some embodiments, the vehicle state data includes the nominal lateral stiffness of the front and rear tires of the target heavy-duty vehicle, the distance from the front and rear axles to the center of gravity, the front wheel steering angle, the vehicle mass, the vehicle longitudinal speed, the direct yaw moment, and the moment of inertia of the vehicle body about the axis.

[0015] The process of establishing a vehicle lateral dynamics model based on the vehicle state data to describe the vehicle's dynamic characteristics, and outputting the center of gravity sideslip angle and yaw angle through the vehicle lateral dynamics model, includes:

[0016] Based on the nominal lateral stiffness of the front and rear tires, the distance from the front and rear axles to the center of gravity, the front wheel steering angle, the vehicle mass, the vehicle longitudinal velocity, the direct yaw moment, and the moment of inertia of the vehicle body about the axis, a vehicle lateral dynamics model that can describe the dynamic characteristics of the vehicle is established.

[0017] The vehicle's lateral dynamics model outputs the sideslip angle and yaw angle.

[0018] In some embodiments, establishing a trajectory tracking error model considering uncertainties based on the centroid sideslip angle, the yaw angle, and the reference trajectory data, and outputting the state variables of the target heavy-duty vehicle containing error information through the trajectory tracking error model, includes:

[0019] Determine the lateral error and heading angle error of the target heavy-duty vehicle;

[0020] Based on the centroid sideslip angle, the yaw angle, the lateral error, the heading angle error, the system control quantity of the target heavy-load vehicle, the measurable curvature disturbance indicated by the reference trajectory data, the change in the system state matrix caused by uncertainty, and the unmeasurable disturbance caused by uncertainty, a trajectory tracking error model considering uncertainty is established.

[0021] The trajectory tracking error model outputs the state variables of the target heavy-duty vehicle, which contain error information.

[0022] In some embodiments, the minimum robust invariant set is obtained through the following steps:

[0023] The model predictive control algorithm based on robust invariant sets, for the target heavy-load vehicle system, uses polyhedral invariant sets to handle asymmetric disturbances and linear inequality constraints, so as to keep the theoretical trajectory of the whole vehicle within the expected limits and obtain the minimum robust invariant set.

[0024] The robust invariant set-based model predictive control algorithm is a model predictive control algorithm based on the support function robust invariant set.

[0025] In some embodiments, the feedback gain is obtained through the following steps:

[0026] During offline calculation, an error system cost function in the infinite time domain is designed based on the heavy-duty vehicle dynamics characteristics of the target heavy-duty vehicle.

[0027] The feedback gain of the closed-loop control system of the target heavy-duty vehicle is obtained by solving the error system cost function in the infinite time domain using the Riccati equation.

[0028] In some embodiments, outputting the nominal control quantity of the target heavy-load vehicle based on the state quantity and the reference trajectory data includes:

[0029] The nominal system output tracking error and control input are determined using the state variables and the reference trajectory data.

[0030] With the goal of minimizing the weighted average of the nominal system output tracking error and the control input, a nominal control sequence in the predicted time domain is obtained through a preset design function and used as the nominal control quantity of the target heavy-duty vehicle.

[0031] To achieve the above objectives, a second aspect of the present disclosure provides a heavy-duty vehicle trajectory tracking and control device, comprising: a data acquisition module, used to acquire vehicle status data of a target heavy-duty vehicle and reference trajectory data of the target heavy-duty vehicle;

[0032] The model building module is used to establish a vehicle lateral dynamics model that can describe the dynamic characteristics of the vehicle and a trajectory tracking error model that considers uncertainties based on the vehicle state data and the reference trajectory data. The module outputs the state variables of the target heavy-load vehicle containing error information through the vehicle lateral dynamics model and the trajectory tracking error model.

[0033] The offline processing module is used to obtain the feedback gain of the closed-loop control system of the target heavy-duty vehicle, the minimum robust invariant set, and the state and control allowable sets of the nominal system obtained through offline calculation.

[0034] An online processing module is used to output the nominal control quantity of the target heavy-load vehicle based on the state quantity and the reference trajectory data, under the constraints of the minimum robust invariant set, the state quantity allowable set and the control quantity allowable set, and to generate the actual control quantity of the target heavy-load vehicle based on the feedback gain and the nominal control quantity.

[0035] The tracking control module is used to control the target heavy-duty vehicle based on the actual control quantity, so that the target heavy-duty vehicle travels along the reference trajectory data.

[0036] To achieve the above objectives, a third aspect of this disclosure provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the heavy-duty vehicle trajectory tracking control method described in the first aspect embodiment.

[0037] To achieve the above objectives, a fourth aspect of the present disclosure provides a storage medium, which is a computer-readable storage medium storing a computer program that, when executed by a processor, implements the heavy-duty vehicle trajectory tracking control method described in the first aspect embodiment.

[0038] This embodiment of the invention, by executing a heavy-duty vehicle trajectory tracking control method, can acquire vehicle state data and reference trajectory data of the target heavy-duty vehicle; establish a vehicle lateral dynamics model describing the vehicle's dynamic characteristics and a trajectory tracking error model considering uncertainties based on the vehicle state data and reference trajectory data; output the state variables of the target heavy-duty vehicle containing error information through the vehicle lateral dynamics model and trajectory tracking error model; acquire the feedback gain, minimum robust invariant set, and nominal system state variable allowable set and control variable allowable set of the target heavy-duty vehicle obtained offline; under the constraints of the minimum robust invariant set, state variable allowable set, and control variable allowable set, output the nominal control variable of the target heavy-duty vehicle based on the state variables and reference trajectory data, and generate the actual control variable of the target heavy-duty vehicle based on the feedback gain and nominal control variable; control the target heavy-duty vehicle based on the actual control variable so that the target heavy-duty vehicle travels along the reference trajectory data.

[0039] Therefore, this embodiment of the present disclosure obtains vehicle state data and reference trajectory data to provide accurate basic input for subsequent control, and establishes a vehicle lateral dynamics model and a trajectory tracking error model that considers uncertainties. This model can accurately reflect the dynamic characteristics of the vehicle under load changes and terrain disturbances, avoiding control deviations caused by model distortion. Then, parameters such as closed-loop feedback gain and minimum robust invariant set are calculated offline to define a safety boundary for the system state and address the stability risks caused by decreased grip. Then, under the above constraints, a nominal control quantity is generated and combined with the feedback gain to obtain the actual control quantity. This can dynamically correct deviations caused by uncertain disturbances and prevent the control quantity from exceeding the safety range and causing instability. Finally, closed-loop control is achieved based on the actual control quantity to ensure that the vehicle travels along the reference trajectory, thereby improving the driving reliability of heavy-duty vehicles on unstructured roads. Attached Figure Description

[0040] Figure 1 This is a flowchart illustrating the heavy-duty vehicle trajectory tracking and control method provided in this embodiment of the disclosure;

[0041] Figure 2 yes Figure 1 A flowchart further includes step S102;

[0042] Figure 3 yes Figure 2 A flowchart further included in step S201;

[0043] Figure 4 yes Figure 2 A flowchart further included in step S202;

[0044] Figure 5 This is a schematic diagram of the vehicle body dynamics model of an intelligent electric-driven heavy-duty vehicle provided in an embodiment of this disclosure;

[0045] Figure 6 This is a schematic diagram of the maximum robust invariant set relationship provided in the embodiments of this disclosure;

[0046] Figure 7 This is a structural diagram of a trajectory tracking controller based on Tube-MPC provided in an embodiment of this disclosure;

[0047] Figure 8 This is a schematic diagram of the trajectory tracking and control method for intelligent electric-driven heavy-duty vehicles provided in this embodiment of the disclosure;

[0048] Figure 9 This is a schematic diagram of the functional modules of the heavy-duty vehicle trajectory tracking and control device provided in this embodiment of the disclosure;

[0049] Figure 10 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this disclosure. Detailed Implementation

[0050] To enable those skilled in the art to better understand the solutions disclosed herein, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this disclosure, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0051] It is understood that in the specific embodiments of this disclosure, which involve retrieving vehicle-related data, when the above embodiments of this disclosure are applied to specific products or technologies, permission or consent from the subject is required, and the collection, use and processing of related data must comply with relevant laws, regulations and standards.

[0052] Furthermore, when this embodiment of the disclosure needs to retrieve vehicle-related data, it will obtain separate permission or separate consent for the vehicle-related data through pop-up windows or redirection to a confirmation page. After clearly obtaining separate permission or separate consent for the vehicle-related data, it will then obtain the necessary vehicle-related data for enabling this embodiment of the disclosure to operate normally.

[0053] In this disclosure, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0054] It should be noted that, in one scenario, the heavy-duty vehicle trajectory tracking control method in this embodiment can be executed by the target heavy-duty vehicle, which is equipped with a processing module to execute the heavy-duty vehicle trajectory tracking control method. Alternatively, the target heavy-duty vehicle can be equipped with a data transmission module, which, after acquiring relevant data, sends it to the server for execution, so that the heavy-duty vehicle trajectory tracking control method can be executed through the server.

[0055] For example, a server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Additionally, a server can also be a node server in a blockchain network.

[0056] It should be noted that the scenarios described in this disclosure are for the purpose of more clearly illustrating the technical solutions of this disclosure, and do not constitute a limitation on the technical solutions provided in this disclosure. Those skilled in the art will understand that, with the evolution of technology and the emergence of new business scenarios, the technical solutions provided in this disclosure are also applicable to similar technical problems. Please refer to... Figure 1 , Figure 1 This is a flowchart illustrating the heavy-duty vehicle trajectory tracking and control method provided in this embodiment. This method can be applied to the target heavy-duty vehicle in the above embodiments, or can be jointly executed by the target heavy-duty vehicle and a server. The heavy-duty vehicle trajectory tracking and control method includes steps S101 to S105:

[0057] Step S101: Obtain the vehicle status data of the target heavy-load vehicle and the reference trajectory data of the target heavy-load vehicle.

[0058] Step S102: Based on vehicle state data and reference trajectory data, establish a vehicle lateral dynamics model that can describe the dynamic characteristics of the vehicle and a trajectory tracking error model that considers uncertainties. Output the state variables of the target heavy-load vehicle containing error information through the vehicle lateral dynamics model and the trajectory tracking error model.

[0059] Step S103: Obtain the feedback gain, minimum robust invariant set, and nominal system state and control allowable sets of the target heavy-duty vehicle obtained through offline calculation.

[0060] Step S104: Under the constraints of the minimum robust invariant set, the state quantity allowable set, and the control quantity allowable set, output the nominal control quantity of the target heavy-load vehicle based on the state quantity and reference trajectory data, and generate the actual control quantity of the target heavy-load vehicle based on the feedback gain and the nominal control quantity.

[0061] Step S105: Control the target heavy-load vehicle based on the actual control quantity to make the target heavy-load vehicle travel along the reference trajectory data. Regarding step S101 above, the target heavy-load vehicle (also referred to as the heavy-load vehicle) refers to an intelligent vehicle suitable for operation on unstructured roads (such as roads with potholes, soft soil, etc.), possessing electric drive characteristics, needing to travel according to a preset reference trajectory, and exhibiting load variations during operation due to transportation needs. Its dynamic characteristics are significantly affected by factors such as load and road surface adhesion. It should be noted that the transportation needs of heavy-load vehicles cause continuous load changes during operation, directly altering the vehicle's dynamic characteristics and making it difficult to accurately obtain vehicle state parameters. This problem is more pronounced on unstructured roads, where potholes and soft soil already reduce vehicle grip and stability. Heavy loads further exacerbate the uncertain disturbances caused by tire nonlinear characteristics and the impact of road surface adhesion changes on tire lateral stiffness, increasing the risk of instability when the vehicle is cornering or avoiding obstacles.

[0062] Vehicle status data refers to key parameters that reflect the real-time dynamic characteristics of the target heavy-duty vehicle. These parameters may include, for example, the sideslip angle, yaw angle, distance from the front and rear axles to the center of gravity, vehicle mass, longitudinal velocity, front wheel steering angle, direct yaw moment, and moment of inertia about the z-axis. These parameters directly determine the vehicle's lateral and yaw response characteristics. Reference trajectory data refers to data related to the target heavy-duty vehicle's preset desired travel path, including the desired position of each point on the reference trajectory, the tangent angle of the desired position, and the measurable curvature. This data serves as the benchmark for determining whether the vehicle deviates from the preset path.

[0063] This disclosure provides accurate basic inputs for subsequent modeling and control. Without accurate vehicle state data, the subsequent model cannot reflect the vehicle's true dynamics; without clear reference trajectory data, the deviation between the actual driving state and the desired driving state cannot be quantified. Therefore, this disclosure establishes a connection between the actual vehicle and the control target by acquiring two types of core data, laying the foundation for building a robust model and generating precise control quantities. It serves as the data entry point for the entire trajectory tracking and control process.

[0064] Regarding step S102 above, the vehicle lateral dynamics model is a mathematical model derived jointly from the lateral motion dynamics equations, yaw motion dynamics equations, and tire lateral force equations of heavy-duty vehicles. For example, core parameters include the nominal lateral stiffness of the front and rear tires, the distance from the front and rear axles to the center of gravity, and the vehicle's mass. Its function is to accurately describe the vehicle's dynamic response under lateral forces and yaw moments (such as the variation patterns of the center of gravity slip angle and yaw angle), and it is the core model reflecting the vehicle's lateral driving characteristics. The trajectory tracking error model, considering uncertainties, can introduce error data to quantify trajectory deviation. Simultaneously, it incorporates uncertain disturbances caused by tire nonlinear characteristics and tire lateral stiffness errors caused by changes in road surface adhesion, transforming uncertainty into calculable mathematical quantities. The state variables containing error information are the system state variables output by the aforementioned trajectory tracking error model. For example, these can include the vehicle's error data, which is a core indicator directly reflecting the degree to which the vehicle deviates from the reference trajectory.

[0065] This disclosure transforms raw data into a mathematical model and error index that can be used for control, addressing the problem of fluctuating vehicle dynamic characteristics on unstructured roads. Because unstructured roads cause changes in vehicle dynamic characteristics due to load variations and terrain disturbances, and increase uncertainties, traditional models, failing to consider these factors, are prone to control inaccuracies. Therefore, establishing a vehicle lateral dynamics model can accurately capture the vehicle's physical motion patterns, avoiding control deviations caused by model distortion. Furthermore, a trajectory tracking error model that considers uncertainties can transform uncontrollable disturbances such as changes in road surface adhesion and tire nonlinearity into quantifiable errors, providing a correction target for subsequent control quantity generation. Ultimately, by outputting state variables containing error information through these two types of models, the transformation of error indices is achieved.

[0066] Further, please refer to Figure 2 , Figure 2 yes Figure 1 The flowchart further includes step S102. In some embodiments, the process of establishing a vehicle lateral dynamics model that describes the dynamic characteristics of the vehicle and a trajectory tracking error model that considers uncertainties based on vehicle state data and reference trajectory data, and outputting the state variables of the target heavy-load vehicle containing error information through the vehicle lateral dynamics model and the trajectory tracking error model, may include steps S201 to S202:

[0067] Step S201: Based on vehicle state data, establish a vehicle lateral dynamics model that can describe the dynamic characteristics of the vehicle, and output the center of gravity sideslip angle and yaw angle through the vehicle lateral dynamics model.

[0068] Step S202: Based on the centroid sideslip angle, yaw angle and reference trajectory data, establish a trajectory tracking error model that considers uncertainties, and output the state variables of the target heavy-load vehicle containing error information through the trajectory tracking error model.

[0069] Furthermore, vehicle status data includes the nominal lateral stiffness of the front and rear tires of the target heavy-duty vehicle, the distance from the front and rear axles to the center of gravity, the front wheel steering angle, the vehicle mass, the vehicle longitudinal velocity, the direct yaw moment, and the vehicle's moment of inertia about the axis; please refer to [link to relevant documentation]. Figure 3 , Figure 3 yes Figure 2 The flowchart further includes step S201. In some embodiments, the process of establishing a vehicle lateral dynamics model that can describe the dynamic characteristics of the vehicle based on vehicle state data, and outputting the center of gravity sideslip angle and yaw angle through the vehicle lateral dynamics model, may include steps S301 to S302:

[0070] Step S301: Based on the nominal lateral stiffness of the front and rear tires, the distance from the front and rear axles to the center of gravity, the front wheel steering angle, the vehicle mass, the vehicle longitudinal velocity, the direct yaw moment, and the moment of inertia of the vehicle body around the axis, establish a vehicle lateral dynamics model that can describe the dynamic characteristics of the vehicle.

[0071] Step S302: Output the sideslip angle and yaw angle of the center of gravity through the vehicle's lateral dynamics model.

[0072] Further, please refer to Figure 4 , Figure 4 yes Figure 2 The flowchart further includes step S202. In some embodiments, the process of establishing a trajectory tracking error model considering uncertainties based on the centroid sideslip angle, yaw angle, and reference trajectory data, and outputting the state variables of the target heavy-load vehicle containing error information through the trajectory tracking error model, may include steps S401 to S403:

[0073] Step S401: Determine the lateral error and heading angle error of the target heavy-load vehicle;

[0074] Step S402: Based on the centroid sideslip angle, yaw angle, lateral error, heading angle error, system control quantity of the target heavy-load vehicle, measurable curvature disturbance indicated by reference trajectory data, system state matrix change caused by uncertainty, and unmeasurable disturbance caused by uncertainty, establish a trajectory tracking error model that considers uncertainty.

[0075] Step S403: Output the state variables of the target heavy-load vehicle containing error information through the trajectory tracking error model.

[0076] In the above steps, this embodiment establishes a mathematical model that accurately reflects the dynamic response characteristics of the heavy-duty vehicle trajectory tracking system, including a vehicle lateral dynamics model and a trajectory tracking error model considering uncertainties. Please refer to... Figure 5 , Figure 5 This is a schematic diagram of the vehicle body dynamics model of an intelligent electric-driven heavy-duty vehicle provided in this embodiment. Based on this diagram, this embodiment can be adapted to... Figure 5 Establish the dynamic equations for the longitudinal, lateral, and yaw motions of the intelligent heavy-duty vehicle:

[0077]

[0078] Among them, l f l r , l, B f and B r These represent the positions of the front and rear axles from the center of gravity, the wheelbase, and the track width between the front and rear wheels, respectively; v x v y ψ and β represent the vehicle's longitudinal velocity, lateral velocity, and heading angle, respectively; β refers to the vehicle's sideslip angle, and r refers to the yaw rate; F xij and F yij This represents the longitudinal and lateral forces acting on the tires in the tire coordinate system. One set of related subscripts, i = f, r, refers to the front and rear axle tires of the vehicle, while the other set, j = l, r, refers to the left and right side tires of the vehicle; δ f Indicates the front wheel steering angle. M z Represents the direct yaw moment; I z The moment of inertia of the vehicle body about the Z-axis is represented by ; m represents the mass of the entire vehicle. Lateral and longitudinal accelerations a x and a y They are represented as follows:

[0079]

[0080] Based on this, the vehicle's lateral dynamics model is derived by combining the dynamic equations of the lateral motion and yaw motion of heavy-duty vehicles with the equations of tire lateral force. The dynamics model is as follows:

[0081]

[0082] Where β is the centroid sideslip angle; r is the yaw angle; and The nominal lateral stiffness of the front and rear tires; f and l r δ represents the distance from the front axle and rear axle to the center of mass, respectively; f The front wheel steering angle is 'm'; the vehicle mass is 'm'; and the steering angle is 'v'. x M represents the longitudinal speed of the vehicle. z Represents the direct yaw moment; Iz This represents the moment of inertia of the vehicle body about the Z-axis.

[0083] Furthermore, this embodiment of the disclosure also establishes a trajectory tracking error model to compare the actual position of the vehicle's center of gravity with the expected position matched on the reference trajectory. In actual tracking, the two positions are not perfectly aligned. The onboard sensors automatically capture terrain information of the mining area road ahead of the heavy-duty vehicle and calculate the relative position information between them in real time. To reflect this characteristic, this embodiment of the disclosure introduces a lateral error e. y and heading angle error e ψ Two variables are used to describe the trajectory. The lateral error is defined as the lateral distance between the actual position of the vehicle's center of gravity and the desired position on the reference trajectory; the heading error is defined as the error between the vehicle's heading angle and the angle between the vehicle's heading angle and the desired position of the center of gravity on the reference trajectory. Therefore, the trajectory tracking model can be expressed as:

[0084]

[0085] Where κ is the measurable curvature, that is, the curvature of the reference trajectory.

[0086] However, considering the uncertain disturbances caused by tire nonlinear characteristics and the mismatch between the actual tire lateral stiffness and the nominal system lateral stiffness when road surface adhesion changes, this disclosure proposes a state-space equation for a heavy-duty vehicle trajectory tracking control system that considers system uncertainties, as a trajectory tracking error model considering uncertainties:

[0087]

[0088] Where, x=[β re y e ψ ] T e represents the system state variable. y e represents the lateral error. ψ Represents the heading angle error; u = [δ] f M z ] T κ represents the system control variable. t The curvature perturbation is the measurable curvature perturbation, i.e., the curvature perturbation of the reference trajectory. ΔA and ΔB are the changes in the system state matrix caused by uncertainties, where ΔA = λ. f A1+λ r A2, ΔB=λ f B1+λ r B2, θ t Unpredictable disturbances caused by uncertainty.

[0089] in: B2 = 0.

[0090] Regarding step S103 above, the present invention discloses a Tube-MPC method based on robust invariant sets. For the linear system of intelligent electric-driven heavy-duty vehicles, it simplifies the calculation process of the minimum robust invariant set, uses polyhedral invariant sets to handle asymmetric disturbances and linear inequality constraints, and keeps the theoretical trajectory of the whole vehicle within the expected limits.

[0091] The feedback gain of a closed-loop control system is a parameter obtained by designing an infinite time-domain error system cost function and solving the Riccati equation in the offline stage. Its function is to correct the nominal control quantity so that the actual system can remain stable under disturbances.

[0092] Minimal Robustly Positively Invariant (mRPI) is a set of computations that uses support functions to simplify calculations. It replaces the traditional Minkowski sum operation to reduce the number of vertices and ensures that the state variables of the system remain within a safe range under uncertain disturbances (such as changes in road surface adhesion or sudden changes in road curvature). It is the core robustness guarantee for handling asymmetric disturbances and linear constraints.

[0093] The nominal system is the ideal system defined in the Tube-MPC related content of this disclosure, that is, a vehicle control system that ignores uncertainty disturbances. The variation law of its state variables and control variables is the reference trajectory of the actual system, which is used to simplify online optimization calculations. The state variable allowable set / control variable allowable set is a set obtained by shrinking based on the physical constraints of the actual vehicle (such as the maximum range of front wheel steering angle and the safe threshold of yaw rate) during offline calculation. It limits the allowable variation range of the nominal system state variables (such as lateral error and heading angle error) and control variables (such as front wheel steering angle adjustment) to avoid exceeding physical limits and causing instability.

[0094] It should be noted that the embodiments of this disclosure obtain key parameters and safety boundaries for robust control in advance through offline calculation, balancing control robustness and real-time performance. Due to the numerous uncertainties and disturbances on unstructured roads, calculating all parameters online would lead to excessive computational load, control delay, and potentially instability. Furthermore, the lack of a minimum robust invariant set and allowable set would result in a lack of safety boundaries for the system, making it susceptible to exceeding control limits due to disturbances. Therefore, offline calculation of the closed-loop feedback gain can determine the error correction coefficient in advance, avoiding online solution of complex equations. Calculating the minimum robust invariant set can predefine the safety domain of state variables to address asymmetric disturbances. In addition, determining the allowable set of state variables / control variables can predefine the parameter range of the nominal system, preventing control variables from exceeding the vehicle's physical limits. By performing these calculations offline, the embodiments of this disclosure reduce the online computational load, ensure control real-time performance, and provide robustness guarantees and safety constraints for subsequent online optimization.

[0095] Furthermore, in some embodiments, the aforementioned minimum robust invariant set is obtained through the following steps, which may include:

[0096] The model predictive control algorithm based on robust invariant sets, for the system of target heavy-load vehicles, adopts polyhedral invariant sets to handle asymmetric disturbances and linear inequality constraints, so as to keep the theoretical trajectory of the whole vehicle within the expected limits and obtain the minimum robust invariant set; among them, the model predictive control algorithm based on robust invariant sets based on support functions is adopted.

[0097] Furthermore, in some embodiments, the feedback gain is obtained through the following steps, which may include:

[0098] During offline calculation, an error system cost function in the infinite time domain is designed based on the dynamic characteristics of the target heavy-load vehicle.

[0099] By solving the Riccati equation for the cost function of the error system in the infinite time domain, the feedback gain of the closed-loop control system of the target heavy-load vehicle is obtained.

[0100] In the above steps, this embodiment of the present disclosure proposes a simplified method for solving the minimum robust invariant set, which addresses the problem that the number of vertices is too large when solving the minimum robust invariant set using the Minkowski summation operation, resulting in a large amount of computation and affecting the real-time performance of the MPC strategy.

[0101] First, define the support function as follows:

[0102]

[0103] Among them, h X (f) represents the set X for row vectors The support function for a parameter matrix h X (F) can be represented as h X (F)=[h X (F1)…h X (F m )], F i This represents the i-th row of the parameter matrix.

[0104] Define sets X, U, and D that satisfy the following conditions:

[0105]

[0106] Where X and U represent the set of state variables and the set of control variables of the actual system, respectively, and the set D satisfies

[0107] The maximum robust invariant set is the set containing all robust invariant sets, and it is also defined based on the sequence limit set:

[0108] Then there is The minimum robust invariant set and the maximum robust invariant set satisfy the following relationship:

[0109]

[0110] Based on the maximum robust invariant set, k iterations are performed to obtain... Among them, sets D and Z max They are all polyhedra, making in Find a set of polyhedra Its largest robust invariant set P ∞ It can be obtained by iterating through the following formula: P0 = C;

[0111] If the invariant set P ∞ Satisfying H c :=H d , When, then P ∞ Able to serve as External approximation solution, such as Figure 6 As shown, Figure 6 This is a schematic diagram illustrating the relationship of the largest robust invariant set provided in this embodiment of the disclosure. ∞ The solution can be achieved through k iterations. in Furthermore, the target heavy-duty vehicle in this embodiment of the disclosure is equipped with a tracking controller for controlling the vehicle's driving state, such as... Figure 7 As shown, Figure 7 This is a structural diagram of a trajectory tracking controller based on Tube-MPC provided in this embodiment. This embodiment designs the tracking controller based on the Tube-MPC optimization mechanism. The controller mainly consists of two parts: offline computation and online solution. The offline computation mainly calculates the feedback gain K of the closed-loop control system and the minimum robust invariant set Z. min and the allowable set of nominal system state variables and control variables. The calculation, in the online part, mainly solves the optimal control quantity of the nominal system based on the real-time status of the vehicle, and finally combines the closed-loop feedback gain to provide control input to the actual system, thereby achieving accurate tracking of the target.

[0112] The cost function of the error system in the infinite time domain can be expressed as:

[0113]

[0114] in, This represents the control input quantity for feedback control, specifically the control input error between the actual system and the nominal system. Available by Ke k Get, e k Q represents the state variable of the error system (i.e., the trajectory tracking error model in the above embodiments) at time k, which is also the state variable output at time k in the above step embodiments. e R represents the control weight of the error system state variables. e This represents the control weight of the error system input.

[0115] Assumption For a feasible solution to the cost function, when the error system in the infinite time domain is asymptotically stable, the feasible solution must satisfy the following conditions: According to Lyapunov's equation, the cost function in the infinite time domain will gradually approximate the cost function in the finite time domain:

[0116]

[0117] Among them, the weight P matrix satisfies the Riccati equation A T (P-PB(R e +B T PB) -1 B T P)A-P+Q e =0, therefore, the feedback gain can be expressed by K = -(R e +B T PB) -1 B T PA is obtained.

[0118] Regarding steps S104 and S105 above, the nominal control quantity is the ideal control quantity obtained by solving the nominal system optimization problem under the constraints of the minimum robust invariant set, the state quantity allowable set, and the control quantity allowable set. It is the benchmark value of the actual control quantity. The actual control quantity is generated jointly by the nominal control quantity, the closed-loop feedback gain, and the state quantity containing error information, according to the optimization mechanism in Appendix Tube-MPC. It is the final control command applied to the vehicle and can offset the effects of uncertain disturbances.

[0119] The constraints are the safety boundaries obtained offline. The minimum robust invariant set constrains the fluctuation range of the nominal system state variables, and the allowable sets of state variables and control variables constrain the limit values ​​of the nominal system state variables and control variables, respectively, to ensure that the parameters do not exceed the safety range during the online optimization process.

[0120] The actual control quantity is the final control command that integrates the nominal control quantity, closed-loop feedback gain, and error state quantity. It is specifically applied to the vehicle's actuators (such as adjusting the front wheel steering angle and controlling the direct yaw moment, which are control parameters derived from the vehicle's lateral dynamics model), and directly determines the vehicle's lateral movement and trajectory correction direction.

[0121] Driving along the reference trajectory data is the control target. It refers to adjusting the vehicle's dynamics through actual control quantities so that the lateral and heading errors between the actual position of the vehicle's center of gravity and the expected position of the reference trajectory approach zero, ensuring that the vehicle does not deviate from the preset path and reducing the risk of instability when following curves and avoiding obstacles.

[0122] It should be noted that the core objective of this disclosure is to transform error indicators into executable actual control quantities under safety constraints, thereby addressing the control deviation problem caused by uncertain disturbances on unstructured roads. Due to the variable curvature and road surface adhesion of unstructured roads, directly generating actual control quantities can easily lead to complex optimization and control inaccuracies due to disturbances. Furthermore, a lack of constraints may cause the control quantities to exceed safe limits, resulting in instability. Therefore, solving for the nominal control quantity under three types of constraints involves first obtaining a reference control command based on an ideal system to simplify the optimization process. Then, combining the closed-loop feedback gain and state variables containing error information, the control quantity is corrected to offset the effects of uncertain disturbances such as tire nonlinearity and changes in road surface adhesion, thus generating the actual control quantity. This logic of first obtaining a reference and then correcting the error ensures the safety of the control quantity through constraints and addresses disturbances through feedback correction. This allows the actual control quantity to both conform to the reference trajectory and adapt to the complex environment of unstructured roads, making it the crucial step in generating control commands for accurate trajectory tracking.

[0123] Furthermore, in the process of outputting the nominal control quantity of the target heavy-load vehicle based on the state quantity and reference trajectory data in the above embodiments, the following steps may be included:

[0124] The nominal system output tracking error and control input are determined using state variables and reference trajectory data;

[0125] With the goal of minimizing the nominal system output tracking error and the weighted control input, a predetermined design function is used to output the nominal control sequence in the predicted time domain, which is then used as the nominal control quantity for the target heavy-duty vehicle.

[0126] In the above steps, the embodiments of this disclosure use the output of the nominal system. and control input We use a weighted sum to design an optimization function, defining the design function as follows:

[0127]

[0128] Among them, the nominal system output D k =I 4×4 y k,ref For the reference output, Q and R are the output error and control quantity, respectively. The weight matrix, ε is the relaxation factor, ρ εThis represents the weighting coefficient for soft constraint penalties.

[0129] The online process can still be as Figure 7 As shown, the constraint settings of the heavy-duty vehicle trajectory tracking controller based on Tube-MPC in this embodiment are mainly based on the allowable range of changes in system state variables and control variables. The boundaries of the two are obtained by shrinking the constraints based on the actual system state and control variables.

[0130] The actual system state constraints should satisfy:

[0131] x min ≤x≤x max ;

[0132] The nominal system state constraints should satisfy:

[0133]

[0134] The actual system control constraints should satisfy:

[0135] u min ≤u≤u max ;

[0136] The nominal system control constraints should satisfy:

[0137]

[0138] Therefore, the control optimization problem of the nominal system of heavy-duty vehicles based on Tube-MPC can be summarized as follows:

[0139]

[0140] Solving the above optimization model yields the nominal system optimal control sequence within the prediction window, i.e., the nominal control quantity can be expressed as:

[0141] Therefore, after incorporating the feedback gain, the actual control quantity of the actual system can be expressed as:

[0142]

[0143] Therefore, this embodiment first obtains the vehicle state data and reference trajectory data of the target heavy-duty vehicle. Based on this, a vehicle lateral dynamics model that accurately describes the vehicle's dynamic characteristics and a trajectory tracking error model incorporating uncertainties such as tire nonlinearity and road surface adhesion changes are established. Then, relying on the robust invariant set model predictive control (Tube-MPC) algorithm, the feedback gain of the closed-loop control system, the minimum robust invariant set simplified by using support functions, and the nominal system's state / control quantity allowable set are calculated offline. Subsequently, in the online stage, under the constraints of the above invariant set and allowable set, the nominal control quantity is solved based on the state quantity containing error information and the reference trajectory data, with the goal of minimizing the weighted average of the nominal system output tracking error and control input. The actual control quantity is then generated by combining the feedback gain obtained offline. Finally, the vehicle actuator is adjusted based on the actual control quantity to achieve stable driving of the heavy-duty vehicle along the reference trajectory on unstructured roads, effectively addressing the fluctuations in vehicle dynamic characteristics and the risk of instability caused by road surface disturbances and load changes.

[0144] For example, please refer to Figure 8 , Figure 8 This is a schematic diagram of the intelligent electric-driven heavy-duty vehicle trajectory tracking control method provided in this embodiment. The control method takes a reference trajectory (i.e., reference trajectory data) as input and obtains the actual state of the target heavy-duty vehicle, including its lateral position, heading angle, yaw rate, and sideslip angle, through a vehicle dynamics model. Based on this, the system adopts the core control strategy of Tube-MPC, which is clearly divided into offline and online parts. The offline part pre-calculates the feedback gain and Tube invariant set (minimum robust invariant set) to set a safety boundary for the system. The online part includes a nominal system and an error system. The nominal system solves the ideal control quantity through MPC optimization under the contraction constraints of state and control, while the error system uses the feedback gain calculated offline to correct the control quantity in real time according to the deviation between the actual state and the nominal state to counteract uncertain disturbances introduced by road surface adhesion, road curvature, and lateral force deviation. Finally, the synthesized actual control quantity is applied to the vehicle to form a closed-loop control system that can ensure that the heavy-duty vehicle can accurately and stably track the reference trajectory under uncertainty.

[0145] In summary, the embodiments of this disclosure, by executing the heavy-duty vehicle trajectory tracking control method in steps S101 to S105, acquire vehicle state data and reference trajectory data to provide accurate basic inputs for subsequent control, and establish a vehicle lateral dynamics model and a trajectory tracking error model considering uncertainties. This accurately reflects the vehicle's dynamic characteristics under load changes and terrain disturbances, avoiding control deviations caused by model distortion. Then, parameters such as closed-loop feedback gain and minimum robust invariant set are calculated offline to define a safety boundary for the system state and address stability risks caused by decreased grip. Under the above constraints, a nominal control quantity is generated and combined with the feedback gain to obtain the actual control quantity. This dynamically corrects deviations caused by uncertain disturbances, preventing the control quantity from exceeding the safety range and causing instability. Finally, closed-loop control is achieved based on the actual control quantity, ensuring that the vehicle travels along the reference trajectory, thereby improving the reliability of heavy-duty vehicles on unstructured roads.

[0146] Please see Figure 9 This disclosure also provides a heavy-duty vehicle trajectory tracking control device, which can implement the above-described heavy-duty vehicle trajectory tracking control method. The heavy-duty vehicle trajectory tracking control device includes:

[0147] The data acquisition module 901 is used to acquire the vehicle status data of the target heavy-load vehicle and the reference trajectory data of the target heavy-load vehicle.

[0148] The model building module 902 is used to build a vehicle lateral dynamics model that can describe the dynamic characteristics of the vehicle and a trajectory tracking error model that considers uncertainties based on vehicle state data and reference trajectory data. The model builds the vehicle lateral dynamics model and the trajectory tracking error model to output the state variables of the target heavy-load vehicle containing error information.

[0149] The offline processing module 903 is used to obtain the feedback gain of the closed-loop control system of the target heavy-duty vehicle, the minimum robust invariant set, and the state variable allowable set and control variable allowable set of the nominal system obtained through offline calculation.

[0150] The online processing module 904 is used to output the nominal control quantity of the target heavy-load vehicle based on the state quantity and reference trajectory data under the constraints of the minimum robust invariant set, the state quantity allowable set and the control quantity allowable set, and to generate the actual control quantity of the target heavy-load vehicle based on the feedback gain and the nominal control quantity.

[0151] The tracking control module 905 is used to control the target heavy-load vehicle based on the actual control quantity, so that the target heavy-load vehicle travels along the reference trajectory data.

[0152] In summary, the heavy-duty vehicle trajectory tracking control device, through the execution of the heavy-duty vehicle trajectory tracking control method in the above embodiments, obtains vehicle state data and reference trajectory data to provide accurate basic inputs for subsequent control, and establishes a vehicle lateral dynamics model and a trajectory tracking error model considering uncertainties. This accurately reflects the vehicle's dynamic characteristics under load changes and terrain disturbances, avoiding control deviations caused by model distortion. Then, it calculates parameters such as closed-loop feedback gain and minimum robust invariant set offline to define a safety boundary for the system state, addressing stability risks caused by decreased grip. Under the above constraints, it generates a nominal control quantity and combines it with the feedback gain to obtain the actual control quantity. This dynamically corrects deviations caused by uncertain disturbances, preventing the control quantity from exceeding the safe range and causing instability. Finally, it achieves closed-loop control based on the actual control quantity, ensuring that the vehicle travels along the reference trajectory, thereby improving the reliability of heavy-duty vehicles on unstructured roads.

[0153] The specific implementation of the heavy-duty vehicle trajectory tracking control device is basically the same as the specific embodiment of the heavy-duty vehicle trajectory tracking control method described above, and will not be repeated here. Subject to meeting the requirements of the embodiments of this disclosure, the heavy-duty vehicle trajectory tracking control device may also be equipped with other functional modules to implement the heavy-duty vehicle trajectory tracking control method in the above embodiments.

[0154] This disclosure also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described heavy-duty vehicle trajectory tracking and control method. This electronic device can be any intelligent terminal, including heavy-duty vehicles, servers, tablet computers, in-vehicle computers, etc.

[0155] Please see Figure 10 , Figure 10 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0156] The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this disclosure.

[0157] The memory 1002 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1002 can store operating devices and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001 to execute the heavy-duty vehicle trajectory tracking control method of the embodiments of this disclosure.

[0158] Input / output interface 1003 is used to implement information input and output;

[0159] The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0160] Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004);

[0161] The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0162] This disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described heavy-duty vehicle trajectory tracking control method.

[0163] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0164] The embodiments described in this disclosure are for the purpose of more clearly illustrating the technical solutions of this disclosure and do not constitute a limitation on the technical solutions provided by this disclosure. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by this disclosure are also applicable to similar technical problems.

[0165] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this disclosure, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0166] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0167] Those skilled in the art will understand that all or some of the steps, apparatuses, or functional modules / units in the methods disclosed above can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0168] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in this disclosure and the foregoing drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0169] It should be understood that in this disclosure, "at least one item" means one or more, and "more than one" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0170] In the several embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0171] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0172] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0173] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0174] The preferred embodiments of the present disclosure have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present disclosure. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and spirit of the present disclosure shall be within the scope of the claims of the present disclosure.

Claims

1. A method for trajectory tracking and control of heavy-duty vehicles, characterized in that, include: Acquire the vehicle status data of the target heavy-load vehicle, as well as the reference trajectory data of the target heavy-load vehicle; Based on the vehicle state data and the reference trajectory data, a vehicle lateral dynamics model that can describe the dynamic characteristics of the vehicle and a trajectory tracking error model that considers uncertainties are established. The state variables of the target heavy-load vehicle containing error information are output through the vehicle lateral dynamics model and the trajectory tracking error model. Obtain the feedback gain, minimum robust invariant set, and nominal system state and control allowable sets of the target heavy-duty vehicle obtained through offline calculation; Under the constraints of the minimum robust invariant set, the state quantity allowable set, and the control quantity allowable set, the nominal control quantity of the target heavy-load vehicle is output based on the state quantity and the reference trajectory data, and the actual control quantity of the target heavy-load vehicle is generated based on the feedback gain and the nominal control quantity. The target heavy-duty vehicle is controlled based on the actual control quantity so that it travels along the reference trajectory data.

2. The heavy-duty vehicle trajectory tracking and control method according to claim 1, characterized in that, The step of establishing a vehicle lateral dynamics model that describes the vehicle's dynamic characteristics and a trajectory tracking error model that considers uncertainties based on the vehicle state data and the reference trajectory data, and outputting the state variables of the target heavy-load vehicle containing error information through the vehicle lateral dynamics model and the trajectory tracking error model, includes: Based on the vehicle state data, a vehicle lateral dynamics model is established that can describe the dynamic characteristics of the vehicle, and the center of gravity sideslip angle and yaw angle are output through the vehicle lateral dynamics model. Based on the centroid sideslip angle, the yaw angle, and the reference trajectory data, a trajectory tracking error model considering uncertainties is established, and the state variables of the target heavy-load vehicle containing error information are output through the trajectory tracking error model.

3. The heavy-duty vehicle trajectory tracking and control method according to claim 2, characterized in that, The vehicle status data includes the nominal lateral stiffness of the front and rear tires of the target heavy-duty vehicle, the distance from the front and rear axles to the center of gravity, the front wheel steering angle, the total vehicle mass, the vehicle longitudinal speed, the direct yaw moment, and the moment of inertia of the vehicle body about the axis. The process of establishing a vehicle lateral dynamics model based on the vehicle state data to describe the vehicle's dynamic characteristics, and outputting the center of gravity sideslip angle and yaw angle through the vehicle lateral dynamics model, includes: Based on the nominal lateral stiffness of the front and rear tires, the distance from the front and rear axles to the center of gravity, the front wheel steering angle, the vehicle mass, the vehicle longitudinal velocity, the direct yaw moment, and the moment of inertia of the vehicle body about the axis, a vehicle lateral dynamics model that can describe the dynamic characteristics of the vehicle is established. The vehicle's lateral dynamics model outputs the sideslip angle and yaw angle.

4. The heavy-duty vehicle trajectory tracking and control method according to claim 2, characterized in that, The process of establishing a trajectory tracking error model considering uncertainties based on the centroid sideslip angle, the yaw angle, and the reference trajectory data, and outputting the state variables of the target heavy-load vehicle containing error information through the trajectory tracking error model, includes: Determine the lateral error and heading angle error of the target heavy-duty vehicle; Based on the centroid sideslip angle, the yaw angle, the lateral error, the heading angle error, the system control quantity of the target heavy-load vehicle, the measurable curvature disturbance indicated by the reference trajectory data, the change in the system state matrix caused by uncertainty, and the unmeasurable disturbance caused by uncertainty, a trajectory tracking error model considering uncertainty is established. The trajectory tracking error model outputs the state variables of the target heavy-duty vehicle, which contain error information.

5. The heavy-duty vehicle trajectory tracking and control method according to claim 1, characterized in that, The minimum robust invariant set is obtained through the following steps: The model predictive control algorithm based on robust invariant sets, for the target heavy-load vehicle system, uses polyhedral invariant sets to handle asymmetric disturbances and linear inequality constraints, so as to keep the theoretical trajectory of the whole vehicle within the expected limits and obtain the minimum robust invariant set. The robust invariant set-based model predictive control algorithm is a model predictive control algorithm based on the support function robust invariant set.

6. The heavy-duty vehicle trajectory tracking and control method according to claim 1, characterized in that, The feedback gain is obtained through the following steps: During offline calculation, an error system cost function in the infinite time domain is designed based on the heavy-duty vehicle dynamics characteristics of the target heavy-duty vehicle. The feedback gain of the closed-loop control system of the target heavy-duty vehicle is obtained by solving the error system cost function in the infinite time domain using the Riccati equation.

7. The heavy-duty vehicle trajectory tracking and control method according to claim 1, characterized in that, The step of outputting the nominal control quantity of the target heavy-load vehicle based on the state quantity and the reference trajectory data includes: The nominal system output tracking error and control input are determined using the state variables and the reference trajectory data. With the goal of minimizing the weighted average of the nominal system output tracking error and the control input, a nominal control sequence in the predicted time domain is obtained through a preset design function and used as the nominal control quantity of the target heavy-duty vehicle.

8. A heavy-duty vehicle trajectory tracking and control device, characterized in that, include: The data acquisition module is used to acquire vehicle status data of the target heavy-load vehicle and reference trajectory data of the target heavy-load vehicle. The model building module is used to establish a vehicle lateral dynamics model that can describe the dynamic characteristics of the vehicle and a trajectory tracking error model that considers uncertainties based on the vehicle state data and the reference trajectory data. The module outputs the state variables of the target heavy-load vehicle containing error information through the vehicle lateral dynamics model and the trajectory tracking error model. The offline processing module is used to obtain the feedback gain of the closed-loop control system of the target heavy-duty vehicle, the minimum robust invariant set, and the state and control allowable sets of the nominal system obtained through offline calculation. An online processing module is used to output the nominal control quantity of the target heavy-load vehicle based on the state quantity and the reference trajectory data, under the constraints of the minimum robust invariant set, the state quantity allowable set and the control quantity allowable set, and to generate the actual control quantity of the target heavy-load vehicle based on the feedback gain and the nominal control quantity. The tracking control module is used to control the target heavy-duty vehicle based on the actual control quantity, so that the target heavy-duty vehicle travels along the reference trajectory data.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the heavy-duty vehicle trajectory tracking control method according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the heavy-duty vehicle trajectory tracking control method according to any one of claims 1 to 8.

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