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] The present disclosure relates to the technical field of vehicle control processing, in particular to a heavy-load vehicle trajectory tracking control method, device, equipment and medium. BACKGROUND

[0002] For intelligent heavy-load vehicles driving on unstructured roads, the terrains such as potholed roads and soft soil make the grip and stability of the vehicle decrease. In addition, the carrying demand of the heavy-load vehicle causes the vehicle load to change constantly during the operation process, and the dynamic characteristics of the vehicle also change, so that the accurate acquisition of the vehicle state parameters becomes a key factor restricting the formation of a safe and reliable control strategy.

[0003] In related technologies, due to the complex and changeable curvature of unstructured roads and the road adhesion, there are more and more uncertain disturbance factors, and the vehicle has a risk of instability in the process of following a curve and avoiding obstacles, which causes the heavy-load vehicle to be difficult to effectively drive on unstructured roads based on a preset reference trajectory, thereby reducing the driving reliability of the heavy-load vehicle on unstructured roads. SUMMARY

[0004] The main purpose of the embodiments of the present disclosure is to propose a heavy-load vehicle trajectory tracking control method, device, equipment and medium, which can improve the driving reliability of the heavy-load vehicle on unstructured roads.

[0005] To achieve the above purpose, a first aspect of the embodiments of the present disclosure proposes a heavy-load vehicle trajectory tracking control method, comprising:

[0006] Obtaining vehicle state data of a target heavy-load vehicle and reference trajectory data of the target heavy-load vehicle;

[0007] Establishing a vehicle lateral dynamics model capable of describing the dynamic characteristics of the vehicle and a trajectory tracking error model considering uncertainties according to the vehicle state data and the reference trajectory data, and outputting state quantities of the target heavy-load vehicle containing error information through the vehicle lateral dynamics model and the trajectory tracking error model;

[0008] Obtaining a closed-loop control system feedback gain, a minimum robust invariant set, and a state quantity allowable set and a control quantity allowable set of a nominal system of the target heavy-load 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, outputting a nominal control quantity of the target heavy-load vehicle according to the state quantity and the reference trajectory data, and generating an actual control quantity of the target heavy-load vehicle based on the feedback gain and the nominal control quantity;

[0010] controlling the target heavy-duty vehicle based on the actual control amount, so that the target heavy-duty vehicle travels along the reference trajectory data.

[0011] In some embodiments, the vehicle lateral dynamics model capable of describing vehicle dynamic characteristics is established according to the vehicle state data and the reference trajectory data, and the state quantity of the target heavy-duty vehicle containing error information is output through the vehicle lateral dynamics model and the trajectory tracking error model, including:

[0012] The vehicle lateral dynamics model capable of describing vehicle dynamic characteristics is established based on the vehicle state data, and the center of mass side slip angle and the yaw angle are output through the vehicle lateral dynamics model.

[0013] The trajectory tracking error model considering uncertainty is established based on the center of mass side slip angle, the yaw angle and the reference trajectory data, and the state quantity of the target heavy-duty vehicle containing error information is output through the trajectory tracking error model.

[0014] In some embodiments, the vehicle state data includes front and rear tire nominal side stiffness, front and rear axle to center of mass distance, front wheel steering angle, vehicle mass, vehicle longitudinal speed, direct yaw moment, and body rotational inertia around the axis.

[0015] The vehicle lateral dynamics model capable of describing vehicle dynamic characteristics is established based on the vehicle state data, and the center of mass side slip angle and the yaw angle are output through the vehicle lateral dynamics model, including:

[0016] The vehicle lateral dynamics model capable of describing vehicle dynamic characteristics is established based on the front and rear tire nominal side stiffness, the front and rear axle to center of mass distance, the front wheel steering angle, the vehicle mass, the vehicle longitudinal speed, the direct yaw moment and the body rotational inertia around the axis.

[0017] The center of mass side slip angle and the yaw angle are output through the vehicle lateral dynamics model.

[0018] In some embodiments, the trajectory tracking error model considering uncertainty is established based on the center of mass side slip angle, the yaw angle and the reference trajectory data, and the state quantity of the target heavy-duty vehicle containing error information is output through the trajectory tracking error model, including:

[0019] The lateral error and the heading angle error of the target heavy-duty vehicle are determined.

[0020] a trajectory tracking error model considering uncertainty is established based on the centroid side slip angle, the yaw angle, the lateral error, the heading angle error, the system control quantity of the target heavy-duty vehicle, a measurable curvature disturbance indicated by the reference trajectory data, a system state matrix variation quantity caused by uncertainty, and an unmeasurable disturbance caused by uncertainty;

[0021] A state quantity of the target heavy-duty vehicle containing error information is output through the trajectory tracking error model.

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

[0023] A model predictive control algorithm based on a robust invariant set is used for the system of the target heavy-duty vehicle, and a polyhedral invariant set is used to process asymmetric disturbance and linear inequality constraints to keep the theoretical trajectory of the whole vehicle within the expected limit, so as to obtain the minimum robust invariant set.

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

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

[0026] In the offline calculation process, an error system cost function in an infinite time domain is designed according to 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 through the Riccati equation.

[0028] In some embodiments, the nominal control quantity of the target heavy-duty vehicle is output according to the state quantity and the reference trajectory data, including:

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

[0030] The nominal control sequence in the prediction time domain is output by a preset design function, and is taken as the nominal control quantity of the target heavy-duty vehicle, aiming at the weighted minimization of the nominal system output tracking error and the control input.

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

[0032] a model construction module, configured to establish a vehicle lateral dynamics model capable of describing vehicle dynamic characteristics and a trajectory tracking error model considering uncertainties according to the vehicle state data and the reference trajectory data, and output state quantities of the target heavy-duty vehicle containing error information through the vehicle lateral dynamics model and the trajectory tracking error model;

[0033] an offline processing module, configured to obtain a closed-loop control system feedback gain, a minimum robust invariant set, and state quantity and control quantity allowable sets of a nominal system of the target heavy-duty vehicle obtained through offline calculation;

[0034] an online processing module, configured to output a nominal control quantity of the target heavy-duty vehicle according to the state quantity and the reference trajectory data under constraints of the minimum robust invariant set, the state quantity allowable set and the control quantity allowable set, and generate an actual control quantity of the target heavy-duty vehicle based on the feedback gain and the nominal control quantity;

[0035] a tracking control module, configured 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 object, a third aspect of embodiments of the present disclosure provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the heavy-duty vehicle trajectory tracking control method of the first aspect of embodiments when executing the computer program.

[0037] To achieve the above object, a fourth aspect of embodiments of the present disclosure provides a storage medium, which is a computer readable storage medium, and the storage medium stores a computer program, and the computer program is executed by a processor to implement the heavy-duty vehicle trajectory tracking control method of the first aspect of embodiments.

[0038] The embodiments of the present disclosure can obtain vehicle state data of a target heavy-load vehicle and reference trajectory data of the target heavy-load vehicle by executing the heavy-load vehicle trajectory tracking control method; a vehicle lateral dynamics model capable of describing vehicle dynamic characteristics and a trajectory tracking error model considering uncertainties are established according to the vehicle state data and the reference trajectory data, and the state quantity of the target heavy-load vehicle containing error information is output through the vehicle lateral dynamics model and the trajectory tracking error model; the feedback gain of the closed-loop control system of the target heavy-load vehicle, the minimum robust invariant set, and the state quantity allowable set and the control quantity allowable set of the nominal system obtained through offline calculation are obtained; the nominal control quantity of the target heavy-load vehicle is output according to 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 the actual control quantity of the target heavy-load vehicle is generated based on the feedback gain and the nominal control quantity; and the target heavy-load vehicle is controlled based on the actual control quantity, so that the target heavy-load vehicle travels along the reference trajectory data.

[0039] Therefore, the embodiments of the present disclosure can provide accurate basic input for subsequent control by obtaining vehicle state data and reference trajectory data, and can accurately reflect vehicle dynamic characteristics under load changes and terrain interference by establishing a vehicle lateral dynamics model and a trajectory tracking error model considering uncertainties, thereby avoiding control deviation caused by model distortion. Then, the parameters such as closed-loop feedback gain and minimum robust invariant set are calculated offline to define a safety boundary for system state and deal with stability risks caused by decreased grip, and then the nominal control quantity is generated under the above constraints and the actual control quantity is obtained in combination with the feedback gain, so as to dynamically correct deviation caused by uncertain disturbances and avoid instability caused by control quantity exceeding a safe range. Finally, closed-loop control is realized based on the actual control quantity to ensure that the vehicle travels along the reference trajectory, thereby improving the driving reliability of the heavy-load vehicle on unstructured roads. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 is a flowchart of a heavy-load vehicle trajectory tracking control method provided by the embodiments of the present disclosure;

[0041] Figure 2 is Figure 1 is a flowchart further included in step S102 in

[0042] Figure 3 is Figure 2 is a flowchart further included in step S201 in

[0043] Figure 4 is Figure 2 is a flowchart further included in step S202 in

[0044] Figure 5 is a schematic diagram of a body dynamics model of an intelligent electrically driven heavy-load vehicle provided by the embodiments of the present disclosure;

[0045] Figure 6 is a maximum robust invariant set set relationship schematic diagram provided by an embodiment of the present disclosure.

[0046] Figure 7 is a Tube-MPC-based trajectory tracking controller structure diagram provided by an embodiment of the present disclosure.

[0047] Figure 8 is a smart electric drive heavy-duty vehicle trajectory tracking control method principle diagram provided by an embodiment of the present disclosure.

[0048] Figure 9 is a functional module schematic diagram of a heavy-duty vehicle trajectory tracking control device provided by an embodiment of the present disclosure.

[0049] Figure 10 is a hardware structure schematic diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0050] In order to enable persons skilled in the art to better understand the schemes of the present disclosure, the technical schemes in the embodiments of the present disclosure will be described clearly and completely below in combination with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by persons skilled in the art without creative labor fall within the scope of protection of the present disclosure.

[0051] It can be understood that in the specific embodiments of the present disclosure, vehicle-related data is involved, and when the above embodiments of the present disclosure are applied to specific products or technologies, the permission or consent of the object needs to be obtained, and the collection, use and processing of the relevant data need to comply with relevant laws, regulations and standards.

[0052] In addition, when the embodiments of the present disclosure need to retrieve vehicle-related data, a separate permission or separate consent of the vehicle-related data is obtained through a pop-up window or jumping to a confirmation page, and after obtaining the separate permission or separate consent of the vehicle-related data, the necessary vehicle-related data for enabling the embodiments of the present disclosure to normally operate is obtained.

[0053] In the embodiments of the present disclosure, the term "module" or "unit" refers to a computer program or a part of a computer program with a predetermined function, and works together with other related parts to achieve a predetermined target, and can be implemented entirely or partially by using software, hardware (such as a processing circuit or a memory) or a combination thereof. Similarly, one processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be a part of an overall module or unit that includes the functions of the module or unit.

[0054] It should be noted that in a scenario, the heavy vehicle trajectory tracking control method in the embodiments of the present disclosure can be executed by a target heavy vehicle, and the target heavy vehicle is provided with a processing module to execute the heavy vehicle trajectory tracking control method. Alternatively, the target heavy vehicle can be provided with a data transmission module, and after obtaining relevant data, it is sent to a server for execution, so as to execute the heavy vehicle trajectory tracking control method through the server.

[0055] Exemplarily, the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing 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 basic cloud computing services such as big data and artificial intelligence platforms. In addition, the server can also be a node server in a blockchain network.

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

[0057] Step S101, obtaining vehicle state data of a target heavy vehicle and reference trajectory data of the target heavy vehicle;

[0058] Step S102, establishing a vehicle lateral dynamics model capable of describing vehicle dynamic characteristics and a trajectory tracking error model considering uncertainties according to the vehicle state data and the reference trajectory data, and outputting state quantities of the target heavy vehicle containing error information through the vehicle lateral dynamics model and the trajectory tracking error model;

[0059] Step S103, obtaining the feedback gain of the closed-loop control system of the target heavy vehicle, the minimum robust invariant set, and the state quantity allowable set and the control quantity allowable set of the nominal system obtained through offline calculation;

[0060] Step S104, outputting a nominal control quantity of the target heavy-duty vehicle according to the state quantity and the reference trajectory data under the constraints of the minimum robust invariant set, the state quantity admissible set and the control quantity admissible set, and generating an actual control quantity of the target heavy-duty vehicle based on the feedback gain and the nominal control quantity;

[0061] Step S105, controlling the target heavy-duty vehicle based on the actual control quantity to make the target heavy-duty vehicle travel along the reference trajectory data. For the above step S101, the target heavy-duty vehicle (which can also be referred to as a heavy-duty vehicle) refers to an intelligent vehicle suitable for operation on unstructured roads (such as roads with potholes, soft soil and other terrains), with electric drive characteristics, needing to travel according to a preset reference trajectory, and having load variation characteristics during operation due to carrying requirements. It should be noted that the carrying requirements of the heavy-duty vehicle result in continuous changes in load during operation, which directly changes the dynamic characteristics of the vehicle, making it difficult to accurately obtain the state parameters of the vehicle. This problem is more prominent on unstructured roads, which will reduce the vehicle's grip and stability due to potholes and soft soil, and the heavy load further exacerbates the uncertain disturbance caused by the non-linear characteristics of the tires, as well as the influence of road adhesion changes on tire cornering stiffness, increasing the risk of instability when following a curve and avoiding obstacles.

[0062] The vehicle state data refers to key parameters that can reflect the real-time dynamic characteristics of the target heavy-duty vehicle, for example, can include the center side slip angle, yaw angle, distance from the front and rear axles to the center of mass, vehicle mass, vehicle longitudinal speed, front wheel angle, direct yaw moment, moment of inertia of the vehicle body around the z-axis, etc. These parameters directly determine the lateral motion and yaw motion response characteristics of the vehicle. The reference trajectory data refers to the expected driving path related data of the target heavy-duty vehicle, including the expected position of each point on the reference trajectory, the tangent angle of the expected position, the measurable curvature, etc., which is the basis for judging whether the vehicle deviates from the preset path.

[0063] The embodiments of the present disclosure can provide accurate basic input for subsequent modeling and control. If accurate vehicle state data cannot be obtained, the subsequent model will not reflect the real dynamics of the vehicle, and if clear reference trajectory data is lacking, the deviation between the actual driving state and the expected driving state cannot be quantified. Therefore, the embodiments of the present disclosure obtain two types of core data to build a connection between the actual vehicle and the control target, lay a foundation for subsequent robust model construction and accurate control quantity generation, and are the data entry of the entire trajectory tracking control process.

[0064] For the above step S102, the vehicle lateral dynamics model is a mathematical model derived from the combined derivation of the heavy vehicle lateral motion dynamics equation, the yaw motion dynamics equation and the tire lateral force equation. Exemplarily, the core parameters include front and rear tire nominal cornering stiffness, front and rear axle to mass center distance, vehicle mass, etc., and its function is to accurately describe the dynamic response of the vehicle under the action of lateral force and yaw moment (such as the change law of mass center side slip angle and yaw angle), which is the core model reflecting the lateral driving characteristics of the vehicle. The trajectory tracking error model considering uncertainty can introduce error data to quantify the trajectory deviation, and the uncertainty caused by the nonlinear characteristics of the tire and the tire cornering stiffness error caused by the change of road adhesion will be converted into a calculable mathematical quantity. The state variable containing error information, i.e. the system state variable output by the above trajectory tracking error model, exemplarily can include error data of the vehicle, which is a core index directly reflecting the degree of deviation of the vehicle from the reference trajectory.

[0065] The embodiments of the present disclosure can convert the original data into a mathematical model and error index that can be used for control, and solve the problem of vehicle dynamic characteristic fluctuation under unstructured road. Since unstructured road will change the vehicle dynamic characteristics due to load change and terrain interference, and the uncertainty disturbance increases, the traditional model is easy to lead to control deviation because it does not consider these factors. Therefore, the establishment of the vehicle lateral dynamics model can accurately capture the physical motion law of the vehicle and avoid control deviation caused by model distortion; and the trajectory tracking error model considering uncertainty can convert the disturbances such as road adhesion change and tire nonlinearity, which are difficult to control directly, into quantifiable errors, providing a correction target for subsequent generation of control quantity. Finally, the state variable containing error information is output through the two types of models, realizing the conversion of error index.

[0066] Further, please refer to Figure 2 , Figure 2 is Figure 1 a flowchart further included in step S102. In some embodiments, a vehicle lateral dynamics model capable of describing the dynamic characteristics of the vehicle and a trajectory tracking error model considering uncertainty are established according to the vehicle state data and the reference trajectory data, and the process of outputting the state variable of the target heavy vehicle containing error information through the vehicle lateral dynamics model and the trajectory tracking error model can include steps S201 to S202:

[0067] Step S201, establishing a vehicle lateral dynamics model capable of describing the dynamic characteristics of the vehicle based on the vehicle state data, and outputting the mass center side slip angle and the yaw angle through the vehicle lateral dynamics model;

[0068] Step S202, based on the centroid side slip angle, yaw angle and reference trajectory data, a trajectory tracking error model considering uncertainty is established, and the state quantity of the target heavy load vehicle containing error information is output through the trajectory tracking error model.

[0069] Further, the vehicle state data includes the front and rear tire nominal cornering stiffness, the distance from the front and rear axles to the centroid, the front wheel steering angle, the vehicle mass, the vehicle longitudinal speed, the direct yaw moment, the moment of inertia of the vehicle body around the axis; please refer to Figure 3 , Figure 3 is Figure 2 The flowchart further included in step S201 in some embodiments. In the process of establishing a vehicle lateral dynamics model capable of describing the dynamic characteristics of the vehicle based on the vehicle state data and outputting the centroid side slip angle and yaw angle, steps S301 to S302 can be included:

[0070] Step S301, based on the front and rear tire nominal cornering stiffness, the distance from the front and rear axles to the centroid, 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 around the axis, a vehicle lateral dynamics model capable of describing the dynamic characteristics of the vehicle is established;

[0071] Step S302, output the centroid side slip angle and yaw angle through the vehicle lateral dynamics model.

[0072] Further, please refer to Figure 4 , Figure 4 is Figure 2 The flowchart further included in step S202 in some embodiments. In the process of establishing a trajectory tracking error model considering uncertainty based on the centroid side slip angle, yaw angle and reference trajectory data, and outputting the state quantity of the target heavy load vehicle containing error information through the trajectory tracking error model, steps S401 to S403 can be included:

[0073] Step S401, determine the lateral error and heading angle error of the target heavy load vehicle;

[0074] Step S402, based on the centroid side slip angle, yaw angle, lateral error, heading angle error, system control quantity of the target heavy load vehicle, measurable curvature disturbance indicated by the reference trajectory data, system state matrix change amount caused by uncertainty and unmeasurable disturbance caused by uncertainty, a trajectory tracking error model considering uncertainty is established;

[0075] Step S403, output the state quantity of the target heavy load vehicle containing error information through the trajectory tracking error model.

[0076] In the above steps, the embodiment of the present disclosure establishes a mathematical model capable of accurately reflecting the dynamic response characteristics of the heavy 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 is the intelligent electric drive heavy vehicle body dynamics model provided by the embodiment of the present disclosure, and on the basis of the diagram, the embodiment of the present disclosure can establish the intelligent heavy vehicle longitudinal motion, lateral motion and yaw motion dynamics equation group according to Figure 5

[0077]

[0078] Wherein, l f , l r , l, B f and B r represent the position of the front axle, the rear axle to the center of mass, the wheelbase and the front and rear wheel track respectively; v x , v y , ψ represent the longitudinal speed, lateral speed and heading angle of the vehicle respectively; β represents the vehicle center of mass side slip angle, r represents the yaw angular velocity; F xij and F yij represent the longitudinal force and lateral force of the tire in the tire coordinate system, one set of related subscripts i=f,r represents the front and rear axle tires of the vehicle, and the other set of related subscripts j=l,r represents the left and right side tires of the vehicle; δ f represents the front wheel steering angle. M z represents the direct yaw moment; I z represents the moment of inertia of the vehicle body around the Z axis; m represents the mass of the vehicle. The lateral and longitudinal accelerations a x and a y are respectively:

[0079]

[0080] Based on this, the vehicle lateral dynamics model is derived from the heavy vehicle lateral motion and yaw motion dynamics equations and the tire lateral force equation, and the dynamics model is as follows:

[0081]

[0082] Wherein, β is the center of mass side slip angle; r is the yaw angle; and are the nominal side stiffness of the front and rear tires; l f and l r represent the distance from the front axle and the rear axle to the center of mass; δ f is the front wheel steering angle; m represents the mass of the vehicle; v x is the vehicle longitudinal speed; M z represents the direct yaw moment; I​z 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=[βr e 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:

[0090] B2 = 0.

[0091] For the above step S103, the Tube-MPC method based on the robust invariant set is adopted in the embodiment of the present disclosure, the calculation process of the minimum robust invariant set is simplified for the linear system of the intelligent electrically-driven heavy-load vehicle, the asymmetric disturbance and the linear inequality constraint are processed by using the polyhedral invariant set, and the theoretical trajectory of the whole vehicle is kept within the expected limit.

[0092] The feedback gain of the closed-loop control system is a parameter obtained by solving Riccati equation through designing an infinite-time error system cost function in the offline stage, and the function is to correct the nominal control quantity, so that the actual system can still maintain stability under disturbance.

[0093] The minimum robustly positively invariant set (mRPI) is a set simplified by using support functions, which replaces the traditional Minkowski sum operation to reduce the number of vertices, and can ensure that the state quantity is always kept within the safe range under uncertain disturbance (such as road adhesion change and road curvature mutation), and is the core robustness guarantee for processing asymmetric disturbance and linear constraint.

[0094] The nominal system is an ideal system defined in the Tube-MPC related content of the embodiment of the present disclosure, that is, the vehicle control system ignoring the uncertainty disturbance, the variation law of the state quantity and the control quantity of the nominal system is the benchmark trajectory of the actual system, and is used to simplify the online optimization calculation. The state quantity allowable set / control quantity allowable set is a set obtained by shrinking based on the physical constraints (such as the maximum range of the front wheel steering angle and the safety threshold of the yaw rate) of the actual vehicle during offline calculation, which respectively limits the allowable variation range of the state quantity (such as the lateral error and the heading angle error) and the control quantity (such as the front wheel steering angle adjustment) of the nominal system, so as to avoid instability caused by exceeding the physical limit.

[0095] It should be noted that the key parameters and safety boundaries of robust control are obtained in advance by offline calculation in the embodiment of the present disclosure, and the robustness and real-time performance of the control are balanced. Since there are many uncertain disturbances on the unstructured road, if all the parameters are calculated online, the calculation amount will be too large and the control delay will be caused, thereby causing the risk of instability. At the same time, the lack of minimum robust invariant set and allowable set will make the system lack safety boundaries, and it is easy to exceed the control range due to disturbance. Therefore, offline calculation of the closed-loop feedback gain can determine the error correction coefficient in advance, avoid solving complex equations online, and calculate the minimum robust invariant set to define the safety domain of the state quantity in advance to cope with asymmetric disturbance. In addition, by determining the allowable set of the state quantity / control quantity, the parameter range of the nominal system can be limited in advance to avoid the control quantity exceeding the physical limit of the vehicle. The embodiment of the present disclosure completes these calculations offline, which not only reduces the online operation load and ensures the real-time performance of the control, but also provides robustness guarantee and safety constraint for subsequent online optimization.

[0096] Further, in some embodiments, the minimum robust invariant set is obtained by the following steps, which can include:

[0097] The model predictive control algorithm based on the robust invariant set is used for a system of a target heavy-duty vehicle, and a polyhedral invariant set is used to process asymmetric interference and linear inequality constraints to keep the theoretical trajectory of the whole vehicle within the expected limit to obtain the minimum robust invariant set.

[0098] Further, in some embodiments, the feedback gain is obtained by the following steps, which can include:

[0099] In the offline calculation process, an error system cost function in an infinite time domain is designed according to the heavy-duty vehicle dynamics characteristics of the target heavy-duty vehicle.

[0100] 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 through the Riccati equation.

[0101] In the above steps, the embodiments of the present disclosure propose a simplified minimum robust invariant set solving method to solve the problem of excessive number of vertices in the Minkowski sum operation of the set, which leads to large amount of calculation and affects the real-time performance of the MPC strategy.

[0102] First, the support function is defined as:

[0103] Wherein, h X (f) represents the support function of the set X to the row vector , and for a parameter matrix h X (F) can be represented as h X (F) = [h X (F1)…h X (F m )], F i represents the i-th row of the parameter matrix.

[0104] The sets X, U and D satisfy the conditions:

[0105]

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

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

[0108] Then 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 Where set D and Z max are polyhedrons, let Where Find a polyhedral set The maximum robust invariant set P ∞ of which can be obtained by iteration as follows: P0=C;

[0111] If the invariant set P ∞ satisfies H c :=H d , Then P ∞ can be used as an approximate solution, as shown in Figure 6 , and Figure 6 is a schematic diagram of the set relationship of the maximum robust invariant set provided by the embodiment of the present disclosure. The solution of P ∞ can be realized by k iterations, Where Further, the target overloaded vehicle of the embodiment of the present disclosure is provided with a tracking controller for controlling the driving state of the vehicle, as shown in Figure 7 , and Figure 7 is a structure diagram of a trajectory tracking controller based on Tube-MPC provided by the embodiment of the present disclosure. The tracking controller is designed based on the Tube-MPC optimization mechanism, and mainly includes offline calculation and online solution. The offline calculation mainly calculates the feedback gain K of the closed-loop control system, the minimum robust invariant set Z min , and the nominal system state quantity and control quantity tolerance set The online part mainly solves the optimal control quantity of the nominal system according to the real-time state of the vehicle, and finally combines the closed-loop feedback gain to control the actual system, so as to realize accurate tracking of the target.

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

[0113]

[0114] Wherein, represents the control input quantity of feedback control, and the control input error of the actual system and the nominal system Ke k is the state of the error system at time k, i.e. the state outputted at time k in the above step embodiment, Q k is the state of the error system at time k, i.e. the state outputted at time k in the above step embodiment, Q e is the state of the error system at time k, i.e. the state outputted at time k in the above step embodiment, Q e is the state of the error system at time k, i.e. the state outputted at time k in the above step embodiment, Q

[0115] Assume is a feasible solution of the cost function, when the error system in infinite time domain is asymptotically stable, the feasible solution needs to satisfy According to Lyapunov equation, the cost function in infinite time domain will gradually approach the cost function in finite time domain:

[0116]

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

[0118] For the above step S104 and step S105, the nominal control quantity is an 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, and is a reference value of the actual control quantity. The actual control quantity is generated by the nominal control quantity, the closed-loop feedback gain and the state quantity containing error information according to the Tube-MPC optimization mechanism, is a final control instruction acting on the vehicle, and can offset the influence of the uncertainty disturbance.

[0119] The constraint condition is the safety boundary obtained offline, wherein the minimum robust invariant set constraint fluctuation range of the nominal system state quantity, and the state quantity / control quantity allowable set respectively constrain the limit value of the nominal system state quantity and the control quantity, and together ensure that the parameters do not exceed the safe range in the online optimization process.

[0120] The actual control quantity is a final control instruction fusing the nominal control quantity, the closed-loop feedback gain and the error state quantity, and is specifically used for the actuator (such as adjusting the front wheel steering angle, controlling the direct yaw moment, and the control parameter derived from the vehicle lateral dynamics model) of the vehicle, and directly determines the lateral motion and trajectory correction direction of the vehicle.

[0121] Driving along the reference trajectory data, that is, the control target, refers to adjusting the vehicle dynamics through the actual control quantity to make the lateral error and the heading error between the actual position of the vehicle center of mass and the expected position of the reference trajectory tend to be zero, so as to ensure that the vehicle does not deviate from the preset path and reduce the risk of instability when following a curve or avoiding obstacles.

[0122] It should be noted that the core purpose of the embodiments of the present disclosure is to convert the error index into an executable actual control quantity under safety constraints, and to solve the control deviation problem caused by uncertain disturbances on unstructured roads. Since the curvature of the unstructured road and the road adhesion are variable, directly generating the actual control quantity is easy to cause optimization complexity and control inaccuracy due to disturbances, and the lack of constraints may cause the control quantity to exceed the safety range and cause instability. Therefore, the nominal control quantity is solved under three types of constraints, the baseline control instruction can be obtained based on the ideal system, the optimization process is simplified, and then the control quantity is corrected in combination with the closed-loop feedback gain and the state quantity containing error information, so as to offset the influence of uncertain disturbances such as tire nonlinearity and road adhesion change, and generate the actual control quantity. This logic of first obtaining the baseline and then correcting the error not only ensures the safety of the control quantity through the constraints, but also responds to the disturbances through the feedback correction, so that the actual control quantity can not only fit the reference trajectory, but also adapt to the complex environment of the unstructured road, which is the control instruction generation link for realizing accurate trajectory tracking.

[0123] Further, in the process of outputting the nominal control quantity of the target heavy vehicle according to the state quantity and the reference trajectory data in the above embodiments, the following steps can be included:

[0124] determining the nominal system output tracking error and the control input based on the state quantity and the reference trajectory data;

[0125] taking the weighted minimization of the nominal system output tracking error and the control input as the target, outputting the nominal control sequence in the prediction time domain through a preset design function, and taking the nominal control sequence as the nominal control quantity of the target heavy vehicle.

[0126] In the above steps, the present disclosure adopts the weighted sum of the output quantity and the control input of the nominal system to design the optimization function, and defines the design function as the following form:

[0127]

[0128] wherein, the nominal system output D k =I 4×4 , y k,ref is the reference output, Q and R are weight matrices of the output error and the control quantity respectively, ε is a relaxation quantity, and ρ ε is a soft constraint penalty weight coefficient.

[0129] The online process can still be as shown Figure 7 The constraint setting of the Tube-MPC based trajectory tracking controller for heavy-duty vehicles in the embodiments of the present disclosure mainly revolves around the allowable variation range of system state variables and control variables, the boundaries of which are obtained by contracting according to the actual system state and control variable constraints.

[0130] The actual system state variable constraint should satisfy:

[0131] x min ≤x≤x max ;

[0132] The nominal system state variable constraint should satisfy:

[0133]

[0134] The actual system control variable constraint should satisfy:

[0135] u min ≤u≤u max ;

[0136] The nominal system control variable constraint should satisfy:

[0137]

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

[0139]

[0140] Solving the above optimization model obtains the optimal control sequence of the nominal system within the prediction window, that is, the nominal control variable can be expressed as:

[0141] Then, after fusing the feedback gain, the actual control variable of the actual system can be expressed as:

[0142] Therefore, the embodiment of the present disclosure first acquires the vehicle state data and the reference trajectory data of the target heavy vehicle, and then establishes a vehicle lateral dynamics model capable of accurately describing the dynamic characteristics of the vehicle and a trajectory tracking error model incorporating uncertainties such as tire nonlinearity and road adhesion variation on the basis of the vehicle state data and the reference trajectory data; then, relying on a Tube-MPC algorithm of robust invariant set, the feedback gain of the closed-loop control system is calculated offline, the minimum robust invariant set is simplified by using a support function, and the state quantity / control quantity allowable set of the nominal system is calculated; subsequently, in the online stage, under the constraints of the above invariant set and allowable set, the nominal control quantity is solved according to the state quantity containing error information and the reference trajectory data, with the weighted minimization of the output tracking error of the nominal system and the control input as the goal, and then the actual control quantity is generated in combination with the feedback gain obtained offline; finally, the vehicle actuator is adjusted based on the actual control quantity, so as to realize stable driving of the heavy vehicle along the reference trajectory on an unstructured road, and effectively cope with the fluctuations and instability risks of the dynamic characteristics of the vehicle caused by road disturbances and load changes.

[0143] Exemplarily, refer to Figure 8 , Figure 8 is a schematic diagram of a trajectory tracking control method for an intelligent electrically driven heavy vehicle provided by the embodiment of the present disclosure. The control method takes a reference trajectory (i.e., reference trajectory data) as input, and obtains the real state including the vehicle lateral position, vehicle heading angle, vehicle yaw rate and vehicle mass center side slip angle of the target heavy vehicle through a vehicle dynamics model. On this basis, the system adopts a Tube-MPC core control strategy, which is clearly divided into offline and online parts. The offline part calculates the feedback gain and Tube invariant set (minimum robust invariant set) in advance, and sets 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 real-time correct the control quantity according to the deviation between the actual state and the nominal state, so as to resist the uncertain disturbances introduced by road adhesion, road curvature and lateral force deviation. Finally, the synthesized actual control quantity is applied to the vehicle, forming a closed-loop control system that can ensure the heavy vehicle to accurately and stably track the reference trajectory under uncertainty.

[0144] In summary, by executing the heavy vehicle trajectory tracking control method in steps S101-S105, the vehicle state data and the reference trajectory data are obtained to provide accurate basic input for subsequent control, and the vehicle lateral dynamics model and the trajectory tracking error model considering uncertainty are established, which can accurately reflect the vehicle dynamic characteristics under the influence of load change and terrain interference, avoid control deviation caused by model distortion, then offline calculate the closed-loop feedback gain, minimum robust invariant set and other parameters to define the safety boundary for system state and deal with the stability hidden danger caused by the grip force reduction, then generate the nominal control quantity under the above constraints and obtain the actual control quantity combined with the feedback gain, which can dynamically correct the deviation caused by uncertainty disturbance and avoid instability caused by control quantity exceeding the safety range, and finally realize closed-loop control based on the actual control quantity to ensure the vehicle to travel along the reference trajectory, thereby improving the driving reliability of the heavy vehicle on unstructured roads.

[0145] Referring to Figure 9 The embodiment of the present disclosure also provides a heavy vehicle trajectory tracking control device, which can implement the heavy vehicle trajectory tracking control method described above, and the heavy vehicle trajectory tracking control device comprises:

[0146] The data acquisition module 901 is configured to acquire vehicle state data of a target heavy vehicle and reference trajectory data of the target heavy vehicle.

[0147] The model construction module 902 is configured to establish a vehicle lateral dynamics model capable of describing vehicle dynamic characteristics and a trajectory tracking error model considering uncertainty according to the vehicle state data and the reference trajectory data, and output state quantities of the target heavy vehicle containing error information through the vehicle lateral dynamics model and the trajectory tracking error model.

[0148] The offline processing module 903 is configured to acquire a closed-loop control system feedback gain of the target heavy vehicle, a minimum robust invariant set, and state quantity allowable set and control quantity allowable set of a nominal system of the target heavy vehicle obtained through offline calculation.

[0149] The online processing module 904 is configured to output a nominal control quantity of the target heavy vehicle according to 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 generate an actual control quantity of the target heavy vehicle based on the feedback gain and the nominal control quantity.

[0150] The tracking control module 905 is configured to control the target heavy vehicle based on the actual control quantity, so that the target heavy vehicle travels along the reference trajectory data.

[0151] In summary, the heavy-load vehicle trajectory tracking control device can accurately reflect the dynamic characteristics of the vehicle under the influence of load changes and terrain disturbances by obtaining vehicle state data and reference trajectory data, providing accurate basic input for subsequent control, establishing a vehicle lateral dynamics model and a trajectory tracking error model considering uncertainty, avoiding control deviation caused by model distortion, then calculating offline closed-loop feedback gain, minimum robust invariant set and other parameters to define a safety boundary for the system state and deal with stability risks caused by reduced grip, then generating a nominal control amount under the above constraints and obtaining an actual control amount combined with the feedback gain, dynamically correcting deviations caused by uncertainty disturbances, avoiding control amounts exceeding the safety range and causing instability, and finally implementing closed-loop control based on the actual control amount to ensure that the vehicle travels along the reference trajectory, thereby improving the driving reliability of the heavy-load vehicle on unstructured roads.

[0152] The specific embodiments of the heavy-load vehicle trajectory tracking control device are basically the same as the specific embodiments of the heavy-load vehicle trajectory tracking control method described above, and will not be repeated here. The heavy-load vehicle trajectory tracking control device can also be provided with other functional modules to implement the heavy-load vehicle trajectory tracking control method in the above embodiments, provided that the requirements of the embodiments of the present disclosure are met.

[0153] The embodiments of the present disclosure also provide an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor implements the heavy-load vehicle trajectory tracking control method described above when executing the computer program. The electronic device can be any intelligent terminal, such as a heavy-load vehicle, a server, a tablet computer, a vehicle-mounted computer, etc.

[0154] Please refer to Figure 10 , Figure 10 The hardware structure of the electronic device of another embodiment is illustrated, which includes:

[0155] The processor 1001 can be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, etc., and is used to execute related programs to implement the technical solutions provided by the embodiments of the present disclosure.

[0156] The memory 1002 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 1002 can store operating devices and other application programs. When the technical solutions provided by the embodiments of the present disclosure are implemented by software or firmware, the related program codes are stored in the memory 1002 and are called and executed by the processor 1001 to implement the overload vehicle trajectory tracking control method of the embodiments of the present disclosure.

[0157] The input / output interface 1003 is configured to realize information input and output.

[0158] The communication interface 1004 is configured to realize the communication interaction between the device and other devices. The communication can be realized by a wired manner (for example, a USB, a network cable, etc.) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, etc.).

[0159] The bus 1005 is configured to transmit information between various components (for example, the processor 1001, the memory 1002, the input / output interface 1003, and the communication interface 1004) of the device.

[0160] The processor 1001, the memory 1002, the input / output interface 1003, and the communication interface 1004 are connected to each other through the bus 1005 to realize the communication connection between the devices.

[0161] The embodiments of the present disclosure also provide a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the overload vehicle trajectory tracking control method.

[0162] The memory is a non-transitory computer readable storage medium, which can be used to store a non-transitory software program and a non-transitory computer executable program. In addition, the memory can include a high-speed random access memory and can also include a non-transitory memory, for example, at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory remotely arranged relative to the processor. These remote memories can be connected to the processor through a network. Examples of the network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0163] The embodiments described in the embodiments of the present disclosure are used to more clearly illustrate the technical solutions of the embodiments of the present disclosure and do not constitute a limitation on the technical solutions provided by the embodiments of the present disclosure. Those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present disclosure are also applicable to similar technical problems.

[0164] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present disclosure, and can include more or fewer steps than the figures, or combine certain steps, or different steps.

[0165] The apparatus embodiments described above are merely illustrative, and units described as separate components can or can not be physically separate, i.e., can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments.

[0166] Those skilled in the art can understand that all or some steps in the above disclosed method, the functions of the modules / units in the device, and the equipment can be implemented as software, firmware, hardware, and appropriate combinations thereof.

[0167] The terms "first", "second", "third", "fourth" and the like (if any) in the description of the present disclosure and the above-described drawings are used to distinguish similar objects, and do not necessarily have to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, device, product or apparatus that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or apparatuses.

[0168] It should be understood that in the present disclosure, "at least one" means one or more, and "multiple" means two or more. "And / or" is used to describe the association relationship between the associated objects, which means that there can be three relationships, for example, "A and / or B" can mean: only A, only B, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally represents that the associated objects before and after are in an "or" relationship. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c, can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0169] In several embodiments provided by the present disclosure, it should be understood that the disclosed apparatus and method can be implemented in other manners. For example, the described apparatus embodiments are merely schematic. The units as divided can be combined or integrated into another unit, and some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, apparatuses or units, and can be in electrical, mechanical or other forms.

[0170] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, can be located in one place, or can be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0171] In addition, each functional unit in the various embodiments of the present disclosure can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0172] If the integrated unit is implemented in the form of 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 solutions of the present disclosure essentially or the part that makes a contribution to the prior art, or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes multiple instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of the present disclosure. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program storage media.

[0173] The preferred embodiments of the present disclosure are described above with reference to the accompanying drawings, and the scope of the present disclosure is not limited thereto. Any modification, equivalent replacement and improvement made by those skilled in the art without departing from the scope and spirit of the present disclosure shall fall within the scope 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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