A trajectory tracking control method, system, storage medium and controller
By setting multiple control time domain lengths in the model predictive controller, conducting performance tests and processing control quantity sets, and optimizing control quantity calculation, the problem of decreased response efficiency caused by increased computational load of the model predictive controller was solved, and efficient trajectory tracking control was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HUIZHOU DESAY SV AUTOMOTIVE
- Filing Date
- 2024-11-30
- Publication Date
- 2026-06-02
AI Technical Summary
When the number of control time domains is increased, the computational load of existing model predictive controllers increases, leading to a decrease in the response efficiency of trajectory tracking control.
By pre-establishing constraint equations, setting multiple control time domain lengths, conducting performance tests, obtaining the target control length, obtaining the set of control quantities, and substituting the control state matrix into the constraint equations, the control quantity calculation process is optimized.
This improves the response speed and computational efficiency of trajectory tracking control, ensuring that the controller remains efficient and stable under various conditions.
Smart Images
Figure CN122131753A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of model optimization technology, and in particular to a trajectory tracking control method, system, storage medium and controller. Background Technology
[0002] In recent years, trajectory tracking control can be broadly categorized into four types based on the methods employed: those represented by classical control theory (PID), those represented by preview control theory, those represented by modern control theory, and those represented by deep learning and reinforcement learning. Among these, model predictive control, represented by modern control theory, is widely used due to its significant advantage in solving constrained optimization problems. Considering vehicle dynamics, driver model preferences, and the characteristics of lateral and longitudinal coupled integrated control, controllers based on model predictive controllers are continuously being innovated and optimized in both academic research and engineering applications.
[0003] In general, the control time domain in a model predictive controller represents the number of control variables at future times. If the number of control time domain variables is increased, the accuracy of the model controller will be improved, but the computation time for solving the objective function will also increase, which will lead to the inability of trajectory tracking control to achieve a high-efficiency response. Summary of the Invention
[0004] To address the aforementioned technical problems, this application provides a trajectory tracking control method, system, storage medium, and controller that can efficiently calculate control quantities, thereby improving the response speed of trajectory tracking control.
[0005] Specifically, this application provides a trajectory tracking control method, which pre-establishes constraint equations and includes the following steps:
[0006] Multiple control time domain lengths are set, and the constraint equations are tested based on each control time domain length to obtain the target control length based on the performance test results.
[0007] Based on the target control length, the replacement values of each control quantity are obtained to obtain a set of control quantities.
[0008] The control state matrix is obtained from the set of control variables, and the control state matrix is substituted into the constraint equation to obtain the control variable corresponding to the current control moment.
[0009] In addition, the vehicle control status at the current control moment is collected, and the vehicle control status is adjusted based on the control quantity to perform trajectory tracking control on the target vehicle.
[0010] In the above technical solution, the control state matrix is transformed by obtaining the substitution value of the control quantity. The transformed control state matrix is then introduced into the constraint equation, reducing the number of degrees of freedom of the control quantity during the optimization process of the equation, thereby reducing the computational load and improving the solution efficiency. At the same time, verification of the calculation method of this application shows that compared with the traditional predictive controller, this application improves the solution accuracy. Therefore, trajectory tracking control based on the control quantity calculated by this application can optimize control efficiency and improve the control response speed.
[0011] Furthermore, the setting of multiple control time domain lengths includes:
[0012] Obtain a predefined control time domain range, and set multiple control time domain lengths according to the control time domain range.
[0013] In the above technical solution, the setting of the control time domain length is not arbitrary, but based on the predefined control time domain range of the controller. This range can be set by those skilled in the art according to the actual application requirements.
[0014] By setting multiple control time domain lengths within a predefined range, the impact of different control time domain lengths on controller performance can be evaluated more comprehensively, providing more accurate data support and thus selecting the optimal control time domain length. By setting multiple control time domain lengths and analyzing their impact on solution efficiency, the computational load and solution time can be reduced to the minimum while ensuring control accuracy, thereby improving the real-time performance of the controller. Reasonable control time domain length settings can also avoid over-prediction and over-control.
[0015] Furthermore, obtaining the target control length includes:
[0016] Simulation tests are performed on the current model predictive controller for each control time domain length to obtain the controller performance for each control time domain length; wherein, the controller performance includes at least response time and computation time.
[0017] Furthermore, the controller performance is evaluated based on predefined performance indicators, and the optimal control time domain length is obtained based on the evaluation results as the target control length.
[0018] In the above technical solution, by conducting simulation tests under multiple control time domain lengths, the impact of each control time domain length on the controller performance can be comprehensively evaluated, thereby avoiding the deviation that may be caused by a single control time domain length. Furthermore, performance evaluation based on simulation test data makes the comparison and selection process of performance indicators more scientific and systematic. By using predefined performance indicators, the performance of each control time domain length can be accurately evaluated, thereby selecting the optimal control time domain length and ensuring that the controller can maintain high efficiency and stability under various conditions.
[0019] Furthermore, the acquisition of the control quantity set includes:
[0020] Multiple control quantities are obtained based on the target control length.
[0021] Under each control variable, the control influence range of the current model predictive controller within a preset time period is analyzed to obtain control variables with the same control influence range, and the control variables with the same control influence range are unified.
[0022] In addition, the set of control variables is obtained based on the unified control variables.
[0023] In the above technical solution, by comparing the influence range of different control quantities, control quantities with the same or similar control effects are identified. These control quantities show consistency in response and dynamic changes. By unifying control quantities with the same or similar control influence range, redundant calculations and processing are avoided, and computational efficiency is significantly improved. All unified control quantities are combined into a set of control quantities, which covers the optimal control inputs in different time periods and states.
[0024] Furthermore, the control state matrix includes at least an identity matrix and a control quantity matrix; obtaining the control state matrix includes:
[0025] The identity matrix and the control quantity matrix are updated based on the set of control quantities, respectively, so as to obtain the control state matrix according to the updated identity matrix and control quantity matrix.
[0026] In the above technical solution, the dynamic updating of the control state matrix ensures that the controller can adaptively adjust the control strategy according to the latest set of control variables, thereby improving the response capability to dynamic changes. By integrating the unified control variable information into the control variable matrix, it is ensured that the control input at each control moment is based on the controller's optimal state and control strategy, thereby improving the precision and accuracy of control.
[0027] Furthermore, the establishment of constraint equations includes at least a controller state matrix, a state weight matrix, a control state matrix, and a control weight matrix.
[0028] In the above technical solution, the method of establishing constraint equations ensures the accuracy, stability and computational efficiency of the model predictive control strategy. This method can not only comprehensively consider multiple factors and optimize the control strategy, but also improve the system performance under various constraints, which has significant advantages and broad application prospects.
[0029] Furthermore, obtaining the control quantity corresponding to the current control moment includes:
[0030] Based on the current model, predict the controller to obtain the controller state matrix, state weight matrix, and control weight matrix.
[0031] Substitute the controller state matrix, state weight matrix, control state matrix, and control weight matrix into the constraint equation to output multiple control quantities in the current control time domain; wherein, the control time domain includes multiple control moments.
[0032] In addition, the control quantity corresponding to the current control moment is obtained based on multiple control quantities.
[0033] In the above technical solution, by using precise matrix calculation and optimization algorithms to select the optimal control quantity at the current control moment, the control error can be effectively reduced and the control accuracy improved.
[0034] Furthermore, based on the same concept, this application also provides a trajectory tracking control system, which at least employs the aforementioned trajectory tracking control method, the trajectory tracking control system comprising:
[0035] The setting module is used to set multiple control time domain lengths and perform performance tests on the constraint equations based on each control time domain length, so as to obtain the target control length according to the performance test results.
[0036] A unified module is used to obtain the replacement value of each control quantity based on the target control length, so as to obtain a set of control quantities.
[0037] The calculation module is used to obtain the control state matrix based on the set of control quantities, and substitute the control state matrix into the constraint equation to obtain the control quantity corresponding to the current control moment.
[0038] And a control module, used to collect the vehicle control status at the current control moment, and adjust the vehicle control status based on the control quantity to perform trajectory tracking control on the target vehicle.
[0039] In the above technical solution, the module performs multi-time-domain performance testing, selects the optimal control time-domain length, and optimizes the overall computational efficiency; the unification module simplifies the calculation process and improves computational efficiency by unifying replacement values; the calculation module adopts efficient optimization algorithms to quickly solve constraint equations and improve real-time response capability; and the control module achieves efficient control of vehicle trajectory tracking based on the calculated control quantities.
[0040] Furthermore, based on the same concept, this application also provides a storage medium storing a computer program, wherein the computer program is configured to execute the trajectory tracking control method at runtime.
[0041] Furthermore, based on the same concept, this application also provides a controller, which is a type of model predictive controller. The controller uses the trajectory tracking control method described above to obtain the control quantity corresponding to the current control moment, and implements the control quantity in the control cycle of the next control time domain.
[0042] Compared with the prior art, the beneficial effects of this application are as follows:
[0043] This application pre-establishes constraint equations, then sets multiple control time domain lengths, and performs performance tests on the constraint equations based on each control time domain length to obtain a target control length based on the performance test results. Further, it obtains substitution values for each control variable based on the target control length to obtain a control variable set. Then, it obtains a control state matrix based on the control variable set and substitutes the control state matrix into the constraint equations to obtain the control variable corresponding to the current control moment. Finally, it collects the vehicle control state at the current control moment and adjusts the vehicle control state based on the control variable to perform trajectory tracking control on the target vehicle. This application can optimize trajectory tracking control efficiency and improve the response speed of trajectory tracking control. Attached Figure Description
[0044] Figure 1 This is a flowchart of the trajectory tracking control method described in this application.
[0045] Figure 2 This is a framework diagram of the trajectory tracking control system described in this application. Detailed Implementation
[0046] The following detailed description of a trajectory tracking control method, system, storage medium, and controller according to this application, with reference to specific embodiments and accompanying drawings, is provided in further detail.
[0047] For details, please see Figure 1 This application provides a trajectory tracking control method, which pre-establishes constraint equations and includes the following steps S100-S400.
[0048] In some embodiments, the performance of different control time domain lengths, such as latency and response time, is tested, and an optimal control time domain length is selected based on performance indicators. Then, the influence range of different control quantities is arbitrated according to this optimal control time domain length, thereby unifying the approximate replacement of different control quantities. Here, the influence range of each control quantity can be obtained to determine whether these control quantities can be unified into a single representation. After integration, a set of control quantities is obtained to modify the contents of the control state matrix. After the modification is completed, the updated control state matrix is substituted into the pre-established constraint equations to solve for the control quantity at the most recent control time (i.e., the current time).
[0049] Steps S100-S400 are described below.
[0050] In step S100: multiple control time domain lengths are set, and the constraint equations are tested based on each control time domain length to obtain the target control length based on the performance test results.
[0051] Furthermore, in step S100, setting multiple control time domain lengths includes:
[0052] Obtain a predefined control time domain range, and set multiple control time domain lengths according to the control time domain range.
[0053] In some embodiments, the control time domain represents the number of control variables at future times, which is the degree of freedom of the control variable set.
[0054] First, obtain a predefined control time domain range. This range can be a set of reasonable values determined based on the physical characteristics of the model predictor, control requirements, and historical data. The control time domain range is such as [5,10].
[0055] Furthermore, based on the obtained predefined control time domain range, multiple specific control time domain lengths are set. These lengths can be uniformly distributed or non-uniformly distributed, such as 5, 6, 7, 8, 9, 10 or 5, 7, 8, 10. These can be adaptively set by those skilled in the art, and are not limited here.
[0056] In the above technical solution, the setting of the control time domain length is not arbitrary, but based on the predefined control time domain range of the controller. This range can be set by those skilled in the art according to the actual application requirements.
[0057] By setting multiple control time domain lengths within a predefined range, the impact of different control time domain lengths on controller performance can be evaluated more comprehensively, providing more accurate data support and thus selecting the optimal control time domain length. By setting multiple control time domain lengths and analyzing their impact on solution efficiency, the computational load and solution time can be reduced to the minimum while ensuring control accuracy, thereby improving the real-time performance of the controller. Reasonable control time domain length settings can also avoid over-prediction and over-control.
[0058] Furthermore, in step S100, obtaining the target control length includes:
[0059] Simulation tests are performed on the current model predictive controller for each control time domain length to obtain the controller performance for each control time domain length; wherein, the controller performance includes at least response time and computation time.
[0060] Furthermore, the controller performance is evaluated based on predefined performance indicators, and the optimal control time domain length is obtained based on the evaluation results as the target control length.
[0061] In some embodiments, the current model predictive controller is simulated for each control time domain length. The purpose of the simulation test is to simulate the actual control process and obtain the controller performance for each control time domain length.
[0062] In the controller performance, response time refers to the time from when the controller receives an instruction to when it actually responds, and calculation time refers to the time required for the controller to calculate the control quantity.
[0063] Furthermore, the predefined performance indicators, such as response time, have a weight of 0.6 and computation time has a weight of 0.4. Assuming that after simulation tests for control time domain lengths of 5, 6, 7, 8, 9, and 10, the response time and computation time for a control time domain length of 8 are 8 milliseconds and 2 milliseconds, respectively, and the overall performance score is the lowest based on the weights of the response time and computation time, 8 is selected as the target control length, i.e., Nc = 8.
[0064] In the above technical solution, by conducting simulation tests under multiple control time domain lengths, the impact of each control time domain length on the controller performance can be comprehensively evaluated, thereby avoiding the deviation that may be caused by a single control time domain length. Furthermore, performance evaluation based on simulation test data makes the comparison and selection process of performance indicators more scientific and systematic. By using predefined performance indicators, the performance of each control time domain length can be accurately evaluated, thereby selecting the optimal control time domain length and ensuring that the controller can maintain high efficiency and stability under various conditions.
[0065] Furthermore, after obtaining the target control length, step S200 can be executed.
[0066] In step S200: Obtain the replacement value of each control quantity based on the target control length to obtain a set of control quantities.
[0067] Furthermore, the acquisition of the control quantity set includes:
[0068] Multiple control quantities are obtained based on the target control length.
[0069] Under each control variable, the control influence range of the current model predictive controller within a preset time period is analyzed to obtain control variables with the same control influence range, and the control variables with the same control influence range are unified.
[0070] In addition, the set of control variables is obtained based on the unified control variables.
[0071] In some embodiments, control quantities Δu1, Δu2, Δu3, Δu4, Δu5, Δu6, Δu7, and Δu8 are obtained based on a target control length Nc = 8.
[0072] Furthermore, for each control variable, the control influence range of the current model predictive controller within a preset time period is analyzed, assuming the preset time period is 20 milliseconds.
[0073] Furthermore, after obtaining the 8 sets of control influence ranges corresponding to the 8 control quantities, the control influence range of each control quantity is compared to find the control quantities with the same or similar influence ranges. Assuming that the second, third, and fourth control quantities are the same, i.e., Δu2 = Δu3 = Δu4, and the fifth control quantity is the same as the other control quantities, i.e., Δu5 = Δu6 = Δu7 = Δu8, then the set of output control quantities can be converted into ΔU = [Δu1 Δu2 Δu2 Δu2 Δu5 Δu5 Δu5 Δu5 Δu5].
[0074] It should be noted that, in other embodiments, those skilled in the art may set the preset time period to other values according to actual application needs, and it is not limited to 20 milliseconds.
[0075] In addition, it should be noted that when comparing whether the range of influence is similar, an upper and lower limit can be set. As long as the range of influence of the two control variables is within the range of this upper and lower limit, they are considered to be similar and can be standardized.
[0076] In the above technical solution, by comparing the influence range of different control quantities, control quantities with the same or similar control effects are identified. These control quantities show consistency in response and dynamic changes. By unifying control quantities with the same or similar control influence range, redundant calculations and processing are avoided, and computational efficiency is significantly improved. All unified control quantities are combined into a set of control quantities, which covers the optimal control inputs in different time periods and states.
[0077] Furthermore, after obtaining the set of control variables, step S300 can be executed.
[0078] In step S300: Obtain the control state matrix based on the control quantity set, and substitute the control state matrix into the constraint equation to obtain the control quantity corresponding to the current control moment.
[0079] Furthermore, the control state matrix includes at least an identity matrix and a control quantity matrix; obtaining the control state matrix includes:
[0080] The identity matrix and the control quantity matrix are updated based on the set of control quantities, respectively, so as to obtain the control state matrix according to the updated identity matrix and control quantity matrix.
[0081] In some embodiments, after obtaining ΔU=[Δu1 Δu2 Δu2 Δu2 Δu5 Δu5 Δu5 Δu5], the following transformation is performed:
[0082]
[0083] in, This represents the updated identity matrix. This represents the control quantity matrix.
[0084] As can be seen, after the above transformation, the degrees of freedom of the control variable set is reduced from 8 to 3, resulting in better computational performance.
[0085] In the above technical solution, the dynamic updating of the control state matrix ensures that the controller can adaptively adjust the control strategy according to the latest set of control variables, thereby improving the response capability to dynamic changes. By integrating the unified control variable information into the control variable matrix, it is ensured that the control input at each control moment is based on the controller's optimal state and control strategy, thereby improving the precision and accuracy of control.
[0086] Furthermore, the establishment of constraint equations includes at least a controller state matrix, a state weight matrix, a control state matrix, and a control weight matrix.
[0087] In some embodiments, the constraint equation is a quadratic programming solution equation:
[0088] J(t)=[ΔU(t) T ,ε] T H t [ΔU(t) T ,ε]+G t [ΔU(t) T ,ε]
[0089] Where ε is the controller state matrix, H t Let G be the state weight matrix. t To control the weight matrix.
[0090] In the above technical solution, the method of establishing constraint equations ensures the accuracy, stability and computational efficiency of the model predictive control strategy. This method can not only comprehensively consider multiple factors and optimize the control strategy, but also improve the system performance under various constraints, which has significant advantages and broad application prospects.
[0091] Furthermore, obtaining the control quantity corresponding to the current control moment includes:
[0092] Based on the current model, predict the controller to obtain the controller state matrix, state weight matrix, and control weight matrix.
[0093] Substitute the controller state matrix, state weight matrix, control state matrix, and control weight matrix into the constraint equation to output multiple control quantities in the current control time domain; wherein, the control time domain includes multiple control moments.
[0094] In addition, the control quantity corresponding to the current control moment is obtained based on multiple control quantities.
[0095] In some embodiments, the controller state matrix represents the state of the model-predicted controller at the current moment, the state weight matrix represents the weight of each state variable, and the control weight matrix represents the weight of each control quantity.
[0096] Among them, state variables include the vehicle's lateral deviation, positional deviation, and speed deviation; control variables include the vehicle's front wheel steering angle and acceleration compensation.
[0097] Furthermore, the controller state matrix, state weight matrix, and control weight matrix are set by combining the functions and states of the model predictor and the importance of the corresponding application functions.
[0098] Furthermore, the set controller state matrix, state weight matrix, and control weight matrix, as well as the control state matrix obtained above, are substituted into the established constraint equations to obtain the control quantity. Then, the control quantity at the first moment is selected for output.
[0099] In the above technical solution, by using precise matrix calculation and optimization algorithms to select the optimal control quantity at the current control moment, the control error can be effectively reduced and the control accuracy improved.
[0100] Once the control quantity is obtained, step S400 can be executed.
[0101] In step S400: the vehicle control state at the current control moment is collected, and the vehicle control state is adjusted based on the control quantity to perform trajectory tracking control on the target vehicle.
[0102] In some embodiments, the vehicle control states, such as acceleration and vehicle turning angle, are adjusted based on the acquired control quantities to achieve corresponding control actions and complete the real-time trajectory tracking control of the target vehicle.
[0103] Furthermore, based on the same concept, please refer to Figure 2 This application also provides a trajectory tracking control system, which employs at least the trajectory tracking control method described above, the trajectory tracking control system comprising:
[0104] The setting module is used to set multiple control time domain lengths and perform performance tests on the constraint equations based on each control time domain length, so as to obtain the target control length according to the performance test results.
[0105] A unified module is used to obtain the replacement value of each control quantity based on the target control length, so as to obtain a set of control quantities.
[0106] The calculation module is used to obtain the control state matrix based on the set of control quantities, and substitute the control state matrix into the constraint equation to obtain the control quantity corresponding to the current control moment.
[0107] In addition, a control module is used to collect the vehicle control status at the current control moment and adjust the vehicle control status based on the control quantity in order to perform trajectory tracking control on the target vehicle.
[0108] It should be noted that the specific implementation of the trajectory tracking control system is the same as the trajectory tracking control method described above, and will not be repeated here.
[0109] In the above technical solution, the module performs multi-time-domain performance testing, selects the optimal control time-domain length, and optimizes the overall computational efficiency; the unification module simplifies the calculation process and improves computational efficiency by unifying replacement values; the calculation module adopts efficient optimization algorithms to quickly solve constraint equations and improve real-time response capability; and the control module achieves efficient control of vehicle trajectory tracking based on the calculated control quantities.
[0110] Furthermore, based on the same concept, this application also provides a storage medium storing a computer program, wherein the computer program is configured to execute the trajectory tracking control method at runtime.
[0111] In some embodiments, the storage medium stores several computer programs to cause a controller to perform all or part of the steps of the methods described in various embodiments of this application.
[0112] The medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.
[0113] Furthermore, based on the same concept, this application also provides a controller, which is a type of model predictive controller. The controller uses the trajectory tracking control method described above to obtain the control quantity corresponding to the current control moment, and implements the control quantity in the control cycle of the next control time domain.
[0114] In summary, this application provides a trajectory tracking control method, system, storage medium, and controller. The trajectory tracking control method, which pre-establishes constraint equations, includes: setting multiple control time domain lengths and performing performance tests on the constraint equations based on each control time domain length to obtain a target control length based on the performance test results; further obtaining substitution values for each control quantity based on the target control length to obtain a set of control quantities; then obtaining a control state matrix based on the control quantity set and substituting the control state matrix into the constraint equations to obtain the control quantity corresponding to the current control moment; finally, acquiring the vehicle control state at the current control moment and adjusting the vehicle control state based on the control quantity to perform trajectory tracking control on the target vehicle. This application can optimize trajectory tracking control efficiency and improve the response speed of trajectory tracking control.
[0115] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of this application. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of this application. All such changes and modifications are intended to be included within the scope of this application as claimed in the appended claims.
[0116] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0117] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units 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.
[0118] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some modules according to the embodiments of this application. This application can also be implemented as an apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.
[0119] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0120] Although the description of this application has been made in conjunction with the specific embodiments described above, it will be apparent to those skilled in the art that many substitutions, modifications, and variations can be made based on the foregoing. Therefore, all such substitutions, modifications, and variations are included within the spirit and scope of the appended claims.
Claims
1. A trajectory tracking control method, characterized in that, The trajectory tracking control method, which pre-establishes constraint equations, includes the following steps: Multiple control time domain lengths are set, and the constraint equations are tested based on each control time domain length to obtain the target control length based on the performance test results. Based on the target control length, the replacement values of each control quantity are obtained to obtain a set of control quantities; The control state matrix is obtained from the set of control variables, and the control state matrix is substituted into the constraint equation to obtain the control variable corresponding to the current control moment. In addition, the vehicle control status at the current control moment is collected, and the vehicle control status is adjusted based on the control quantity to perform trajectory tracking control on the target vehicle.
2. The trajectory tracking control method according to claim 1, characterized in that, The setting of multiple control time domain lengths includes: Obtain a predefined control time domain range, and set multiple control time domain lengths according to the control time domain range.
3. The trajectory tracking control method according to claim 2, characterized in that, The process of obtaining the target control length includes: performing simulation tests on the current model predictive controller at each control time domain length to obtain the controller performance at each control time domain length; wherein, the controller performance includes at least response time and computation time; Furthermore, the controller performance is evaluated based on predefined performance indicators, and the optimal control time domain length is obtained based on the evaluation results as the target control length.
4. The trajectory tracking control method according to claim 3, characterized in that, The set of control quantities to be acquired includes: Multiple control quantities are obtained based on the target control length; Under each control variable, the control influence range of the current model predictive controller within a preset time period is analyzed to obtain control variables with the same control influence range, and the control variables with the same control influence range are unified. In addition, the set of control variables is obtained based on the unified control variables.
5. The trajectory tracking control method according to claim 4, characterized in that, The control state matrix includes at least an identity matrix and a control quantity matrix; The process of obtaining the control state matrix includes: The identity matrix and the control quantity matrix are updated based on the set of control quantities, respectively, so as to obtain the control state matrix according to the updated identity matrix and control quantity matrix.
6. The trajectory tracking control method according to claim 1, characterized in that, The established constraint equations include at least the controller state matrix, the state weight matrix, the control state matrix, and the control weight matrix.
7. The trajectory tracking control method according to any one of claims 5-6, characterized in that, Obtaining the control quantity corresponding to the current control moment includes: Based on the current model, predict the controller to obtain the controller state matrix, state weight matrix, and control weight matrix; Substituting the controller state matrix, state weight matrix, control state matrix, and control weight matrix into the constraint equation, multiple control quantities in the current control time domain are output; wherein, the control time domain includes multiple control moments; In addition, the control quantity corresponding to the current control moment is obtained based on multiple control quantities.
8. A trajectory tracking control system, characterized in that, The trajectory tracking control system comprises at least the trajectory tracking control method as described in any one of claims 1-7, wherein the trajectory tracking control system includes: The setting module is used to set multiple control time domain lengths and perform performance tests on the constraint equations based on each control time domain length, so as to obtain the target control length based on the performance test results. A unified module is used to obtain the replacement value of each control quantity based on the target control length, so as to obtain a set of control quantities; The calculation module is used to obtain the control state matrix based on the set of control quantities, and substitute the control state matrix into the constraint equation to obtain the control quantity corresponding to the current control moment. And a control module, used to collect the vehicle control status at the current control moment, and adjust the vehicle control status based on the control quantity to perform trajectory tracking control on the target vehicle.
9. A storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is configured to execute the trajectory tracking control method as described in any one of claims 1-7 when it is run.
10. A controller, characterized in that, The controller is a type of model predictive controller. The controller uses the trajectory tracking control method as described in any one of claims 1-7 to obtain the control quantity corresponding to the current control moment, and implements the control quantity in the control cycle of the next control time domain.