Robot trajectory tracking control method, device, equipment, medium and product

Abstract

The embodiment of the invention provides a trajectory tracking control method and device of a robot, equipment, a medium and a product. The method comprises the steps of obtaining an expected trajectory, an actual pose and an actual movement parameter of the robot; determining a pose tracking error according to the expected trajectory and the actual pose; according to a preset kinematics controller, performing error processing on the pose tracking error and the actual movement parameter to obtain a speed tracking error; generating a theoretical control torque according to a preset dynamic controller, the speed tracking error and the adaptive estimated value of the lumped uncertain item; when the theoretical control torque meets the preset event triggering requirement, the robot is controlled to track the expected trajectory according to the theoretical control torque, the movement control process of the robot is completed, and high-precision and low-resource-consumption robot trajectory tracking in the complex dynamic environment is achieved.

Description

Technical Field

[0001] This application relates to the field of robot control technology, and in particular to a method, device, equipment, medium and product for trajectory tracking control of a robot. Background Technology

[0002] Wheeled mobile robots are widely used in fields such as power grid inspection due to their simple structure and flexible movement. In power grid inspection, robots need to traverse narrow spaces and adapt to uneven ground conditions, while simultaneously adjusting their posture in real time to cope with sudden disturbances. Therefore, how to achieve highly robust trajectory tracking control of wheeled mobile robots in complex dynamic environments and effectively reduce the frequency of control command updates has become a current research hotspot and challenge.

[0003] In existing technologies, robot trajectory tracking control methods mainly include periodic control and event-triggered control. High-frequency sampling ensures control accuracy, or trigger conditions are set, and control commands are updated only when the trigger conditions are met, thereby reducing invalid communication.

[0004] However, existing technologies cannot be compatible with low transmission load and high control precision, which reduces the control accuracy and efficiency of robot trajectory tracking. Summary of the Invention

[0005] This application provides a robot trajectory tracking control method, apparatus, device, medium, and product to solve the technical problem that the prior art cannot be compatible with low transmission burden and high control precision, thus reducing the control precision and efficiency of robot trajectory tracking.

[0006] Firstly, a trajectory tracking control method for a robot includes:

[0007] The robot's desired trajectory, actual pose, and actual movement parameters are obtained, including actual velocity and actual angular velocity.

[0008] The pose tracking error is determined based on the desired trajectory and the actual pose.

[0009] Based on the preset kinematics controller, the pose tracking error and the actual movement parameters are processed to obtain the velocity tracking error;

[0010] Based on the preset dynamic controller, the adaptive estimate of the speed tracking error and the lumped uncertainty term, a theoretical control torque is generated. The lumped uncertainty term is used to characterize the model uncertainty and external disturbances of the robot.

[0011] When the theoretical control torque meets the preset event triggering requirements, the robot is controlled to follow the desired trajectory according to the theoretical control torque, thus completing the movement control process of the robot.

[0012] The preset event triggering requirement is the difference requirement between the theoretical control torque and the initial theoretical control torque, wherein the initial theoretical control torque is the torque generated before the theoretical control torque was generated.

[0013] Furthermore, when the theoretical control torque meets the preset event triggering requirements, the robot is controlled to track the desired trajectory according to the theoretical control torque, completing the movement control process of the robot, including:

[0014] Determine the initial theoretical control torque;

[0015] The initial theoretical control torque is compared with the theoretical control torque to obtain the torque difference.

[0016] When the torque difference meets the dynamic threshold, it indicates that the theoretical control torque meets the preset event triggering requirements;

[0017] The robot is controlled to follow the desired trajectory based on the theoretical control torque, thus completing the movement control process of the robot.

[0018] Furthermore, the dynamic threshold is positively correlated with the norm of the speed tracking error and the adaptive estimate of the lumped uncertainty term.

[0019] Furthermore, after comparing the initial theoretical control torque with the theoretical control torque to obtain the torque difference, the method further includes:

[0020] If the torque difference does not meet the preset event triggering requirements, the robot continues to track the desired trajectory according to the initial theoretical control torque.

[0021] Further, based on a preset kinematic controller, error processing is performed on the pose tracking error and the actual movement parameters to obtain the velocity tracking error, including:

[0022] Based on a preset kinematic controller, the pose tracking error is processed to generate virtual movement commands, which include virtual velocity commands and virtual angular velocity commands.

[0023] The speed tracking error is obtained by comparing the virtual movement command with the actual movement parameters.

[0024] Further, based on the preset dynamic controller and the adaptive estimate of the speed tracking error and lumped uncertainty, a theoretical control torque is generated, including:

[0025] Based on the speed tracking error, a fixed-time convergence term is constructed, and the fixed-time control term is used to drive the speed tracking error to converge within a fixed time.

[0026] An adaptive robust term is constructed based on the adaptive estimate of the lumped uncertainty term;

[0027] By combining the fixed-time convergence term and the adaptive robust term, a continuous-time fixed-time control law is obtained.

[0028] The theoretical control torque is generated based on the preset dynamic controller and the fixed-time control law of the continuous time.

[0029] Furthermore, when the theoretical control torque meets the preset event triggering requirements, the robot is controlled to move according to the theoretical control torque. After completing the control process of the robot tracking the desired trajectory, the method further includes:

[0030] Based on the theoretical control torque, the adaptive estimate in the lumped uncertainty term is updated to complete the update of the preset dynamic controller.

[0031] Secondly, this application proposes a trajectory tracking control device for a robot, comprising:

[0032] The acquisition module is used to acquire the robot's desired trajectory, actual pose, and actual movement parameters, wherein the actual movement parameters include actual velocity and actual angular velocity.

[0033] The pose tracking error determination module is used to determine the pose tracking error based on the expected trajectory and the actual pose.

[0034] The velocity tracking error acquisition module is used to perform error processing on the pose tracking error and the actual movement parameters according to a preset kinematic controller to obtain the velocity tracking error;

[0035] The theoretical control torque generation module is used to generate theoretical control torque based on the preset dynamic controller, the speed tracking error, and the adaptive estimate of the lumped uncertainty term. The lumped uncertainty term is used to characterize the model uncertainty and external disturbances of the robot.

[0036] The trajectory tracking control module is used to control the robot to track the desired trajectory according to the theoretical control torque when the theoretical control torque meets the preset event triggering requirements, thereby completing the movement control process of the robot.

[0037] Thirdly, embodiments of this application provide a trajectory tracking and control device for a robot, including: a memory and a processor;

[0038] The memory stores computer-executed instructions;

[0039] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0040] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0041] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0042] The robot trajectory tracking control method, apparatus, device, medium, and product provided in this application acquire the robot's desired trajectory, actual pose, and actual movement parameters; determine the pose tracking error based on the desired trajectory and actual pose; perform error processing on the pose tracking error and actual movement parameters according to a preset kinematic controller to obtain the velocity tracking error; generate a theoretical control torque based on the preset dynamic controller, the velocity tracking error, and the adaptive estimate of the lumped uncertainty term; when the theoretical control torque meets the preset event triggering requirements, control the robot to track the desired trajectory according to the theoretical control torque, thus completing the robot's movement control process and achieving high-precision, low-resource-consumption robot trajectory tracking in complex dynamic environments. Attached Figure Description

[0043] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0044] Figure 1 A flowchart illustrating an embodiment of a robot trajectory tracking control method provided in this application;

[0045] Figure 2 A flowchart illustrating a second embodiment of a robot trajectory tracking control method provided in this application;

[0046] Figure 3 A control block diagram of the robot trajectory tracking control method provided in this application;

[0047] Figure 4 This is a structural schematic diagram of the wheeled mobile robot provided in this application;

[0048] Figure 5A schematic diagram illustrating the simulation results of planar trajectory tracking for the wheeled mobile robot provided in this application;

[0049] Figure 6 A schematic diagram of simulation results for the position and attitude tracking error of the wheeled mobile robot provided in this application;

[0050] Figure 7 A schematic diagram of simulation results for the speed and angular velocity tracking errors of the wheeled mobile robot provided in this application;

[0051] Figure 8 A schematic diagram of the simulation results of the control torque of the wheeled mobile robot provided in this application;

[0052] Figure 9 A schematic diagram of the simulation results for the adaptive parameter estimation provided in this application;

[0053] Figure 10 A schematic diagram of the simulation results for the control signal event triggering time interval provided in this application;

[0054] Figure 11 A schematic diagram of the trajectory tracking control device for the robot provided in this application;

[0055] Figure 12 A schematic diagram of the trajectory tracking control device for the robot provided in this application.

[0056] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0057] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0058] The technical solution of this application is applicable to wheeled mobile robot systems in scenarios such as logistics transportation, power grid inspection, and disaster relief. In logistics scenarios, the robot needs to navigate autonomously in dynamic environments, reducing communication overhead through event-triggered control; in power grid inspection scenarios, the robot needs to traverse narrow spaces and adapt to uneven ground, compensating for model uncertainties through parameter adaptation; in disaster relief scenarios, the robot needs to respond quickly to commands in unknown environments, ensuring real-time performance through a fixed-time convergence strategy. In terms of network architecture, the control system consists of a kinematic controller, a dynamic controller, a parameter adaptation module, and an event-triggered module, achieving coordinated optimization of position, velocity, and control commands through hierarchical processing.

[0059] The existing technology has the following problems in the trajectory tracking of wheeled mobile robots: (1) Periodic control methods rely on high-frequency sampling, which leads to a waste of communication resources and cannot adapt to sudden disturbances in dynamic environments; (2) Event-triggered control schemes are mostly designed based on kinematic models, without considering the parameter uncertainty in the dynamic model, resulting in insufficient robustness of the controller; (3) Fixed-time convergence strategy is lacking, and the convergence time of tracking error depends on the initial state, which makes it difficult to meet the real-time requirements.

[0060] Starting from the shortcomings of existing technologies, this application first analyzes the dynamic model characteristics of wheeled mobile robots. By comparing the advantages and disadvantages of periodic control and event-triggered control, it proposes a hierarchical control approach: first, an initial position and attitude deviation is eliminated through a kinematic controller, and then dynamic uncertainties are compensated through a dynamic controller. Furthermore, parameter adaptive technology is introduced to dynamically estimate the upper bound of the lumped uncertainty term, improving robustness. Finally, combining fixed-time convergence theory, event triggering conditions are designed to ensure that control commands are updated only when necessary, thereby reducing communication overhead and avoiding Zeno's phenomenon.

[0061] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0062] Figure 1 This is a flowchart illustrating an embodiment of a robot trajectory tracking control method provided in this application. Figure 1 As shown, it includes:

[0063] S101. Obtain the robot's desired trajectory, actual pose, and actual movement parameters.

[0064] The actual movement parameters include actual velocity and actual angular velocity. This step systematically acquires the robot's desired trajectory, actual pose, and actual movement parameters, providing a complete and accurate input basis for subsequent error calculation and control decisions, and ensuring the information completeness of the robot trajectory tracking control loop.

[0065] S102. Determine the pose tracking error based on the desired trajectory and the actual pose.

[0066] In the embodiments of this application, the primary objective of robot control is to eliminate deviations from the desired trajectory. Without explicit error quantification, the controller will lack clear target guidance.

[0067] Based on this, this step transforms the complex "trajectory tracking" problem into a clear "error adjustment" problem by accurately calculating the pose tracking error, providing a clear and quantifiable control objective for the kinematic controller. The specific calculation process is shown below.

[0068] S103. Based on the preset kinematic controller, perform error processing on the pose tracking error and the actual movement parameters to obtain the velocity tracking error.

[0069] Among them, the preset kinematic controller can ensure that the tracking error between the position and attitude of the wheeled mobile robot and the desired trajectory gradually converges to zero.

[0070] Specifically, in this embodiment, the preset kinematic controller is designed based on the robot's kinematic model, meaning a pre-constructed kinematic model of the robot is required. This application uses a wheeled mobile robot as an example, where the front wheels of the wheeled mobile robot's kinematic model are auxiliary wheels, and the two rear wheels are drive wheels, with a radius of [missing information]. .

[0071] Assuming the center of mass of the wheeled mobile robot coincides with its geometric center, an inertial coordinate system is established. and body coordinate system Describe the planar motion of a wheeled mobile robot. The mass and moment of inertia of the wheeled mobile robot are respectively... and The distance between the drive wheel and the geometric center is .

[0072] Without considering slippage and sideslip, the kinematic equations of the wheeled mobile robot are:

[0073]

[0074] in, For the position and orientation of the wheeled mobile robot, For the speed and angular velocity of the wheeled mobile robot, Given the velocity transformation matrix, the nonholonomic constraints of a wheeled mobile robot can be described as follows: .

[0075] Then, based on the kinematic model of the wheeled mobile robot described above, a preset kinematic controller is designed so that the position and attitude of the wheeled mobile robot can track the desired position and attitude, i.e. the desired trajectory.

[0076] Further, in this step, the pose tracking error is processed according to the preset dynamic controller to generate virtual movement commands; the virtual movement commands include virtual velocity commands and virtual angular velocity commands; then the virtual movement commands are compared with the actual movement parameters of the wheeled mobile robot to obtain the velocity tracking error.

[0077] For example, the desired trajectory of a wheeled mobile robot is given as follows:

[0078]

[0079] Then, based on the actual pose of the wheeled robot, the pose tracking error of the wheeled mobile robot is calculated, as shown in the following formula:

[0080]

[0081] Based on the above pose tracking error, the kinematic error equation of the wheeled mobile robot can be expressed as:

[0082]

[0083] Finally, combining the desired trajectory and kinematic error equations of the wheeled mobile robot described above, the kinematic controller of the wheeled mobile robot can be designed as follows:

[0084]

[0085] in,, , , , and These are virtual velocity and virtual angular velocity. Therefore, under the action of the aforementioned kinematic controller, the position and attitude tracking errors of the wheeled mobile robot can asymptotically converge to zero, achieving the effect of eliminating position and attitude deviations through virtual control quantities.

[0086] This application introduces a hierarchical control structure. A controller designed based on a kinematic model transforms the robot's pose tracking error into virtual velocity commands, ensuring the robot can track the desired trajectory along the geometric path. Then, by comparing the virtual velocity commands with the robot's actual movement parameters, the velocity tracking error is generated and passed to the lower-level dynamics controller. This achieves a reasonable decomposition of the control task, simplifies the design, and improves the robot's control accuracy.

[0087] S104. Generate the theoretical control torque based on the adaptive estimate of the preset dynamic controller, speed tracking error, and lumped uncertainty term.

[0088] The lumped uncertainty term is used to characterize the model uncertainty and external disturbances of the preset dynamic controller. In practical applications, the robot will encounter lumped uncertainties such as inaccurate model parameters, load changes, and unknown external disturbances (e.g., uneven ground, wind force) during actual operation. These lumped uncertainties will significantly reduce the accuracy of the robot's motion control; at the same time, conventional motion control methods are difficult to guarantee convergence speed, which will reduce the efficiency of the robot's motion control.

[0089] Therefore, in the steps of this application, the upper bound of the lumped uncertainty term is estimated online using parameter adaptive technology, and an adaptive robust term is constructed for real-time compensation.

[0090] Specifically, based on the speed tracking error obtained from the above steps, a fixed-time convergence term is constructed, where the fixed-time control term drives the speed tracking error to converge within a fixed time. Then, based on the adaptive estimate of the lumped uncertainty term, an adaptive robust term is constructed. Subsequently, based on a time-triggered adaptive controller, the fixed-time convergence term and the adaptive robust term are combined to obtain a continuous-time fixed-time control law. This solves the problem of the convergence time of traditional asymptotic convergence methods being dependent on the initial value and unpredictable, and is particularly suitable for application scenarios with strict requirements on convergence time.

[0091] Finally, based on the preset dynamic controller and the fixed-time control law of continuous time, the theoretical control torque is generated, which enables the system to achieve high-performance trajectory tracking within a fixed time even under complex uncertainties.

[0092] For example, in this step, the dynamic equations are first constructed, as follows:

[0093]

[0094] in, This is the control torque; External disturbances, i.e., lumped uncertainties; It is the inertia matrix, and the formula is as follows:

[0095]

[0096] It is the centripetal force and Coriolis force matrix, and the formula is: .

[0097] It is the gravity vector, and the formula is: .

[0098] The input transformation matrix is ​​given by the following formula:

[0099]

[0100] It is a nonholonomic constraint vector, and its formula is as follows:

[0101]

[0102] For Lagrange multipliers, their expression is: Additionally, model uncertainty is considered as... ,in, and These represent the nominal portion and the uncertain portion, respectively.

[0103] Then, by differentiating the kinematic model with respect to time, we can obtain the following formula:

[0104]

[0105] Substituting the above equation into the dynamic model and applying the relation... You can then obtain:

[0106]

[0107] In the formula, and This represents the lumped uncertainty term. The specific formula is as follows:

[0108] ,

[0109]

[0110] Assuming lumped uncertainty terms It is bounded, meaning there exists an unknown constant. Make , .

[0111] Through the above optimization process of the preset dynamic controller, it is possible to ensure that the speed and angular velocity tracking errors of the wheeled mobile robot converge to the neighborhood of zero within a fixed time and effectively avoid the occurrence of Zeno's phenomenon.

[0112] Furthermore, the pre-defined dynamic controller is designed based on a fixed-time adaptive control method, employing parameter adaptive technology to estimate the upper bound of the lumped uncertainty term. This makes the controller highly robust to model uncertainties and external disturbances. This allows the controller to "learn" and overcome unknown disturbances and model uncertainties, significantly improving the system's control accuracy and stability.

[0113] S105. When the theoretical control torque meets the preset event triggering requirements, the robot is controlled to track the desired trajectory according to the theoretical control torque, thus completing the robot's movement control process.

[0114] This step introduces an event-triggered mechanism, whereby the theoretical control torque is updated and transmitted only "when needed" (i.e., when the triggering conditions are met), significantly reducing the computational load on the controller and the communication burden on the actuators. This ensures that while reducing updates, the system's tracking performance does not significantly degrade, achieving an optimal balance between resource conservation and control performance.

[0115] Specifically, the event-triggered mechanism dynamically monitors tracking errors and system state changes, triggering control command updates only when necessary. For example, when the robot is in a stable operating condition (such as low-speed uniform motion), the triggering condition is relaxed, reducing control command updates; while in dynamic environments (such as high-speed motion or sudden disturbances), the triggering condition is sensitive, ensuring control accuracy. This technique significantly reduces the communication burden between the control system and the actuator by reducing the frequency of control command updates, while avoiding resource waste caused by high-frequency sampling. Furthermore, the event-triggered mechanism can adapt to changes in the dynamic environment, avoiding the response lag problem caused by the fixed sampling period in traditional periodic control methods, thereby improving the real-time performance and adaptability of the controller.

[0116] This application's embodiments, through the establishment of kinematic and dynamic models, can quantify the robot's motion characteristics in complex environments and provide a mathematical basis for subsequent controller design. The kinematic model is used to eliminate initial deviations, while the dynamic model is used to compensate for uncertainties; the combination of the two achieves high-precision tracking.

[0117] Meanwhile, the kinematic and dynamic dual-layer control architecture, combined with fixed-time convergence control and parameter adaptive technology, effectively overcomes model uncertainty and external disturbances while ensuring asymptotic convergence of pose tracking error and stability of velocity tracking error within a fixed time. Furthermore, the introduction of an event triggering mechanism significantly reduces the frequency of control command updates, saves system communication and computing resources, and strictly avoids Zeno's phenomenon, thus achieving high-precision, low-energy, and robust trajectory tracking control for the robot.

[0118] Figure 2 This is a flowchart illustrating a second embodiment of the robot trajectory tracking control method proposed in this application. Figure 2 As shown, based on Embodiment 1, when the theoretical control torque meets the preset event triggering requirements, the robot is controlled to track the desired trajectory according to the theoretical control torque, completing the robot's movement control process, including:

[0119] S201. Determine the initial theoretical control torque and compare the initial theoretical control torque with the theoretical control torque to obtain the torque difference.

[0120] The initial theoretical control torque refers to the control torque output at the previous trigger moment.

[0121] This step provides a clear quantitative basis for event-triggered decisions by comparing the control torque of adjacent calculation cycles, ensuring that the necessity of control command updates is judged based on continuous changes in system state, thus laying the foundation for resource-saving control.

[0122] S202. When the torque difference meets the dynamic threshold, it indicates that the theoretical control torque meets the preset event triggering requirements.

[0123] The preset event triggering requirement is the difference between the theoretical control torque and the initial theoretical control torque; while the dynamic threshold is positively correlated with the norm of the speed tracking error calculated above and the adaptive estimate of the lumped uncertainty term.

[0124] Furthermore, the core of the event triggering conditions involved in this application's embodiments lies in the use of adaptive dynamic thresholds, rather than the fixed thresholds commonly used in traditional event triggering. Specifically, the dynamic multi-condition event triggering mechanism dynamically adjusts the triggering threshold by introducing multi-dimensional triggering conditions (such as tracking error, state change rate, and environmental disturbance intensity). For example, in high-speed motion or high-disturbance scenarios, the triggering threshold is lowered to improve response speed; in low-speed or stable scenarios, the triggering threshold is raised to reduce update frequency. This technique optimizes the performance balance of the event triggering mechanism under different operating conditions through adaptive threshold adjustment, avoiding over-triggering or response lag problems that may be caused by fixed thresholds, and further reducing communication resource consumption. Ultimately, it achieves synergistic optimization of control accuracy and resource efficiency in complex dynamic environments, significantly improving the flexibility and adaptability of event-triggered control.

[0125] Meanwhile, this dynamic threshold is also positively correlated with the lumped uncertainty of the system estimated online by the adaptive law. When the system is identified as being in a complex environment with high model uncertainty or strong external disturbances, the dynamic threshold will increase accordingly. This design enables the control system to "be more vigilant" when facing challenges, enhancing the robustness and intervention power of the control by increasing the communication frequency, thus ensuring the stable operation of the control system in unknown environments.

[0126] In addition, if the torque difference does not meet the preset event triggering requirements, the robot will continue to track the desired trajectory based on the initial theoretical control torque.

[0127] Specifically, this "keep-update" working mode ensures that the control commands applied to the actuator are continuous and constant within the trigger interval, effectively avoiding high-frequency jumps in control commands.

[0128] The adaptive controller and dynamic event triggering mechanism in this application ensure that all signals in the entire closed-loop control system are consistent and eventually bounded and stable. This means that the robot's trajectory tracking error will eventually converge and stabilize within a controllable small range, mathematically guaranteeing the global stability of the system.

[0129] S203. Control the robot to track the desired trajectory according to the theoretical control torque, and complete the robot's movement control process.

[0130] In this step, based on the dynamic model of the wheeled mobile robot, a preset dynamic controller is designed to enable the wheeled mobile robot's velocity and angular velocity to track virtual velocity and angular velocity. First, the velocity and angular velocity tracking errors of the wheeled mobile robot are defined using the following formulas:

[0131]

[0132] Based on the above formula, the dynamic error equation of the wheeled mobile robot can be expressed as:

[0133]

[0134] Furthermore, in this application, the fixed-time control law can be determined based on an event-triggered adaptive controller. The event-triggered fixed-time adaptive controller is designed as follows:

[0135]

[0136]

[0137]

[0138] In the formula, , , , , , , , ;

[0139] in addition, This indicates the measurement error of the control command; and This indicates the trigger times of two adjacent control commands. Indicates to The estimated value.

[0140] For a given vector and scalar ,symbol Defined as:

[0141]

[0142] in, ;symbol Defined as:

[0143]

[0144] Furthermore, the adaptive law for parameters is chosen as follows:

[0145]

[0146] In the formula, , , .

[0147] In addition, the event-triggered adaptive controller only updates the control command when the event triggering condition is met, thereby reducing the information transmission between the wheeled mobile robot control system and the actuator and saving more information transmission resources.

[0148] Finally, based on the theoretical control torque, the adaptive estimate in the lumped uncertainty term is updated, thus completing the update of the preset dynamic controller.

[0149] This application's embodiments utilize a dynamic controller design to estimate lumped uncertainties in real time using an adaptive parameter law, dynamically compensating for uncertainties in model parameters (such as deviations in the inertia matrix and Coriolis force matrix) and external disturbances (such as changes in ground friction and wind interference). Secondly, fixed-time convergence theory ensures that the tracking error converges to a zero neighborhood within a finite time, and that the convergence time is independent of the initial state, solving the problem of excessively long convergence times in traditional asymptotic convergence strategies. An event-triggered mechanism further reduces the frequency of control command updates, avoiding the waste of communication resources caused by high-frequency sampling. This technique, through the synergistic optimization of hierarchical control (kinematic controller eliminating initial deviations + dynamic controller compensating for uncertainties) and the event-triggered mechanism, achieves highly robust and low-resource-consumption trajectory tracking in complex dynamic environments. Finally, the controller effectively copes with model uncertainties and external disturbances, significantly improving tracking accuracy and reducing communication overhead by minimizing invalid control command updates.

[0150] Figure 3 This is a control block diagram of the trajectory tracking control method for the robot provided in this application. Figure 3 As shown, the overall control flow of the robot trajectory tracking control method includes a model design unit 301 and a controller design unit 302. The model design unit 301 is used to calculate the kinematic model and the dynamic model; the controller design unit 302 is used to calculate the kinematic controller and the dynamic controller.

[0151] Figure 4 This is a structural schematic diagram of the wheeled mobile robot provided in this application. Figure 4 As shown, the center of mass of the wheeled mobile robot coincides with its geometric center, and the inertial coordinate system... and body coordinate system Describes the planar motion of a wheeled mobile robot, where x, y, and θ refer to the current position of the wheeled mobile robot relative to the inertial coordinate system.

[0152] Figure 5 This is a schematic diagram illustrating the simulation results of planar trajectory tracking for the wheeled mobile robot provided in this application. Figure 5 As shown, considering model uncertainty and external disturbances, the proposed event-triggered controller can effectively track the desired circular trajectory.

[0153] Figure 6 A schematic diagram of the simulation results of the position and attitude tracking error of the wheeled mobile robot provided in this application.

[0154] Figure 7 This is a schematic diagram illustrating the simulation results of the speed and angular velocity tracking errors of the wheeled mobile robot provided in this application. Figure 6 and Figure 7 As shown, the proposed event-triggered controller can ensure that the tracking errors of the wheeled mobile robot's position, attitude, speed and angular velocity converge to zero, and has high control accuracy and fast convergence speed.

[0155] Figure 8 This is a schematic diagram illustrating the simulation results of the control torque of the wheeled mobile robot provided in this application. Under the action of the proposed event-triggered controller, the control torque curve exhibits quantitative characteristics, which is due to the non-periodic sampling caused by the introduction of the event-triggered mechanism.

[0156] Figure 9 This is a schematic diagram of the simulation results for the adaptive parameter estimation provided in this application. The adaptive parameters change smoothly over time and eventually converge to a constant value.

[0157] Figure 10 A schematic diagram of the simulation results for the control signal event triggering time interval provided in this application. (See diagram below.) Figure 10 As shown, using the proposed event-triggered controller, the control torque is controlled within 20 seconds. Triggered 209 times, controlling torque It was triggered 199 times. However, using a time-triggered controller, the control torque is achieved within 20 seconds. and Each event is triggered 2000 times. Under the proposed event-triggered controller, the frequency of information transmission between the wheeled mobile robot control system and the actuator is significantly reduced, avoiding waste of information transmission resources. The designed controller is easy to implement, consumes few resources, has good tracking performance, and is suitable for tasks such as logistics transportation, power grid inspection, and disaster relief, with a wide range of applications.

[0158] Figure 11 This is a schematic diagram of the trajectory tracking control device for the robot provided in this application. Figure 11 As shown, the robot trajectory tracking control device 110 provided in this embodiment includes:

[0159] The acquisition module 1101 is used to acquire the robot's desired trajectory, actual pose, and actual movement parameters, including actual velocity and actual angular velocity.

[0160] The pose tracking error determination module 1102 is used to determine the pose tracking error based on the desired trajectory and the actual pose.

[0161] The velocity tracking error acquisition module 1103 is used to perform error processing on the pose tracking error and actual movement parameters according to the preset kinematic controller to obtain the velocity tracking error;

[0162] The theoretical control torque generation module 1104 is used to generate theoretical control torque based on the adaptive estimate of the preset dynamic controller, velocity tracking error and lumped uncertainty term. The lumped uncertainty term is used to characterize the model uncertainty and external disturbance of the robot.

[0163] The trajectory tracking control module 1105 is used to control the robot to track the desired trajectory according to the theoretical control torque when the theoretical control torque meets the preset event triggering requirements, thereby completing the movement control process of the robot.

[0164] In one possible implementation, the trajectory tracking control module 1105 is further specifically used for:

[0165] Determine the initial theoretical control torque;

[0166] The initial theoretical control torque is compared with the theoretical control torque to obtain the torque difference;

[0167] When the torque difference meets the dynamic threshold, it indicates that the theoretical control torque meets the preset event triggering requirements;

[0168] The robot is controlled to follow the desired trajectory based on the theoretical control torque, thus completing the movement control process of the robot.

[0169] Furthermore, the dynamic threshold is positively correlated with the norm of the velocity tracking error and the adaptive estimate of the lumped uncertainty term.

[0170] In one possible implementation, the trajectory tracking control module 1105 is further specifically used for:

[0171] If the torque difference does not meet the preset event triggering requirements, the robot will continue to track the desired trajectory based on the initial theoretical control torque.

[0172] In one possible implementation, the velocity tracking error obtaining module 1103 is further specifically used for:

[0173] Based on the preset dynamic controller, the pose tracking error is processed to generate virtual movement commands, which include virtual velocity commands and virtual angular velocity commands.

[0174] The speed tracking error is obtained by comparing the virtual movement command with the actual movement parameters.

[0175] In one possible implementation, the theoretical control torque generation module 1104 is further specifically used for:

[0176] Based on the speed tracking error, a fixed-time convergence term is constructed, and a fixed-time control term is used to drive the speed tracking error to converge within a fixed time.

[0177] An adaptive robust term is constructed based on the adaptive estimate of the lumped uncertainty term;

[0178] By combining the fixed-time convergence term and the adaptive robust term, a continuous-time fixed-time control law is obtained;

[0179] The theoretical control torque is generated based on the preset dynamic controller and the fixed-time control law for continuous time.

[0180] In one possible implementation, the robot's trajectory tracking control device 110 is also specifically used for:

[0181] Based on the theoretical control torque, the adaptive estimate in the lumped uncertainty term is updated to complete the update of the preset dynamic controller.

[0182] The robot trajectory tracking control device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0183] Figure 12 A schematic diagram of the trajectory tracking control device for the robot provided in this application. Figure 12 As shown, the electronic device 120 provided in this embodiment includes at least one processor 1201 and a memory 1202. Optionally, the device 120 further includes a communication component 1203. The processor 1201, the memory 1202, and the communication component 1203 are connected via a bus 1204.

[0184] In a specific implementation, at least one processor 1201 executes computer execution instructions stored in memory 1202, causing at least one processor 1201 to perform the above-described method.

[0185] The specific implementation process of processor 1201 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0186] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0187] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0188] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0189] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0190] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0191] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0192] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0193] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0194] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0195] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0196] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0197] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0198] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A trajectory tracking control method for a robot, characterized in that, include: The robot's desired trajectory, actual pose, and actual movement parameters are obtained, including actual velocity and actual angular velocity. The pose tracking error is determined based on the desired trajectory and the actual pose. Based on the preset kinematics controller, the pose tracking error and the actual movement parameters are processed to obtain the velocity tracking error; Based on the preset dynamic controller, the adaptive estimate of the speed tracking error and the lumped uncertainty term, a theoretical control torque is generated. The lumped uncertainty term is used to characterize the model uncertainty and external disturbances of the robot. When the theoretical control torque meets the preset event triggering requirements, the robot is controlled to follow the desired trajectory according to the theoretical control torque, thus completing the movement control process of the robot. The preset event triggering requirement is the difference requirement between the theoretical control torque and the initial theoretical control torque, wherein the initial theoretical control torque is the torque generated before the theoretical control torque was generated.

2. The trajectory tracking control method according to claim 1, characterized in that, When the theoretical control torque meets the preset event triggering requirements, the robot is controlled to track the desired trajectory according to the theoretical control torque, thus completing the movement control process of the robot, including: Determine the initial theoretical control torque; The initial theoretical control torque is compared with the theoretical control torque to obtain the torque difference. When the torque difference meets the dynamic threshold, it indicates that the theoretical control torque meets the preset event triggering requirements; The robot is controlled to follow the desired trajectory based on the theoretical control torque, thus completing the movement control process of the robot.

3. The trajectory tracking control method according to claim 2, characterized in that, The dynamic threshold is positively correlated with the norm of the speed tracking error and the adaptive estimate of the lumped uncertainty term.

4. The trajectory tracking control method according to claim 2, characterized in that, After comparing the initial theoretical control torque with the theoretical control torque to obtain the torque difference, the method further includes: If the torque difference does not meet the preset event triggering requirements, the robot will continue to track the desired trajectory according to the initial theoretical control torque.

5. The trajectory tracking control method according to any one of claims 1 to 4, characterized in that, Based on a preset kinematic controller, error processing is performed on the pose tracking error and the actual movement parameters to obtain the velocity tracking error, including: Based on a preset kinematic controller, the pose tracking error is processed to generate virtual movement commands, which include virtual velocity commands and virtual angular velocity commands. The speed tracking error is obtained by comparing the virtual movement command with the actual movement parameters.

6. The trajectory tracking control method according to any one of claims 1 to 4, characterized in that, Based on the preset dynamic controller, the adaptive estimate of the speed tracking error and the lumped uncertainty term, a theoretical control torque is generated, including: Based on the speed tracking error, a fixed-time convergence term is constructed, and the fixed-time control term is used to drive the speed tracking error to converge within a fixed time. An adaptive robust term is constructed based on the adaptive estimate of the lumped uncertainty term; Based on the event-triggered adaptive controller, the fixed-time convergence term and the adaptive robust term are combined to obtain a continuous-time fixed-time control law; The theoretical control torque is generated based on the preset dynamic controller and the fixed-time control law of the continuous time.

7. The trajectory tracking control method according to any one of claims 1 to 4, characterized in that, When the theoretical control torque meets the preset event triggering requirements, the robot is controlled to move according to the theoretical control torque. After completing the control process of the robot tracking the desired trajectory, the method further includes: Based on the theoretical control torque, the adaptive estimate in the lumped uncertainty term is updated to complete the update of the preset dynamic controller.

8. A trajectory tracking control device for a robot, characterized in that, include: The acquisition module is used to acquire the robot's desired trajectory, actual pose, and actual movement parameters, wherein the actual movement parameters include actual velocity and actual angular velocity. The pose tracking error determination module is used to determine the pose tracking error based on the expected trajectory and the actual pose. The velocity tracking error acquisition module is used to perform error processing on the pose tracking error and the actual movement parameters according to a preset kinematic controller to obtain the velocity tracking error; The theoretical control torque generation module is used to generate theoretical control torque based on the preset dynamic controller, the speed tracking error, and the adaptive estimate of the lumped uncertainty term. The lumped uncertainty term is used to characterize the model uncertainty and external disturbances of the robot. The trajectory tracking control module is used to control the robot to track the desired trajectory according to the theoretical control torque when the theoretical control torque meets the preset event triggering requirements, thereby completing the movement control process of the robot.

9. A trajectory tracking control device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

11. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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