Robot controller design method, robot control method, device and equipment

Abstract

The invention relates to a robot controller design method, a robot control method, a robot controller design device and robot control equipment, and relates to the technical field of robot control. The robot controller design method comprises the steps that a dynamic model of a robot mechanical arm is established, and the dynamic model is converted into a state equation of the robot mechanical arm; defining a trajectory tracking error of the robot mechanical arm, and determining a boundary constraint condition of the trajectory tracking error; according to the preset observation convergence time of the robot mechanical arm, the observation error of the robot mechanical arm is determined; and according to the state equation, the boundary constraint condition and the observation error, an anti-interference tracking controller of the robot mechanical arm is established. By adopting the method, the accuracy and stability of trajectory tracking of the mechanical arm of the robot can be ensured within finite time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of robot control, in particular to a robot controller design method, a robot control method, device and equipment. BACKGROUND

[0002] With the development of robot technology, various robots are widely used in various application scenarios, such as sweeping robots, AGV (Automated Guided Vehicle) transport robots, palletizing robots, and disassembling and sorting robots. Among them, some robots such as palletizing robots and disassembling and sorting robots are provided with mechanical arms to achieve work tasks by controlling the movement of the robot mechanical arm.

[0003] Taking a railway scattered pile of loading and unloading robot as an example, the unloading robot is composed of a multi-axis mechanical arm, which contains a hydraulic mechanical arm, which realizes precise object operation and path tracking through multiple joints and complex hydraulic systems. However, the inherent nonlinearity, time-varying characteristics of the hydraulic system and external disturbances have a significant impact on the control accuracy of the robot mechanical arm. Among them, the robot mechanical arm often needs to accurately track the robot mechanical arm on a trajectory with constraints when performing tasks, and the robustness of the robot mechanical arm is particularly important when dealing with unknown disturbances.

[0004] Therefore, how to ensure the accuracy and stability of the trajectory tracking of the robot mechanical arm within a limited time is a technical problem to be solved. SUMMARY

[0005] Therefore, it is necessary to provide a robot controller design method, a robot control method, device and equipment to ensure the accuracy and stability of the trajectory tracking of the robot mechanical arm within a limited time.

[0006] In a first aspect, the present application provides a robot controller design method, comprising:

[0007] establishing a dynamics model of a robot mechanical arm, and converting the dynamics model into a state equation of the robot mechanical arm;

[0008] defining a trajectory tracking error of the robot mechanical arm, and determining a boundary constraint condition of the trajectory tracking error;

[0009] determining an observation error of the robot mechanical arm according to a preset observation convergence time of the robot mechanical arm;

[0010] establishing an anti-interference tracking controller of the robot mechanical arm according to the state equation, the boundary constraint condition and the observation error.

[0011] In one embodiment, establishing a dynamic model of a robotic arm includes: determining motion parameters and disturbance parameters of the robotic arm; wherein the motion parameters include the rotation angle, angular velocity, and angular acceleration of the robotic arm; establishing an inertia matrix, an acceleration matrix, a gravity vector, and a friction torque of the robotic arm based on the motion parameters; and establishing a dynamic model of the robotic arm based on the inertia matrix, the acceleration matrix, the gravity vector, the friction torque, and the disturbance parameters.

[0012] In one embodiment, converting the dynamic model into a state equation of the robot arm includes: converting the dynamic model into a state equation of the robot arm using the rotation angle and angular velocity as state quantities of the equation.

[0013] In one embodiment, determining the boundary constraint conditions of the trajectory tracking error includes: designing a prescribed performance function for limiting the motion range of the robot manipulator based on a preset motion space of the robot manipulator; wherein the prescribed performance function is a bounded and strictly monotonically decreasing smooth function; and determining the value range of the function value of the prescribed performance function as the boundary constraint conditions of the trajectory tracking error.

[0014] In one embodiment, an observation error of the robot arm is determined based on a preset observation convergence time of the robot arm, including: establishing an extended state observer of the robot arm based on the preset observation convergence time of the robot arm; and determining an observation error of the extended state observer based on the extended state observer and a state equation as the observation error of the robot arm.

[0015] In one embodiment, an anti-interference tracking controller for a robot manipulator is established based on a state equation, boundary constraints, and observation errors, including: determining a first controller state quantity based on the trajectory tracking error and the boundary constraints, and determining a second controller state quantity based on the state equation and a backstepping virtual controller; and establishing an anti-interference tracking controller for the robot manipulator using the backstepping method, the Lyapunov stability function, the first controller state quantity, the second controller state quantity, and the observation error.

[0016] In a second aspect, the present application further provides a robot control method, comprising:

[0017] Obtain the current motion trajectory of the robot arm; compare the current motion trajectory with the preset motion trajectory of the robot arm to obtain a trajectory comparison result; use the anti-interference tracking controller of the robot arm to control the movement of the robot arm according to the trajectory comparison result; wherein the anti-interference tracking controller is obtained according to the various method embodiments provided in the first aspect.

[0018] In a third aspect, the present application further provides a robot controller design device, comprising:

[0019] A state determination module is used to establish a dynamic model of the robot manipulator and convert the dynamic model into a state equation of the robot manipulator;

[0020] A condition determination module is used to define the trajectory tracking error of the robot manipulator and determine the boundary constraint conditions of the trajectory tracking error;

[0021] an error determination module, configured to determine an observation error of the robot arm based on a preset observation convergence time of the robot arm;

[0022] The tracker establishment module is used to establish the anti-interference tracking controller of the robot manipulator based on the state equation, boundary constraints and observation errors.

[0023] In a fourth aspect, the present application further provides a robot control device, comprising:

[0024] The trajectory acquisition module is used to obtain the current motion trajectory of the robot arm;

[0025] The trajectory comparison module is used to compare the current motion trajectory with the preset motion trajectory of the robot manipulator to obtain the trajectory comparison result;

[0026] A robot control module is used to control the movement of the robot arm based on the trajectory comparison result by using the anti-interference tracking controller of the robot arm;

[0027] Among them, the anti-interference tracking controller is obtained according to the various method embodiments provided in the first aspect.

[0028] In a fifth aspect, the present application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method embodiments when executing the computer program.

[0029] In a sixth aspect, the present application further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned method embodiments are implemented.

[0030] In a seventh aspect, the present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned method embodiments when executed by a processor.

[0031] The robot controller design method, robot control method, device, and apparatus described above establish a dynamic model of a robot arm and convert the dynamic model into a state equation for the robot arm; define the trajectory tracking error of the robot arm and determine the boundary constraints of the trajectory tracking error; determine the observation error of the robot arm based on a preset observation convergence time of the robot arm; and then establish an anti-interference tracking controller for the robot arm based on the state equation, boundary constraints, and observation error. Thus, when designing the anti-interference tracking controller for the robot arm, on the one hand, by setting the observation convergence time, the robot arm can quickly reach a steady state within a relatively short convergence time when performing a task, thereby ensuring the response speed and control efficiency of the robot arm and significantly improving the task execution efficiency of the robot arm. On the other hand, by introducing the observation error, the external interference and internal uncertainty of the robot arm can be estimated and compensated in real time, thereby improving the anti-interference performance of the robot arm in complex environments and ensuring that the robot arm can still operate stably even when subjected to external interference. On the other hand, by setting the boundary constraint conditions of the trajectory tracking error of the robot arm, the output constraint control is adopted for the robot arm, so that the movement of the robot arm is always within the predetermined physical range and does not exceed the allowable operation boundary, so as to avoid causing damage to other equipment and the surrounding environment in the environment. Therefore, using the designed anti-interference tracking controller to control the movement of the robot arm can ensure the accuracy and stability of the robot arm trajectory tracking within a limited time, thereby making the motion error of the robot arm converge quickly within the predetermined time range, achieving high-precision trajectory tracking of the robot arm, meeting the requirements of the high-precision operation task of the robot arm, and thus the dynamic response and steady-state performance of the robot arm can meet higher requirements. In addition, the above-mentioned anti-interference tracking controller is applicable to multi-joint hydraulic manipulators and other nonlinear robot arm systems, as well as manipulators with various drive modes such as electric drive, hydraulic drive, and pneumatic drive. It can cope with trajectory tracking tasks of different types of robot arms and has broad industrial application prospects and application value in intelligent manufacturing, automated assembly and high-precision operation tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0033] Figure 1A flowchart of a robot controller design method provided in some embodiments of the present application;

[0034] Figure 2 A schematic diagram of a process for establishing a kinetic model provided in some embodiments of the present application;

[0035] Figure 3 A schematic flow chart of a robot controller design method provided in some other embodiments of the present application;

[0036] Figure 4 A schematic diagram of a process for determining boundary constraints provided in some embodiments of the present application;

[0037] Figure 5 A schematic diagram of a process for determining observation errors provided in some embodiments of the present application;

[0038] Figure 6 A schematic diagram of a flow chart for establishing an anti-interference tracking controller according to some embodiments of the present application;

[0039] Figure 7A The obtained anti-interference tracking controller is used to control the robot arm, and the rotation angle tracking diagram of the robot arm is shown in FIG.

[0040] Figure 7B Angular velocity tracking diagram of the robot arm during the process of the robot arm being controlled by the obtained anti-interference tracking controller;

[0041] Figure 7C The observed error tracking diagram of the robot arm during the process of the robot arm being controlled by the obtained anti-interference tracking controller;

[0042] Figure 7D The tracking error performance boundary diagram of the robot manipulator during the process of the obtained anti-interference tracking controller controlling the robot manipulator;

[0043] Figure 8 A flowchart of a robot controller design method provided in some embodiments of the present application;

[0044] Figure 9 A schematic flow chart of a robot control method provided in some embodiments of the present application;

[0045] Figure 10 A structural block diagram of a robot controller design device provided in some embodiments of the present application;

[0046] Figure 11 A structural block diagram of a robot control device provided in some embodiments of the present application;

[0047] Figure 12 Fig. 1 is a schematic diagram of an internal structure of a computer device according to some embodiments of the present application. DETAILED DESCRIPTION

[0048] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not intended to limit the present application.

[0049] In the conventional technology, some robots are provided with hydraulic mechanical arms, and the inherent nonlinearity, time-varying characteristics and external disturbances of the hydraulic system have a significant impact on the control accuracy of the robot mechanical arm. Therefore, how to ensure the accuracy and stability of the trajectory tracking of the robot mechanical arm within a limited time is a technical problem to be solved.

[0050] Based on this, in order to solve the above technical problems, in an exemplary embodiment, a robot controller design method is provided, which can be applied to a computer device, which can be a server or a terminal. Wherein, in the case that the method is applied to the server, the server can be a stand-alone physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0051] Wherein, the robot mechanical arm is a key component of the robot, which is usually composed of a base, a connecting rod, a joint and an end effector, etc. The base is the supporting foundation of the mechanical arm; the connecting rod is a component connecting each joint, which plays a role in transmitting force and motion; the joint is the key part to realize the motion of the mechanical arm, which can be divided into rotary joints and moving joints, through the motion combination of the joints, the mechanical arm can reach different positions and attitudes in space; the end effector is a tool installed at the end of the mechanical arm, such as a clamp, a welding gun, a spray gun, a camera, etc., which can be replaced according to different task requirements to complete specific operations such as grasping, welding, spraying and detection. During the motion of the mechanical arm, the motion control of the mechanical arm can be realized through the controller (control system), including position control, speed control and force control, etc. Among them, the position control is to make the end effector of the mechanical arm accurately reach the specified spatial position; the speed control is used to adjust the motion speed of the mechanical arm to meet the requirements of different tasks; the force control is to accurately control the size of the force when the mechanical arm interacts with the external environment.

[0052] Next, taking the application of the robot controller design method to the server as an example, as shown in Figure 1 The method can include the following steps:

[0053] S101, a dynamics model of the robot mechanical arm is established, and the dynamics model is converted into a state equation of the robot mechanical arm.

[0054] Generally, when the robot manipulator performs a task, the motion state of the robot manipulator is affected by driving force, gravity, centripetal force and the like, and centripetal force moment and the like, so that the motion state of the robot manipulator has a correlation relationship with the force and moment acting on the robot manipulator, and the correlation relationship between the motion state of the robot manipulator and the force and moment acting on the robot manipulator can be represented by a dynamics model of the robot manipulator, so that the driving force and moment required by the robot manipulator in different motion states can be analyzed by using the dynamics model, so as to calculate the corresponding joint moment according to the expected force output, realize accurate force control of the robot manipulator, and optimize the motion trajectory of the end effector of the robot manipulator when the robot manipulator performs a task by using the dynamics model, so as to reduce energy consumption in the task execution process and improve motion efficiency.

[0055] Among them, the dynamics model of the robot manipulator can be established according to the driving system characteristics, the structure, size, motion limitation conditions and the connection relationship and driving relationship between the parts of the robot manipulator, and the relevant kinematics principles. Further, the dynamics model of the robot manipulator can be converted into a state equation of the robot manipulator to describe the motion state of the robot manipulator.

[0056] S102, defining the trajectory tracking error of the robot manipulator, and determining the boundary constraint condition of the trajectory tracking error.

[0057] When the robot manipulator performs a task, the motion trajectory of the end effector of the robot manipulator is usually determined according to the task demand as a desired movement trajectory, and due to the influence of air resistance, gravity, equipment wear and other interference factors, there is usually an error between the actual movement trajectory of the end effector of the robot manipulator and the desired movement trajectory as a trajectory tracking error. In order to realize accurate control of the robot manipulator, the trajectory tracking error needs to be limited within a set range. Based on this, the trajectory tracking error of the robot manipulator can be defined first, and then the boundary constraint condition of the trajectory tracking error can be determined according to the requirements of actual application. The boundary constraint condition limits the range of the trajectory tracking error, and performs output constraint control on the motion process of the robot manipulator, so that the motion of the robot manipulator is always limited within a predetermined physical range during the motion process of the robot manipulator, and will not exceed the allowed operation boundary, so as to avoid damaging other equipment and environment in the environment where the robot is located.

[0058] S103, determining the observation error of the robot manipulator according to the preset observation convergence time of the robot manipulator.

[0059] Typically, at the beginning of a task being performed by a robotic arm, there is often a large trajectory tracking error between the actual movement trajectory of the robotic arm and the desired movement trajectory. As the robotic arm continues to move, the actual movement trajectory of the robotic arm can gradually approach the desired movement trajectory, thereby gradually reducing the above-mentioned trajectory tracking error until the actual movement trajectory can be considered to coincide with the desired movement trajectory. However, the process of reducing the above-mentioned trajectory movement error often takes a long time, thereby greatly affecting the precise control of the robotic arm during the task execution. Based on this, in order to ensure the accuracy and stability of the trajectory tracking of the robotic arm within a limited time, so that during the task execution, the trajectory tracking error between the actual movement trajectory of the robotic arm and the desired movement trajectory can be quickly reduced, thereby achieving the coincidence of the actual movement trajectory and the desired movement trajectory within a set short time, thereby improving the precise control of the robotic arm during the task execution, a preset observation convergence time of the robotic arm can be pre-set.

[0060] The so-called preset observation convergence time refers to the convergence time of the robot arm's observer. Observer convergence refers to the observer's estimation and compensation of observation errors, reducing the observation errors to a value that meets the control accuracy requirement. The predicted observation convergence time can be understood as the duration from the start of the robot arm's task execution to the moment the robot arm's observer compensates for the observation errors to a value that meets the control accuracy requirement. Furthermore, the preset observation convergence time can be limited based on the actual task requirements of the robot arm, for example, to 0.2 seconds. This embodiment does not impose any specific limitations on this.

[0061] Optionally, considering the material and mechanical properties of the robotic arm, and to protect the movement safety of the robotic arm and avoid physical damage to the robotic arm, the preset observation convergence time is generally not too short, for example, not less than 0.1. The lower limit of the preset observation convergence time can also be set based on the actual task requirements of the robotic arm, as well as the material and mechanical properties of the robotic arm, and this embodiment also does not impose any specific limitations on this.

[0062] In addition, when the robot acquires external environment information through the sensor, the inherent precision limitation of the camera, laser radar, ultrasonic sensor and the like of the sensor and the factors such as illumination, temperature, humidity, electromagnetic interference of the environment where the robot is located often interfere with the external environment information acquired by the robot, so that there is a deviation between the external environment information acquired by the robot and the real information of the external environment. The deviation can be used as an observation error, and the observation error will affect the control accuracy of the subsequent robot arm. In order to ensure the accuracy and stability of the robot arm trajectory tracking in a limited time, the observation error of the robot arm needs to be compensated within the preset observation convergence time. Therefore, the observation error of the robot arm can be determined according to the preset observation convergence time after the preset observation convergence time is determined.

[0063] S104, establishing an anti-interference tracking controller of the robot arm according to the state equation, the boundary constraint condition and the observation error.

[0064] After obtaining the state equation of the robot arm, the boundary constraint condition of the trajectory tracking error of the robot arm, and the observation error of the robot arm, an anti-interference tracking controller of the robot arm can be established according to the state equation, the boundary constraint condition and the observation error.

[0065] The so-called anti-interference tracking refers to the resistance to external interference such as gravity, friction, environmental temperature change, and internal uncertainty such as transmission error, controller error and driving force torque fluctuation through disturbance compensation, so as to realize the tracking of the actual motion trajectory of the robot arm to the expected motion trajectory, so that the actual motion trajectory of the robot arm can converge to the expected motion trajectory in a short time. Thus, the motion control of the robot arm by using the anti-interference tracking controller can ensure the accuracy and stability of the robot arm trajectory tracking in a limited time.

[0066] The above-mentioned robot controller design method establishes a dynamic model of the robot manipulator and converts the dynamic model into the state equation of the robot manipulator; defines the trajectory tracking error of the robot manipulator and determines the boundary constraints of the trajectory tracking error; determines the observation error of the robot manipulator based on the preset observation convergence time of the robot manipulator; and then establishes an anti-interference tracking controller for the robot manipulator based on the above-mentioned state equation, boundary constraints, and observation error. In this way, when designing the anti-interference tracking controller for the robot manipulator, on the one hand, by setting the observation convergence time, the robot manipulator can quickly reach a steady state within a short convergence time when performing a task, thereby ensuring the response speed and control efficiency of the robot manipulator and significantly improving the task execution efficiency of the robot manipulator. On the other hand, by introducing the observation error, the external interference and internal uncertainty of the robot manipulator can be estimated and compensated in real time, thereby improving the anti-interference performance of the robot manipulator in complex environments and ensuring that the robot manipulator can still operate stably despite external interference. On the other hand, by setting the boundary constraint conditions of the trajectory tracking error of the robot arm, the output constraint control is adopted for the robot arm, so that the movement of the robot arm is always within the predetermined physical range and does not exceed the allowable operation boundary, so as to avoid causing damage to other equipment and the surrounding environment in the environment. Therefore, using the designed anti-interference tracking controller to control the movement of the robot arm can ensure the accuracy and stability of the robot arm trajectory tracking within a limited time, thereby making the motion error of the robot arm converge quickly within the predetermined time range, achieving high-precision trajectory tracking of the robot arm, meeting the requirements of the high-precision operation task of the robot arm, and thus the dynamic response and steady-state performance of the robot arm can meet higher requirements. In addition, the above-mentioned anti-interference tracking controller is applicable to multi-joint hydraulic manipulators and other nonlinear robot arm systems, as well as manipulators with various drive modes such as electric drive, hydraulic drive, and pneumatic drive. It can cope with trajectory tracking tasks of different types of robot arms and has broad industrial application prospects and application value in intelligent manufacturing, automated assembly and high-precision operation tasks.

[0067] On the basis of the above embodiment, in an exemplary embodiment, the establishment of the kinetic model in the above S101 is further refined, optionally, as follows: Figure 2 As shown, the following steps may be included:

[0068] S201, determining motion parameters and disturbance parameters of a robot manipulator arm.

[0069] Among them, the motion parameters include the rotation angle, angular velocity and angular acceleration of the robot arm.

[0070] As previously mentioned, the dynamic model of a robotic arm is used to characterize the relationship between the arm's motion state and the forces and torques acting on it. The arm's motion state is often characterized by motion parameters such as rotation angle, angular velocity, and angular acceleration. Therefore, to establish the dynamic model of the robotic arm, the arm's motion parameters must be determined. These parameters include rotation angle, angular velocity, and angular acceleration. Furthermore, the arm's motion can be subject to various disturbances, such as external interference and internal uncertainties. Therefore, to compensate for these disturbances and improve the arm's control accuracy, the arm's disturbance parameters must be considered when establishing the dynamic model. For example, external disturbances such as gravity, friction, and temperature variations, as well as internal uncertainties such as transmission errors, controller errors, and fluctuations in the driving force and torque, are considered.

[0071] S202: Establish an inertia matrix, an acceleration matrix, a gravity vector, and a friction torque of the robot arm according to the motion parameters.

[0072] After determining the above motion parameters, the robot arm's inertia matrix, acceleration matrix, gravity vector, and friction torque can be constructed based on these motion parameters. Optionally, the acceleration matrix can be a centripetal-Coriolis matrix. Using the centripetal-Coriolis matrix, the driving torque required to apply to each joint in order for the robot arm to move along the desired trajectory is determined.

[0073] S203 , establishing a dynamic model of the robot arm according to the inertia matrix, the acceleration matrix, the gravity vector, the friction torque, and the disturbance parameters.

[0074] After obtaining the inertia matrix, acceleration matrix, gravity vector and friction torque of the above-mentioned robot manipulator, a dynamic model of the robot manipulator can be established based on the above-mentioned inertia matrix, acceleration matrix, gravity vector, friction torque, and disturbance parameters.

[0075] In an optional embodiment, the established dynamic model of the robot manipulator can be expressed as follows (1):

[0076]

[0077] in, is the rotation angle, is the angular velocity, is the angular acceleration, is the disturbance parameter, and , , , , express is a vector space.

[0078] is the inertia matrix of the robot manipulator, which is a symmetric positive definite inertia matrix, and, , yes The set of real matrices of ; is the acceleration matrix of the robot manipulator, which is a centripetal-Coriolis matrix, and, ; is the gravity vector of the robot arm, and, ; is the friction torque of the robot arm, and, ; is the rotation control vector of the robot arm, .

[0079] In this embodiment, by introducing disturbance parameters into the dynamic model of the robot arm, the resulting anti-interference tracking controller can achieve resistance to external interferences such as gravity, friction, and ambient temperature changes, as well as internal uncertainties such as transmission errors, controller errors, and driving force torque fluctuations through disturbance compensation when controlling the robot arm, thereby improving the control accuracy of the robot arm.

[0080] On the basis of the above embodiments, in an exemplary embodiment, the determination of the state equation in the above S101 is further refined, optionally, as follows: Figure 3 As shown, the robot controller design method may include the following steps:

[0081] S301, determining motion parameters and disturbance parameters of a robot manipulator.

[0082] S302: Establish the inertia matrix, acceleration matrix, gravity vector, and friction torque of the robot arm according to the motion parameters.

[0083] S303 , establishing a dynamic model of the robot manipulator according to the inertia matrix, acceleration matrix, gravity vector, friction torque, and disturbance parameters.

[0084] Among them, the specific implementation method of the above S301-S303 is the same as the specific implementation method of the above S201-S203, and will not be repeated here.

[0085] S304 , using the rotation angle and angular velocity as equation state quantities, converting the dynamic model into a state equation of the robot manipulator.

[0086] After obtaining the dynamic model of the robot manipulator, the dynamic model can be converted into a state equation of the robot manipulator by taking the rotation angle and the angular velocity in the motion parameters of the robot manipulator as equation state quantities. The so-called equation state quantity can be understood as a variable of the state equation of the robot manipulator.

[0087] In an optional embodiment, the set equation state quantity is: , . Among them, can be the first equation state quantity, can be the second equation state quantity. In this embodiment, the converted state equation of the robot manipulator can be as shown in the following equations (2)-(4):

[0088]

[0089]

[0090]

[0091] Among them, and represent disturbances, , , represent the output of the state equation of the robot manipulator, represent the input control, , and is also used to represent the anti-interference tracking controller of the robot manipulator to be designed finally by the robot controller design method.

[0092] And the state of the robot manipulator does not violate the full state constraint, and all control signals of the anti-interference tracking controller finally obtained are bounded in the presence of disturbances, wherein the so-called full state constraint refers to the condition of limiting the value range or change rule of all equation state quantities of the robot manipulator in the entire task process of the robot manipulator. Further, in the process of executing the task of the robot manipulator, the first equation state quantity can track the expected reference trajectory (expected movement trajectory) , and the transient and steady-state performance of the robot manipulator does not violate the specified performance limit, that is, the trajectory tracking error of the robot manipulator cannot exceed the boundary constraint condition of the trajectory tracking error.

[0093] S305, defining the trajectory tracking error of the robot manipulator and determining the boundary constraint condition of the trajectory tracking error.

[0094] S306, determining the observation error of the robot manipulator according to the preset observation convergence time of the robot manipulator.

[0095] S307, establishing an anti-interference tracking controller for the robot manipulator according to the state equation, boundary constraints and observation errors.

[0096] The specific implementation of the above S305-S307 is the same as the specific implementation of the above S102-S104, and will not be repeated here.

[0097] In this embodiment, by using the rotation angle and angular velocity as variables (equation state quantities) of the state equation of the robot arm, the dynamic model can be converted into the state equation of the robot arm. Therefore, when the disturbance parameter is introduced into the dynamic model of the robot arm, the disturbance parameter is also introduced into the state equation of the obtained robot arm. Furthermore, when the anti-interference tracking controller finally obtained controls the robot arm, the actual movement trajectory of the robot arm can track the expected movement trajectory, and the transient and steady-state performance of the robot arm do not violate the specified performance limits. Therefore, while improving the control accuracy of the robot arm, the stability of the robot arm control is improved, ensuring the accuracy and stability of the robot arm trajectory tracking.

[0098] On the basis of the above embodiments, in an exemplary embodiment, the determination of the boundary constraint condition in the above S102 is further refined, optionally, as follows: Figure 4 As shown, the following steps may be included:

[0099] S401 : Designing a prescribed performance function for limiting the motion range of the robot arm according to a preset motion space of the robot arm.

[0100] The performance function is specified to be a bounded and strictly monotonically decreasing smooth function.

[0101] Typically, during the motion of a robotic arm, to avoid damaging other devices within the robot's environment and the surrounding environment, a motion space for the robotic arm can be set so that the robotic arm's motion always remains within a predetermined physical range. Based on this, the robotic arm's motion space can be set based on the robot's task requirements and the environmental conditions of the robot's environment, such as the size of the robot's environment and the distance between the robotic arm and other devices in the robot's environment. This motion space can also contribute to defining the trajectory tracking error of the robotic arm. Therefore, a prescribed performance function for defining the range of motion of the robotic arm can be designed based on this preset motion space.

[0102] In the field of robotic control, a "prescribed performance function" (PPF) typically refers to a predefined mathematical expression used to quantitatively evaluate a robot's dynamic performance to meet specific task requirements (such as trajectory tracking, obstacle avoidance, and operational accuracy). This type of function must be designed based on the robot's dynamic characteristics, control objectives, and physical constraints, and is commonly found in robotic control algorithms (such as adaptive control and robust control). In this embodiment, since a PPF is required to define the boundary constraints for trajectory tracking error, the PPF can be designed as a bounded, strictly monotonically decreasing smooth function.

[0103] S402: Determine the value range of the function value of the prescribed performance function as the boundary constraint condition of the trajectory tracking error.

[0104] After the above-mentioned specified performance function is designed, since the specified performance function is a bounded and strictly monotonically decreasing smooth function, the value range of the function value of the specified performance function can be determined, and thus the above-mentioned value range can be determined as the boundary constraint condition of the trajectory tracking error.

[0105] In an optional embodiment, the trajectory tracking error of the robot manipulator is defined as: ,in, is the trajectory tracking error. Furthermore, the prescribed performance function designed to limit the motion range of the robot manipulator is shown in the following formula (5):

[0106]

[0107] in, is the specified performance function, yes The steady-state value of It’s time; and is the tuning gain, , ; is a time-varying scaling function, , It is an anti-interference tracking controller for the robot manipulator (i.e. ) preset convergence time, , , It is a preset parameter.

[0108] Furthermore, by using the value range of the function value of the specified performance function, the boundary constraint condition of the trajectory tracking error is determined as shown in the following formula (6):

[0109]

[0110] In this embodiment, It can be a trajectory tracking performance function in the field of robot control, such as position error function, velocity error function, comprehensive error performance index, etc., which is used to measure the error between the actual motion trajectory of the robot manipulator and the expected motion trajectory to ensure motion accuracy. It uses a preset performance boundary function (such as an exponential decay function) to force the tracking error between the actual motion trajectory of the robot manipulator and the expected motion trajectory to converge within a specified range. For example, when the robot manipulator tracks a sinusoidal trajectory, the controller parameters are optimized by minimizing the comprehensive error performance index to ensure that the position error of the end effector of the robot manipulator is within a specified threshold (such as centimeter).

[0111] The so-called steady-state value of the specified performance function refers to the limit value of the specified performance function when time approaches infinity, which directly determines the maximum allowable range of the tracking error between the actual motion trajectory of the robot manipulator and the expected motion trajectory in the steady state.

[0112] In this embodiment, by designing a prescribed performance function for limiting the range of motion of the robot arm, and determining the range of function values ​​of the prescribed performance function as the boundary constraint condition of the trajectory tracking error, output constraint control can be introduced into the final anti-interference tracking controller. Thus, when the final anti-interference tracking controller controls the robot arm, the movement of the robot arm can always be within the predetermined physical range and will not exceed the allowable operating boundary, so as to avoid damage to other equipment and the surrounding environment in the environment, thereby improving the stability of the robot arm control.

[0113] On the basis of the above embodiments, in an exemplary embodiment, the determination of the observation error in the above S103 is further refined, optionally, as follows: Figure 5 As shown, the following steps may be included:

[0114] S501 : establishing an extended state observer of the robot arm according to a preset observation convergence time of the robot arm.

[0115] In order to ensure the accuracy and stability of the robot arm trajectory tracking in a limited time, the observation convergence time can be preset according to the task requirements of the robot arm and the material performance and mechanical performance of the robot arm. Since one of the purposes of the obtained anti-interference tracking controller is to compensate for the observation error of the robot arm within the preset observation convergence time to make the observation error decay to a value that meets the control accuracy requirement, the extended state observer of the robot arm can be determined according to the preset observation convergence time.

[0116] The so-called extended state observer (ESO) is an observer for estimating system states and unknown disturbances, which estimates unknown disturbances in the system together with system states, so as to realize simultaneous observation of system states and unknown disturbances by constructing an augmented system containing system states and extended states (usually regarding unknown disturbances as extended states), and then designing an observer to estimate the state of the augmented system.

[0117] S502, determining the observation error of the extended state observer as the observation error of the robot arm according to the extended state observer and the state equation.

[0118] After the extended state observer is designed, in order to realize the compensation of the observation error of the robot arm within the preset observation convergence time after the robot arm starts moving, so as to make the extended state observer of the robot arm converge within a short convergence time, the observation error of the extended state observer can be determined as the observation error of the robot arm according to the extended state observer and the state equation.

[0119] In an optional embodiment, in the case that the state equation of the robot arm is as shown in the above formula (2), the following formula (3) is defined first Thus, the state equation of the robot arm shown in the above formula (2) can be converted into the following formula (2), (8)-(9):

[0120]

[0121]

[0122]

[0123] wherein, is bounded, smooth, continuous, , is a positive constant.

[0124] Further, the extended state observer of the robot manipulator is shown in the following equations (10)-(12):

[0125]

[0126]

[0127]

[0128] wherein, , , is a preset observation convergence time; is a Lipschitz constant, and ; , and are design parameters, and , , ; , and are preset gain values, and the values thereof are preset positive constant values; is a switching function, ;

[0129] represents ; represents ; represents ; wherein, represents a sign function.

[0130] The observation error of the above-mentioned extended state observer of the robot manipulator is shown in the following equations (13)-(15):

[0131]

[0132]

[0133]

[0134] wherein, , , .

[0135] In the embodiment, by introducing the observation error of the extended state observer, the finally obtained anti-interference tracking controller can make the robot manipulator reach a steady state in a limited time when controlling the robot manipulator, so as to guarantee the response speed and control efficiency of the robot manipulator, significantly improve the task execution efficiency of the robot manipulator, and can estimate and compensate the external interference and internal uncertainty of the robot manipulator in real time, improve the anti-interference performance of the robot manipulator in a complex environment, and ensure that the robot manipulator can still operate stably when receiving external interference.

[0136] On the basis of the above embodiments, in an exemplary embodiment, the establishment of the anti-interference tracking controller in S104 is further refined, which can include the following steps as shown in Figure 6

[0137] S601, determining a first controller state quantity according to the trajectory tracking error and the boundary constraint condition, and determining a second controller state quantity according to the state equation and the backstepping virtual controller.

[0138] After obtaining the state equation of the robot manipulator, the boundary constraint condition of the trajectory tracking error of the robot manipulator, and the observation error of the robot manipulator, the controller state quantity of the finally obtained anti-interference tracking controller can be determined first. The so-called controller state quantity can be understood as the variable of the anti-interference tracking controller of the robot manipulator. In this way, the first controller state quantity of the anti-interference tracking controller of the robot manipulator can be determined according to the trajectory tracking error and the boundary constraint condition, and then the backstepping virtual controller is defined, and the second controller state quantity of the anti-interference tracking controller of the robot manipulator is determined according to the state equation of the robot manipulator and the backstepping virtual controller. The so-called backstepping virtual controller is a controller for a control system designed based on backstepping.

[0139] S602, using backstepping, using Lyapunov stability function, first controller state quantity, second controller state quantity and observation error to establish the anti-interference tracking controller of the robot manipulator.

[0140] ​After obtaining the first controller state quantity and the second controller state quantity of the anti-interference tracking controller of the robot manipulator, the backstepping method can be used to introduce the Lyapunov stability function, and the first controller state quantity, the second controller state quantity and the observation error are used to establish the anti-interference tracking controller of the robot manipulator. Among them, the so-called backstepping method is a recursive design method based on Lyapunov stability theory, which decomposes a complex nonlinear system into multiple subsystems, and gradually designs virtual control laws and actual control laws to make each subsystem meet the stability requirements, and ultimately ensure the stability and performance of the entire system. The so-called Lyapunov stability function is a positive definite function used to judge the stability of the system. For the system , if there exists a positive definite function V(x) with continuous first-order partial derivatives, and along the trajectory of the system, its time derivative is If certain conditions are met, the stability of the system can be judged based on these conditions.

[0141] In an optional embodiment, the determined first controller state quantity is: , the second controller state quantity is .in, is the first controller state quantity, is the state quantity of the second controller, The backstepping virtual controller is shown in Equation (16).

[0142]

[0143] in, and is the preset gain.

[0144] The Lyapunov stability function includes Lyapunov stability function 1 and Lyapunov stability function 2, wherein Lyapunov stability function 1 is shown in the following formula (17), and Lyapunov stability function 2 is shown in the following formula (18).

[0145]

[0146] (18)

[0147] in, is a positive boundary value among the boundary values ​​in the boundary constraint conditions of the trajectory tracking error; is the Lyapunov stability function1, is the Lyapunov stability function 2. Then, the backstepping method is used to establish the anti-interference tracking controller of the robot manipulator using the Lyapunov stability function, the first controller state quantity, the second controller state quantity and the observation error, as shown in the following equations (19)-(24):

[0148]

[0149]

[0150]

[0151]

[0152] in, is the preset gain.

[0153]

[0154]

[0155] in, and is the preset gain, This is the final anti-interference tracking controller of the robot manipulator.

[0156] Furthermore, in order to prove the above-mentioned anti-interference tracking controller of the robot manipulator The control performance can be proved to be stable. The proof process is shown in the following equations (25)-(27):

[0157] (25)

[0158] Furthermore, the Young inequality is used to scale the above formula (25) to obtain the following formula (26):

[0159] (26)

[0160] according to and , which can be calculated as follows (27):

[0161] (27)

[0162] in, , , is the specified performance function, It can be proved that in the above anti-disturbance tracking controller Under the control of the above-mentioned anti-interference tracking controller (i.e. ) preset convergence time Converges to a steady state.

[0163] In addition, the above preset convergence time It can be understood as the time between the start of the robot arm's task and the moment when the actual movement trajectory of the robot arm begins to overlap with the expected movement trajectory, and the preset convergence time Convergence time with the above preset observation The values ​​can be the same or different.

[0164] Further, if Figures 7A-7D As shown, this is the anti-interference tracking controller of the robot manipulator obtained above The simulation results of the control performance are verified. The parameters in the above formulas are set as follows: , , , , , , , , , , , , , , , Specifically:

[0165] Figure 7A The obtained anti-interference tracking controller is used to control the robot arm, and the rotation angle tracking diagram of the robot arm is shown in FIG. Figure 7A As shown in the figure, the solid line curve shows the change of the actual rotation angle of the robot arm over time, and the dotted line curve shows the change of the expected rotation angle of the robot arm over time. It can be seen that in the initial stage, there is a large difference between the actual rotation angle and the expected rotation angle, especially at the starting moment ( However, after a very short time ( ), the actual rotation angle quickly approaches the expected rotation angle, and the error between the two gradually decreases until they almost completely coincide. The whole process takes place within the preset time ( ), indicating that the anti-interference tracking controller obtained above has a very fast response speed and can accurately complete the trajectory tracking. Figure 7B The angular velocity tracking diagram of the robot arm during the process of the obtained anti-interference tracking controller controlling the robot arm. Figure 7B As shown in the figure, the solid line curve shows the actual angular velocity of the robot arm over time, and the dotted line curve shows the expected angular velocity of the robot arm over time. It can be seen that in the initial stage, the actual angular velocity of the robot arm fluctuates, but in a short time ( ) quickly converges and stabilizes around the desired angular velocity, and the deviation between the actual angular velocity of the robot manipulator and the desired value gradually decreases over time. Figure 7C The tracking error performance boundary of the robot manipulator during the control of the obtained anti-interference tracking controller is shown in FIG. 8. As shown in FIG. 8, the tracking error is shown to change over time and the range of the performance boundary in which the error is located. It can be seen that the tracking error is large (0.5) at the initial stage, but quickly decreases and finally remains within the strict performance boundary (0.05). This indicates that the obtained anti-interference tracking control not only can achieve accurate trajectory tracking within the preset time, but also can ensure that the tracking error is always within the set constraint condition. Figure 7B As shown in FIG. 7, the estimation error of the extended state observer for the external disturbance is shown. It can be seen that the curve shows that the observation error is large at the initial stage, and the observation error quickly decays over time and almost decreases to 0 in about 2 seconds. This indicates that the extended state observer can quickly estimate and compensate for the disturbance existing in the movement of the robot manipulator, so that the robot manipulator can achieve high-precision interference suppression within the preset time. Figure 7D Figure 7D The tracking error performance boundary of the robot manipulator during the control of the obtained anti-interference tracking controller is shown in FIG. 8. As shown in FIG. 8, the tracking error is shown to change over time and the range of the performance boundary in which the error is located. It can be seen that the tracking error is large (0.5) at the initial stage, but quickly decreases and finally remains within the strict performance boundary (0.05). This indicates that the obtained anti-interference tracking control not only can achieve accurate trajectory tracking within the preset time, but also can ensure that the tracking error is always within the set constraint condition.

[0166] As shown in FIG. 7, the estimation error of the extended state observer for the external disturbance is shown. It can be seen that the curve shows that the observation error is large at the initial stage, and the observation error quickly decays over time and almost decreases to 0 in about 2 seconds. This indicates that the extended state observer can quickly estimate and compensate for the disturbance existing in the movement of the robot manipulator, so that the robot manipulator can achieve high-precision interference suppression within the preset time. Figures 7A-7D As shown in FIG. 8, the tracking error performance boundary of the robot manipulator during the control of the obtained anti-interference tracking controller is shown. As shown in FIG. 8, the tracking error is shown to change over time and the range of the performance boundary in which the error is located. It can be seen that the tracking error is large (0.5) at the initial stage, but quickly decreases and finally remains within the strict performance boundary (0.05). This indicates that the obtained anti-interference tracking control not only can achieve accurate trajectory tracking within the preset time, but also can ensure that the tracking error is always within the set constraint condition.

[0167] Figure 8 As shown in FIG. 9, the robot controller design method can include the following steps:

[0168] S801, determine the motion parameters of the robot manipulator and the disturbance parameters of the robot manipulator.

[0169] S802, according to the motion parameters, establish the inertia matrix, acceleration matrix, gravity vector and friction torque of the robot manipulator.

[0170] S803, according to the inertia matrix, acceleration matrix, gravity vector, friction torque, and disturbance parameters, establish the dynamics model of the robot manipulator.

[0171] S804, taking the rotation angle and angular velocity as the equation state quantity, convert the dynamics model into the state equation of the robot manipulator.​​​

[0172] S805 , designing a prescribed performance function for limiting the motion range of the robot arm according to the preset motion space of the robot arm.

[0173] S806: Determine the value range of the function value of the prescribed performance function as the boundary constraint condition of the trajectory tracking error.

[0174] S807: Establish an extended state observer of the robot arm according to the preset observation convergence time of the robot arm.

[0175] S808 , determining an observation error of the extended state observer according to the extended state observer and the state equation, as the observation error of the robot manipulator.

[0176] S809: Determine the state quantity of the first controller according to the trajectory tracking error and the boundary constraint condition, and determine the state quantity of the second controller according to the state equation and the backstepping virtual controller.

[0177] S810, adopting the backstepping method, using the Lyapunov stability function, the first controller state quantity, the second controller state quantity and the observation error, establishes an anti-interference tracking controller for the robot manipulator.

[0178] The specific implementation of S801-S810 is the same as that in the above embodiments, and will not be repeated here.

[0179] Based on the same inventive concept, an embodiment of the present application further provides a robot control method based on the robot controller design method described above. This method can be applied to a computer device, which can be a server or a terminal. When the method is applied to a server, the server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. When the method is applied to a terminal, the terminal can be a robot.

[0180] The following is an example of applying the robot control method to a robot. Figure 9 As shown, the method may include the following steps:

[0181] S901, obtaining the current motion trajectory of the robot arm.

[0182] S902 , comparing the current motion trajectory with the preset motion trajectory of the robot manipulator to obtain a trajectory comparison result.

[0183] S903: Using the anti-interference tracking controller of the robot arm, the robot arm is controlled to move according to the trajectory comparison result. The anti-interference tracking controller is obtained according to the various embodiments of the design method of the robot controller.

[0184] The robot control method, by utilizing the anti-interference tracking controller for the robot arm established by the robot controller design method during the motion of the robot arm, can ensure the accuracy and stability of the robot arm's trajectory tracking within a limited time. This allows the robot arm's motion error to converge quickly within a predetermined time range, achieving high-precision trajectory tracking for the robot arm and meeting the requirements of high-precision manipulation tasks for the robot arm. Furthermore, the dynamic response and steady-state performance of the robot arm can meet high requirements.

[0185] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0186] Based on the same inventive concept, embodiments of the present application also provide a robot controller design device for implementing the aforementioned robot controller design method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations in one or more of the following embodiments of the robot controller design device can be found in the aforementioned limitations of the robot controller design method and will not be further elaborated here.

[0187] In an exemplary embodiment, Figure 10 As shown, a robot controller design device is provided, including: a state determination module 1010, a condition determination module 1020, an error determination module 1030 and a tracker establishment module 1040, wherein:

[0188] A state determination module 1010 is configured to establish a dynamic model of the robot arm and convert the dynamic model into a state equation of the robot arm;

[0189] a condition determination module 1020, configured to define a trajectory tracking error of the robot manipulator and determine a boundary constraint condition of the trajectory tracking error;

[0190] an error determination module 1030 for determining an observation error of the robotic arm based on a preset observation convergence time of the robotic arm;

[0191] The tracker establishment module 1040 is used to establish an anti-interference tracking controller of the robot manipulator according to the state equation, the boundary constraint condition and the observation error.

[0192] In an exemplary embodiment, the state determination module 1010 is specifically used to: determine the motion parameters of the robot arm and the disturbance parameters of the robot arm; wherein the motion parameters include the rotation angle, angular velocity and angular acceleration of the robot arm; based on the motion parameters, establish the inertia matrix, acceleration matrix, gravity vector and friction torque of the robot arm; based on the inertia matrix, the acceleration matrix, the gravity vector, the friction torque, and the disturbance parameters, establish a dynamic model of the robot arm.

[0193] In an exemplary embodiment, the state determination module 1010 is specifically configured to: transform the dynamic model into a state equation of the robot arm by taking the rotation angle and the angular velocity as equation state quantities.

[0194] In an exemplary embodiment, the condition determination module 1020 is specifically used to: design a prescribed performance function for limiting the motion range of the robot arm based on the preset motion space of the robot arm; wherein the prescribed performance function is a bounded and strictly monotonically decreasing smooth function; and determine the value range of the function value of the prescribed performance function as the boundary constraint condition of the trajectory tracking error.

[0195] In an exemplary embodiment, the error determination module 1030 is specifically used to: establish an extended state observer of the robotic arm based on a preset observation convergence time of the robotic arm; and determine the observation error of the extended state observer based on the extended state observer and the state equation as the observation error of the robotic arm.

[0196] In an exemplary embodiment, the tracker establishment module 1040 is specifically used to: determine the first controller state quantity based on the trajectory tracking error and the boundary constraint condition, and determine the second controller state quantity based on the state equation and the backstepping virtual controller; adopt the backstepping method, use the Lyapunov stability function, the first controller state quantity, the second controller state quantity and the observation error to establish the anti-interference tracking controller of the robot manipulator.

[0197] Based on the same inventive concept, embodiments of the present application also provide a robot control device for implementing the aforementioned robot control method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations in one or more robot control device embodiments provided below can be found in the above-described limitations on the robot control method and will not be further elaborated here.

[0198] In an exemplary embodiment, Figure 11 As shown, a robot control device is provided, including: a trajectory acquisition module 1110, a trajectory comparison module 1120 and a robot control module 1130, wherein:

[0199] The trajectory acquisition module 1110 is used to obtain the current motion trajectory of the robot manipulator;

[0200] a trajectory comparison module 1120 for comparing the current motion trajectory with a preset motion trajectory of the robot manipulator to obtain a trajectory comparison result;

[0201] The robot control module 1130 is used to control the movement of the robot arm according to the trajectory comparison result by using the anti-interference tracking controller of the robot arm; wherein the anti-interference tracking controller is obtained according to the various method embodiments of the above-mentioned robot controller design method.

[0202] Each module in the robot controller design device and robot control device described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0203] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 12As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store data such as motion parameters of the robot manipulator arm. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a robot controller design method and / or a robot control method is implemented.

[0204] Those skilled in the art will understand that Figure 12 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0205] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of each embodiment of the above-mentioned robot controller design method and / or robot control method are implemented.

[0206] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of each embodiment of the above-mentioned robot controller design method and / or robot control method are implemented.

[0207] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of each embodiment of the above-mentioned robot controller design method and / or robot control method.

[0208] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0209] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0210] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A robot controller design method, characterized in that: The method comprises: Establishing a dynamic model of the robot manipulator and converting the dynamic model into a state equation of the robot manipulator; Defining a trajectory tracking error of the robot manipulator and determining boundary constraints of the trajectory tracking error; determining an observation error of the robotic arm according to a preset observation convergence time of the robotic arm; An anti-interference tracking controller for the robot manipulator is established according to the state equation, the boundary constraint conditions and the observation error.

2. The method according to claim 1, characterized in that The establishment of a dynamic model of the robot manipulator includes: Determining motion parameters of a robotic arm and disturbance parameters of the robotic arm; wherein the motion parameters include a rotation angle, an angular velocity, and an angular acceleration of the robotic arm; Establishing an inertia matrix, an acceleration matrix, a gravity vector, and a friction torque of the robot manipulator according to the motion parameters; A dynamic model of the robot arm is established according to the inertia matrix, the acceleration matrix, the gravity vector, the friction torque, and the disturbance parameter.

3. The method according to claim 2, characterized in that The converting the dynamic model into the state equation of the robot manipulator comprises: The dynamic model is converted into a state equation of the robot arm using the rotation angle and the angular velocity as equation state quantities.

4. The method according to claim 1, wherein The boundary constraint conditions for determining the trajectory tracking error include: Designing a prescribed performance function for limiting the range of motion of the robotic arm according to a preset motion space of the robotic arm; wherein the prescribed performance function is a bounded and strictly monotonically decreasing smooth function; The value range of the function value of the prescribed performance function is determined as the boundary constraint condition of the trajectory tracking error.

5. The method according to claim 1, wherein The determining the observation error of the robotic arm according to the preset observation convergence time of the robotic arm includes: establishing an extended state observer of the robotic arm according to a preset observation convergence time of the robotic arm; According to the extended state observer and the state equation, an observation error of the extended state observer is determined as an observation error of the robot manipulator.

6. The method according to any one of claims 1 to 5, characterized in that The step of establishing an anti-interference tracking controller for the robot manipulator according to the state equation, the boundary constraint condition, and the observation error comprises: Determine a first controller state quantity according to the trajectory tracking error and the boundary constraint condition, and determine a second controller state quantity according to the state equation and the backstepping virtual controller; An anti-interference tracking controller for the robot manipulator is established by adopting a backstepping method and utilizing a Lyapunov stability function, the first controller state quantity, the second controller state quantity and the observation error.

7. A robot control method, characterized in that: The method comprises: Get the current motion trajectory of the robot arm; Comparing the current motion trajectory with a preset motion trajectory of the robot manipulator to obtain a trajectory comparison result; Using the anti-interference tracking controller of the robot arm, the robot arm is controlled to move according to the trajectory comparison result; Wherein, the anti-interference tracking controller is obtained according to the design method of the robot controller according to any one of claims 1-6.

8. A robot controller design device, characterized in that: The device comprises: A state determination module, configured to establish a dynamic model of the robot manipulator and convert the dynamic model into a state equation of the robot manipulator; a condition determination module, configured to define a trajectory tracking error of the robot manipulator and determine a boundary constraint condition of the trajectory tracking error; an error determination module, configured to determine an observation error of the robotic arm based on a preset observation convergence time of the robotic arm; A tracker establishment module is used to establish an anti-interference tracking controller of the robot manipulator according to the state equation, the boundary constraint condition and the observation error.

9. A robot control device, characterized in that: The device comprises: The trajectory acquisition module is used to obtain the current motion trajectory of the robot arm; A trajectory comparison module is used to compare the current motion trajectory with the preset motion trajectory of the robot manipulator to obtain a trajectory comparison result; a robot control module, configured to control the movement of the robot arm according to the trajectory comparison result by utilizing the anti-interference tracking controller of the robot arm; Wherein, the anti-interference tracking controller is obtained according to the design method of the robot controller according to any one of claims 1-6.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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