Mechanical arm disturbance observation method and device and nonvolatile storage medium

Abstract

The invention discloses a mechanical arm disturbance observation method and device and a nonvolatile storage medium. The method comprises the steps that a dynamic model of the mechanical arm is determined according to system parameters of the mechanical arm, and the system parameters comprise the mass of the mechanical arm, the length of the mechanical arm and the joint angular position of the mechanical arm; the kinetic model is converted into a task space model, parameters of the task space model comprise a known part and an unknown part, the known part comprises parameters supporting determination through model calculation, and the unknown part comprises a disturbance value to be observed; and according to the task space model and the disturbance observer model, the disturbance value of the mechanical arm is determined, and the observation error of the disturbance observer model converges to zero within the preset duration. The technical problem that the operation stability of a mechanical arm system is affected due to the fact that existing disturbance observation cannot obtain a result within preset time is solved.

Description

Technical Field

[0001] The present application relates to the field of automatic control, and in particular to a method and device for observing disturbances of a robotic arm and a non-volatile storage medium. Background Art

[0002] In the field of industrial production, people often use robots to improve work efficiency and quality. The precise operation, continuous operation ability and repeatability of robots make them key factors in improving the level of automation of production lines. However, robotics technology is not perfect in practical applications. Its model uncertainty and susceptibility to external environmental interference have severely limited its extensiveness and stability in practical applications, posing considerable challenges to the in-depth promotion and use of robots. Faced with this dilemma, there is an urgent need to create a method to solve the disturbance problem of the robot system and ensure that the robot can exert its maximum potential in various production scenarios.

[0003] In practical applications, it is necessary not only to effectively handle model uncertainty and external interference, determine the disturbance value, but also to meet time limits for solving the problem. If the problem solving time cannot be assessed or is too long, it will seriously affect the stability of the system, overall operating efficiency, and actual expectations.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present application provide a robot arm disturbance observation method, device and non-volatile storage medium to at least solve the technical problem of affecting the operating stability of the robot arm system due to the inability of existing disturbance observation to obtain results within a predetermined time.

[0006] According to one aspect of an embodiment of the present application, a method for observing disturbances of a robotic arm is provided, comprising: determining a dynamic model of the robotic arm based on system parameters of the robotic arm, wherein the system parameters include the mass of the robotic arm, the length of the robotic arm, and the joint angle positions of the robotic arm; converting the dynamic model into a task space model, wherein the parameters of the task space model include a known part and an unknown part, the known part includes parameters supported by model calculation, and the unknown part includes a disturbance value to be observed; determining the disturbance value of the robotic arm based on the task space model and a disturbance observer model, wherein the observation error of the disturbance observer model converges to zero within a preset time length.

[0007] Optionally, the interference observer model is used at least to represent the functional relationship between preset observation parameters, parameters of the task space model, and observation values ​​of unknown parts of the task space model, wherein the task space model is used at least to represent the functional relationship between the inertia matrix, centrifugal force and Coriolis force matrix, gravity vector, robot arm joint angular acceleration, robot arm joint angular velocity, robot arm joint angle, and external interference vector.

[0008] Optionally, the disturbance observer model is determined by the following formula: in, is the derivative of z, z is The observed value of is the known angular velocity of the manipulator joint in the task space model, u is the input torque of the manipulator, D0 is the known part of the inertia matrix in the task space model, C0 is the known part of the centrifugal force and Coriolis force matrix in the task space model, G0 is the known part of the gravity vector in the task space model, is the derivative of ω, ω is the observed value of the unknown disturbance value in the task space model, a, b, c, k, σ are preset observation parameters, ε T is the transposed matrix of ε, and n is the number of joints of the robot.

[0009] Optionally, the preset observation parameters meet the following preset conditions: Among them, a, b, c, r, σ, are preset observation parameters. n is the number of joints of the robot arm, t is the observation time, and the observation time is less than or equal to the preset time.

[0010] Optionally, the dynamic model is converted into a task space model, including: constructing a Jacobian matrix of the robotic arm; converting the dynamic model into a task space model through the Jacobian matrix, wherein the task space model includes an inertia matrix, a centrifugal force and Coriolis force matrix, a gravity vector, a robotic arm joint angular acceleration, a robotic arm joint angular velocity, a robotic arm joint angle, and an external interference vector, the inertia matrix includes a known part and an unknown part, the centrifugal force and Coriolis force matrix include a known part and an unknown part, and the gravity vector includes a known part and an unknown part.

[0011] Optionally, the task space model is represented as: in, H is the total disturbance value, D0(q) is the known part of the inertia matrix, is the known part of the centrifugal force and Coriolis force matrix, G0(q) is the known part of the gravity vector, ΔD(q) is the unknown part of the inertia matrix, is the unknown part of the centrifugal force and Coriolis force matrix, ΔG(q) is the unknown part of the gravity vector, d is the external interference vector, is the angular acceleration of the robot joint, is the angular velocity of the robot joint, and x is the angle of the robot joint.

[0012] Optionally, after determining the disturbance value of the robot arm according to the task space model and the disturbance observer, the method further includes: compensating for the disturbance of the robot arm according to the disturbance value.

[0013] According to another aspect of an embodiment of the present application, a robotic arm disturbance observation device is also provided, including: a first determination module, used to determine the dynamic model of the robotic arm based on the system parameters of the robotic arm, wherein the system parameters include the mass of the robotic arm, the length of the robotic arm and the joint angle position of the robotic arm; a conversion module, used to convert the dynamic model into a task space model, wherein the parameters of the task space model include a known part and an unknown part, the known part includes parameters supported by model calculation, and the unknown part includes a disturbance value to be observed; a second determination module, used to determine the disturbance value of the robotic arm based on the task space model and the disturbance observer model, wherein the observation error of the disturbance observer model converges to zero within a preset time length.

[0014] According to another aspect of an embodiment of the present application, a non-volatile storage medium is further provided, in which a program is stored, wherein when the program is running, a device where the non-volatile storage medium is located is controlled to execute a robot arm disturbance observation method.

[0015] According to another aspect of an embodiment of the present application, there is also provided an electronic device, including: a memory and a processor, the processor being configured to run a program stored in the memory, wherein the robot arm disturbance observation method is executed when the program is run.

[0016] According to another aspect of an embodiment of the present application, a computer program product is also provided, including a computer program, and when the computer program is executed by a processor, a method for observing disturbances of a robotic arm is implemented.

[0017] In an embodiment of the present application, a dynamic model of the robot arm is determined based on the system parameters of the robot arm, wherein the system parameters include the mass of the robot arm, the length of the robot arm, and the joint angle position of the robot arm; the dynamic model is converted into a task space model, wherein the parameters of the task space model include a known part and an unknown part, the known part includes parameters supported by model calculation, and the unknown part includes a disturbance value to be observed; based on the task space model and the disturbance observer model, the disturbance value of the robot arm is determined, wherein the observation error of the disturbance observer model converges to zero within a preset time length, and by adopting a non-singular fixed-time interferer, the purpose of accurately observing and compensating for the total disturbance of the robot system within a preset time that is not affected by the initial error is achieved, thereby achieving the technical effect of improving the stability of the system, and further solving the technical problem of affecting the operational stability of the robot arm system caused by the inability of existing disturbance observations to obtain results within the predetermined time. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 is a schematic diagram of the basic principle of an interference observer provided according to an embodiment of the present application;

[0020] Figure 2 is a structural schematic diagram of a computer terminal provided according to an embodiment of the present application;

[0021] Figure 3 It is a flow chart of a method for observing disturbances of a manipulator provided in accordance with an embodiment of the present application;

[0022] Figure 4 is a schematic diagram of a robotic arm provided according to an embodiment of the present application;

[0023] Figure 5 is a schematic diagram of a simulation experiment result provided according to an embodiment of the present application;

[0024] Figure 6 It is a structural schematic diagram of a robot arm disturbance observation device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] In order to better understand the embodiments of the present application, the technical terms involved in the embodiments of the present application are explained as follows:

[0028] Robot dynamics equation: It is an equation that describes the relationship between the force and motion of the robot. It only describes the relationship between force and motion, without considering the force and torque that produce motion. Among them, the Euler-Lagrange equation describes the change of force and motion over time of a mechanical system under complete constraints and whose constraint forces satisfy the principle of virtual work.

[0029] Disturbance Observer: Disturbance Observer is a control system design method used to estimate and compensate for system model uncertainties and external disturbances. The basic idea of ​​disturbance observer is to estimate the total disturbance of the system (including external disturbances, unmodeled dynamics and model uncertainties) by constructing an observer, and then compensate for these disturbances in the control law, thereby improving the performance of the system.

[0030] Fixed-time stability: For a system that achieves fixed-time stability, its convergence time is only affected by system parameters and has nothing to do with the initial state of the system.

[0031] Lyapunov stability theorem: an important tool for analyzing the stability of nonlinear systems. It evaluates the stability of a system by simulating a function of the system's energy to determine whether the system can maintain a stable state.

[0032] In related technologies, the methods used to solve robot disturbance problems have limitations and cannot meet actual needs. For example, the method of using neural networks and fuzzy logic systems to approximate the unknown parts of the system can only guarantee the consistent limit boundedness of the closed-loop system due to the existence of theoretical approximation errors. However, it is very difficult to ensure that the appropriate robust terms are selected to ensure that the system error converges to zero. In related technologies, it is often assumed that all uncertain factors change slowly or tend to be constant over time. Based on this assumption, a traditional disturbance observer is proposed for a certain nonlinear system. The basic principle of the disturbance observer is as follows: Figure 1 Shown: Among them is the equivalent disturbance, which is used to compensate for the observed disturbance, d is the observed disturbance, which is the external disturbance and internal uncertainty in the system, u is the input signal, which is usually generated by the controller and is used to drive the system to achieve the desired output, ε is the error signal, which is the error between the system output and the desired output, and is used for feedback control and disturbance observation, G P (s) is the transfer function, which describes the dynamic relationship between the input and output of the system. The inverse transfer parameter is used to process the error signal in the disturbance observer. If you want to ensure that the system reaches stability within a finite and measurable time, the external disturbance and internal uncertainty of the system must also be accurately estimated within a measurable and finite time. The general method to achieve this goal is to use a finite-time disturbance observer. However, there is a regrettable problem that when the initial error norm of the disturbance observer increases infinitely, the estimation time of the disturbance will tend to be infinitely long, which will undoubtedly affect the actual performance and stability of the system.

[0033] In order to solve the above problems, relevant solutions are provided in the embodiments of the present application, which are described in detail below.

[0034] According to an embodiment of the present application, a method embodiment of a robot arm disturbance observation method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0035] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 2 FIG. 1 shows a hardware structure block diagram of a computer terminal for implementing a method for observing disturbances of a manipulator. Figure 2As shown, the computer terminal 20 may include one or more (202a, 202b, ..., 202n are used to illustrate) processors 202 (the processor 202 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 204 for storing data, and a transmission device 206 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 2 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 2 More or fewer components as shown, or with Figure 2 Different configurations are shown.

[0036] It should be noted that the one or more processors 202 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 20. As involved in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0037] The memory 204 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the robot arm disturbance observation method in the embodiment of the present application. The processor 202 executes various functional applications and data processing by running the software programs and modules stored in the memory 204, that is, the above-mentioned robot arm disturbance observation method is realized. The memory 204 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 204 may further include a memory remotely arranged relative to the processor 202, and these remote memories may be connected to the computer terminal 20 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0038] The transmission device 206 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 20. In one example, the transmission device 206 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 206 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0039] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 20 .

[0040] In the above operating environment, the present application embodiment provides a method for observing disturbances of a robot arm, such as Figure 3 As shown, the method comprises the following steps:

[0041] Step S302 , determining a dynamic model of the robotic arm according to system parameters of the robotic arm, wherein the system parameters include the mass of the robotic arm, the length of the robotic arm, and the joint angle positions of the robotic arm.

[0042] Optionally, the system parameters of the robot are determined by measurement: a suitable robot is selected according to actual needs, and the number of joints and their connection parts of the robot is clarified, and the constraint relationship between them is established to determine the degree of freedom of the robot system. Next, the system parameters of the robot are measured, including but not limited to the mass, length and joint angle position of the robot, so as to construct a nonlinear Euler-Lagrangian dynamic model of the robot. Among them, the degree of freedom is the number of dimensions in which the robot can move independently. Each joint or moving part of the robot may increase the degree of freedom of the system. For example, a joint that can rotate along one axis adds one rotational degree of freedom. In a multi-joint robot, the number of degrees of freedom reflects the complexity of the spatial position and posture that the robot can achieve.

[0043] Optionally, Figure 4 A mechanical arm is shown. Figure 4 As an example, assuming that the robot arm is a rigid joint structure with n degrees of freedom, the steps of establishing the nonlinear Euler-Lagrangian dynamic model of the robot arm with n degrees of freedom are as follows: Figure 4 The length (such as l1, l2) and mass (such as m1, m2) of the connecting rod shown, and based on these parameters, the centrifugal force and Coriolis force matrix, gravity matrix, and symmetric positive definite inertia matrix are calculated. The joint space dynamics model based on the kinetic energy and potential energy of the manipulator is constructed as follows:

[0044]

[0045] in, Represents the joint angular acceleration vector, joint angular velocity vector and joint angle vector, G(q)∈R n is the gravity vector, Refers to the centrifugal force and Coriolis force matrix, D(q)∈R nxn represents the (symmetric positive definite) inertia matrix, u∈R n is the input torque of the controller, d∈R n is the inevitable external interference vector.

[0046] Step S304, converting the dynamic model into a task space model, wherein the parameters of the task space model include known parts and unknown parts, the known parts include parameters supported by model calculations, and the unknown parts include disturbance values ​​to be observed.

[0047] As an optional implementation, the dynamic model is converted into a task space model, including: constructing a Jacobian matrix of the robot arm; converting the dynamic model into a task space model through the Jacobian matrix, wherein the task space model includes an inertia matrix, a centrifugal force and Coriolis force matrix, a gravity vector, a robot arm joint angular acceleration, a robot arm joint angular velocity, a robot arm joint angle, and an external interference vector, the inertia matrix includes a known part and an unknown part, the centrifugal force and Coriolis force matrix include a known part and an unknown part, and the gravity vector includes a known part and an unknown part.

[0048] Optionally, the task space model describes the position and posture of the end of the robot in space from the perspective of the task. Unlike the joint space model (dynamic model) which focuses on describing the independent motion of each joint of the robot, the task space model focuses on the working points and postures that the robot can reach in three-dimensional space, and how to control these parameters to complete specific task requirements.

[0049] By converting the joint motion of the robot into changes in position and posture in the task space, the task space model can intuitively show how the robot moves its end effector in space to reach the target position and perform the task. This conversion involves the Jacobian matrix, which is a bridge between the joint space and the task space, and can map the joint velocity to the velocity in the task space. The construction and application of the task space model is crucial to the design of the robot control strategy because it allows the control system to directly optimize the operation target without paying attention to the specific internal structure or joint motion of the robot. For example, when the robot needs to perform a grasping or welding task at a specific point, the task space model enables the control algorithm to directly calculate how to adjust the position and posture of the end effector of the robot to meet the operation requirements without having to have a deep understanding of the individual control of each joint. In addition, the task space model can also simplify motion planning by planning the path in space without calculating the trajectory of each joint in detail. This is especially important for complex multi-joint robots, which can significantly reduce the computational burden and improve the efficiency and flexibility of control.

[0050] In summary, the task space model provides an ability to understand the motion of the robot arm from a global perspective. It transforms the control problem of the robot arm into an operation task problem in space, so that the control strategy can be more directly optimized for the target position and posture, thereby improving the control performance and task completion capability of the robot arm in practical applications.

[0051] Optionally, the task space model is represented as: in, H is the total disturbance value, D0(q) is the known part of the inertia matrix, is the known part of the centrifugal force and Coriolis force matrix, G0(q) is the known part of the gravity vector, ΔD(q) is the unknown part of the inertia matrix, is the unknown part of the centrifugal force and Coriolis force matrix, ΔG(q) is the unknown part of the gravity vector, d is the external interference vector, is the angular acceleration of the robot joint, is the angular velocity of the robot joint, and x is the angle of the robot joint.

[0052] Alternatively, analyzing the motion and posture of the robot arm in the task space is more practical for industrial production. Therefore, in order to simplify the analysis, the joint space dynamics model of the robot arm needs to be converted to the task space model. Such a conversion not only helps to simplify the model structure and improve the convenience of analysis, but also more intuitively reflects the relationship between the parameters in the task space, thereby providing strong theoretical support for the subsequent control strategy design and optimization. Specifically, by introducing the Jacobian matrix J(q)∈R n , velocity variable in joint space and the speed variable in the task space The conversion relationship is defined as: Through the above relationship, the dynamic model of the robot arm in the task space is further defined as:

[0053]

[0054] Where, d = (J -1 ) T d q , G=(J -1 ) T G q , D=(J -1 ) T D q J -1 , u=J T u q .

[0055] In actual engineering, due to unavoidable errors, such as modeling errors, measurement errors, etc., the parameter matrix of the robot often has certain deviations. In order to facilitate subsequent analysis, a strategy of decomposing the parameter matrix can be adopted. Specifically, the parameter matrix is ​​divided into two parts: one part is the known and relatively accurate parameters (i.e., the known part), which can be obtained directly; the other part is the unknown or uncertain parameters (i.e., the unknown part), which may be affected by many factors and is difficult to measure accurately by conventional means. Through such decomposition, the total unknown part (including internal uncertainty of the system and external interference) can be integrated and corresponding observation and compensation measures can be taken, thereby improving the performance and reliability of the entire robotic arm system. Specifically, the known system parameter matrix is ​​divided into a known part and an unknown part, that is, D(q)=D0(q)+ΔD(q) is defined, G(q)=G0(q)+ΔG(q), where D0(q), G0(q) represents the part that can be accurately obtained, ΔD(q), ΔG(q) represents the inevitable but uncertain part, namely the interference value term.

[0056] Through further mathematical derivation, the dynamic model of the robot in the task space can be converted into:

[0057]

[0058] in, is the known part, H is the unknown term including internal uncertainty and external disturbance (i.e. the total disturbance value),

[0059] Step S306 , determining the disturbance value of the robot arm according to the task space model and the disturbance observer model, wherein the observation error of the disturbance observer model converges to zero within a preset time period.

[0060] As an optional implementation, the disturbance observer model is at least used to represent the functional relationship between preset observation parameters, parameters of the task space model, and observation values ​​of the unknown parts of the task space model, wherein the task space model is at least used to represent the functional relationship between the inertia matrix, centrifugal force and Coriolis force matrix, gravity vector, robot arm joint angular acceleration, robot arm joint angular velocity, robot arm joint angle, and external disturbance vector.

[0061] Optionally, the disturbance observer model is determined by the following formula:

[0062]

[0063] in, is the derivative of z, z is The observed value of is the known angular velocity of the manipulator joint in the task space model, u is the input torque of the manipulator, D0 is the known part of the inertia matrix in the task space model, C0 is the known part of the centrifugal force and Coriolis force matrix in the task space model, G0 is the known part of the gravity vector in the task space model, is the derivative of ω, ω is the observed value of the unknown disturbance value in the task space model, a, b, c, k, σ are preset observation parameters, εT T

[0064] ε is the transposed matrix of ε, and n is the number of joints of the robot.

[0065] Optionally, assume that the first-order derivative of the unknown nonlinear term H satisfies L is assumed to be a known constant. To determine the total disturbance value, an observation value is defined for the total unknown disturbance, and then the dynamic relationship of the observation value is defined as the expression of the disturbance observer. Ultimately, it is ensured that the observation error of the total disturbance can be gradually reduced within a predetermined fixed time and finally converge to zero. This means that through a carefully designed disturbance observer strategy, it can ensure that the total disturbance of the system can be accurately and effectively estimated and compensated within a fixed time, thereby ensuring that the control system can achieve the expected effect. Specifically, based on the task space model, a fixed-time disturbance observer is defined for the total unknown term, and ensuring that the observation error can converge to zero in a fixed time that is not affected by the initial value includes:

[0066] is the total unknown term H of the system and Set the observations and define the dynamic relationship between the observations as the expression of the disturbance observer. yes The observed value of H is defined as

[0067] The fixed time interference observer set in this method embodiment is as follows:

[0068]

[0069] Where a>0, b>0, c>0, c>0,k>0,a≥L,σ>1,n>0,and

[0070] Optionally, the preset observation parameters meet the following preset conditions:

[0071]

[0072] Among them, a, b, c, r, σ, are preset observation parameters. n is the number of joints of the robot arm, t is the observation time, and the observation time is less than or equal to the preset time.

[0073] Optionally, the Lyapunov stability theorem is used to derive the constraints of the relevant parameters in the fixed-time disturbance observer. Combined with the requirements of the actual application for the disturbance observation duration, the values ​​of the relevant parameters are determined to complete the design of the entire fixed-time disturbance observer, ensuring that the observation error converges to zero within a controllable fixed time:

[0074] Based on the Lyapunov stability theorem, the disturbance observer can calculate the unknown term H and The observation error will converge to zero within a fixed time T, satisfying:

[0075]

[0076] For example, in practical applications, the observer parameters are selected as a=50, b=12, c=2, k=2. n = 6. Then the fixed-time disturbance observer using this set of parameters can ensure that the unknown total disturbance term of the system will converge to zero within t ≤ 1.1 s.

[0077] Optionally, since the observation time is a fixed value that can be set manually, this means that in engineering practice, operators can flexibly adjust the observation time according to specific needs to ensure that the internal and external interference of the system is accurately captured and evaluated in the shortest time. Such a design greatly improves the robustness and adaptability of the system in the face of uncertain factors, and provides strong technical support for ensuring the stable operation of the system and optimizing the control strategy.

[0078] Specifically, the Lyapunov function is a positive definite function of the system state, which means that its value is positive when the system state deviates from the equilibrium point and is zero at the equilibrium point. When the time derivative of the Lyapunov function is negative definite (that is, the system energy decreases with time), it can be proved that the system is stable and that under certain conditions, the state error can be guaranteed to converge to zero in a finite time.

[0079] In the design of the fixed-time disturbance observer, the observation error dynamic equation is defined, and the Lyapunov function is constructed by selecting appropriate preset observation parameters to analyze the convergence of the observation error. The design of the Lyapunov function involves the analysis of the system dynamic equation to ensure that its derivative is negative definite under the control of the observer dynamic equation. This means that no matter what the initial error state of the observer is, as long as these parameters are within the appropriate range, the energy of the observation error will decrease over time until it finally converges to zero within the preset fixed time T.

[0080] Specifically, the selection of preset observation parameters in the fixed-time disturbance observer is determined based on the derivative analysis of the Lyapunov function in order to meet the conditions for non-singular fixed-time convergence. For example, parameters a and b need to be large enough to ensure the convergence speed, while the values ​​of c and ε need to be precisely adjusted to ensure that the observation error can converge to zero within a fixed time while avoiding instability or oscillation in the system. The selection of these parameters depends on a deep understanding of the system model and consideration of the convergence time requirements for the observation error in actual application scenarios.

[0081] In practical applications, combined with the dynamic model of the robot system and the specific disturbance observation requirements, the constraint range of the parameters can be determined by solving the condition that the derivative of the Lyapunov function is less than zero. The values ​​of the parameters must meet these constraints to ensure that under any initial observation error, the observer can converge the error to zero within a limited, preset time period. In this way, not only can the robustness and adaptability of the fixed-time disturbance observer be guaranteed, but also it can ensure that the robot system can maintain its stable performance and operation accuracy in the face of uncertainty.

[0082] In summary, according to the Lyapunov stability theorem, by carefully setting the dynamic equations and parameter constraints of the fixed-time disturbance observer, it can be ensured that even when the initial observation value has a large error, the observer can still accurately estimate the system disturbance within a fixed time, thereby providing a solid foundation for the subsequent disturbance compensation control strategy and significantly improving the stability and reliability of the robot system in complex environments.

[0083] Optionally, Figure 5A simulation experiment result is shown. Figure 5 As shown, select the robot system and observer parameters:

[0084] l1=0.7m, l2=0.5m, m1=10kg, m2=5kg, g=9.18m / s 2

[0085] a=50,b=12,c=2,k=2, n=6

[0086] The observation time of the observer is t≤1.1s, and the observation value ω in the simulation diagram m,1 (ω m,1 : = [ω m,1 (1) ,ω m,1 (2) ] T ) for the total disturbance term H m,1 (H m,1 :=[H m,1 (1) ,H m,1 (2) ] T ), the observation time in the actual simulation experiment can indeed satisfy T≤1.1s.

[0087] As an optional implementation, after determining the disturbance value of the robot arm according to the task space model and the disturbance observer, the method further includes: compensating for the disturbance of the robot arm according to the disturbance value.

[0088] Optionally, compensating for the disturbance of the manipulator includes designing a disturbance compensation control law, which is used to feed back the observed disturbance value to the control loop in real time, and then adjust the control signal of the manipulator. The control law ensures that the generation of the compensation signal can not only respond to the disturbance change quickly, but also match the motion state of the manipulator, avoiding over-compensation or under-compensation. For example, assuming that the manipulator encounters wind disturbance during operation, the observer can quickly identify this external disturbance and transmit the disturbance information to the controller. After receiving the disturbance value, the controller will increase the torque output of a joint of the manipulator accordingly to offset the influence of wind on the stability of the manipulator. This real-time disturbance compensation ensures that the manipulator can maintain an accurate operation trajectory and maintain its proper stability even in the face of a variable interference environment. According to the task space model and the disturbance value obtained by NTDO, by implementing the disturbance compensation control law, the stability and control accuracy of the manipulator in actual industrial scenarios can be significantly improved, and the technical problems affecting the operation stability of the manipulator system caused by model uncertainty and external disturbances can be effectively solved, providing a strong guarantee for the in-depth application of robot technology.

[0089] Through the above steps, a fixed-time disturbance observer can be realized to ensure that the observation time of the robot's total disturbance is not affected by the initial observation error, and the observation time can be preset according to actual needs, so that the robot can complete the task within a predictable fixed time, greatly improving the application prospects of the robot. An effective method is proposed for disturbances such as model uncertainty and external environment that seriously limit the popularity and stability of robots. While maintaining the stability of the robot system, the robot system can complete the task within a predictable fixed time, and the total disturbance of the system can be resolved within a fixed time that is not affected by the norm of the initial error. And the observation time can be preset according to actual needs, thereby improving the adaptability and reliability of the system. Specifically, the method embodiment of the present application has the following advantages:

[0090] 1. In actual engineering applications, it is possible to achieve an accurate observation that is not affected by the initial error state of the observation for the total interference caused by factors such as modeling errors, measurement errors, and external interference: Compared with traditional methods, such as the neural network approximation method that can only ensure that the closed-loop system has a bounded consistent limit, or a finite time observer whose disturbance estimation time tends to be infinite when the initial error norm increases infinitely. The new fixed-time interference observer designed by the present invention can not only accurately observe the internal uncertainty and external interference of the robot system, but also the observation time is not affected by the initial error norm of the observer. The method embodiment of the present application does not need to establish a complex mathematical model for the unknown model and external interference, and can avoid falling into cumbersome mathematical modeling links. Thereby simplifying the interference estimation process, it is more in line with the actual characteristics that interference signals are often difficult to accurately describe. In traditional interference estimation methods, in order to predict interference signals, a large number of mathematical operations may be required, which is not only time-consuming, but also has high requirements on computing resources.

[0091] 2. In the method embodiment of the application, the various parameter matrices of the system model (inertia matrix, centrifugal force and Coriolis force matrix, gravity vector matrix) are cleverly divided into known and unknown parts according to the characteristics of the robot model, and then all the known parts and all the unknown parts (unknown system parameter parts and external interference) of the robot task space model are extracted and integrated. Thus, through precise mathematical expressions, the design goal of the fixed-time disturbance observer is established, providing a clear direction and theoretical basis for the construction of the observer.

[0092] 3. The interference observer model provided by the method embodiment of the present application is simpler in the selection of model parameters than other methods. Compared with other methods such as neural network methods, the parameter selection process is relatively complicated, and not only the number of layers, the number of neurons in each layer, the activation function, but also the loss function during the training process, the selection of optimization algorithms, etc. The simple feature of model parameter selection makes the interference observer model of the method embodiment of the present application more suitable for actual production, and can quickly adapt to different application scenarios without the need for cumbersome parameter tuning processes.

[0093] The present application provides a robot arm disturbance observation device, Figure 6 is a schematic diagram of the structure of the device, such as Figure 6 As shown, the device includes: a first determination module 60, used to determine the dynamic model of the robot arm according to the system parameters of the robot arm, wherein the system parameters include the mass of the robot arm, the length of the robot arm and the joint angle position of the robot arm; a conversion module 62, used to convert the dynamic model into a task space model, wherein the parameters of the task space model include a known part and an unknown part, the known part includes parameters that support determination through model calculation, and the unknown part includes a disturbance value to be observed; a second determination module 64, used to determine the disturbance value of the robot arm according to the task space model and the disturbance observer model, wherein the observation error of the disturbance observer model converges to zero within a preset time length.

[0094] In some embodiments of the present application, the interference observer model is at least used to represent the functional relationship between preset observation parameters, parameters of the task space model, and observation values ​​of the unknown parts of the task space model, wherein the task space model is at least used to represent the functional relationship between the inertia matrix, centrifugal force and Coriolis force matrix, gravity vector, robot arm joint angular acceleration, robot arm joint angular velocity, robot arm joint angle, and external interference vector.

[0095] In some embodiments of the present application, the disturbance observer model is determined by the following formula: in, is the derivative of z, z is The observed value of is the known angular velocity of the manipulator joint in the task space model, u is the input torque of the manipulator, D0 is the known part of the inertia matrix in the task space model, C0 is the known part of the centrifugal force and Coriolis force matrix in the task space model, G0 is the known part of the gravity vector in the task space model, is the derivative of ω, ω is the observed value of the unknown disturbance value in the task space model, a, b, c, k, σ are preset observation parameters, ε Tis the transposed matrix of ε, and n is the number of joints of the robot.

[0096] In some embodiments of the present application, the preset observation parameters meet the following preset conditions: Among them, a, b, c, r, σ, are preset observation parameters. n is the number of joints of the robot arm, t is the observation time, and the observation time is less than or equal to the preset time.

[0097] In some embodiments of the present application, a dynamic model is converted into a task space model, including: constructing a Jacobian matrix of a robotic arm; converting the dynamic model into a task space model through the Jacobian matrix, wherein the task space model includes an inertia matrix, a centrifugal force and Coriolis force matrix, a gravity vector, a robotic arm joint angular acceleration, a robotic arm joint angular velocity, a robotic arm joint angle, and an external interference vector, the inertia matrix includes a known part and an unknown part, the centrifugal force and Coriolis force matrix include a known part and an unknown part, and the gravity vector includes a known part and an unknown part.

[0098] In some embodiments of the present application, the task space model is expressed as: in, H is the total disturbance value, D0(q) is the known part of the inertia matrix, is the known part of the centrifugal force and Coriolis force matrix, G0(q) is the known part of the gravity vector, ΔD(q) is the unknown part of the inertia matrix, is the unknown part of the centrifugal force and Coriolis force matrix, ΔG(q) is the unknown part of the gravity vector, d is the external interference vector, is the angular acceleration of the robot joint, is the angular velocity of the robot joint, and 6 is the joint angle of the robot arm.

[0099] In some embodiments of the present application, after determining the disturbance value of the robot arm according to the task space model and the disturbance observer, the method further includes: compensating the disturbance of the robot arm according to the disturbance value.

[0100] It should be noted that the various modules in the above-mentioned robotic arm disturbance observation device can be program modules (for example, a set of program instructions that implement a certain specific function) or hardware modules. For the latter, it can be expressed in the following forms, but is not limited to this: the expression form of each of the above-mentioned modules is a processor, or the functions of each of the above-mentioned modules are implemented by a processor.

[0101] An embodiment of the present application provides a non-volatile storage medium, in which a program is stored, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the following robot arm disturbance observation method: determine the dynamic model of the robot arm based on the system parameters of the robot arm, wherein the system parameters include the mass of the robot arm, the length of the robot arm and the joint angle position of the robot arm; convert the dynamic model into a task space model, wherein the parameters of the task space model include a known part and an unknown part, the known part includes parameters that support determination through model calculation, and the unknown part includes a disturbance value to be observed; determine the disturbance value of the robot arm based on the task space model and the disturbance observer model, wherein the observation error of the disturbance observer model converges to zero within a preset time length.

[0102] An embodiment of the present application provides an electronic device, including: a memory and a processor, the processor is used to run a program stored in the memory, wherein the following robot arm disturbance observation method is executed when the program is running: determining the dynamic model of the robot arm based on the system parameters of the robot arm, wherein the system parameters include the mass of the robot arm, the length of the robot arm and the joint angle position of the robot arm; converting the dynamic model into a task space model, wherein the parameters of the task space model include a known part and an unknown part, the known part includes parameters that support determination through model calculation, and the unknown part includes a disturbance value to be observed; determining the disturbance value of the robot arm based on the task space model and the disturbance observer model, wherein the observation error of the disturbance observer model converges to zero within a preset time length.

[0103] An embodiment of the present application provides a computer program product, including a computer program, which implements the following robot arm disturbance observation method when executed by a processor: determining the dynamic model of the robot arm based on the system parameters of the robot arm, wherein the system parameters include the mass of the robot arm, the length of the robot arm, and the joint angle position of the robot arm; converting the dynamic model into a task space model, wherein the parameters of the task space model include a known part and an unknown part, the known part includes parameters that support determination through model calculation, and the unknown part includes a disturbance value to be observed; determining the disturbance value of the robot arm based on the task space model and the disturbance observer model, wherein the observation error of the disturbance observer model converges to zero within a preset time length.

[0104] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0105] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0106] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0107] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0108] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the relevant technology or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0109] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for observing disturbance of a robot arm, characterized in that: include: Determining a dynamic model of the robotic arm according to system parameters of the robotic arm, wherein the system parameters include the mass of the robotic arm, the length of the robotic arm, and the joint angle position of the robotic arm; Converting the dynamic model into a task space model, wherein the parameters of the task space model include known parts and unknown parts, the known parts include parameters supported by model calculation and determination, and the unknown parts include disturbance values ​​to be observed; A disturbance value of the robot arm is determined according to the task space model and the disturbance observer model, wherein an observation error of the disturbance observer model converges to zero within a preset time length.

2. The method for observing disturbances of a manipulator according to claim 1, characterized in that: The disturbance observer model is at least used to represent the functional relationship between preset observation parameters, the known part of the task space model and the observation values ​​of the unknown part of the task space model, wherein the task space model is at least used to represent the functional relationship between the inertia matrix, the centrifugal force and Coriolis force matrix, the gravity vector, the robot arm joint angular acceleration, the robot arm joint angular velocity, the robot arm joint angle and the external disturbance vector.

3. The robot arm disturbance observation method according to claim 2, characterized in that: The disturbance observer model is determined by the following formula: in, is the derivative of z, z is The observed value of is the known angular velocity of the manipulator joint in the task space model, u is the input torque of the manipulator, D0 is the known part of the inertia matrix in the task space model, C0 is the known part of the centrifugal force and Coriolis force matrix in the task space model, G0 is the known part of the gravity vector in the task space model, is the derivative of ω, ω is the observed value of the unknown disturbance value in the task space model, a, b, c, k, σ are preset observation parameters, ε T is the transposed matrix of ε, and n is the number of joints of the robotic arm.

4. The robot arm disturbance observation method according to claim 2, characterized in that: The preset observation parameters meet the following preset conditions: Among them, a, b, c, r, σ, are preset observation parameters. n is the number of joints of the robotic arm, t is the observation time, and the observation time is less than or equal to the preset time.

5. The robot arm disturbance observation method according to claim 1, characterized in that: Converting the dynamic model into a task space model includes: Constructing a Jacobian matrix of the robotic arm; The dynamic model is converted into a task space model through the Jacobian matrix, wherein the task space model includes an inertia matrix, a centrifugal force and Coriolis force matrix, a gravity vector, a robot arm joint angular acceleration, a robot arm joint angular velocity, a robot arm joint angle, and an external interference vector, the inertia matrix includes a known part and an unknown part, the centrifugal force and Coriolis force matrix include a known part and an unknown part, and the gravity vector includes a known part and an unknown part.

6. The robot arm disturbance observation method according to claim 5, characterized in that: The task space model is expressed as: in, H is the total disturbance value, D0(q) is the known part of the inertia matrix, is the known part of the centrifugal force and Coriolis force matrix, G0(q) is the known part of the gravity vector, ΔD(q) is the unknown part of the inertia matrix, is the unknown part of the centrifugal force and Coriolis force matrix, ΔG(q) is the unknown part of the gravity vector, d is the external interference vector, is the angular acceleration of the robot joint, is the angular velocity of the robot joint, and x is the angle of the robot joint.

7. The robot arm disturbance observation method according to claim 1, characterized in that: After determining the disturbance value of the robot arm according to the task space model and the disturbance observer, the method further includes: compensating for the disturbance of the robot arm according to the disturbance value.

8. A robot arm disturbance observation device, characterized in that: include: A first determination module is used to determine the dynamic model of the robotic arm according to the system parameters of the robotic arm, wherein the system parameters include the mass of the robotic arm, the length of the robotic arm, and the joint angle position of the robotic arm; A conversion module, used for converting the dynamic model into a task space model, wherein the parameters of the task space model include known parts and unknown parts, the known parts include parameters supported by model calculation and determination, and the unknown parts include disturbance values ​​to be observed; The second determination module is used to determine the disturbance value of the robot arm according to the task space model and the disturbance observer model, wherein the observation error of the disturbance observer model converges to zero within a preset time length.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores a program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the robot arm disturbance observation method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: A memory and a processor, wherein the processor is used to run a program stored in the memory, wherein the robot arm disturbance observation method described in any one of claims 1 to 7 is executed when the program is run.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the robot arm disturbance observation method according to any one of claims 1 to 7 is implemented.

Images