Robot arm disturbance observation method, device, and nonvolatile storage medium

Abstract

The application discloses a mechanical arm disturbance observation method and device and a nonvolatile storage medium. The method comprises the following steps: determining a dynamic model of a mechanical arm according to system parameters of the mechanical arm, wherein the system parameters comprise the mass of the mechanical arm, the length of the mechanical arm and the joint angle position of the mechanical arm; converting the dynamic model into a task space model, wherein the parameters of the task space model comprise a known part and an unknown part, the known part comprises parameters determined by model calculation, and the unknown part comprises disturbance values to be observed; and determining the disturbance values of the mechanical arm according to 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. The application solves the technical problem of affecting the operation stability of the mechanical arm system due to the fact that the existing disturbance observation cannot obtain results within a predetermined time.

Description

Technical Field

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

[0002] In industrial production, robots are often used to improve efficiency and quality. Their precise operation, continuous operation, and repeatability make them key factors in enhancing production line automation. However, robotics technology is not without flaws in practical applications. Model uncertainty and susceptibility to external environmental interference severely limit its widespread and stable application, posing significant challenges to the widespread and widespread adoption of robots. Faced with this dilemma, there is an urgent need to develop methods to address disturbances in robotic systems and ensure that robots can maximize their potential in various production scenarios.

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

[0004] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0005] The embodiments of the present application provide a method, device and non-volatile storage medium for observing disturbances of a robotic arm, so as to at least solve the technical problem of affecting the operational stability of the robotic 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 angular position 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 matrices, 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 robotic arm.

[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 robotic 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 includes 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 arm joint, is the angular velocity of the robot arm joint, and x is the angle of the robot arm joint.

[0012] Optionally, after determining the disturbance value of the robotic arm according to the task space model and the disturbance observer, the method further includes: compensating for the disturbance of the robotic 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 provided, in which a program is stored. 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.

[0015] According to another aspect of an embodiment of the present application, an electronic device is further provided, 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 further provided, including a computer program, which implements a robotic arm disturbance observation method when executed by a processor.

[0017] In an embodiment of the present application, a dynamic model of the robotic arm is determined 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 angular position of the robotic 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 robotic arm is determined, wherein the observation error of the disturbance observer model converges to zero within a preset time length. 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 robotic 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 diagram of a computer terminal provided according to an embodiment of the present application;

[0021] Figure 3 1 is a flow chart of a method for observing disturbances of a robotic arm 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 2 is a schematic structural diagram of a robotic 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 present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this 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 sequential order. 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 a sequence 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 equations describe the relationship between forces and motion in a robot. They only describe the relationship between forces and motion, without considering the forces and torques that generate motion. The Euler-Lagrange equations describe the time-dependent evolution of forces and motion in a mechanical system under holonomic constraints, where the constraints satisfy the principle of virtual work.

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

[0030] Fixed-time stability: The system convergence time of a system that achieves fixed-time stability is only affected by the 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, thereby determining whether the system can maintain a stable state.

[0032] In related technologies, the methods used to solve the robot disturbance problem 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 uniform 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, 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, usually generated by the controller, 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, 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. To ensure that the system reaches stability within a finite, measurable time, the system's external disturbances and internal uncertainties must also be accurately estimated within a measurable and finite time. A common approach to achieving this goal is to employ a finite-time disturbance observer. However, a regrettable problem is that when the initial error norm of the disturbance observer increases infinitely, the disturbance estimation time tends to be infinite, which undoubtedly affects 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 robotic 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 a 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 (illustrated as 202a, 202b, ..., 202n in the figure) 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 will 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 than shown, or with Figure 2 Different configurations 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 circuitry." The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be incorporated in whole or in part into any of the other components of the computer terminal 20. As described in the embodiments of the present application, the data processing circuitry serves 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, realizing the above-mentioned robot arm disturbance observation method. 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 located 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 combinations thereof.

[0038] Transmission device 206 is configured to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by the communications provider of computer terminal 20. In one embodiment, transmission device 206 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, transmission device 206 may be a radio frequency (RF) module configured 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 embodiment of the present application provides a method for observing disturbances of a manipulator, such as Figure 3 As shown, the method includes 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 robotic arm are determined by measurement: a suitable robotic arm is selected according to actual needs, and the number of joints and their connection parts of the robotic arm is clarified, and the constraint relationship between them is established to determine the degrees of freedom of the robotic arm system. Next, the system parameters of the robotic arm are measured, including but not limited to the mass, length and joint angular position of the robotic arm, so as to construct a nonlinear Euler-Lagrangian dynamic model of the robotic arm. Among them, the degree of freedom is the number of dimensions in which the robotic arm can move independently. Each joint or moving part of the robotic arm 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 robotic arm, the number of degrees of freedom reflects the complexity of the spatial positions and postures that the robotic arm can achieve.

[0043] Optionally, Figure 4 A robotic arm is shown. Figure 4 For example, assuming that the manipulator is a rigid joint structure with n degrees of freedom, the steps for establishing the nonlinear Euler-Lagrangian dynamic model of the manipulator with n degrees of freedom are as follows: Figure 4 The lengths (e.g., l1, l2) and masses (e.g., m1, m2) of the connecting rods are shown, and based on these parameters, the centrifugal force and Coriolis force matrices, the gravity matrix, and the 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] wherein, represents joint angle acceleration vector, joint angle velocity vector and joint angle vector, G(q) e R n is the gravity vector, denotes centrifugal force and Coriolis force matrix, D(q) e R nxn represents (symmetric positive definite) inertia matrix, u e R n is the input torque of the controller, d e R n is the unavoidable external disturbance vector.

[0046] In step S304, the dynamics model is converted into a task space model, wherein parameters of the task space model include known parts and unknown parts, the known parts include parameters determined by model calculation, and the unknown parts include disturbance values to be observed.

[0047] As an optional implementation, converting the dynamics model into the task space model comprises: constructing a Jacobian matrix of the robot arm; and converting the dynamics model into the 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 angle acceleration, a robot arm joint angle velocity, a robot arm joint angle, and an external disturbance vector, the inertia matrix includes known parts and unknown parts, the centrifugal force and Coriolis force matrix includes known parts and unknown parts, and the gravity vector includes known parts and unknown parts.

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

[0049] By converting the joint motions of a robotic arm into changes in position and posture in task space, a task space model intuitively demonstrates how the robotic arm moves its end effector in space to reach a target position and perform a task. This conversion involves the Jacobian matrix, which bridges the joint space and task space and maps joint velocities to velocities in task space. The construction and application of a task space model is crucial for the design of robotic arm control strategies because it allows the control system to directly optimize for the task objective without focusing on the specific internal structure or joint motions of the robotic arm. For example, when a robotic arm 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 robotic arm's end effector to meet the task requirements without requiring a detailed understanding of the individual control of each joint. Furthermore, the task space model simplifies motion planning by allowing the robot to plan a path in space without having to calculate the detailed trajectory of each joint. This is particularly important for complex multi-joint robotic arms, significantly reducing the computational burden and improving control efficiency and flexibility.

[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, enabling the control strategy to 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 arm joint, is the angular velocity of the robot arm joint, and x is the angle of the robot arm 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 various 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 ​​split 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 various 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 deduction, the dynamic model of the robot arm 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 manipulator 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 embodiment, the interference observer model is used to at least 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 to at least represent the functional relationship between the inertia matrix, centrifugal force and Coriolis force matrices, gravity vector, robot arm joint angular acceleration, robot arm joint angular velocity, robot arm joint angle, and external interference 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 robotic arm.

[0065] Alternatively, assume that the first-order derivative of the unknown nonlinear term H satisfies L is assumed to be a known constant. In order 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 eventually converge to zero. This means that through a carefully designed disturbance observer strategy, it is possible to ensure that the total disturbance of the system is accurately and effectively estimated and compensated within a fixed time, thereby ensuring that the control system achieves 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 within 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 observation value and then define the dynamic relationship of the observation value 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 robotic 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 on the relevant parameters in the fixed-time disturbance observer. Combined with the actual application requirements for the disturbance observation duration, the values ​​of the relevant parameters are determined, thus completing the design of the entire fixed-time disturbance observer and ensuring that the observation error converges to zero within a controllable fixed time:

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

[0075]

[0076] For example, in practical applications, the parameters of the observer are selected as a=50, b=12, c=2, k=2. n = 6. 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] Alternatively, since the observation duration is a fixed, customizable value, operators can flexibly adjust it based on specific needs in engineering practice to ensure accurate capture and assessment of internal and external disturbances within the system in the shortest possible time. This design significantly improves the system's robustness and adaptability in the face of uncertainty, providing strong technical support for ensuring stable system operation and optimizing control strategies.

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

[0079] In the design of a fixed-time disturbance observer, the observation error dynamic equation is defined. By selecting appropriate preset observation parameters, a Lyapunov function is constructed to analyze the convergence of the observation error. Designing the Lyapunov function involves analyzing the system dynamic equations to ensure that their derivatives are negative definite under the control of the observer dynamic equations. This means that regardless of the initial error state of the observer, as long as these parameters are within an appropriate range, the energy of the observation error will decrease over time until it eventually converges to zero within a preset fixed time T.

[0080] The selection of the preset observation parameters in the fixed-time disturbance observer is based on derivative analysis of the Lyapunov function, aiming to meet the requirements for non-singular fixed-time convergence. For example, parameters a and b need to be sufficiently large to ensure rapid convergence, while the values ​​of c and ε require precise tuning to ensure that the observation error converges to zero within the fixed time while avoiding system instability or oscillation. The selection of these parameters relies on a deep understanding of the system model and consideration of the observation error convergence time requirements in actual application scenarios.

[0081] In practical applications, by combining the dynamic model of the robotic system with the specific disturbance observation requirements, the parameter constraints can be determined by solving the condition that the derivative of the Lyapunov function is less than zero. The parameter values ​​must meet these constraints to ensure that, given any initial observation error, the observer can converge to zero within a finite, preset time period. This approach not only ensures the robustness and adaptability of the fixed-time disturbance observer, but also ensures that the robotic system can maintain stable performance and operational accuracy in the face of uncertainty.

[0082] In summary, based on 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 in FIG. 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 is 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 meet T≤1.1s.

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

[0088] Optionally, compensating for disturbances in the manipulator includes designing a disturbance compensation control law that feeds observed disturbance values ​​into the control loop in real time, thereby adjusting the manipulator's control signals. This control law ensures that the compensation signal generated is both responsive to disturbance changes and consistent with the manipulator's motion state, avoiding over- or under-compensation. For example, if the manipulator encounters wind disturbances during operation, the observer can quickly identify this external disturbance and transmit the disturbance information to the controller. Upon receiving the disturbance value, the controller will correspondingly increase the torque output of a joint in the manipulator to offset the wind's impact on the manipulator's stability. This real-time disturbance compensation ensures that the manipulator maintains a precise operational trajectory and maintains its desired stability even in the face of variable disturbances. By implementing the disturbance compensation control law based on the task space model and the disturbance values ​​obtained by the NTDO, the manipulator's stability and control accuracy can be significantly improved in real-world industrial scenarios. This effectively addresses the technical issues that affect the operational stability of the manipulator system caused by model uncertainty and external disturbances, providing a strong foundation for the in-depth application of robotics technology.

[0089] Through the above steps, a fixed-time interference 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, which greatly improves the application prospects of the robot. An effective method is proposed to address 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, it 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 of the total interference caused by factors such as modeling errors, measurement errors, and external interference, which is not affected by the initial error state of the observation: Compared with traditional methods, such as the neural network approximation method that can only ensure that the consistency limit of the closed-loop system is bounded, or the 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 the tedious mathematical modeling link. This simplifies the interference estimation process and 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 the interference signal, 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 characteristics of the robot model are cleverly used to divide the system model's parameter matrices (inertia matrix, centrifugal and Coriolis force matrices, gravity vector matrix) into known and unknown parts. This allows the extraction and integration of all known and unknown parts of the robot's task space model (unknown system parameters and external disturbances). This precise mathematical formulation establishes the design goals of a fixed-time disturbance observer, providing a clear direction and theoretical foundation for its construction.

[0092] 3. The interference observer model provided by the method embodiment of the present application simplifies the selection of model parameters compared to other methods. Compared to other methods, such as neural network methods, the parameter selection process is relatively complex, requiring consideration not only of the number of layers, the number of neurons in each layer, and the activation function, but also of the loss function and optimization algorithm used during training. The simplicity of model parameter selection makes the interference observer model of the method embodiment of the present application more practical, enabling rapid adaptation to different application scenarios without the need for tedious parameter tuning.

[0093] The present invention provides a device for observing disturbances in a robotic arm. Figure 6 is a structural diagram 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 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 angular 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 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.

[0094] In some embodiments of the present application, 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 the 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.

[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 robotic arm.

[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 robotic 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, 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 includes 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 arm joint, is the angular velocity of the robot arm joint, and 6 is the robot arm joint angle.

[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: 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 supported by 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.

[0102] An embodiment of the present application provides an electronic device, comprising: a memory and a processor, the processor being configured to run a program stored in the memory, wherein the program executes the following manipulator disturbance observation method when running: determining a dynamic model of the manipulator based on system parameters of the manipulator, wherein the system parameters include the mass of the manipulator, the length of the manipulator, and the joint angular position of the manipulator; 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 including parameters supported by model calculation, and the unknown part including a disturbance value to be observed; determining the disturbance value of the manipulator 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.

[0103] An embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the following method for observing disturbances in a robotic arm: 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 angular 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, wherein 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.

[0104] In the above embodiments of the present application, the description of each embodiment has its own focus. 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 exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as 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 separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple 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, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or 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 is essentially 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, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0109] The above is only a preferred embodiment 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 disturbances of a robotic arm, characterized in that: include: 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 angular positions of the robotic arm; Converting the dynamic model into a task space model, wherein parameters of the task space model include known parts and unknown parts, the known parts include parameters determined by model calculation, and the unknown parts include disturbance values ​​to be observed; The disturbance value of the manipulator is determined 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. The disturbance observer model is determined by the following formula: ； in, is the derivative of z, for The observed value of is the known angular velocity of the manipulator joint in the task space model, , is the input torque of the manipulator, is the known part of the inertia matrix in the task space model, are the known parts of the centrifugal and Coriolis force matrices in the task space model, is the known part of the gravity vector in the mission space model, for The derivative of is the observed value of the unknown disturbance value in the task space model, , a, b, c, k, To preset observation parameters, , for The transposed matrix of , n is the number of joints of the robotic arm.

2. The method for observing disturbances of a manipulator according to claim 1, wherein: 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 method for observing disturbances of a manipulator according to claim 2, wherein: The preset observation parameters meet the following preset conditions: ； Among them, a, b, c, r, , 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.

4. The method for observing disturbances of a manipulator according to claim 1, wherein: 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 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 includes a known part and an unknown part, and the gravity vector includes a known part and an unknown part.

5. The method for observing disturbances of a manipulator according to claim 4, wherein: The task space model is expressed as: ； in, , is the total disturbance value, , is the known part of the inertia matrix, are the known parts of the centrifugal and Coriolis force matrices, is the known part of the gravity vector, is the unknown part of the inertia matrix, is the unknown part of the centrifugal and Coriolis force matrices, is the unknown part of the gravity vector, d is the external interference vector, is the angular acceleration of the robot arm joint, is the angular velocity of the robot arm joint is the joint angle of the robotic arm.

6. The method for observing disturbances of a manipulator according to claim 1, wherein: After determining the disturbance value of the manipulator according to the task space model and the disturbance observer, the method further includes: compensating for the disturbance of the manipulator according to the disturbance value.

7. A robotic arm disturbance observation device, characterized in that: include: a first determining module, configured to determine a dynamic model of the robotic arm according to system parameters of the robotic arm, wherein the system parameters include a mass of the robotic arm, a length of the robotic arm, and joint angular positions of the robotic arm; a conversion module, configured to convert 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 determined by model calculation, and the unknown parts include disturbance values ​​to be observed; A second determination module is configured to determine a disturbance value of the robotic arm based on the task space model and a disturbance observer model, wherein an observation error of the disturbance observer model converges to zero within a preset time period, and the disturbance observer model is determined by the following formula: ； in, is the derivative of z, for The observed value of is the known angular velocity of the manipulator joint in the task space model, , is the input torque of the manipulator, is the known part of the inertia matrix in the task space model, are the known parts of the centrifugal and Coriolis force matrices in the task space model, is the known part of the gravity vector in the mission space model, for The derivative of is the observed value of the unknown disturbance value in the task space model, , a, b, c, k, To preset observation parameters, , for The transposed matrix of , n is the number of joints of the robotic arm.

8. 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 6.

9. 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 according to any one of claims 1 to 6 is executed when the program is run.

10. 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 6 is implemented.

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