Method, device, equipment and storage medium for estimating interference torque of robotic arm

Abstract

The present application discloses a method, device, equipment and storage medium for estimating the interference torque of a robotic arm. The method includes: establishing a dynamic model of the robotic arm and obtaining a traditional momentum observer based on it; combining the traditional momentum observer with the super-twisting algorithm to obtain an observer based on the momentum state using a sigmoid function; constructing an interference observer according to the observer based on the momentum state; and estimating the interference torque based on the interference observer. While maintaining stability, it overcomes the chattering drawback of similar sliding mode methods.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial robots, and particularly relates to a method, device, equipment and storage medium for estimating the interference torque of a robotic arm. Background Art

[0002] In recent years, "human-robot interaction" and "human-robot collaboration" have become very important concerns in the field of robotics, and their safety issues have become the most basic considerations. In actual robot operations, there are mainly two types of scenarios where contact with humans may occur. One is contact collision during dynamic operation, and the other is contact collision in a static state. To prevent contact collisions, researchers have divided the motion collisions of robots into several stages: the collision avoidance stage (before collision), during collision (detecting collision), and after collision (stopping motion). The main task of the detecting collision stage is to detect that a collision has occurred, which is essentially how to quickly and accurately estimate the appearance of an interference external torque on the robotic arm. On the other hand, real-time estimation of the interference external torque of the robotic arm can effectively compensate the output of the underlying controller of the robotic arm, reducing the influence of external interference torque or internal modeling error on the control accuracy. Therefore, how to effectively and real-time estimate the external interference torque of the robotic arm is a difficult point and key technology in the field of human-robot interaction and control of robotic arms.

[0003] Currently, for the estimation of the interference torque of robotic arms, there are mainly the following two types of methods:

[0004] 1. Rely on new torque sensors such as torque sensors and electronic skins for real-time detection. This type of method mainly relies on external sensors, and additional external sensors need to be added to the robotic arm body, which will make the structure complex, reduce the system reliability, and increase the cost.

[0005] 2. Do not rely on additional sensors, only rely on basic joint angle information, current information, etc. of the body, use the output of the controller and the state quantity of the robotic arm, design an interference observer algorithm to estimate the interference torque in real time, and compensate the controller. This type of method only needs software algorithm design to achieve interference estimation, with low cost, so it has become the forefront hot spot for most researchers and companies. However, this type of method mostly uses interference observers with algorithms such as sliding mode, and there will be a chattering phenomenon, which is not smooth enough. Summary of the Invention

[0006] The embodiments of the present invention provide a method, device, equipment and storage medium for estimating the interference torque of a robotic arm, which can estimate the interference torque of the robotic arm in real time, and while maintaining stability, overcome the chattering drawback of methods similar to the sliding mode method.

[0007] In a first aspect, the embodiments of the present invention provide a method for estimating the interference torque of a robotic arm, and the method includes:

[0008] Establish a dynamic model of the robotic arm and obtain a traditional momentum observer based on it;

[0009] Combine the traditional momentum observer with the super-twisting algorithm to obtain a momentum-state-based observer using the sigmoid function;

[0010] Construct a disturbance observer according to the momentum-state-based observer;

[0011] Estimate the disturbance torque based on the disturbance observer.

[0012] In a second aspect, an embodiment of the present invention provides a robotic arm disturbance torque estimation device, including:

[0013] A robotic arm dynamic model construction module, configured to establish a dynamic equation of the robotic arm system model according to the Euler-Lagrange formula;

[0014] A traditional momentum observer acquisition module, configured to obtain a traditional momentum observer based on the robotic arm dynamic model;

[0015] A momentum-state observation module, configured to combine the traditional momentum observer with the super-twisting algorithm to obtain a momentum-state-based observation module using the sigmoid function;

[0016] A disturbance observation module, configured to construct a disturbance observation module according to the momentum-state-based observation module;

[0017] A disturbance torque estimation module, configured to estimate the disturbance torque based on the disturbance observation module.

[0018] In a third aspect, the present invention provides a device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, the above-mentioned robotic arm disturbance torque estimation method is implemented.

[0019] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned robotic arm disturbance torque estimation method is implemented.

[0020] The technical solution of the embodiment of the present invention, compared with the traditional method of estimating the external torque using a disturbance observer, can avoid solving the inverse model of the manipulator, which is relatively simple to implement. It estimates the disturbance using the deviation of the state quantity of momentum, which has a good physical meaning. Essentially, it has generality and stability. Compared with the existing manipulator torque estimation method based on a generalized momentum observer, the estimation method of the embodiment of the present invention combines the sigmoid function and a structure similar to "proportional + integral", which has fast convergence while maintaining the estimation stability. In addition, an adaptive rule is added to make the gain adjustment autonomous and improve the robustness of the system. Compared with the existing momentum observer estimation method based on sliding mode technology, the estimation method of the embodiment of the present invention uses the sigmoid function, which can effectively reduce the chattering phenomenon. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flowchart of the method for estimating the disturbance torque of the manipulator in the embodiment of the present invention;

[0022] Figure 2 It is a schematic diagram of the 2R rigid manipulator model in the embodiment of the present invention;

[0023] Figure 3 It is a comparison diagram of the estimated results of the external torque in the embodiment of the present invention;

[0024] Figure 4 It is a schematic diagram of the structure of the manipulator disturbance torque estimation device in the embodiment of the present invention;

[0025] Figure 5 It is a principle block diagram of the method for estimating the disturbance torque of the manipulator provided in the embodiment of the present invention;

[0026] Figure 6 It is a schematic diagram of the structure of an industrial robot provided in the embodiment of the present invention;

[0027] Figure 7 It is a module schematic diagram of the computer-readable storage medium in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] The present invention will be further described in detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present invention, rather than limiting the present invention. It should be noted that, for the sake of description, only parts related to the present invention are shown in the drawings, rather than all the structures.

[0029] Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts depict the operations (or steps) as sequential processes, many of the operations can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operations are completed, but there can also be additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc. In addition, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0030] In addition, in the embodiments of the present invention, words such as "optionally" or "exemplarily" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "optionally" or "exemplarily" in the embodiments of the present invention should not be construed as being more preferred or more advantageous than other embodiments or design solutions. Rather, the use of words such as "optionally" or "exemplarily" is intended to present the relevant concepts in a specific manner.

[0031] Figure 1 The flowchart of a method for estimating the interference torque of a robotic arm provided for the embodiments of the present invention can be applied to an industrial robot to estimate the interference torque of the robotic arm. This method can be executed by the device for estimating the interference torque of the robotic arm provided by the embodiments of the present application, and this device can be implemented in a software and / or hardware manner. In a specific embodiment, this device can be integrated into the industrial robot. The following embodiments will be described by taking this device integrated into the industrial robot as an example. Refer to Figure 1 , the method provided by the embodiments of the present application can specifically include but is not limited to the following steps:

[0032] S101. Establish a dynamic model of the robotic arm and obtain a traditional momentum observer based on it;

[0033] Among them, establishing the dynamic model of the robotic arm includes: establishing the dynamic equation of the robotic arm system model according to the Euler-Lagrange formula. The dynamic equation is as follows,

[0034]

[0035] Among them, M(q) is a matrix of second-order angular derivative terms related to the mass, centroid position, and angle of the actual model of the robotic arm; is a matrix of first-order angular derivative terms related to the mass, centroid position, and angle of the actual model of the robotic arm; g(q) is the gravitational vector of the gravity acting on the robotic arm; τ m is the joint torque; d is the interference quantity, that is, the external interference torque quantity that needs to be estimated.

[0036] The dynamic model of the robotic arm is established and a traditional momentum observer is obtained based on it. Among them, obtaining the traditional momentum observer includes:

[0037] 1) The momentum of the robotic arm is defined as follows:

[0038]

[0039] At the same time, its derivative term matrix M(q) satisfies

[0040] 2) According to the momentum definition of the robotic arm and the dynamic model of the robotic arm, the state equation of the robotic arm momentum is obtained as follows:

[0041]

[0042] Among them, is the transpose of the matrix ;

[0043] 3) Based on the state equation of the robotic arm momentum, a traditional momentum observer is obtained as follows:

[0044]

[0045] S102. Combine the traditional momentum observer with the super-twisting algorithm to obtain an observer based on momentum state using the sigmoid function; The sigmoid function is a common S-shaped function in biology, also known as the S-shaped growth curve. In information science, due to its properties such as being monotonically increasing and having a monotonically increasing inverse function, the sigmoid function is often used as the activation function of neural networks to map variables between 0 and 1.

[0046] The super-twisting algorithm is a type of second-order sliding mode control. Compared with traditional sliding mode techniques, the main idea of the super-twisting algorithm is to remove the restriction that the relative order of the standard sliding surface is 1 and increase the order.

[0047] The super-twisting algorithm can be described as:

[0048]

[0049] Among them, x1 and x2 are state variables. k1 and k2 are control gains to be designed. ρ1 and ρ2 are uncertain terms. Under certain conditions, k1 and k2 can have a robust effect on bounded uncertainties. The sigmoid function has good noise suppression ability. Compared with the existing momentum observer estimation method based on the sliding mode technique, the estimation method provided by the embodiment of the present invention uses the sigmoid function, designs the momentum state observer equation with ρ1(x,t) and ρ2(x,t) using the sigmoid function, realizes the estimation of the momentum state based on the momentum deviation, avoids the generation of chattering phenomenon, and improves the fast convergence performance of the external torque estimation.

[0050] S103. Construct an interference observer according to the observer based on the momentum state;

[0051] The interference observer constructed by the embodiment of the present invention can overcome the chattering drawback that appears in the similar sliding mode method while ensuring stability.

[0052] S104. Estimate the interference torque based on the interference observer.

[0053] When the working robotic arm is subjected to an external interference torque, by using the torque signal output, joint angle and angular velocity output of the controller, combined with the momentum state observer, the estimated value of the momentum of the robotic arm can be calculated, and the estimated value of the momentum is compared with the actual momentum value of the robotic arm to obtain a momentum deviation value, and this momentum deviation value contains information about the amount of the external interference torque, which is applied to the interference observer based on the sigmoid function proposed above, and then the estimated value of the external interference torque is obtained.

[0054] In one example, combining the traditional momentum observer with the super-twisting algorithm to obtain an observer based on the momentum state using the sigmoid function may include, but is not limited to, the following steps:

[0055] 1) After combining the traditional momentum observer with the super-twisting algorithm, obtain the following form based on the sign function:

[0056]

[0057]

[0058] Among them, Q, T ∈ R n×n is a positive definite symmetric matrix;

[0059] The formula of the traditional momentum observer is: Equivalent to relying only on the momentum deviation Solve interference by using multiples, and the embodiments of the present invention utilize the idea of the high-order sliding mode technique (super-twisting algorithm) in the original sliding mode technique to transform the deviation for transformation.

[0060] It is known that the idea of the super-twisting algorithm is: The super-twisting algorithm is a type of second-order sliding mode control, initially proposed by Levant. Compared with the traditional sliding mode technique, the main idea of the super-twisting algorithm is to remove the restriction on the relative order of 1 of the standard sliding mode surface and increase the order. The super-twisting algorithm can be described as:

[0061]

[0062] where x1, x2 are state variables. k1, k2 are control gains to be designed. ρ1, ρ2 are uncertain terms. Under certain conditions, k1, k2 can have a robust effect on the bounded uncertainties ρ1(x,t), ρ2(x,t).

[0063] In the prior art, the super-twisting algorithm has been combined with a momentum observer, and the form is as follows:

[0064]

[0065]

[0066] where, Q, T ∈ R n×n is a positive definite symmetric matrix, and this structure can be used to estimate τ ext , that is, σ ≈ τ ext . The embodiments of the present invention further design the controller structure to weaken the chattering problem caused by high-frequency switching.

[0067] 2) Replace the above sign function with a sigmoid function, where the sigmoid function is defined as follows:

[0068] sigm(x; a, b) = a[(1 + e -b ) -1 -0.5]

[0069] The sigmoid function has better noise suppression ability, and it is also a combination of a linear term and a non-linear term, which can effectively weaken the chattering problem caused by high-frequency switching.

[0070] 3) Add a proportional term on the above basis which can improve the rapidity, and at the same time set T, Q as time-varying parameters ψ(t), so as to obtain an observer based on the momentum state, as follows:

[0071]

[0072] wherein, represents the momentum deviation of the robotic arm, sig(·) represents the sigmoid function, σ is an intermediate variable, ψ(t) and represent gain coefficients that vary with time; the sigmoid function is as follows:

[0073] sig(x; a, b) = a[(1 + e -bx ) -1 -0.5] where a and b are constants greater than zero.

[0074] Set T and Q as parameters ψ(t) that vary with time, which can enable the observer provided by the embodiment of the present invention to be adjusted in real time according to external disturbances, and can estimate the disturbance torque of the robotic arm in real time.

[0075] In this embodiment of the present invention, while maintaining the stability of the momentum observer method, the rapidity of estimating the disturbance torque is improved, the chattering drawback of similar sliding mode methods is overcome, and at the same time, the disturbance torque of the robotic arm can be estimated in real time.

[0076] In one example, a disturbance observer is constructed according to the observer based on the momentum state. The disturbance observer is constructed in the form of "proportional + integral" as follows:

[0077]

[0078] It is used for the estimation of the external disturbance torque.

[0079] Since traditional sliding mode observers, observers based on the super-twisting algorithm, or extended state observers all obtain disturbance observation quantities in the form of deviation integration, which will cause the observed disturbance speed to be too slow. Therefore, the embodiment of the present invention constructs a disturbance observer in a form similar to "proportional + integral" based on the above-mentioned constructed momentum observer for the estimation of the external disturbance torque, further increasing the rapidity of the observer.

[0080] In another example, for the obtained disturbance observer, an adaptive rule for the gain parameter is further designed. The ψ(t) and vary with time and are adjusted by the adaptive rule. Specifically, the adjustment steps of the adaptive rule include obtaining the momentum estimation value of the robotic arm in real time according to the momentum state observer; comparing the momentum estimation value with the actual momentum value of the robotic arm to obtain a momentum deviation value; designing an adaptive rule according to the momentum deviation value to adjust the parameter gain. The specific adaptive rule is as follows:

[0081]

[0082] wherein, a, b, τ, χ, ψ m are normal constants.

[0083] By adopting the above adaptive rule, it can be ensured that the designed disturbance observer is convergent and stable within a finite time, which facilitates the self-regulation of parameters, is convenient for extension to various robotic arm platforms for transplantation and parameter adjustment, and ensures the safety and anti-interference ability during the operation of the entire robotic arm.

[0084] Exemplarily, the steps of the robotic arm disturbance torque estimation method provided by the embodiment of the present invention are as follows: First, establish a robotic arm dynamics model and obtain a traditional momentum observer based on it; combine the traditional momentum observer with a super-twisting algorithm to obtain an observer based on momentum state using a sigmoid function; construct a disturbance observer according to the observer based on momentum state; and estimate the disturbance torque based on the disturbance observer.

[0085] The specific implementation steps are as follows:

[0086] In the first step, establish a robotic arm dynamics model equation. Here, a 2R rigid robotic arm model is taken as an example. As Figure 2 shown, the established equation is specifically as follows:

[0087]

[0088] wherein,

[0089]

[0090]

[0091] C0 = m2r1r c2 sin q2,

[0092] G1 = (m1r c1 + m2r1)gcosq1 + m2gr c2 cos(q1 + q2),

[0093] G2 = m2gr c2 cos(q1 + q2)

[0094] wherein, m1 and m2 are the masses of joint 1 and joint 2 respectively, r1 and r2 are the lengths of joint 1 and joint 2 respectively, r c1 , r c2are the distances from the centers of mass of joints 1 and 2 to the joints respectively, q1 and q2 are the angles of joints 1 and 2 respectively, and g is the acceleration due to gravity. In this way, the dynamic model of the robotic arm can be established using the specific information of the robotic arm model. Here, it is sufficient to take m1 = m2 = 0.55 kg; rc1 = rc2 = 0.18 m, and r1 = r2 = 0.36 m.

[0095] In the second step, a momentum-based observer using the sigmoid function is obtained by combining the traditional momentum observer and the super-twisting algorithm:

[0096]

[0097] where, represents the momentum deviation of the robotic arm, sig(·) represents the sigmoid function, σ is an intermediate variable, ψ(t) and represent gain coefficients that vary with time, and their variation rules are determined by the adaptive rules. The sigmoid function is as follows:

[0098] sig(x; a, b) = a[(1 + e -bx ) -1 -0.5], where a and b are constants greater than zero. Here, a = b = 2 can be taken;

[0099] In the third step, a disturbance observer is constructed based on the momentum-based observer as follows:

[0100]

[0101] In the fourth step, an adaptive rule is designed based on the momentum deviation as follows:

[0102]

[0103] where, the adjustment parameters are set as a = 2, b = 10, τ = 0.01, χ = 0.01, ψ m = 10, which can ensure the finite-time stability of the disturbance torque estimation of the robotic arm of the present invention.

[0104] Constructing an adaptive rule based on the momentum deviation can automatically adjust the gain parameters of the disturbance observer, realize self-adjustment of the parameters, improve the robustness of the external torque estimation of the system, and is easy to transplant, with application and promotion potential.

[0105] In the embodiment of the present invention, simulation verification is carried out for the above specific examples.

[0106] To verify the effectiveness of the estimation method designed by the present invention, in the simulation, an external disturbance torque of 6 N·m was applied to the joint 1 of the robotic arm within the time range of 5 s ≤ t ≤ 8 s. At the same time, the traditional generalized momentum observer estimation method and the sliding-mode-based momentum observer estimation method were compared. The simulation diagrams of the estimation of the external disturbance torque by these three methods are as Figure 3 shown.

[0107] As can be seen from Figure 2 it, compared with the other two external torque estimation methods, the robotic arm disturbance torque estimation method provided by the present invention has better rapidity while ensuring stability, and at the same time overcomes the chattering defect in the sliding-mode technology, and can complete the estimation of the magnitude of the external torque.

[0108] Figure 4 The structural schematic diagram of a robotic arm disturbance torque estimation device provided by an embodiment of the present application is as Figure 4 shown. The device may include: a robotic arm dynamics model construction module, a traditional momentum observer acquisition module, a momentum state observation module, a disturbance observation module, and a disturbance torque estimation module.

[0109] Among them, the robotic arm dynamics model construction module is used to establish the dynamic equation of the robotic arm system model according to the Euler-Lagrange formula;

[0110] The traditional momentum observer acquisition module is used to obtain a traditional momentum observer based on the robotic arm dynamics model;

[0111] The momentum state observation module is used to obtain an observation module based on the momentum state using a sigmoid function after combining the traditional momentum observer with the super-twisting algorithm;

[0112] The disturbance observation module is used to construct a disturbance observation module according to the observation module based on the momentum state;

[0113] The disturbance torque estimation module is used to estimate the disturbance torque based on the disturbance observation module.

[0114] The above-mentioned robotic arm disturbance torque estimation device can execute Figure 1 the provided robotic arm disturbance torque estimation method, and has the corresponding devices and beneficial effects in this method.

[0115] Figure 5The figure is a schematic diagram of the principle of the method for estimating the interference torque of the robotic arm provided by the embodiments of the present invention. The entire system consists of a controller, an actuator, a robotic arm dynamics model, a momentum state observer, an adaptive rule, and a sigmoid function-based interference observer. When the working robotic arm is subjected to an external interference torque, by using the torque signal output, joint angle and angular velocity output of the controller, combined with the momentum state observer, the estimated value of the momentum of the robotic arm can be calculated in real time. Then, the estimated value of the momentum is subtracted from the actual momentum value of the robotic arm to obtain a momentum deviation value, which contains information about the amount of the external interference torque. Based on the momentum deviation value, an adaptive rule is designed to adjust the parameter gain, which is directly applied to the sigmoid function-based interference observer proposed above, and then the estimated value of the external interference torque is obtained.

[0116] Figure 6 The figure is a schematic structural diagram of an industrial robot provided by an embodiment of the present application. As Figure 6 shown, the industrial robot includes a controller, a memory, an input device, and an output device. The number of controllers in the industrial robot can be one or more. Figure 6 Here, one controller is taken as an example. The controller, memory, input device, and output device in the industrial robot can be connected through a bus or other means. Figure 6 Here, the connection through the bus is taken as an example.

[0117] The memory, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as Figure 1 the program instructions / modules corresponding to the method for estimating the interference torque of the robotic arm in the embodiment. The controller executes various functional applications and data processing in the industrial robot by running the software programs, instructions, and modules stored in the memory, that is, implements the above-mentioned method for estimating the interference torque of the robotic arm.

[0118] The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the terminal, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some instances, the memory can further include a memory remotely set relative to the controller, and these remote memories can be connected to the terminal / server through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and their combinations.

[0119] The input device can be used to receive input digital or character information and generate key signal inputs related to user settings and function control of the industrial robot. The output device can include display devices such as a display screen.

[0120] As Figure 7 shown, an embodiment of the present application further provides a computer-readable storage medium and a computer controller. The computer-executable instructions, when executed by the computer controller, are used to execute a method for estimating the interference torque of a robotic arm. The method includes Figure 1 the steps shown.

[0121] From the above description of the embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software and necessary general hardware. Of course, it can also be implemented by hardware, but in many cases, the former is a better implementation. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as a floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk, or optical disc of a computer, etc., including several instructions for enabling the industrial robot to implement the methods or functions described in various embodiments of the present application.

[0122] It should be noted that the modules included in the above-mentioned robotic arm interference torque estimation device are only divided according to functional logic, but are not limited to the above division method. As long as the corresponding functions can be realized, it is not used to limit the protection scope of the present application.

[0123] Note that the above is only a preferred embodiment of the present application and the applied technical principle. Those skilled in the art will understand that the present application is not limited to the specific embodiments described here. Various obvious changes, re-adjustments, and substitutions can be made by those skilled in the art without departing from the protection scope of the present application. Therefore, although the present application has been described in detail through the above embodiments, the present application is not limited to the above embodiments. Without departing from the concept of the present application, more other equivalent embodiments can be included, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. A method for estimating the interference torque of a robotic arm, characterized in that, The method includes: Establish a manipulator dynamic model and obtain a traditional momentum observer based on it; Combine the traditional momentum observer with the super-twisting algorithm to obtain a momentum-state-based observer using the sigmoid function; Construct a disturbance observer according to the momentum-state-based observer; Estimate the disturbance torque based on the disturbance observer; The establishment of the manipulator dynamic model and obtaining the traditional momentum observer based on it, where the establishment of the manipulator dynamic model is specifically: Establish the dynamic equation of the manipulator system model according to the Euler-Lagrange formula. The dynamic equation is as follows, where M(q) is a matrix of second-order angular derivative terms related to the mass, center-of-mass position, and angle of the actual manipulator model; is the first-order angular derivative term matrix related to the mass, center of mass position, and angle of the actual model of the robotic arm; g(q) is the gravity The gravity vector acting on the robotic arm; τ m is the joint torque; d is the disturbance quantity, that is, the external disturbance torque quantity to be estimated; The establishment of the manipulator dynamic model and obtaining the traditional momentum observer based on it, where obtaining the traditional momentum observer includes: 1) The momentum of the manipulator is defined as follows: Meanwhile, its derivative term matrix M(q) satisfies 2) According to the momentum definition of the manipulator and the manipulator dynamic model, obtain the state equation of the manipulator momentum as follows: wherein, is the matrix transpose; 3) Obtain the traditional momentum observer based on the state equation of the manipulator momentum, as follows: Combining the traditional momentum observer with the super-twisting algorithm to obtain a momentum-state-based observer using the sigmoid function includes: 1) After combining the traditional momentum observer with the super-twisting algorithm, obtain the following form based on the sign function: wherein, Q, T ∈ R n×n is a positive definite symmetric matrix; 2) Replace the above sign function with the sigmoid function, where the sigmoid function is defined as follows: sigm(x;a,b)=a[(1+e -b ) -1 -0.5] 3) Add a proportional term on the above basis At the same time, set T and Q as parameters ψ(t) that vary with time Thus, an observer based on the momentum state is obtained as follows: Among them, represents the momentum deviation of the robotic arm, sig(·) represents the sigmoid function, σ is an intermediate variable, ψ(t) and represent the gain coefficients that vary with time; the sigmoid function is as follows: sig(x; a, b) = a[(1 + e -bx ) -1 - 0.5], where a and b are constants greater than zero.

2. The method for estimating the interference torque of the robotic arm according to claim 1, wherein The said according to the momentum state Construct a disturbance observer based on the observer. The disturbance observer is constructed in the form of "proportional + integral" as follows: It is used for the estimation of the external disturbance torque.

3. The method for estimating the interference torque of the robotic arm according to claim 2, wherein The ψ(t) and vary with time and are adjusted by an adaptive rule.

4. The method for estimating the interference torque of the robotic arm according to claim 3, characterized in that, The adjustment steps of the said adaptive rule include obtaining the estimated value of the manipulator momentum in real time according to the momentum-state observer; Compare the estimated value of the momentum with the actual momentum value of the manipulator to obtain the momentum deviation value; design the adaptive rule according to the momentum deviation value and adjust the parameter gain. The specific adaptive rule is as follows: where a, b, τ, χ, ψ m are positive constants.

5. An apparatus, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the manipulator disturbance torque estimation method described in any one of claims 1-4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the manipulator disturbance torque estimation method described in any one of claims 1-4.

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