Method and device for determining approximation interfered parameter, medium and electronic equipment

Abstract

The embodiment of the invention provides a method and a device for determining an approximation interfered parameter, a computer readable medium and electronic equipment. The method comprises the following steps: acquiring a fuzzy inference rule; acquiring current position attitude information and current speed information of the robot; for each fuzzy inference rule, determining a first membership degree of a first target variable value in a first target fuzzy set according to a first membership function of the first target fuzzy set, a first target variable corresponding to the first target fuzzy set and the first target variable value in the current position attitude information, determining a second membership degree of a second target variable value in the second target fuzzy set according to a second membership function of the second target fuzzy set, a second target variable corresponding to the second target fuzzy set and the second target variable value in the current position attitude information; and performing fuzzy logic reasoning according to the first membership degree and the second membership degree corresponding to each fuzzy reasoning rule to obtain an approximation interfered parameter. The robot control precision can be improved.

Description

Technical Field

[0001] The present application relates to the field of robot control, and in particular, to a method, device, computer-readable medium and electronic device for determining approximate disturbed parameters. Background Art

[0002] Currently, in the field of robot control, the main method to solve the unknown parameters of the model is to establish a data set based on the input-output data pairs for parameter identification.

[0003] However, in the absence of a large enough data set, the accuracy of parameter identification using data will be low, and parameter identification requires operators to be proficient in system modeling methods, and the threshold for controlling the robot is high. In addition, when mechanical components need to be added during the operation, the parameter identification scheme often needs to be redesigned, and the general applicability of the parameter identification scheme is poor. Summary of the invention

[0004] The embodiments of the present application provide a method, device, computer-readable medium and electronic device for determining an approximate disturbed parameter, thereby at least to a certain extent being able to improve the control accuracy of the robot while lowering the threshold of robot control.

[0005] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0006] According to one aspect of an embodiment of the present application, a method for determining an approximate disturbed parameter is provided, the method comprising: obtaining a plurality of fuzzy inference rules in a rule base, the fuzzy inference rules comprising the name of a first target fuzzy set and the name of a second target fuzzy set, the first target fuzzy set being any first fuzzy set among a plurality of first fuzzy sets, the second target fuzzy set being any second fuzzy set among a plurality of second fuzzy sets, the plurality of first fuzzy sets comprising a fuzzy set defined for a first target variable involved in a position and posture vector of a robot, the plurality of second fuzzy sets comprising a fuzzy set defined for a second target variable involved in a speed vector of the robot, the second target variable corresponding to the first target variable; obtaining current position and posture information and current speed information of the robot, the current position and posture information comprising first variable values ​​corresponding to a plurality of first variables, the current speed information comprising second variable values ​​corresponding to a plurality of first variables; for the rules Then, for each fuzzy inference rule in the library, a first membership of the first target variable value in the first target fuzzy set is determined according to the first membership function of the first target fuzzy set corresponding to the name of the first target fuzzy set in the fuzzy inference rule, the first target variable corresponding to the first target fuzzy set, and the first target variable value corresponding to the first target variable in the current position and posture information; and a second membership of the second target variable value in the second target fuzzy set is determined according to the second membership function of the second target fuzzy set corresponding to the name of the second target fuzzy set in the fuzzy inference rule, the second target variable corresponding to the second target fuzzy set, and the second target variable value corresponding to the second target variable in the current position and posture information; fuzzy logic reasoning is performed according to the first membership and the second membership respectively determined for each fuzzy inference rule in the rule library to obtain the approximate disturbed parameters of the robot.

[0007] According to one aspect of an embodiment of the present application, a device for determining an approximate disturbed parameter is provided, the device comprising: a first acquisition unit, used to acquire multiple fuzzy inference rules in a rule base, the fuzzy inference rules comprising the name of a first target fuzzy set and the name of a second target fuzzy set, the first target fuzzy set is any first fuzzy set among multiple first fuzzy sets, the second target fuzzy set is any second fuzzy set among multiple second fuzzy sets, the multiple first fuzzy sets comprise a fuzzy set defined for a first target variable involved in a position and posture vector of a robot, the multiple second fuzzy sets comprise a fuzzy set defined for a second target variable involved in a speed vector of the robot, the second target variable corresponds to the first target variable; a second acquisition unit, used to acquire current position and posture information and current speed information of the robot, the current position and posture information comprising first variable values ​​corresponding to multiple first variables, the current speed information comprising second variable values ​​corresponding to multiple first variables; a membership determination unit , used for determining, for each fuzzy inference rule in the rule base, a first membership degree of the first target variable value in the first target fuzzy set according to a first membership function of the first target fuzzy set corresponding to the name of the first target fuzzy set in the fuzzy inference rule, the first target variable corresponding to the first target fuzzy set, and a first target variable value corresponding to the first target variable in the current position and posture information, and determining a second membership degree of the second target variable value in the second target fuzzy set according to a second membership function of the second target fuzzy set corresponding to the name of the second target fuzzy set in the fuzzy inference rule, the second target variable corresponding to the second target fuzzy set, and a second target variable value corresponding to the second target variable in the current position and posture information; a fuzzy logic reasoning unit, used for performing fuzzy logic reasoning according to the first membership degree and the second membership degree respectively determined for each fuzzy inference rule in the rule base to obtain the approximate disturbed parameters of the robot.

[0008] Optionally, based on the above-mentioned scheme, the current position posture information is the current position posture vector, the current speed information is the current speed vector, and the index of the first target variable value in the current position posture vector is the same as the index of the second target variable value in the current speed vector.

[0009] Optionally, based on the above scheme, the first acquisition unit is configured to: acquire multiple groups of fuzzy inference rules in the rule base, each group of fuzzy inference rules includes multiple inference fuzzy inference rules, each group of fuzzy inference rules corresponds to a target variable pair, and the two variables in the target variable pair are variables corresponding to the same index in the position posture vector and the velocity vector; the membership determination unit is configured to: for each fuzzy inference rule in each group of fuzzy inference rules in the rule base, according to the first membership function of the first target fuzzy set corresponding to the name of the first target fuzzy set in the fuzzy inference rule, the first target variable corresponding to the first target fuzzy set and the first target variable value corresponding to the first target variable in the current position posture information, determine the first membership of the first target variable value in the first target fuzzy set; and, according to the The second membership function of the second target fuzzy set corresponding to the name of the second target fuzzy set in the fuzzy inference rule, the second target variable corresponding to the second target fuzzy set and the second target variable value corresponding to the second target variable in the current position posture information are used to determine the second membership of the second target variable value in the second target fuzzy set; the fuzzy logic reasoning unit is configured as follows: for each group of fuzzy inference rules in the rule base, fuzzy logic reasoning is performed according to the first membership and the second membership respectively determined for each fuzzy inference rule in the group of fuzzy inference rules to obtain the approximation parameters of a component of the interference force and the interference force vector corresponding to the group of fuzzy inference rules received by the robot; the interference force and the interference force vector received by the robot are constructed according to the approximation parameters of each component of the interference force and the interference force vector received by the robot.

[0010] Optionally, based on the above-mentioned scheme, the device also includes a control unit; after constructing the interference force and interference force vector exerted on the robot, the control unit is used to: control the robot according to the interference force and interference force vector exerted on the robot.

[0011] Optionally, based on the above-mentioned scheme, the control unit is configured to: differentiate the current velocity vector to obtain the derivative of the current velocity vector; determine the force and torque required to be output to the control of the robot according to the derivative of the current velocity vector, the interference force and interference force vector exerted on the robot, and the system inertia matrix of the robot; and control the robot according to the force and torque required to be output to the control of the robot.

[0012] Optionally, based on the aforementioned scheme, the first fuzzy set is used to describe the degree of deviation between the variable values ​​in the position and posture vector and the corresponding variable values ​​defined by the expected trajectory, and the second fuzzy set is used to describe the degree of deviation between the variable values ​​in the velocity vector and the corresponding variable values ​​in the expected velocity vector.

[0013] Optionally, based on the aforementioned scheme, the fuzzy logic reasoning unit is configured as: for each fuzzy reasoning rule, fuzzy logic reasoning is performed according to the first membership and the second membership determined for the fuzzy reasoning rule to obtain the reasoning result of the fuzzy reasoning rule; a fuzzy output result is obtained by performing a union operation on the reasoning results of each fuzzy reasoning rule; and a defuzzification operation is performed on the fuzzy output result to obtain the approximate interference parameters of the robot.

[0014] According to one aspect of an embodiment of the present application, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for determining the approximate disturbed parameter as described in the above embodiment is implemented.

[0015] According to one aspect of an embodiment of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method for determining the approximate interference parameters as described in the above embodiments.

[0016] According to one aspect of an embodiment of the present application, a computer program product is provided, which includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method for determining the approximate interference parameters as described in the above embodiments.

[0017] In the technical solutions provided in some embodiments of the present application, a plurality of fuzzy inference rules are first obtained from a rule base, the fuzzy inference rules including the name of a first target fuzzy set and the name of a second target fuzzy set, and the first target fuzzy set is any first fuzzy set among a plurality of first fuzzy sets, the second target fuzzy set is any second fuzzy set among a plurality of second fuzzy sets, the plurality of first fuzzy sets are fuzzy sets defined for first target variables involved in the position and posture vectors of the robot, the plurality of second fuzzy sets include fuzzy sets defined for second target variables involved in the speed vector of the robot, and the second target variable corresponds to the first target variable; then the current position and posture information and the current speed information of the robot are obtained, and for each fuzzy inference rule, on the one hand, according to the first target fuzzy set in the fuzzy inference rule The first membership function of the first target fuzzy set corresponding to the name, the first target variable corresponding to the first target fuzzy set and the first target variable value corresponding to the first target variable in the current position and posture information is used to determine the first membership of the first target variable value in the first target fuzzy set. On the other hand, the second membership function of the second target fuzzy set corresponding to the name of the second target fuzzy set in the fuzzy inference rule, the second target variable corresponding to the second target fuzzy set and the second target variable value corresponding to the second target variable in the current position and posture information is used to determine the second membership of the second target fuzzy set. Finally, fuzzy logic reasoning is performed according to the first membership and the second membership determined for each fuzzy inference rule in the rule base, so as to obtain the approximate disturbed parameters of the robot.

[0018] It can be seen that the scheme of the embodiment of the present application directly applies expert experience to the determination process of approximating the disturbed parameters for the first time, thereby improving the accuracy of determining the approximated disturbed parameters, reducing system errors, and improving the motion control performance of the robot; since the scheme of the embodiment of the present application summarizes routine operations and approximates the black box system parameters of the robot using relevant operation information, even operators who are unfamiliar with the system modeling method can use imprecise fuzzy language to describe the system, thereby significantly lowering the threshold for controlling the robot; in addition, the scheme of the embodiment of the present application can also be applied to the control of various other robots with similar routine operations. When mechanical components are added due to work needs, parameter approximation can still be performed based on the scheme of the embodiment of the present application, which can adapt to changes in model parameters and has strong universal applicability.

[0019] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0021] Figure 1 A flow chart of a method for determining an approximate disturbed parameter according to an embodiment of the present application is shown;

[0022] Figure 2 A flowchart of performing fuzzy logic reasoning to obtain approximate interference parameters of a robot according to an embodiment of the present application according to the first membership degree and the second membership degree respectively determined for each fuzzy reasoning rule in the rule base is shown;

[0023] Figure 3 A flow chart of controlling a robot according to a disturbance force and a disturbance force vector applied to the robot according to an embodiment of the present application is shown;

[0024] Figure 4 A schematic diagram showing a comparison between the actual parameters and the approximation parameter modulus values ​​of a robot according to an embodiment of the present application is shown;

[0025] Figure 5 A schematic diagram showing a comparison of a real parameter and a first component of an approximation parameter of a robot according to an embodiment of the present application is shown;

[0026] Figure 6 A schematic diagram showing a comparison of a real parameter and a second component of an approximation parameter of a robot according to an embodiment of the present application is shown;

[0027] Figure 7 A schematic diagram showing a comparison of the third component of the real parameters and the approximation parameters of the robot according to an embodiment of the present application is shown;

[0028] Figure 8 A schematic diagram showing a comparison of the fourth component of the real parameters and the approximation parameters of the robot according to an embodiment of the present application is shown;

[0029] Fig. 9 A schematic diagram showing a comparison of the fifth component of the real parameters and the approximation parameters of the robot according to an embodiment of the present application is shown;

[0030] Fig.10 A schematic diagram showing a comparison of the sixth component of the real parameters and the approximation parameters of the robot according to an embodiment of the present application is shown;

[0031] Fig.11A block diagram of a device for determining an approximate disturbed parameter according to an embodiment of the present application is shown;

[0032] Fig.12 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0033] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0034] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0035] In the embodiments of the present application, the term "module" or "unit" refers to a computer program or a part of a computer program with a predetermined function, and works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as processing circuits or memories) or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be part of an overall module or unit that includes the function of the module or unit.

[0036] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0037] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0038] In the related technologies in the field of robot control, the main method to solve the unknown parameters of the model is to perform parameter identification based on the input-output data set. For example, there are two parameter identification solutions in the related technology:

[0039] Solution 1: An excitation trajectory optimization method for dynamic parameter identification of a space robot.

[0040] This solution establishes the state space equation of the space robot. Due to the incomplete characteristics of the robot, the regression matrix of the parameter identification model contains the posture information of the robot arm and the base. This patent uses the least squares method to estimate the parameters, and then selects the spectral conditional function of the regression matrix to optimize the excitation trajectory.

[0041] Solution 2: In-situ calibration and synchronous identification method combining the robot body and external force sensing.

[0042] This solution collects the joint current of the moving industrial robot and the external force sensing information of the base, and then uses an iterative algorithm to identify the parameters.

[0043] The inventors of the present application have discovered that mainstream parameter identifiers in the related art all perform parameter identification by establishing data sets based on input-output data pairs. In the absence of a sufficiently large data set, the accuracy of parameter identification using data pairs will be low; in addition, expert experience cannot be directly applied to existing mainstream parameter identifiers.

[0044] However, before the robot performs autonomous motion, in addition to data pairs, expert experience is another important source of information: they are familiar with the system operation and can adjust the output according to the difference between the position and speed and the expected value without understanding the system model. Therefore, this type of expert experience can help us understand the unknown parameters of the system.

[0045] To this end, the embodiment of the present application first provides a method for determining an approximate disturbed parameter. The method for determining an approximate disturbed parameter provided by the embodiment of the present application can overcome the above defects, can apply expert experience to the determination process of the approximate disturbed parameter, can implement a nonlinear system identifier based on a fuzzy controller, can comprehensively use language description information and input-output pair information, and can adapt to changes in model parameters.

[0046] The implementation details of the technical solution of the embodiment of the present application are described in detail below:

[0047] Figure 1A flow chart of a method for determining an approximate disturbed parameter according to an embodiment of the present application is shown. The method for determining an approximate disturbed parameter can be executed by various devices capable of controlling a robot, and specifically, can be executed by a controller on the robot. Figure 1 As shown, the method for determining the approximate disturbed parameter at least comprises the following steps:

[0048] Step 110, obtain multiple fuzzy inference rules in the rule base, the fuzzy inference rules include the name of the first target fuzzy set and the name of the second target fuzzy set, the first target fuzzy set is any first fuzzy set among the multiple first fuzzy sets, the second target fuzzy set is any second fuzzy set among the multiple second fuzzy sets, the multiple first fuzzy sets include a fuzzy set defined for a first target variable related to the robot's position and posture vector, the multiple second fuzzy sets include a fuzzy set defined for a second target variable related to the robot's velocity vector, and the second target variable corresponds to the first target variable.

[0049] Before introducing step 110 in detail, relevant contents about the robot in the embodiment of the present application are first introduced.

[0050] The robot in the embodiment of the present application can be an industrial robot, specifically a drone or an autonomous underwater robot (Autonomous Underwater Vehicle, AUV). In addition, the robot in the embodiment of the present application can be a six-degree-of-freedom robot. The degree of freedom (DOF) of a robot refers to the number of parameters that a robot can move independently, which is an important technical indicator for measuring the maneuverability of a robot. For a rigid body, it usually has six degrees of freedom: including three translational motions (T1, T2, T3) along the coordinate axes ox, oy and oz, and three rotational motions (R1, R2, R3) around these axes. This means that a rigid body can change its position and direction in three-dimensional space.

[0051] Assume that the state space form of the six-degree-of-freedom industrial robot model is:

[0052]

[0053] Among them, x is the position and posture vector of the robot, x′ is the derivative of the position and posture vector of the robot with respect to time, u is the control output force and torque of the robot, M is the system inertia matrix of the robot, f is the interference force and interference torque received by the robot during movement, v is the velocity vector of the robot, and v′ is the derivative of the velocity vector of the robot with respect to time.

[0054] Each element in the position and attitude vector x corresponds to a position and attitude variable.

[0055] For example, the robot's position posture vector x can be expressed as x=[x, y, z, α, β, γ], where x, y, z are the three position components of the robot in three-dimensional space, α is the roll angle (roll), β is the pitch angle (pitch) and γ is the yaw angle (yaw). These angles are attitude angles and are used to describe the robot's posture.

[0056] Similarly, the velocity vector v may also include 6 elements, which may specifically include the linear velocity in the three coordinate axis directions of the reference coordinate system and the angular velocity around each coordinate axis. These 6 elements also correspond to 6 velocity variables respectively.

[0057] The robot's control output force and torque can also include three forces and three torques, which correspond one-to-one to the six elements in the position attitude vector and the velocity vector, respectively. The force corresponds to the position component and the linear velocity, and the torque corresponds to the attitude angle and the angular velocity.

[0058] The interference force and interference torque that the robot is subject to during movement may be caused by external interference factors such as wind, water flow, and external collision.

[0059] The interference forces and interference torques to which the robot is subjected during the motion process may also include three interference forces and three interference torques, which may be opposite to the control output forces and torques of the robot respectively.

[0060] Before obtaining multiple fuzzy inference rules in the rule base, it is necessary to first perform fuzzification and build the rule base.

[0061] In one embodiment of the present application, the first fuzzy set is used to describe the degree of deviation between the variable values ​​in the position and posture vector and the corresponding variable values ​​defined by the expected trajectory, and the second fuzzy set is used to describe the degree of deviation between the variable values ​​in the velocity vector and the corresponding variable values ​​in the expected velocity vector.

[0062] The expected trajectory is a function of the predefined position and posture information over time; the expected trajectory can be defined as the expected variable value corresponding to each variable at each moment; the expected velocity vector can be obtained in advance by differentiating the expected trajectory with respect to time.

[0063] Different first fuzzy sets can be used to describe different degrees of deviation between the variable values ​​in the position and attitude vector and the corresponding variable values ​​defined by the expected trajectory, and different second fuzzy sets can be used to describe different degrees of deviation between the variable values ​​in the velocity vector and the corresponding variable values ​​in the expected velocity vector.

[0064] Specifically, in the fuzzification stage, it is necessary to first define a fuzzy set for each variable and determine the membership function corresponding to the fuzzy set.

[0065] In the embodiment of the present application, a first fuzzy set is defined for the variable corresponding to each element in each position and posture vector, and a second fuzzy set is defined for the variable corresponding to each element in the velocity vector.

[0066] Experts can summarize their experience based on routine operations to obtain the first fuzzy set and the second fuzzy set.

[0067] For example, for the variables corresponding to each element in the position and attitude vector, five first fuzzy sets can be defined respectively: not keeping up, a little not keeping up, keeping up, a little over keeping up, and over keeping up; for the variables corresponding to each element in the speed vector, five second fuzzy sets can be defined respectively: slow, a little slow, appropriate, a little fast, and fast. Of course, for the variables corresponding to each element in the position and attitude vector and the variables corresponding to each element in the speed vector, other fuzzy sets can also be defined, for example, extremely slow, very slow, very slow, a little slow, etc. can be defined. It can be seen that the above first fuzzy set and the second fuzzy set are defined according to the criteria of negative medium (Negative Medium, NM), negative small (Negative Small, NS), zero (Zero), positive small (PositviveSmall, PS), and positive medium (Positvive Medium, PM). Negative medium and positive medium represent large deviations from the positive direction and negative direction, respectively, negative small and positive small represent small deviations from the positive direction and negative direction, respectively, and zero represents no deviation. The above variables are all input variables of the system. Similarly, fuzzy sets can also be defined for the output variables of the system. The output variables are the interference force and interference torque that the robot is subjected to during movement. The variables corresponding to its six components are the six output variables. The following fuzzy sets can be defined for each output variable: large, slightly larger, appropriate, slightly smaller, and small.

[0068] Then, the membership functions of the fuzzy sets can be determined.

[0069] Assume that the input of the fuzzy logic system is a = [x1,x2,x3,x4,x5,x6,v1,v2,v3,v4,v5,v6,] T , where x i Represents the i-th element of the input position and posture vector x, v i represents the i-th element of the input velocity vector v. i , the following five membership functions can be defined respectively:

[0070]

[0071] Among them, a i is the i-th element of the input vector a of the fuzzy logic system, i.e., the i-th input of the fuzzy logic system, μ NM (a i ), μNS (a i ), μ Z (a i ), μ PS (a i ), μ PM (a i ) represent the membership functions corresponding to Negative Medium (NM), Negative Small (NS), Zero (Zero), Positive Small (PS), and Positive Medium (PM), respectively.

[0072] Therefore, for the five first fuzzy sets of not keeping up, not keeping up a little, keeping up, keeping up a little, and keeping up, the above five membership functions can be used respectively; similarly, for the five second fuzzy sets of slow, a little slow, suitable, a little fast, and fast, the above five membership functions can also be used respectively. Although in the embodiment of the present application, the same five membership functions are used for each first fuzzy set and each second fuzzy set, in other embodiments of the present application, different membership functions can also be used for each first fuzzy set and each second fuzzy set.

[0073] Through the above membership functions, the i-th input of the fuzzy logic system can be fuzzified respectively.

[0074] The membership function corresponding to the fuzzy set of the output variable can be defined as 1.

[0075] The following describes how to build a rule base.

[0076] Experts can summarize their experience based on routine operations to develop fuzzy reasoning rules, and use the fuzzy reasoning rules to build a rule base.

[0077] Specifically, the fuzzy reasoning rules formulated in the embodiment of the present application can be shown in Table 1:

[0078]

[0079] Table 1

[0080] In the embodiment of the present application, the fuzzy reasoning rule is expressed in the form of: if A, then B. Wherein, A is the premise part (antecedent), which usually includes one or more fuzzy propositions; B is the conclusion part (consequent), which is usually also a fuzzy proposition. The fuzzy reasoning rule in the embodiment of the present application may include two fuzzy propositions, one fuzzy proposition includes the name of a fuzzy set of position and posture variables, and the other fuzzy proposition includes the name of a fuzzy set of speed variables corresponding to the position and posture variables.

[0081] The mapping relationship between the names of the fuzzy sets of position and posture variables, the names of the fuzzy sets of speed variables, and the names of the corresponding fuzzy sets of output variables in the fuzzy inference rules is shown in Table 1.

[0082] Taking x1 and v1 in the input of the fuzzy logic system as an example for analysis, when x1 does not keep up with the expected trajectory and the speed v1 is slower than the expected speed, it means that the force output by the control robot is not large enough. According to the second formula in the above state space form, the sum of the force output by the control robot and f is fixed, which means that the parameter f1 calculated in the previous cycle (that is, the inferred interference force on the robot and the first component of the interference force vector) accounts for a large proportion of the sum of the force output by the control robot and f, and needs to be reduced, which corresponds to the fuzzy reasoning rule in Table 1: "If the posture does not keep up and the speed is slow, f is large".

[0083] In step 110, the second target variable corresponds to the first target variable, which means that the index of the variable value corresponding to the first target variable in the position and posture vector is the same as the index of the variable value corresponding to the second target variable in the velocity vector.

[0084] In one embodiment of the present application, step 110 may specifically include: obtaining multiple groups of fuzzy inference rules in the rule base, each group of fuzzy inference rules includes multiple inference fuzzy inference rules, each group of fuzzy inference rules corresponds to a target variable pair, and the two variables in the target variable pair are variables corresponding to the same index in the position posture vector and the velocity vector.

[0085] Specifically, the position and attitude vector and the velocity vector each have 6 variables, and the variables corresponding to the same index in the two vectors include 6 pairs of variables; an example of the variables corresponding to the same index in the two vectors may be: the first position and attitude variable in the position and attitude vector and the first velocity variable in the velocity vector. A set of fuzzy inference rules may be formulated for each pair of variables.

[0086] If fuzzy inference rules are constructed based on the form of the input vector a of the fuzzy logic system, then, since there are 6 pairs of variables, 150 fuzzy inference rules can be designed. The form of fuzzy inference rules can be expressed as follows: If a i yes And a i+6 yes but yes Among them, a i is the i-th component of the input vector a, a i+6 is the i+6th component of the input vector a, which ensures that the indexes of the two variables in the corresponding vectors are consistent. A is the fuzzy set of a, Represents the variable a i The definition of the firstm The name of the fuzzy set, Represents the variable a i+6 The definition of the first n The name of the fuzzy set, is the i-th component of the fuzzy system output, B is the fuzzy set of the fuzzy system output, Express The definition of the first p fuzzy sets, i = 1, 2, ... 6, j = 1, 2, ... 6, k = 1, 2, ... 6, l m =1,2,…5,l n =1,2,…5.

[0087] Step 120, obtaining current position and posture information and current speed information of the robot, wherein the current position and posture information includes first variable values ​​corresponding to a plurality of first variables, and the current speed information includes second variable values ​​corresponding to a plurality of first variables.

[0088] The current position and posture information and the current speed information can both be collected by corresponding sensors on the robot.

[0089] In one embodiment of the present application, the current position posture information is the current position posture vector, the current speed information is the current speed vector, and the index of the first target variable value in the current position posture vector is the same as the index of the second target variable value in the current speed vector.

[0090] The steps of the embodiment of the present application may be executed cyclically at regular time intervals. For example, each time the current position and posture information and current speed information of the robot are obtained, the solution of the embodiment of the present application is executed once, and the execution cycle may be 0.02 seconds.

[0091] Step 130, for each fuzzy inference rule in the rule base, determine the first membership of the first target variable value in the first target fuzzy set according to the first membership function of the first target fuzzy set corresponding to the name of the first target fuzzy set in the fuzzy inference rule, the first target variable corresponding to the first target fuzzy set, and the first target variable value corresponding to the first target variable in the current position and posture information; and determine the second membership of the second target variable value in the second target fuzzy set according to the second membership function of the second target fuzzy set corresponding to the name of the second target fuzzy set in the fuzzy inference rule, the second target variable corresponding to the second target fuzzy set, and the second target variable value corresponding to the second target variable in the current position and posture information.

[0092] For a certain fuzzy inference rule, it is necessary to determine the membership degree of the fuzzy set corresponding to the variable value in the premise part of the fuzzy inference rule according to the variable value in the current position and posture information and the current speed information.

[0093] For example, the first membership degree determined for a fuzzy inference rule can be The second membership degree can be in, For variable a i In the first m The membership degree of a fuzzy set is For variable a i+6 In the first n The membership degree of a fuzzy set.

[0094] In one embodiment of the present application, step 130 may include the following steps: for each fuzzy inference rule in each group of fuzzy inference rules in the rule base, determine the first membership of the first target variable value in the first target fuzzy set according to the first membership function of the first target fuzzy set corresponding to the name of the first target fuzzy set in the fuzzy inference rule, the first target variable corresponding to the first target fuzzy set, and the first target variable value corresponding to the first target variable in the current position and posture information; and determine the second membership of the second target variable value in the second target fuzzy set according to the second membership function of the second target fuzzy set corresponding to the name of the second target fuzzy set in the fuzzy inference rule, the second target variable corresponding to the second target fuzzy set, and the second target variable value corresponding to the second target variable in the current position and posture information.

[0095] For each fuzzy reasoning rule in each group of fuzzy reasoning rules, it is necessary to calculate the degree of membership using the method provided in the embodiment of the present application.

[0096] Step 140 , performing fuzzy logic reasoning according to the first membership degree and the second membership degree respectively determined for each fuzzy reasoning rule in the rule base, to obtain the approximate interference parameters of the robot.

[0097] The approximate disturbance parameter of the robot is an inferred disturbance force on the robot and an element of the disturbance force vector.

[0098] Figure 2 The flowchart of performing fuzzy logic reasoning according to the first membership degree and the second membership degree respectively determined for each fuzzy reasoning rule in the rule base to obtain the approximate interference parameter of the robot according to one embodiment of the present application is shown. Figure 2 As shown, according to the first membership degree and the second membership degree respectively determined for each fuzzy reasoning rule in the rule base, fuzzy logic reasoning is performed to obtain the approximate interference parameter of the robot, which may specifically include the following steps:

[0099] Step 210 , for each fuzzy reasoning rule, fuzzy logic reasoning is performed according to the first membership degree and the second membership degree determined for the fuzzy reasoning rule to obtain a reasoning result for the fuzzy reasoning rule.

[0100] Specifically, the process of fuzzy logic reasoning can be as follows:

[0101] 1. Use the product inference engine to perform premise reasoning of the rule and obtain the following premise reasoning results:

[0102]

[0103] 2. Use non-single-value fuzzifier to solve That is, the input value corresponding to the maximum value of the membership function.

[0104] 3. Use the product inference engine to realize the reasoning of rule premise and rule conclusion. The reasoning result is:

[0105]

[0106] Step 220, performing a AND operation on the inference results of each fuzzy inference rule to obtain a fuzzy output result.

[0107] After step 3, the fuzzy logic reasoning process may also include:

[0108] 4. Perform a AND operation on the inference results obtained from the 25 fuzzy inference rules of the i-th component, and the output of the fuzzy system is:

[0109]

[0110] Step 230, performing a defuzzification operation on the fuzzy output result to obtain the approximate disturbed parameters of the robot.

[0111] After step 4, the fuzzy logic reasoning process may also include:

[0112] 5. The reasoning results obtained from the 25 fuzzy reasoning rules of the i-th component are combined and the approximate parameters of the i-th component of the disturbance force on the robot and the disturbance force vector f are:

[0113]

[0114] Of course, in other embodiments of the present application, other defuzzifiers may also be used to perform the defuzzification operation.

[0115] In one embodiment of the present application, step 140 may specifically include the following steps: for each group of fuzzy reasoning rules in the rule base, fuzzy logic reasoning is performed according to the first membership and the second membership respectively determined for each fuzzy reasoning rule in the group of fuzzy reasoning rules to obtain the approximation parameters of a component of the interference force and the interference force vector corresponding to the group of fuzzy reasoning rules received by the robot; according to the approximation parameters of each component of the interference force and the interference force vector received by the robot, the interference force and the interference force vector received by the robot are constructed.

[0116] For each set of fuzzy inference rules, the disturbance force on the robot and the approximate parameters of a component of the disturbance force vector can be determined.

[0117] The interference force and interference force vector acting on the robot may include various forces and torques that are unrelated to the thrust and can affect the system, such as Coriolis force, fluid force, etc.

[0118] In one embodiment of the present application, after constructing the interference force and the interference force vector received by the robot, the method further includes: controlling the robot according to the interference force and the interference force vector received by the robot.

[0119] Figure 3 FIG. 1 is a flowchart showing a method of controlling a robot according to a disturbance force and a disturbance force vector applied to the robot according to an embodiment of the present application. Figure 3 As shown, according to the interference force and interference force vector received by the robot, the robot is controlled, which may specifically include the following steps:

[0120] Step 310, deriving the current velocity vector to obtain the derivative of the current velocity vector.

[0121] Step 320, determining the force and torque required to be output to the control of the robot according to the derivative of the current velocity vector, the disturbance force and the disturbance force vector on the robot, and the system inertia matrix of the robot.

[0122] The force and torque required for the robot's control output can be calculated according to the second formula in the above state space form, that is, the interference force and interference force vector acting on the robot are multiplied by the robot's system inertia matrix, and the difference between the derivative of the current velocity vector and the product is calculated to obtain the force and torque required for the robot's control output.

[0123] Step 330, controlling the robot according to the force and torque output required for the robot.

[0124] According to the force and torque required to control the robot output, control signals are sent to various driving units (such as motors) of the robot for control.

[0125] The experimental results of the embodiments of the present application are introduced below. The embodiment of the present invention adopts the simulation experiment platform SIMULINK, ODE3 as the solver, sets the step size to 0.1s, uses the data analysis platform MATLAB R2021b, the development language and toolkit are MATLAB language, S-Function function framework, and simulates and verifies that the robot moves in a straight line from (0,0,0) to (20,0,0), and the specific trajectory expression is:

[0126]

[0127] Where A = 1m / s. The experimental results are as follows: The robot stability error is within 0.02m. The upper limit of the absolute error on the x-axis is 0.026m, and the root mean square error is 0.002m; the upper limit of the absolute error on the y-axis is 0.002m, and the root mean square error is 0.0003m; the upper limit of the absolute error on the z-axis is 0.0043m, and the root mean square error is 0.0026m. The upper limit of the absolute error of the parameter modulus is 0.814, and the root mean square error is 0.084. Through data analysis, it is found that the absolute error upper limit of f1 is 0.985, and the root mean square error is 0.095; the absolute error upper limit of f2 is 0.041, and the root mean square error is 0.016; the absolute error upper limit of f3 is 0.125, and the root mean square error is 0.039; the absolute error upper limit of f4 is 0.226, and the root mean square error is 0.0776; the absolute error upper limit of f5 is 0.043, and the root mean square error is 0.0003; the absolute error upper limit of f6 is 0.105, and the root mean square error is 0.053.

[0128] Please continue to see Figure 4-Figure 10 , these figures show the comparison results between the real parameters of the robot and the approximation parameters inferred by the embodiment scheme of the present application. The real parameters of the robot are determined by simulation. It can be seen that both the modulus of the approximation parameters and the various components of the approximation parameters are very close to the corresponding real parameters of the robot, which shows that the parameter approximation can be achieved very accurately based on the embodiment scheme of the present application.

[0129] In summary, according to a method for determining an approximate disturbed parameter provided in an embodiment of the present application, at least the following beneficial effects can be achieved:

[0130] 1. Summarize and summarize routine operations, and use relevant operation information to approximate the black box system parameters of the 6-DOF robot. Even operators who are not familiar with the system modeling method can use imprecise fuzzy language to describe the system.

[0131] 2. As you become proficient in routine operations, you can summarize new operations and improve the fuzzy logic system parameter approximation results.

[0132] 3. It is suitable for controlling a 6-DOF robot with similar conventional operations. When mechanical components are added due to work needs, the solution of the embodiment of the present application can still be used for parameter approximation, and has strong universal adaptability.

[0133] The following describes an apparatus embodiment of the present application, which can be used to execute the method for determining the approximate disturbed parameter in the above-mentioned embodiment of the present application. For details not disclosed in the apparatus embodiment of the present application, please refer to the above-mentioned embodiment of the method for determining the approximate disturbed parameter of the present application.

[0134] Fig.11 FIG. 4 is a block diagram of a device for determining an approximate disturbed parameter according to an embodiment of the present application. Fig.11As shown, according to an embodiment of the present application, an apparatus 1100 for determining an approximate disturbed parameter includes: a first acquisition unit 1110, a second acquisition unit 1120, a membership determination unit 1130, and a fuzzy logic reasoning unit 1140. The first acquisition unit 1110 is used to acquire multiple fuzzy reasoning rules in a rule base, wherein the fuzzy reasoning rules include the name of a first target fuzzy set and the name of a second target fuzzy set, the first target fuzzy set is any first fuzzy set among multiple first fuzzy sets, the second target fuzzy set is any second fuzzy set among multiple second fuzzy sets, the multiple first fuzzy sets include fuzzy sets defined for first target variables involved in the position and posture vector of the robot, the multiple second fuzzy sets include fuzzy sets defined for second target variables involved in the velocity vector of the robot, and the second target variable corresponds to the first target variable; the second acquisition unit 1120 is used to acquire current position and posture information and current velocity information of the robot, the current position and posture information includes first variable values ​​corresponding to multiple first variables, and the current velocity information includes second variable values ​​corresponding to multiple first variables; the membership determination unit 1130 is used to determine the membership of each model in the rule base. Fuzzy reasoning rules, determine the first membership of the first target variable value in the first target fuzzy set according to the first membership function of the first target fuzzy set corresponding to the name of the first target fuzzy set in the fuzzy reasoning rules, the first target variable corresponding to the first target fuzzy set and the first target variable value corresponding to the first target variable in the current position and posture information; and determine the second membership of the second target variable value in the second target fuzzy set according to the second membership function of the second target fuzzy set corresponding to the name of the second target fuzzy set in the fuzzy reasoning rules, the second target variable corresponding to the second target fuzzy set and the second target variable value corresponding to the second target variable in the current position and posture information; the fuzzy logic reasoning unit 1140 is used to perform fuzzy logic reasoning according to the first membership and the second membership respectively determined for each fuzzy reasoning rule in the rule base to obtain the approximate interference parameters of the robot.

[0135] Optionally, based on the above-mentioned scheme, the current position posture information is the current position posture vector, the current speed information is the current speed vector, and the index of the first target variable value in the current position posture vector is the same as the index of the second target variable value in the current speed vector.

[0136] Optionally, based on the above scheme, the first acquisition unit 1110 is configured to: acquire multiple groups of fuzzy inference rules in the rule base, each group of fuzzy inference rules includes multiple inference fuzzy inference rules, each group of fuzzy inference rules corresponds to a target variable pair, and the two variables in the target variable pair are variables corresponding to the same index in the position posture vector and the velocity vector; the membership determination unit 1130 is configured to: for each fuzzy inference rule in each group of fuzzy inference rules in the rule base, according to the first membership function of the first target fuzzy set corresponding to the name of the first target fuzzy set in the fuzzy inference rule, the first target variable corresponding to the first target fuzzy set, and the first target variable value corresponding to the first target variable in the current position posture information, determine the first membership of the first target variable value in the first target fuzzy set; and, according to the The second membership function of the second target fuzzy set corresponding to the name of the second target fuzzy set in the fuzzy inference rule, the second target variable corresponding to the second target fuzzy set and the second target variable value corresponding to the second target variable in the current position posture information are used to determine the second membership of the second target variable value in the second target fuzzy set; the fuzzy logic reasoning unit 1140 is configured as: for each group of fuzzy inference rules in the rule base, fuzzy logic reasoning is performed according to the first membership and the second membership respectively determined for each fuzzy inference rule in the group of fuzzy inference rules to obtain the approximation parameters of a component of the interference force and the interference force vector corresponding to the group of fuzzy inference rules received by the robot; the interference force and the interference force vector received by the robot are constructed according to the approximation parameters of each component of the interference force and the interference force vector received by the robot.

[0137] Optionally, based on the above-mentioned scheme, the device also includes a control unit; after constructing the interference force and interference force vector exerted on the robot, the control unit is used to: control the robot according to the interference force and interference force vector exerted on the robot.

[0138] Optionally, based on the above-mentioned scheme, the control unit is configured to: differentiate the current velocity vector to obtain the derivative of the current velocity vector; determine the force and torque required to be output to the control of the robot according to the derivative of the current velocity vector, the interference force and interference force vector exerted on the robot, and the system inertia matrix of the robot; and control the robot according to the force and torque required to be output to the control of the robot.

[0139] Optionally, based on the aforementioned scheme, the first fuzzy set is used to describe the degree of deviation between the variable values ​​in the position and posture vector and the corresponding variable values ​​defined by the expected trajectory, and the second fuzzy set is used to describe the degree of deviation between the variable values ​​in the velocity vector and the corresponding variable values ​​in the expected velocity vector.

[0140] Optionally, based on the above-mentioned scheme, the fuzzy logic reasoning unit 1140 is configured as: for each fuzzy reasoning rule, fuzzy logic reasoning is performed according to the first membership and the second membership determined for the fuzzy reasoning rule to obtain the reasoning result of the fuzzy reasoning rule; according to the reasoning results of each fuzzy reasoning rule, a fuzzy output result is obtained by performing a union operation on the reasoning results of each fuzzy reasoning rule; and defuzzifying the fuzzy output result to obtain the approximate interference parameters of the robot.

[0141] Fig.12 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.

[0142] It should be noted that Fig.12 The computer system 1200 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0143] like Fig.12 As shown, the computer system 1200 includes a central processing unit (CPU) 1201, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1202 or the program loaded from the storage part 1208 to the random access memory (RAM) 1203, such as executing the method described in the above embodiment. In the RAM 1203, various programs and data required for system operation are also stored. The CPU 1201, the ROM 1202 and the RAM 1203 are connected to each other through the bus 1204. The input / output (I / O) interface 1205 is also connected to the bus 1204.

[0144] The following components are connected to the I / O interface 1205: an input section 1206 including a keyboard, a mouse, etc.; an output section 1207 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1208 including a hard disk, etc.; and a communication section 1209 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 1209 performs communication processing via a network such as the Internet. A drive 1210 is also connected to the I / O interface 1205 as needed. A removable medium 1211, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1210 as needed so that a computer program read therefrom is installed into the storage section 1208 as needed.

[0145] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 1209, and / or installed from a removable medium 1211. When the computer program is executed by a central processing unit (CPU) 1201, various functions defined in the system of the present application are executed.

[0146] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by an instruction execution system, device or device or used in combination with it. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0147] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0148] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0149] As one aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method described in the above embodiment.

[0150] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0151] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0152] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

[0153] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for determining an approximate disturbed parameter, characterized in that: The method comprises: Acquire a plurality of fuzzy inference rules in a rule base, wherein the fuzzy inference rules include a name of a first target fuzzy set and a name of a second target fuzzy set, the first target fuzzy set is any first fuzzy set among a plurality of first fuzzy sets, the second target fuzzy set is any second fuzzy set among a plurality of second fuzzy sets, the plurality of first fuzzy sets include a fuzzy set defined for a first target variable involved in a position and posture vector of the robot, the plurality of second fuzzy sets include a fuzzy set defined for a second target variable involved in a velocity vector of the robot, and the second target variable corresponds to the first target variable; Acquire current position and posture information and current speed information of the robot, wherein the current position and posture information includes first variable values ​​corresponding to a plurality of first variables, and the current speed information includes second variable values ​​corresponding to a plurality of first variables; For each fuzzy inference rule in the rule base, determine a first membership of the first target variable value in the first target fuzzy set according to a first membership function of the first target fuzzy set corresponding to the name of the first target fuzzy set in the fuzzy inference rule, the first target variable corresponding to the first target fuzzy set, and a first target variable value corresponding to the first target variable in the current position and posture information, and determine a second membership of the second target variable value in the second target fuzzy set according to a second membership function of the second target fuzzy set corresponding to the name of the second target fuzzy set in the fuzzy inference rule, the second target variable corresponding to the second target fuzzy set, and a second target variable value corresponding to the second target variable in the current position and posture information; According to the first membership degree and the second membership degree respectively determined for each fuzzy reasoning rule in the rule base, fuzzy logic reasoning is performed to obtain the approximate disturbed parameters of the robot.

2. The method for determining the approximate disturbed parameter according to claim 1, characterized in that: The current position and posture information is a current position and posture vector, the current speed information is a current speed vector, and the index of the first target variable value in the current position and posture vector is the same as the index of the second target variable value in the current speed vector.

3. The method for determining the approximate disturbed parameter according to claim 2, characterized in that: The step of obtaining multiple fuzzy inference rules in the rule base includes: Acquire multiple groups of fuzzy inference rules in the rule base, each group of fuzzy inference rules includes multiple inference fuzzy inference rules, each group of fuzzy inference rules corresponds to a target variable pair, and two variables in the target variable pair are variables corresponding to the same index in the position posture vector and the velocity vector; For each fuzzy inference rule in the rule base, according to the first membership function of the first target fuzzy set corresponding to the name of the first target fuzzy set in the fuzzy inference rule, the first target variable corresponding to the first target fuzzy set, and the first target variable value corresponding to the first target variable in the current position and posture information, determine the first membership of the first target variable value in the first target fuzzy set; and according to the second membership function of the second target fuzzy set corresponding to the name of the second target fuzzy set in the fuzzy inference rule, the second target variable corresponding to the second target fuzzy set, and the second target variable value corresponding to the second target variable in the current position and posture information, determine the second membership of the second target variable value in the second target fuzzy set, including: For each fuzzy inference rule in each group of fuzzy inference rules in the rule base, determine a first membership degree of the first target variable value in the first target fuzzy set according to a first membership function of the first target fuzzy set corresponding to the name of the first target fuzzy set in the fuzzy inference rule, the first target variable corresponding to the first target fuzzy set, and a first target variable value corresponding to the first target variable in the current position and posture information; and determine a second membership degree of the second target variable value in the second target fuzzy set according to a second membership function of the second target fuzzy set corresponding to the name of the second target fuzzy set in the fuzzy inference rule, the second target variable corresponding to the second target fuzzy set, and a second target variable value corresponding to the second target variable in the current position and posture information; The method of performing fuzzy logic reasoning according to the first membership degree and the second membership degree respectively determined for each fuzzy reasoning rule in the rule base to obtain the approximate interference parameter of the robot includes: For each group of fuzzy inference rules in the rule base, fuzzy logic reasoning is performed according to the first membership degree and the second membership degree respectively determined for each fuzzy inference rule in the group of fuzzy inference rules to obtain the interference force on the robot and the approximation parameter of a component of the interference force vector corresponding to the group of fuzzy inference rules; The disturbance force and the disturbance force vector suffered by the robot are constructed according to the approximation parameters of the components of the disturbance force and the disturbance force vector suffered by the robot.

4. The method for determining the approximate disturbed parameter according to claim 3, characterized in that: After constructing the disturbance force and the disturbance force vector on the robot, the method further includes: The robot is controlled according to the interference force and the interference force vector received by the robot.

5. The method for determining the approximate disturbed parameter according to claim 4, characterized in that: The controlling of the robot according to the interference force and the interference force vector applied to the robot comprises: Derivative the current velocity vector to obtain a derivative of the current velocity vector; Determine the force and torque required for the control output of the robot according to the derivative of the current velocity vector, the disturbance force and the disturbance force vector exerted on the robot, and the system inertia matrix of the robot; The robot is controlled according to the force and torque required to be output by the control of the robot.

6. The method for determining the approximate disturbed parameter according to claim 1, characterized in that: The first fuzzy set is used to describe the degree of deviation between the variable values ​​in the position and posture vector and the corresponding variable values ​​defined by the expected trajectory, and the second fuzzy set is used to describe the degree of deviation between the variable values ​​in the velocity vector and the corresponding variable values ​​in the expected velocity vector.

7. The method for determining the approximate disturbed parameter according to claim 1, characterized in that: The method of performing fuzzy logic reasoning according to the first membership degree and the second membership degree respectively determined for each fuzzy reasoning rule in the rule base to obtain the approximate interference parameter of the robot includes: For each fuzzy reasoning rule, fuzzy logic reasoning is performed according to the first membership degree and the second membership degree determined for the fuzzy reasoning rule to obtain a reasoning result for the fuzzy reasoning rule; According to the reasoning results of each fuzzy reasoning rule, a fuzzy output result is obtained; A defuzzification operation is performed on the fuzzy output result to obtain an approximate disturbed parameter of the robot.

8. A device for determining an approximate disturbed parameter, characterized in that: The device comprises: A first acquisition unit is used to acquire a plurality of fuzzy inference rules in a rule base, wherein the fuzzy inference rules include a name of a first target fuzzy set and a name of a second target fuzzy set, the first target fuzzy set is any first fuzzy set among a plurality of first fuzzy sets, the second target fuzzy set is any second fuzzy set among a plurality of second fuzzy sets, the plurality of first fuzzy sets include a fuzzy set defined for a first target variable involved in a position and posture vector of the robot, the plurality of second fuzzy sets include a fuzzy set defined for a second target variable involved in a velocity vector of the robot, and the second target variable corresponds to the first target variable; A second acquisition unit is used to acquire current position and posture information and current speed information of the robot, wherein the current position and posture information includes first variable values ​​corresponding to a plurality of first variables, and the current speed information includes second variable values ​​corresponding to a plurality of first variables; a membership determination unit, for determining, for each fuzzy inference rule in the rule base, a first membership of the first target variable value in the first target fuzzy set according to a first membership function of the first target fuzzy set corresponding to the name of the first target fuzzy set in the fuzzy inference rule, the first target variable corresponding to the first target fuzzy set, and a first target variable value corresponding to the first target variable in the current position and posture information, and determining a second membership of the second target variable value in the second target fuzzy set according to a second membership function of the second target fuzzy set corresponding to the name of the second target fuzzy set in the fuzzy inference rule, the second target variable corresponding to the second target fuzzy set, and a second target variable value corresponding to the second target variable in the current position and posture information; The fuzzy logic reasoning unit is used to perform fuzzy logic reasoning according to the first membership degree and the second membership degree respectively determined for each fuzzy reasoning rule in the rule base, so as to obtain the approximate interference parameter of the robot.

9. A computer readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for determining an approximate disturbed parameter according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: one or more processors; A storage device, used to store one or more programs, when the one or more programs are executed by the one or more processors, enables the one or more processors to implement the method for determining the approximate interference parameter as described in any one of claims 1 to 7.

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