An exoskeleton variable admittance control method and device, a terminal device and a medium

Abstract

The application is suitable for the technical field of exoskeleton robots, and provides an exoskeleton variable admittance control method and device, a terminal device and a medium. Training data is collected. A human-robot interaction force prediction model is constructed according to joint angles at multiple historical moments and human-robot interaction forces at multiple historical moments, and a human-robot interaction force prediction value at a to-be-tested moment is obtained. A difference between the human-robot interaction force prediction value and a pre-set human-robot interaction force expectation value is calculated. An admittance control model for controlling the exoskeleton is constructed, and parameters of the admittance control model are adjusted according to the difference, and a variable admittance control model is obtained. The expected joint angle of the exoskeleton at the to-be-tested moment is obtained according to the variable admittance control model. The exoskeleton is controlled by using a position controller according to the expected joint angle. The application can improve the accuracy of exoskeleton control.

Description

Technical Field

[0001] This application belongs to the field of exoskeleton robot technology, and in particular relates to an exoskeleton variable admittance control method, device, terminal equipment and medium. Background Technology

[0002] In recent years, there has been an urgent need for the intervention of exoskeleton robots to assist in rehabilitation treatment and training for hemiplegic and paralyzed patients, helping them to return to a normal level of life as soon as possible. This is of great significance to society and these disabled patients.

[0003] Exoskeleton robots are wearable medical assistive devices that improve, assist, and extend bodily functions. They can help people with lower limb motor dysfunction perform daily activities such as walking upright, standing up, sitting down, and climbing stairs. Rehabilitation training generally involves steps such as assessment, gait design, and strategy development. During the rehabilitation process, the human body interacts with the exoskeleton robot, generating human-machine interaction forces. Controlling these interaction forces within the designed range to ensure the comfort of the human wearing the exoskeleton robot is a significant challenge.

[0004] In response, researchers in related fields have proposed an impedance / admittance control strategy. This strategy primarily adjusts the human-machine interaction force by setting appropriate parameters such as impedance, stiffness, and inertia, thereby improving the comfort of the wearer and ensuring the natural and compliant control of the exoskeleton robot's continuous movement. However, traditional admittance control is a global, fixed-parameter control that is not well-suited for different stages of human walking, resulting in low accuracy in exoskeleton control. Summary of the Invention

[0005] This application provides a method, device, terminal equipment, and medium for controlling variable admittance of exoskeletons, which can solve the problem of low accuracy in current exoskeleton control.

[0006] In a first aspect, this application provides a method for controlling the variable admittance of an exoskeleton, including:

[0007] Collect training data; the training data includes joint angles and human-computer interaction forces at multiple historical moments;

[0008] Based on the joint angles and human-computer interaction forces at multiple historical moments, a human-computer interaction force prediction model is constructed to obtain the predicted value of the human-computer interaction force at the moment to be measured.

[0009] Calculate the difference between the predicted value of human-computer interaction force and the pre-set expected value of human-computer interaction force;

[0010] An admittance control model for controlling the exoskeleton is constructed, and the parameters of the admittance control model are adjusted according to the difference to obtain a variable admittance control model; the parameters include stiffness coefficient and damping coefficient.

[0011] Based on the variable admittance control model, the expected joint angle of the exoskeleton at the time of test is obtained;

[0012] The exoskeleton is controlled using a position controller based on the desired joint angle.

[0013] Optionally, the expression for the human-computer interaction force prediction model is as follows:

[0014]

[0015] Where F(t) represents the predicted value of the human-computer interaction force at the t-th test time, λ i Φ represents the Lagrange multiplier corresponding to the training data at the i-th historical moment, where i = 1, 2, ..., N, and N represents the total number of historical moments. k (·) represents the membership function of the k-th fuzzy rule, and Z(ht) represents the variable value of the input fuzzy model at the ht-th historical moment, including the joint angle and the human-computer interaction force at that historical moment. v k (Z(ht)) represents the total product of the membership functions of the fuzzy sets in the k-th fuzzy rule. R represents the number of fuzzy rules. Let K(·) represent the membership function of the q-th dimension fuzzy set in the k-th fuzzy rule, and let K(·) represent the spatial kernel function. β k This represents the consequent parameter of the k-th fuzzy rule.

[0016] Optionally, the expression for the admittance control model is as follows:

[0017]

[0018] Among them, M d B represents the coefficient of inertia. d K represents the damping coefficient. d F represents the elastic coefficient. d F represents the expected value of human-computer interaction capability, and F represents the predicted value of human-computer interaction capability. The second derivative represents the desired location of the admittance output. It represents the second derivative at a given desired position.

[0019] Optionally, the parameters of the admittance control model can be adjusted based on the difference, including:

[0020] By calculating formula K d (t)=χΔF(t), to obtain the adjusted stiffness coefficient K d (t); where ΔF(t) represents the difference, ΔF(t) = F d(t)-F(t), F d F(t) represents the expected value of the human-computer interaction force at the t-th test time, F(t) represents the predicted value of the human-computer interaction force at the t-th test time, and χ represents the stiffness adjustment parameter.

[0021] By calculating formula B d (t)=δΔF(t), to obtain the adjusted damping coefficient B. d (t), where δ represents the damping adjustment parameter.

[0022] Optionally, the expression for the variable admittance control model is as follows:

[0023]

[0024]

[0025] in, This represents the second derivative of the desired position of the admittance output at the t-th time point to be measured. Let represent the second derivative of the desired position at the t-th time point to be measured.

[0026] Optionally, based on the variable admittance control model, the expected joint angles of the exoskeleton at the time of test are obtained, including:

[0027] Through calculation formula

[0028]

[0029] The desired joint angle r(t) is obtained; where r(t) represents the desired joint angle at the t-th time to be measured.

[0030] Optionally, the position controller is a PD controller.

[0031] Optionally, the exoskeleton can be controlled using a position controller based on the desired joint angle, including:

[0032] Through calculation formula

[0033]

[0034]

[0035] e = r(t) - x(t)

[0036] The control signal τ output by the position controller is obtained; where L d L represents the differential gain. p denoted by , and e representing the error between the desired joint angle and the actual joint angle of the exoskeleton acquired at the time of measurement. The first derivative represents the error;

[0037] The exoskeleton is controlled based on control signals.

[0038] Secondly, this application provides an exoskeleton variable admittance control device, comprising:

[0039] The acquisition module is used to collect training data; the training data includes joint angles and human-computer interaction forces at multiple historical moments.

[0040] The human-computer interaction force prediction module is used to construct a human-computer interaction force prediction model based on the joint angles and human-computer interaction forces at multiple historical moments, and obtain the predicted value of the human-computer interaction force at the moment to be measured.

[0041] The interaction force difference calculation module is used to calculate the difference between the predicted value of human-computer interaction force and the pre-set expected value of human-computer interaction force.

[0042] The variable admittance control module is used to construct an admittance control model for controlling the exoskeleton, and adjusts the parameters of the admittance control model according to the difference to obtain the variable admittance control model; the parameters include stiffness coefficient and damping coefficient.

[0043] The desired joint angle module is used to obtain the desired joint angle of the exoskeleton at the time of test based on the variable admittance control model.

[0044] The exoskeleton control module is used to control the exoskeleton using a position controller according to the desired joint angle.

[0045] Thirdly, this application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described exoskeleton variable admittance control method.

[0046] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described exoskeleton variable admittance control method.

[0047] The above-mentioned solution in this application has the following beneficial effects:

[0048] This application collects joint angles and human-computer interaction forces at multiple historical moments, constructs a human-computer interaction force prediction model based on this data, calculates the difference between the predicted human-computer interaction force value and the pre-set expected human-computer interaction force value, and adjusts the parameters of the admittance control model to obtain a variable admittance control model. Finally, based on the expected joint angle output by the variable admittance control model, a position controller is used to control the exoskeleton. By adjusting the parameters of the admittance control model by calculating the difference between the predicted and expected human-computer interaction force values, the admittance parameters can be controlled and adjusted in real time, making them more closely match actual movement needs and improving the accuracy of exoskeleton control.

[0049] Other beneficial effects of this application will be described in detail in the following detailed description section. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A flowchart of an exoskeleton variable admittance control method provided in an embodiment of this application;

[0052] Figure 2 A flowchart for constructing a human-computer interaction force model is provided as an embodiment of this application;

[0053] Figure 3 A flowchart illustrating the specific implementation of the exoskeleton variable admittance control method provided in one embodiment of this application;

[0054] Figure 4 This is a schematic diagram of the structure of an exoskeleton variable admittance control device provided in an embodiment of this application;

[0055] Figure 5 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation

[0056] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0057] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0058] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0059] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0060] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0061] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0062] To address the issue of low accuracy in current exoskeleton control, this application provides an exoskeleton variable admittance control method, device, terminal equipment, and medium. By collecting joint angles and human-machine interaction forces at multiple historical moments, a human-machine interaction force prediction model is constructed. The difference between the predicted human-machine interaction force value and the pre-set expected human-machine interaction force value is then calculated to adjust the parameters of the admittance control model, resulting in a variable admittance control model. Finally, based on the desired joint angle output by the variable admittance control model, a position controller controls the exoskeleton. The method of adjusting the admittance control model parameters by calculating the difference between the predicted and expected human-machine interaction force values ​​allows for real-time control and adjustment of the admittance parameters, making them more closely match actual movement requirements and improving the accuracy of exoskeleton control.

[0063] like Figure 1 As shown, the exoskeleton variable admittance control method provided in this application includes the following steps:

[0064] Step 11: Collect training data.

[0065] In the embodiments of this application, the training data includes joint angles at multiple historical moments and human-computer interaction forces at multiple historical moments.

[0066] For joint angles, common joint angle acquisition methods can be used, such as: photoelectric motion capture systems (using multiple cameras to capture body movement trajectories and joint angles, and processing and calculating the joint angles through software), inertial measurement units (by wearing an inertial measurement unit, measuring data such as the angular velocity and acceleration of a moving object, and calculating joint angles), and sensors (installed near the joint to measure the position and angle of the joint).

[0067] For human-computer interaction force, human-computer interaction force data can be collected through exoskeleton feedback.

[0068] Step 12: Based on the joint angles and human-computer interaction forces at multiple historical moments, construct a human-computer interaction force prediction model to obtain the predicted human-computer interaction force value at the moment to be measured.

[0069] Specifically, the expression for the human-computer interaction force prediction model is as follows:

[0070]

[0071] Where F(t) represents the predicted value of the human-computer interaction force at the t-th test time, λ i Φ represents the Lagrange multiplier corresponding to the training data at the i-th historical moment, where i = 1, 2, ..., N, and N represents the total number of historical moments. k(·) represents the membership function of the k-th fuzzy rule, and Z(ht) represents the variable value of the input fuzzy model at the ht-th historical moment, including the joint angle and the human-computer interaction force at that historical moment. v k (Z(ht)) represents the total product of the membership functions of the fuzzy sets in the k-th fuzzy rule. R represents the number of fuzzy rules. Let K(·) represent the membership function of the q-th dimension fuzzy set in the k-th fuzzy rule, and let K(·) represent the spatial kernel function. β k This represents the consequent parameter of the k-th fuzzy rule.

[0072] The process of constructing the human-computer interaction force prediction model in the embodiments of this application is illustrated below.

[0073] Step 12.1: Construct a TS fuzzy model of the exoskeleton robot system based on the joint angles and human-computer interaction forces at multiple historical moments.

[0074] The TS fuzzy model is a control stability analysis method used to handle nonlinear systems.

[0075] In the embodiments of this application, the expression of the above-mentioned TS fuzzy model is:

[0076]

[0077] in,

[0078]

[0079] In the above formula, x1(t) represents the hip joint angle at the t-th historical moment, x2(t) represents the knee joint angle at the t-th historical moment, x3(t) represents the ankle joint angle at the t-th historical moment, and F(t) represents the human-computer interaction force at the t-th historical moment.

[0080] Step 12.2: The regular TS fuzzy model rules are fused and projected into a high-dimensional space through a spatial kernel function.

[0081] The expression for the new TS fuzzy model obtained after step 12.2 is as follows:

[0082]

[0083]

[0084]

[0085] in,

[0086] This represents the consequent variable projected into a higher-dimensional space.

[0087] Step 12.3: Solve for the parameters in the new TS fuzzy model.

[0088] The above parameters include α k and β k .

[0089] Step 12.3.1, based on the new TS fuzzy model, construct the following objective function:

[0090]

[0091]

[0092] Among them, e(Z) i ,t) represents the error, e(Z) i ,t)=R(t)-F(t), ξ represents the regularization factor that balances approximate accuracy and generalization.

[0093] Step 12.3.2: Construct the Lagrange function based on the objective function.

[0094] The expression for the Lagrange function is as follows:

[0095]

[0096] Where, λ i It represents the Lagrange multiplier.

[0097] Step 12.3.3: Solve for the Lagrange function.

[0098] Specifically, the solution for the Lagrange function is:

[0099]

[0100]

[0101]

[0102]

[0103] Step 12.3.4: Convert the solution of the Lagrange function into a matrix to obtain the intermediate TS fuzzy model.

[0104] The solution to the Lagrange function, after being transformed into a matrix, takes the following specific form:

[0105]

[0106]

[0107]

[0108]

[0109] By introducing a kernel function (such as a radial basis function (RBF)), we obtain the parameter γ. i and β k .

[0110] α k Substituting (t) into the output of the rule, we obtain the intermediate TS fuzzy model, the specific expression of which is as follows:

[0111]

[0112] Step 12.3.5: Weight the intermediate TS fuzzy model using the membership function to obtain the human-computer interaction force prediction model.

[0113] The specific process of step 12 above is as follows: Figure 2 As shown, firstly, joint angles and human-computer interaction forces at multiple historical moments (such as...) were collected. Figure 2 In step 21), the regular TS fuzzy model rules are then fused and projected to a high-dimensional space using a spatial kernel function to obtain the TS fuzzy model (e.g., ...). Figure 2 Step 22; where step 22.1 represents the fusion of TS fuzzy model rules, and step 22.2 represents projecting the fused TS fuzzy model rules to a high-dimensional space through a spatial kernel function, and then combining this with the collected human-computer interaction forces to obtain a human-computer interaction force prediction model (such as...). Figure 2 Step 23).

[0114] In one embodiment of this application, the expression for the rule fusion of the TS fuzzy model described above is as follows:

[0115]

[0116] The specific process is to merge 1-l1 fuzzy rules into a new fuzzy rule, merge l1+1-l2 fuzzy rules into a new fuzzy rule, and so on until all L fuzzy rules are merged, where l1,l2∈1,2,...,L.

[0117] In one embodiment of this application, the expression for projecting the fused TS fuzzy model rules into a high-dimensional space using a spatial kernel function is as follows:

[0118]

[0119] in, Representing new fuzzy rules (nonlinear), through the projection function Projecting it into a high-dimensional space yields linear fuzzy rules.

[0120] Step 13: Calculate the difference between the predicted value of human-computer interaction force and the pre-set expected value of human-computer interaction force.

[0121] The aforementioned expected value of human-computer interaction force can be obtained based on expert knowledge. For example, in the embodiments of this application, an expert knowledge database is pre-constructed, which includes several expert knowledge (e.g., the expected value of human-computer interaction force that should be applied to the exoskeleton at a certain stage of human movement). When executing step 13, the expected value of human-computer interaction force corresponding to the movement stage of the predicted value of human-computer interaction force is obtained from the database.

[0122] Step 14: Construct an admittance control model for controlling the exoskeleton, and adjust the parameters of the admittance control model according to the difference to obtain a variable admittance control model.

[0123] The parameters include stiffness coefficient and damping coefficient.

[0124] Specifically, the expression for the admittance control model is as follows:

[0125]

[0126] Among them, M d B represents the coefficient of inertia. d K represents the damping coefficient. d F represents the elastic coefficient. d F represents the expected value of human-computer interaction capability, and F represents the predicted value of human-computer interaction capability. The second derivative represents the desired location of the admittance output. It represents the second derivative at a given desired position.

[0127] In the embodiments of this application, the expression of the variable admittance control model is as follows:

[0128]

[0129]

[0130] in, This represents the second derivative of the desired position of the admittance output at the t-th time point to be measured. Let represent the second derivative of the desired position at the t-th time point to be measured.

[0131] Step 15: Based on the variable admittance control model, obtain the expected joint angle of the exoskeleton at the time to be measured.

[0132] Specifically, through calculation formula

[0133]

[0134] The desired joint angle r(t) is obtained.

[0135] Where r(t) represents the expected joint angle at the y-th time to be measured.

[0136] Step 16: Control the exoskeleton using a position controller according to the desired joint angle.

[0137] In the embodiments of this application, the position controller is a PD controller.

[0138] The following is an illustrative example of the process of adjusting the parameters of the admittance control model based on the difference in step 14 (constructing an admittance control model for controlling the exoskeleton, and adjusting the parameters of the admittance control model according to the difference to obtain a variable admittance control model).

[0139] Step 14.1, calculate K using formula d (t)=χΔF(t), to obtain the adjusted stiffness coefficient K d (t).

[0140] Where ΔF(t) represents the difference, ΔF(t) = F d (t)-F(t), F d F(t) represents the expected value of the human-computer interaction force at the t-th test time, F(t) represents the predicted value of the human-computer interaction force at the t-th test time, and χ represents the stiffness adjustment parameter.

[0141] Step 14.2, calculate formula B. d (t)=δΔF(t), to obtain the adjusted damping coefficient B. d (t), where δ represents the damping adjustment parameter.

[0142] The following is an exemplary description of the specific process of step 16 (controlling the exoskeleton using a position controller according to the desired joint angle).

[0143] Step 16.1, using the calculation formula

[0144]

[0145]

[0146] e = r(t) - x(t)

[0147] The control signal τ output by the position controller is obtained.

[0148] Among them, L d L represents the differential gain.p denoted by , and e representing the error between the desired joint angle and the actual joint angle of the acquired exoskeleton. The first derivative of the error is represented.

[0149] Step 16.2: Control the exoskeleton according to the control signals.

[0150] Controlling the exoskeleton based on control signals is common knowledge to those skilled in the art, and will not be elaborated upon here.

[0151] As can be seen from the above steps, the exoskeleton variable admittance control method provided in this application collects joint angles and human-computer interaction forces at multiple historical moments, constructs a human-computer interaction force prediction model based on this data, calculates the difference between the predicted human-computer interaction force value and the pre-set expected human-computer interaction force value, and adjusts the parameters of the admittance control model to obtain a variable admittance control model. Finally, based on the expected joint angle output by the variable admittance control model, a position controller is used to control the exoskeleton. In particular, by calculating the difference between the predicted and expected human-computer interaction force values ​​to adjust the parameters of the admittance control model, the admittance parameters can be controlled and adjusted in real time, making them more closely match actual movement needs and improving the accuracy of exoskeleton control.

[0152] The exoskeleton variable admittance control method provided in this application will be illustrated below with reference to specific embodiments.

[0153] like Figure 3 As shown, this exoskeleton variable admittance control method first constructs a human-computer interaction force prediction model based on the joint angles and human-computer interaction forces collected at multiple historical moments, and obtains the predicted human-computer interaction force value at the time to be measured (e.g., ...). Figure 3 In step 31), the difference between the predicted human-computer interaction force value and the pre-set expected human-computer interaction force value is calculated (e.g., ...). Figure 3 In step 32), an admittance control model for controlling the exoskeleton is constructed, and the parameters of the admittance control model are adjusted according to the difference to obtain a variable admittance control model (e.g., Figure 3 In step 33), the desired joint angle of the exoskeleton at the time of test is obtained based on the variable admittance control model (e.g., ...). Figure 3 In step 34), finally, based on the desired joint angle, the exoskeleton is controlled using a position controller (e.g., ...). Figure 3 (Step 35)

[0154] The exoskeleton variable admittance control device provided in this application is described below by way of example.

[0155] like Figure 4 As shown, the exoskeleton variable admittance control device 400 includes:

[0156] The acquisition module 401 is used to acquire training data; the training data includes joint angles at multiple historical moments and human-computer interaction forces at multiple historical moments.

[0157] The human-computer interaction force prediction module 402 is used to construct a human-computer interaction force prediction model based on the joint angles and human-computer interaction forces at multiple historical moments, and obtain the predicted value of the human-computer interaction force at the moment to be measured.

[0158] The interaction force difference calculation module 403 is used to calculate the difference between the predicted value of human-computer interaction force and the pre-set expected value of human-computer interaction force.

[0159] The variable admittance control module 404 is used to construct an admittance control model for controlling the exoskeleton, and adjust the parameters of the admittance control model according to the difference to obtain the variable admittance control model; wherein, the parameters include stiffness coefficient and damping coefficient.

[0160] The desired joint angle module 405 is used to obtain the desired joint angle of the exoskeleton at the time to be measured based on the variable admittance control model.

[0161] The exoskeleton control module 406 is used to control the exoskeleton using a position controller according to the desired joint angle.

[0162] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0163] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0164] like Figure 5 As shown, embodiments of this application provide a terminal device, such as... Figure 5As shown, the terminal device D10 of this embodiment includes: at least one processor D100 ( Figure 5 The diagram shows only one processor, a memory D101, and a computer program D102 stored in the memory D101 and executable on the at least one processor D100, wherein the processor D100 executes the computer program D102 to implement the steps in any of the above method embodiments.

[0165] Specifically, when the processor D100 executes the computer program D102, it collects joint angles and human-computer interaction forces at multiple historical moments, constructs a human-computer interaction force prediction model, calculates the difference between the predicted human-computer interaction force value and the pre-set expected human-computer interaction force value, adjusts the parameters of the admittance control model to obtain a variable admittance control model, and finally controls the exoskeleton using a position controller based on the expected joint angle output by the variable admittance control model. By calculating the difference between the predicted and expected human-computer interaction force values ​​to adjust the parameters of the admittance control model, the admittance parameters can be adjusted in real time, making them more closely match actual movement needs and improving the accuracy of exoskeleton control.

[0166] The processor D100 can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0167] In some embodiments, the memory D101 may be an internal storage unit of the terminal device D10, such as a hard disk or memory of the terminal device D10. In other embodiments, the memory D101 may be an external storage device of the terminal device D10, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the terminal device D10. Furthermore, the memory D101 may include both internal and external storage units of the terminal device D10. The memory D101 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory D101 can also be used to temporarily store data that has been output or will be output.

[0168] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0169] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0170] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to the exoskeleton variable admittance control device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0171] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0172] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0173] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0174] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0175] The exoskeleton variable admittance control method provided in this application has the following advantages:

[0176] (1) A TS fuzzy model was designed. This model uses a spatial kernel function to reduce fuzzy rules, so that the fuzzy model can be better applied.

[0177] (2) A prediction method for human-computer interaction force was established. Since the interaction force between the wearer and the exoskeleton robot has periodic characteristics in a gait cycle, a TS fuzzy model was established by taking the joint angle and the human-computer interaction force at the previous moment as input to complete the prediction of human-computer interaction force.

[0178] (3) The walking is divided into different stages. Different expected auxiliary forces of the exoskeleton are given by the expert experience knowledge. The force is compared with the interaction force predicted by the fuzzy model at the next moment. The admittance parameters are adjusted in real time through this difference, which improves the problem of the global invariance of the admittance control parameters and enhances the comfort of human-computer interaction and the smoothness of movement.

[0179] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principles described in this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for controlling variable admittance of an exoskeleton, characterized in that, include: Collect training data; The training data includes joint angles at multiple historical moments and human-computer interaction forces at multiple historical moments; Based on the joint angles and human-computer interaction forces at the multiple historical moments, a human-computer interaction force prediction model is constructed to obtain the predicted value of the human-computer interaction force at the moment to be measured. Calculate the difference between the predicted human-computer interaction force value and the preset expected human-computer interaction force value; An admittance control model for controlling the exoskeleton is constructed, and the parameters of the admittance control model are adjusted according to the difference to obtain a variable admittance control model; wherein the parameters include stiffness coefficient and damping coefficient. Based on the variable admittance control model, the expected joint angle of the exoskeleton at the time of test is obtained; The exoskeleton is controlled using a position controller based on the desired joint angle. The expression for the admittance control model is as follows: in, Represents the coefficient of inertia. Indicates the damping coefficient. Represents the elastic coefficient. This represents the expected value of the human-computer interaction force. This represents the predicted value of the human-computer interaction force. The second derivative represents the desired location of the admittance output. This represents the second derivative at a given desired position. The step of adjusting the parameters of the admittance control model based on the difference includes: Through calculation formula The adjusted stiffness coefficient is obtained. ;in, This represents the difference. , Indicates the first Expected value of human-computer interaction force at each test time. Indicates the first Predicted human-computer interaction force at each test time. This indicates the stiffness adjustment parameter; Through calculation formula The adjusted damping coefficient is obtained. , Indicates the damping adjustment parameter; The expression for the variable admittance control model is as follows: in, Indicates the first The second derivative of the admittance output at the desired position at each time point to be measured. Indicates the first The second derivative of a given desired position at a given time point.

2. The exoskeleton variable admittance control method according to claim 1, characterized in that, The expression for the human-computer interaction force prediction model is as follows: in, Indicates the first The predicted value of human-computer interaction force at each test time. Indicates the first The Lagrange multipliers corresponding to the training data at each historical moment. , This represents the total number of historical moments. Indicates the first The membership function of a fuzzy rule. Indicates the input fuzzy model number. The variable values ​​at each historical moment, including the joint angle and the human-computer interaction force at that historical moment. , Indicates the first The product of the membership functions of fuzzy sets in a fuzzy rule. , Indicates the number of fuzzy rules. Indicates the first The first fuzzy rule Membership function of a multidimensional fuzzy set Represents the spatial kernel function, , Indicates the first The consequent parameters of a fuzzy rule.

3. The exoskeleton variable admittance control method according to claim 1, characterized in that, The step of obtaining the expected joint angle of the exoskeleton at the time of measurement based on the variable admittance control model includes: Through calculation formula The desired joint angle is obtained. ;in, Indicates the first The expected joint angle at each test moment.

4. The exoskeleton variable admittance control method according to claim 3, characterized in that, The position controller is a PD controller; The step of controlling the exoskeleton using a position controller based on the desired joint angle includes: Through calculation formula The control signal output by the position controller is obtained. ;in, Represents differential gain. Indicates proportional gain. This represents the error between the desired joint angle and the actual joint angle of the exoskeleton acquired at the time of measurement. The first derivative of the error is represented by the first derivative. The exoskeleton is controlled according to the control signal.

5. An exoskeleton variable admittance control device, characterized in that, include: The acquisition module is used to collect training data; The training data includes joint angles at multiple historical moments and human-computer interaction forces at multiple historical moments; The human-computer interaction force prediction module is used to construct a human-computer interaction force prediction model based on the joint angles and human-computer interaction forces at the multiple historical moments, and to obtain the predicted value of the human-computer interaction force at the moment to be measured. The interaction force difference calculation module is used to calculate the difference between the predicted human-computer interaction force value and the pre-set expected human-computer interaction force value. A variable admittance control module is used to construct an admittance control model for controlling the exoskeleton, and to adjust the parameters of the admittance control model according to the difference to obtain a variable admittance control model; wherein, the parameters include stiffness coefficient and damping coefficient; The desired joint angle module is used to obtain the desired joint angle of the exoskeleton at the time to be measured based on the variable admittance control model. An exoskeleton control module is used to control the exoskeleton using a position controller according to the desired joint angle; The expression for the admittance control model is as follows: in, Represents the coefficient of inertia. Indicates the damping coefficient. Represents the elastic coefficient. This represents the expected value of the human-computer interaction force. This represents the predicted value of the human-computer interaction force. The second derivative represents the desired location of the admittance output. This represents the second derivative at a given desired position. The step of adjusting the parameters of the admittance control model based on the difference includes: Through calculation formula The adjusted stiffness coefficient is obtained. ;in, This represents the difference. , Indicates the first Expected value of human-computer interaction force at each test time. Indicates the first Predicted human-computer interaction force at each test time. This indicates the stiffness adjustment parameter; Through calculation formula The adjusted damping coefficient is obtained. , Indicates the damping adjustment parameter; The expression for the variable admittance control model is as follows: in, Indicates the first The second derivative of the admittance output at the desired position at each time point to be measured. Indicates the first The second derivative of a given desired position at a given time point.

6. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the exoskeleton variable admittance control method as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the exoskeleton variable admittance control method as described in any one of claims 1 to 4.

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