An exoskeleton control method, device, terminal equipment and medium

Abstract

The application is suitable for the technical field of exoskeleton control, and provides an exoskeleton control method, device, terminal equipment and medium, which collects a first exoskeleton joint angle and a first motor output torque in an empty load state; according to the data collected in the empty load state, a first model parameter corresponding to a lower limb exoskeleton dynamics model is identified to obtain a first model parameter estimation value; a second exoskeleton joint angle, a second motor output torque and a subject joint angle in a load state are collected to calculate an actual interaction torque; a subject joint motion model is constructed to identify a second model parameter corresponding to the subject joint motion model to obtain a second model parameter estimation value; an exoskeleton state space equation is established; a control law is obtained; an adaptive law of an uncertain parameter in the control law is calculated; and the exoskeleton is controlled according to the control law and the adaptive law. The application can improve the accuracy of exoskeleton control.

Description

Technical Field

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

[0002] In recent years, various types of exoskeletons have been increasingly applied in the field of medical rehabilitation. These exoskeletons can help patients with hemiplegia and other mobility impairments to undergo rehabilitation training and regain their ability to walk.

[0003] Exoskeletons are wearable robots that aid in rehabilitation. Unlike traditional industrial robots, exoskeletons exhibit highly nonlinear motion patterns, primarily due to the intricate coupling between the patient and the exoskeleton and the strong nonlinear interactions between them. This complexity presents challenges to the precise control of exoskeletons.

[0004] To address these challenges, scholars have developed two main approaches.

[0005] The first category is model-based control methods, such as dynamic modeling, backstepping controllers, sliding mode controllers, and sensitivity amplification control. Some researchers, building upon dynamic modeling and identification, have introduced techniques such as neural network theory, robust control theory, iterative learning control theory, and disturbance observers to improve the stability and accuracy of trajectory tracking control. However, due to the highly nonlinear, highly coupled, and highly uncertain interactive characteristics of lower limb exoskeleton systems, dynamic modeling and identification of human-machine coupling are quite difficult. Human-machine interactions within exoskeleton systems often reduce tracking control accuracy and even affect system stability.

[0006] The second category of model-free control methods mainly includes intelligent adaptive control, active disturbance rejection control, data-driven adaptive model-free control, and model-free control based on algebraic estimation. However, it is worth noting that due to the limited system modeling capabilities of exoskeletons and the complexity of the interaction between the patient and the exoskeleton, the control accuracy of this method still falls short of the required standards. Summary of the Invention

[0007] This application provides an exoskeleton control method, device, terminal equipment, and medium, which can improve the accuracy of exoskeleton control.

[0008] In a first aspect, this application provides an exoskeleton control method, comprising:

[0009] The data collection included the first exoskeleton joint angle and the first motor output torque used to control the exoskeleton when the exoskeleton moved along a normal human gait trajectory under no-load conditions.

[0010] Based on the first joint angle and the first motor output torque, the first model parameters corresponding to the pre-constructed lower limb exoskeleton dynamic model are identified, and the estimated values ​​of the first model parameters are obtained.

[0011] Under load, the exoskeleton moves along a normal human gait trajectory, and the joint angles of the subject are collected. Based on the estimated values ​​of the first model parameters, the actual interaction torque is calculated.

[0012] A joint motion model of the subject is constructed, and based on the actual interaction torque, the subject's joint angle, and the pre-collected information of the subject's affected leg, the second model parameters corresponding to the subject's joint motion model are identified, and the estimated values ​​of the second model parameters are obtained.

[0013] Based on the lower limb exoskeleton dynamics model, the parameter estimates of the first model, and the parameter estimates of the second model, the state space equation of the exoskeleton is established.

[0014] Based on TS fuzzy theory, the desired joint angle is obtained, and the initial control law is obtained according to the desired joint angle and the exoskeleton state space equation.

[0015] Based on Lyapunov theory, the adaptive law for uncertain parameters in the initial control law is calculated;

[0016] The exoskeleton is controlled according to the initial control law and the adaptive law.

[0017] Optionally, the subject's joint angle includes the subject's knee joint angle. and subject's hip joint angle

[0018] The expression for the subject's joint motion model is as follows:

[0019]

[0020] Where, τ int This represents the interaction torque between the subject and the exoskeleton. This represents the subject's own inertial torque. This represents the subject's own gravitational torque. This represents the leg torque of the test subject. Leg torque is the torque generated by the force of the human leg itself. Let g represent the mass of the lower leg, and g represent the acceleration due to gravity. J represents the location of the center of mass of the lower leg. h This represents the virtual moment of inertia of the human leg. This represents the angular acceleration of the subject's knee joint. This indicates the maximum torque generated by the affected leg of the subject.

[0021] Optionally, the first model parameter estimates include estimates of the exoskeleton's inertia matrix. Coriolis matrix estimates of exoskeletons and the estimated gravity term of the exoskeleton

[0022] The second model parameter estimates include the predicted virtual rotational inertia of the human legs. Predicted maximum torque generated by the affected leg of the subject

[0023] The expression for the state-space equation of the exoskeleton is as follows:

[0024]

[0025] in, τ represents the angular velocity of the exoskeleton joints. act Indicates the motor output torque. The value represents the predicted interaction torque between the exoskeleton and the subject, where f represents the uncertain parameter. q represents the exoskeleton joint angle. This represents the angular acceleration of the exoskeleton joints. M(q) represents the actual value of the exoskeleton's inertia matrix. C(q) represents the actual value of the Coriolis matrix of the exoskeleton. This represents the angular acceleration of the subject's knee joint. Indicates the angular velocity of the subject's knee joint.

[0026] Optionally, based on TS fuzzy theory, the desired joint angle is obtained, and the initial control law is obtained according to the desired joint angle and the exoskeleton state space equation, including:

[0027] Establish fuzzy rules for joint angles; the fuzzy rules for joint angles are expressed as follows:

[0028] if x (1) is G p1 and x (2) is G p2 and...and x (m) is G pm

[0029] Then f(x) = M p x+N p p = 1, 2, ..., r

[0030] x (1) ,...,x(m) All represent joint angles, m represents the total number of joint angles collected, and G represents the total number of joint angles collected. p1 ,...,G pm Both represent fuzzy sets, r represents the number of clusters, and M represents the number of clusters. p N p Represents the fuzzy coefficient;

[0031] Based on the joint angle fuzzy rule, obtain the desired joint angle x. d ;

[0032] Through calculation formula

[0033]

[0034] Obtain the initial control law T act Where ε represents the constant velocity approach law, ε>0, and s represents the sliding surface equation. g represents gravitational acceleration, sgn(s) represents the sign function, k represents the exponential reaching law, k>0, and c represents the slope of the sliding surface, c>0.

[0035] Optionally, the formula for calculating the adaptive law is as follows: Where γ1 represents the regularization factor, and ξ1(x) represents the angle-related vector in the TS fuzzy model.

[0036] Secondly, this application provides an exoskeleton control device, comprising:

[0037] The no-load data acquisition module is used to collect the first exoskeleton joint angle and the first motor output torque for controlling the exoskeleton when the exoskeleton moves in a normal human gait trajectory under no-load conditions.

[0038] The first parameter identification module is used to identify the first model parameters corresponding to the pre-built lower limb exoskeleton dynamic model based on the first joint angle and the first motor output torque, and obtain the estimated values ​​of the first model parameters.

[0039] The load data acquisition module is used to collect the joint angles of the second exoskeleton, the output torque of the second motor, and the joint angles of the subject when the exoskeleton moves in a normal human gait trajectory under load, and to calculate the actual interaction torque based on the estimated values ​​of the first model parameters.

[0040] The second parameter estimation module is used to construct the subject's joint motion model and identify the second model parameters corresponding to the subject's joint motion model based on the actual interaction torque, the subject's joint angle and the pre-collected information of the subject's affected leg, and obtain the estimated values ​​of the second model parameters.

[0041] The state space equation module is used to establish the state space equation of the exoskeleton based on the lower limb exoskeleton dynamics model, the estimated values ​​of the first model parameters, and the estimated values ​​of the second model parameters.

[0042] The control law acquisition module is used to obtain the desired joint angle based on TS fuzzy theory, and to obtain the initial control law based on the desired joint angle and the exoskeleton state space equation.

[0043] The adaptive law acquisition module is used to calculate the adaptive law of the uncertain parameters in the initial control law based on Lyapunov theory.

[0044] The exoskeleton control module is used to control the exoskeleton according to the initial control law and the adaptive law.

[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, wherein the processor executes the computer program to implement the exoskeleton control method described above.

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

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

[0048] The exoskeleton control method provided in this application identifies model parameters in the lower limb exoskeleton dynamic model using data collected under unloaded conditions. This avoids the difficulties in model parameter identification and low model fitting caused by high human-machine coupling, thus improving control accuracy. Similarly, based on the identification results of model parameters in the lower limb exoskeleton dynamic model, the method scientifically models the subject's joint movements and identifies the corresponding model parameters of the subject's joint movement model according to the actual interaction torque, the subject's joint angle, and the information of the subject's affected leg. This reduces the impact of high human-machine coupling, decreases the number of unknowns, reduces the degree of nonlinearity, and improves the fitting degree of the subject's joint movement model, thereby improving control accuracy. Furthermore, by calculating the adaptive law of uncertain parameters in the control law, the control law adapts during exoskeleton control, improving tracking control accuracy and enhancing the stability 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 control method provided in an embodiment of this application;

[0052] Figure 2 This is a schematic diagram of the structure of an exoskeleton control device provided in an embodiment of this application;

[0053] Figure 3 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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]."

[0058] 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.

[0059] 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.

[0060] To address the issue of low control precision in traditional exoskeletons, this application provides an exoskeleton control method, device, terminal equipment, and medium. The method identifies model parameters in the lower limb exoskeleton dynamics model using data collected from the exoskeleton in an unloaded state. This avoids the difficulties in model parameter identification and low model fit caused by high human-machine coupling, thus improving control precision. Similarly, based on the identification results of model parameters in the lower limb exoskeleton dynamics model, and through scientific modeling of the subject's joint movements, and by identifying the corresponding model parameters of the subject's joint movement model according to the actual interaction torque, the subject's joint angles, and the information of the subject's affected leg, the influence of high human-machine coupling can be reduced, the number of unknowns can be decreased, the degree of nonlinearity can be reduced, and the fit of the subject's joint movement model can be improved, thereby increasing control precision. Furthermore, by calculating the adaptive law of uncertain parameters in the control law, the control law adapts during exoskeleton control, improving tracking control precision and enhancing the stability of exoskeleton control.

[0061] The exoskeleton control method, device, terminal equipment, and medium provided in this application are described in detail below.

[0062] like Figure 1 As shown, the exoskeleton control method provided in this application specifically includes the following steps:

[0063] Step 11: Collect the first exoskeleton joint angle and the first motor output torque used to control the exoskeleton when the exoskeleton moves along a normal human gait trajectory under no-load conditions.

[0064] For example, an exoskeleton using PID control (Proportional-Integral-Derivative Control, a common closed-loop control method consisting of proportional, integral, and derivative components) moves along a normal human gait trajectory. The control process is common knowledge and will not be elaborated here.

[0065] The joint angles of the exoskeleton can be acquired by sensors pre-installed on the exoskeleton, and the motor output torque is fed back in real time by the motor installed on the exoskeleton.

[0066] It should be noted that the above-mentioned normal human gait trajectory refers to the gait trajectory of a healthy person walking. Generally speaking, this gait trajectory includes a stance phase and a swing phase.

[0067] Specifically, the stance phase indicates that the body weight is borne on one foot, typically including: Initial Contact: the foot lands, initiating a new gait cycle; Loading Response: the body gradually presses down, preparing to support its weight; Mid Stance: the transition from this phase to the other foot; and Terminal Stance: the body's center of gravity gradually shifts forward to the other foot. The swing phase indicates that the body's center of gravity shifts to the other foot, typically including: Pre-Swing: the foot lifts off the ground, initiating the swing phase; Initial Swing: the leg begins to swing forward, taking a large step; Mid Swing: the leg continues to swing forward, and the knee begins to bend; and Terminal Swing: the leg continues to swing forward, preparing to land.

[0068] The aforementioned unloaded state refers to the state in which the exoskeleton is not worn by the subject.

[0069] In one embodiment of this application, joint angle data corresponding to normal human gait trajectory movement can be determined through preliminary sample experiments.

[0070] Step 12: Based on the first joint angle and the first motor output torque, identify the first model parameters corresponding to the pre-constructed lower limb exoskeleton dynamic model to obtain the estimated values ​​of the first model parameters.

[0071] Specifically, the expression for the lower limb exoskeleton dynamic model mentioned above is as follows:

[0072]

[0073] Where q1 represents the first exoskeleton joint angle collected. Indicates the angular velocity of the first exoskeleton joint. Represents the angular acceleration and angular velocity of the first exoskeleton joint. The angular acceleration of the first exoskeleton joint is obtained by differentiating the first exoskeleton joint angle q1. From the angular velocity of the first exoskeleton joint The relationship between the subject's joint angles, joint velocities, and joint accelerations is obtained by differentiation. Similarly, M represents the exoskeleton's inertia matrix, G represents the exoskeleton's Coriolis matrix, and Ggravity represents the exoskeleton's gravitational term. τ represents the output torque of the first motor. int It represents the human-computer interaction force.

[0074] In the embodiments of this application, the first joint angle q1 and the first motor output torque are obtained. Then, the least squares method can be used to identify M, C, and G respectively, and their corresponding estimated values ​​(first model parameter estimates) can be obtained:

[0075] The identification of M, C, and G using the above-mentioned least squares method is common knowledge in this field and will not be elaborated upon here.

[0076] Step 13: Under load, collect the joint angles of the second exoskeleton, the output torque of the second motor, and the joint angles of the subject when the exoskeleton moves along a normal human gait trajectory. Calculate the actual interaction torque based on the estimated values ​​of the first model parameters.

[0077] Specifically, the estimated values ​​of the first model parameters obtained in step 12 are substituted into the lower limb exoskeleton dynamic model, that is: Then, the collected second exoskeleton joint angle q2 and the output torque of the second motor are... By substituting the values, the actual interaction torque τ can be calculated. int .

[0078] Correspondingly, in the embodiments of this application, after identifying the above model parameters (M, C, G), if the interaction torque τ can be determined... int By making accurate predictions, the required motor output torque can be obtained. By designing appropriate control laws, the motor output torque of the exoskeleton can meet the requirements, thereby assisting hemiplegic or mobility-impaired patients in rehabilitation training.

[0079] The aforementioned load state refers to the state in which the exoskeleton is worn by the subject.

[0080] Step 14: Construct a joint motion model of the subject, and identify the second model parameters corresponding to the subject's joint motion model based on the actual interaction torque, the subject's joint angle, and the pre-collected information of the subject's affected leg, and obtain the estimated values ​​of the second model parameters.

[0081] In the embodiments of this application, the aforementioned subject joint angle includes the subject's knee joint angle. and subject's hip joint angle

[0082] Specifically, the expression for the subject's joint motion model is as follows:

[0083]

[0084] Where, τ int This represents the interaction torque between the subject and the exoskeleton. This represents the subject's own inertial torque. This represents the subject's own gravitational torque. This represents the leg torque of the test subject. Leg torque is the torque generated by the force of the human leg itself. Let g represent the mass of the lower leg, and g represent the acceleration due to gravity. J represents the location of the center of mass of the lower leg. h This represents the virtual moment of inertia of the human leg. This represents the angular acceleration of the subject's knee joint. This indicates the maximum torque generated by the affected leg of the subject.

[0085] For example, the actual interaction torque, the subject's joint angle, and pre-collected information on the subject's affected leg can be used. Substituting the data into the subject's joint motion model, we can obtain the estimated values ​​of the second model parameters.

[0086] Step 15: Based on the lower limb exoskeleton dynamic model, the estimated parameters of the first model, and the estimated parameters of the second model, establish the state-space equation of the exoskeleton.

[0087] Specifically, the expression for the exoskeleton state-space equation is as follows:

[0088]

[0089] in, τ represents the angular velocity of the exoskeleton joints. act Indicates the motor output torque. The value represents the predicted interaction torque between the exoskeleton and the subject, where f represents the uncertain parameter. q represents the exoskeleton joint angle. This represents the angular acceleration of the exoskeleton joints. M(q) represents the actual value of the exoskeleton's inertia matrix. C(q) represents the actual value of the Coriolis matrix of the exoskeleton. This represents the angular acceleration of the subject's knee joint. Indicates the angular velocity of the subject's knee joint.

[0090] In the above formula, Uncertainty caused by inaccurate parameters in the dynamic model. To address the uncertainty caused by inaccurate interaction torques, adaptive modeling is required to make the system more accurate and stable. See step 16 for details.

[0091] Step 16: Based on TS fuzzy theory, obtain the desired joint angle, and obtain the initial control law according to the desired joint angle and the exoskeleton state space equation.

[0092] The aforementioned TS fuzzy method is a control stability analysis method used to handle nonlinear systems.

[0093] Specifically, in step 16.1, establish fuzzy rules for joint angles;

[0094] The joint angle fuzzy rule is expressed as follows:

[0095] Rule p: if x (1) is G p1 and x (2) is G p2 and...and x (m) is G pm

[0096] Then f(x) = M p x + Np, p = 1, 2, ..., r

[0097] x (1) ,...,x (m) All represent joint angles, m represents the total number of joint angles collected, and G represents the total number of joint angles collected. p1 ,...,G pm Both represent fuzzy sets, r represents the number of clusters (number of fuzzy rules), and M represents the number of clusters. p N p This represents the fuzzy coefficient.

[0098] Step 16.2: Obtain the desired joint angle x according to the joint angle fuzzy rule. d .

[0099] It should be noted that the process of establishing fuzzy rules and obtaining the desired joint angle can be specifically referred to in the patent application "A method, device, terminal equipment and medium for controlling the variable admittance of an exoskeleton" (patent application number: 202310542596.7).

[0100] Step 16.3, using the calculation formula

[0101]

[0102] Obtain the initial control law T act Where ε represents the constant velocity approach law, ε>0, and s represents the sliding surface equation. g represents gravitational acceleration, sgn(s) represents the sign function, k represents the exponential reaching law, k>0, and c represents the slope of the sliding surface, c>0.

[0103] The process of obtaining the initial control law is illustrated below.

[0104] Specifically, based on the exoskeleton state-space equation and the desired joint angles, the error can be calculated as follows:

[0105] e = x1 - x d

[0106]

[0107] Therefore, the equation for the sliding surface is established: Substituting the exoskeleton state-space equation into the sliding surface equation, we get:

[0108]

[0109] Then the control quantity:

[0110] Using the exponential reaching law, we obtain:

[0111]

[0112] It is not difficult to see that:

[0113]

[0114] Substituting the approach law into the control variable expression yields the aforementioned control law.

[0115] Based on the above fuzzy model, the uncertain parameter f is modeled as follows:

[0116]

[0117] in, This represents a fuzzy model, which is an approximation of the unknown nonlinear d(t).

[0118] Each parameter in the model satisfies its corresponding condition:

[0119]

[0120] Where, ω p (x) represents the membership degree of x to the p-th fuzzy rule, which can be expressed by the radial basis function as:

[0121]

[0122] u p σ represents the center of the fuzzy set. p The variance of the fuzzy set can be obtained through clustering algorithms such as C-means.

[0123] Define variables μ1 = [UV], ξ1(x) = [ω(x)xω(x)] T U and V represent r-dimensional vectors, μ1 represents the adaptive fuzzy coefficient, and ξ1(x) represents a vector related to the joint angle.

[0124] Therefore, the above formula can be transformed into When μ1 takes its optimal value, it should satisfy the following condition: Let μ1 be the range of values. ε1 represents the approximation error of f(x), and

[0125] make but: This formula represents the error between the uncertain parameter and its theoretical optimal value. This error will affect the subsequent control stability. Therefore, it is necessary to obtain the adaptive law of the uncertain parameter according to the stability theorem. See step 17 for details.

[0126] Step 17: Based on Lyapunov theory, calculate the adaptive law of the uncertain parameters in the initial control law.

[0127] Lyapunov theory is used to determine whether a dynamical system will tend toward a stable state after a certain period of time.

[0128] Specifically, the Lyapunov function is defined as follows:

[0129]

[0130] We can obtain:

[0131]

[0132] When satisfied When, γ1 represents the regularization factor, and ξ1(x) represents the angle-related vector in the TS fuzzy model.

[0133] The above formula can be further transformed into:

[0134]

[0135] By Lyapunov's theorem, when V(x) ≥ 0 and The system is uniformly convergent and stable.

[0136] Combining sliding surface equations The adaptive law design is as follows

[0137] Step 18: Control the exoskeleton according to the initial control law and the adaptive law.

[0138] For example, when the subject's gait changes during exercise, the initial control law is adjusted through an adaptive law to dynamically adapt to the gait change, ensuring the accuracy of tracking control and assisting patients with hemiplegia or leg inconvenience in rehabilitation training.

[0139] As can be seen, the exoskeleton control method provided in this application identifies model parameters in the lower limb exoskeleton dynamic model by collecting data in the unloaded state of the exoskeleton. This avoids the problems of difficult model parameter identification and low model fitting caused by high human-machine coupling, thus improving control accuracy. Similarly, based on the identification results of model parameters in the lower limb exoskeleton dynamic model, by scientifically modeling the subject's joint motion and identifying the model parameters corresponding to the subject's joint motion model according to the actual interaction torque, the subject's joint angle, and the information of the subject's affected leg, the influence of high human-machine coupling can be reduced, the number of unknowns can be reduced, the degree of nonlinearity can be reduced, and the fitting degree of the subject's joint motion model can be improved, thereby improving control accuracy. In addition, by calculating the adaptive law of the uncertain parameters in the control law, the control law is made to change adaptively during exoskeleton control, which improves tracking control accuracy and enhances the stability of exoskeleton control.

[0140] The exoskeleton control device provided in this application is described below by way of example.

[0141] like Figure 2 As shown, the exoskeleton control device 200 includes:

[0142] The no-load data acquisition module 201 is used to acquire the first exoskeleton joint angle and the first motor output torque for controlling the exoskeleton when the exoskeleton moves in a normal human gait trajectory under no-load conditions.

[0143] The first parameter identification module 202 is used to identify the first model parameters corresponding to the pre-built lower limb exoskeleton dynamic model based on the first joint angle and the first motor output torque, and obtain the estimated value of the first model parameters.

[0144] The load data acquisition module 203 is used to acquire the joint angles of the second exoskeleton, the output torque of the second motor, and the joint angles of the subject when the exoskeleton moves in a normal human gait trajectory under load, and to calculate the actual interaction torque based on the estimated values ​​of the first model parameters.

[0145] The second parameter estimation module 204 is used to construct a subject joint motion model and, based on the actual interaction torque, the subject joint angle, and the pre-collected information of the subject's affected leg, identify the second model parameters corresponding to the subject joint motion model and obtain the estimated values ​​of the second model parameters.

[0146] The state space equation module 205 is used to establish the state space equation of the exoskeleton based on the lower limb exoskeleton dynamics model, the estimated values ​​of the first model parameters, and the estimated values ​​of the second model parameters.

[0147] The control law acquisition module 206 is used to acquire the desired joint angle based on TS fuzzy theory, and to acquire the initial control law based on the desired joint angle and the exoskeleton state space equation.

[0148] The adaptive law acquisition module 207 is used to calculate the adaptive law of the uncertain parameters in the initial control law based on Lyapunov theory.

[0149] The exoskeleton control module 208 is used to control the exoskeleton according to the initial control law and the adaptive law.

[0150] 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.

[0151] 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.

[0152] like Figure 3 As shown, embodiments of this application provide a terminal device, such as... Figure 3 As shown, the terminal device D10 of this embodiment includes: at least one processor D100 ( Figure 3 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.

[0153] Specifically, when the processor D100 executes the computer program D102, it collects the first exoskeleton joint angles and the first motor output torque used to control the exoskeleton when the exoskeleton moves along a normal human gait trajectory under no-load conditions. Based on the first joint angles and the first motor output torque, it identifies the first model parameters corresponding to the pre-constructed lower limb exoskeleton dynamics model to obtain estimated values ​​of the first model parameters. Under load conditions, it collects the second exoskeleton joint angles, the second motor output torque, and the subject's joint angles when the exoskeleton moves along a normal human gait trajectory. Based on the estimated values ​​of the first model parameters, it calculates the actual interaction torque and constructs the subject's joint... The motion model is used, and based on the actual interaction torque, the subject's joint angles, and pre-collected information on the affected leg of the subject, the second model parameters corresponding to the subject's joint motion model are identified to obtain the estimated values ​​of the second model parameters. Based on the lower limb exoskeleton dynamic model, the estimated values ​​of the first model parameters, and the estimated values ​​of the second model parameters, the exoskeleton state space equation is established. Based on TS fuzzy theory, the desired joint angle is obtained. Based on the desired joint angle and the exoskeleton state space equation, the initial control law is obtained. Based on Lyapunov theory, the adaptive law of the uncertain parameters in the initial control law is calculated. Finally, the exoskeleton is controlled according to the initial control law and the adaptive law. Specifically, by identifying model parameters in the lower limb exoskeleton dynamics model using data collected under unloaded conditions, the problem of difficult model parameter identification and low model fit caused by high human-machine coupling can be avoided, thus improving control accuracy. Similarly, based on the identification results of model parameters in the lower limb exoskeleton dynamics model, by scientifically modeling the subject's joint movements and identifying the corresponding model parameters of the subject's joint movement model according to the actual interaction torque, subject's joint angle, and information of the subject's affected leg, the influence of high human-machine coupling can be reduced, the number of unknowns can be reduced, the degree of nonlinearity can be reduced, and the fit of the subject's joint movement model can be improved, thereby improving control accuracy. In addition, by calculating the adaptive law of uncertain parameters in the control law, the control law can be adaptively changed during exoskeleton control, improving tracking control accuracy and enhancing the stability of exoskeleton control.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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 computer program code to the exoskeleton 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The exoskeleton control method provided in this application has the following advantages:

[0164] (1) A method for estimating human-computer interaction torque is proposed, which can model and estimate the interaction torque. This method analyzes the physical quantities related to human-computer interaction torque, establishes a prediction model of the interaction torque and these physical quantities, and substitutes the model into the exoskeleton system for dynamic analysis and control.

[0165] (2) The system state equation and sliding surface control equation based on the human-machine interaction torque were derived, and the system's stable control law was obtained. This method substitutes the human-machine interaction torque model into the exoskeleton's dynamic equation and state space, and establishes the sliding surface equation, based on which the system's stable control law is derived.

[0166] (3) An adaptive law for the human-machine interaction torque model was designed, which enables the parameters of the human-machine interaction model to change adaptively during the control process, thereby stabilizing the system. Based on Lyapunov's theorem, the adaptive law for the human-machine interaction torque was derived, enabling the system to stably tend towards the sliding surface equation.

[0167] 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. An exoskeleton control device, characterized in that, include: The no-load data acquisition module is used to acquire the first exoskeleton joint angle and the first motor output torque for controlling the exoskeleton when the exoskeleton moves in a normal human gait trajectory under no-load conditions. The first parameter identification module is used to identify the first model parameters corresponding to the pre-constructed lower limb exoskeleton dynamic model based on the first exoskeleton joint angle and the first motor output torque, and obtain the estimated value of the first model parameters. The load data acquisition module is used to collect the joint angles of the second exoskeleton, the output torque of the second motor, and the joint angles of the subject when the exoskeleton moves in a normal human gait trajectory under load, and to calculate the actual interaction torque based on the estimated values ​​of the first model parameters. The second parameter estimation module is used to construct a subject joint motion model and, based on the actual interaction torque, the subject joint angle, and the pre-collected information of the subject's affected leg, identify the second model parameters corresponding to the subject joint motion model and obtain the estimated values ​​of the second model parameters. The state space equation module is used to establish the state space equation of the exoskeleton based on the lower limb exoskeleton dynamic model, the first model parameter estimate, and the second model parameter estimate. The control law acquisition module is used to obtain the desired joint angle based on TS fuzzy theory, and to obtain the initial control law according to the desired joint angle and the exoskeleton state space equation. The adaptive law acquisition module is used to calculate the adaptive law of the uncertain parameters in the initial control law based on Lyapunov theory. An exoskeleton control module is used to control the exoskeleton according to the initial control law and the adaptive law; The subject's joint angles include the subject's knee joint angles. and subject's hip joint angle ; The expression for the subject's knee joint motion model is as follows: in, This represents the interaction torque between the subject and the exoskeleton. This represents the subject's own inertial torque. This represents the subject's own gravitational torque. This refers to the leg torque of the subject, which represents the torque generated by the force of the human leg itself. Indicates the mass of the lower leg. Represents gravitational acceleration. This indicates the location of the center of mass of the human lower leg. This represents the virtual moment of inertia of the human leg. This represents the angular acceleration of the subject's knee joint. This indicates the maximum torque generated by the affected leg of the subject; The first model parameter estimates include the exoskeleton's inertia matrix estimates. Coriolis matrix estimate of exoskeleton and the estimated gravity term of the exoskeleton ; The second model parameter estimate includes the predicted virtual rotational inertia of the human leg. The predicted maximum torque generated by the affected leg of the subject ; The expression for the exoskeleton state-space equation is as follows: in, , Indicates the angular velocity of the exoskeleton joints. Indicates the motor output torque. This represents the predicted interaction torque between the exoskeleton and the subject. Indicates the uncertain parameter, , , Indicates the angle of the exoskeleton joints. This represents the angular acceleration of the exoskeleton joints. , This represents the actual value of the exoskeleton's inertia matrix. , The actual values ​​of the Coriolis matrix representing the exoskeleton. , , This represents the angular acceleration of the subject's knee joint. Indicates the angular velocity of the subject's knee joint. , .

2. The exoskeleton control device according to claim 1, characterized in that, The method involves obtaining the desired joint angle based on TS fuzzy theory, and then obtaining the initial control law based on the desired joint angle and the exoskeleton state space equation, including: Establish fuzzy rules for joint angles; the fuzzy rules for joint angles are as follows: ， Both represent joint angles. This represents the total number of joint angles collected. Both represent fuzzy sets. Indicates the number of clusters. Represents the fuzzy coefficient; Based on the joint angle fuzzy rule, obtain the desired joint angle. ; Through calculation formula The initial control law is obtained. ;in, This represents the isokinetic approach law. , Represent the equation of the sliding surface. , Represents gravitational acceleration. Represents a symbolic function. This represents the exponential law of convergence. , Indicates the slope of the sliding surface. .

3. The exoskeleton control device according to claim 1, characterized in that, The formula for calculating the adaptive law is as follows: ;in, Represents the regularization factor. This represents the angle-related vector in the TS fuzzy model.

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