Self-adaptive control method and system for wearing hip joint booster in real time and storage medium

Abstract

The invention discloses a self-adaptive control method and system for wearing a hip joint booster in real time and a storage medium, and relates to the technical field of self-adaptive control of hip joint boosters. The self-adaptive control method for wearing the hip joint booster in real time comprises the following steps: acquiring movement data; obtaining a feedback error compensation value; generating a motion data curve graph; and obtaining a feedback error reduction amount. According to the method, the motion data of the next moment is predicted through the obtained movement data to obtain the feedback error compensation value, then the self-adaptive learning parameters are dynamically adjusted according to the obtained feedback error compensation value to generate the motion data curve graph, and finally the convergence condition of the feedback error compensation value is monitored in real time to obtain the feedback error reduction amount. The effect of improving the self-adaptive control accuracy in the power assisting process of the real-time wearing hip joint booster is achieved, and the problem that in the prior art, the self-adaptive control accuracy is low in the power assisting process of the real-time wearing hip joint booster is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of adaptive control of hip joint boosters, and in particular to a method, system and storage medium for adaptive control of a real-time wearable hip joint booster. Background Art

[0002] In recent years, wearable technology and sensor technology have made great progress. Various high-precision and high-sensitivity sensors are widely used to detect the real-time motion state of the human body, such as angle sensors, acceleration sensors, gyroscopes, etc. These sensors can obtain key information such as the motion angle and acceleration of the hip joint in real time, providing reliable data support for adaptive control methods. Among them, the hip joint booster or hip joint exoskeleton robot is a robot used for lower limb walking assistance and power assistance, thereby reducing human metabolism.

[0003] The existing technology detects the pulse signal of the current human hip joint through an encoder to obtain the movement angle position, and obtains the power assistance data based on the feedback of the encoder and the output of the gait planning module. The motor power assistance movement is controlled according to the obtained power assistance data and the predefined gait trajectory, thereby realizing precise control of the power assistance movement.

[0004] For example, the invention patent with announcement number: CN115741637B announces a hybrid control method for hip joint exoskeleton lifting and walking assistance, including: using the angle and angular velocity sensors on the left and right sides of the hip joint exoskeleton to obtain the angles and angular velocities of the left and right hip joints, and determining the first linkage state variable x and the second linkage state variable y based on the angles of the left and right hip joints; determining the wearer's movement mode based on the first linkage state variable x and the second linkage state variable y; when the wearer performs walking or lifting movements, the power assistance controller controls the exoskeleton to enter the corresponding left and right hip joint assistance states, and according to the angular velocities of the left and right hip joints, the power assistance controller applies auxiliary force to the left and right hip joints.

[0005] For example, the invention patent with announcement number CN112548993B announces a lasso-driven hip joint series elastic power-assisted exoskeleton robot, including: a waist connecting mechanism and a leg connecting mechanism, leg connecting mechanisms are arranged on both sides of the waist connecting mechanism, series elastic modules at the motor are arranged on both sides of the waist connecting mechanism, and a series elastic module at the hip joint is arranged on the top of the leg connecting mechanism, wherein the series elastic module at the hip joint and the series elastic module at the motor are connected and transmitted through a lasso transmission mechanism, and the series elastic module at the hip joint, the series elastic module at the motor and the lasso transmission mechanism provide control and power through a driving and sensing system.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the existing technology, the human hip joint can only passively accept the drive of the power-assisting robot, and cannot couple the movement of the human lower limb hip joint and the power-assisting delay well. There is a lack of recognition of the movement state and intention of the human lower limb hip joint, which will lack flexibility at the moments of gait changing and walking and stopping. There is a problem of low adaptive control accuracy during the real-time power-assisting process of wearing a hip joint booster. Summary of the invention

[0008] The embodiments of the present application solve the problem of low accuracy of adaptive control during the assisting process of a real-time wearable hip joint booster in the prior art by providing a real-time wearable hip joint booster adaptive control method, system and storage medium, thereby improving the accuracy of adaptive control during the assisting process of a real-time wearable hip joint booster.

[0009] The embodiment of the present application provides a real-time wearable hip joint booster adaptive control method, comprising the following steps: step 1, detecting the motion data of the human body's double leg lifting at the current moment through the exoskeleton booster encoder and performing low-pass filtering, and acquiring movement data according to the motion state of the booster motor hip joint, the motion data including the angular position and angular velocity of the booster motor hip joint, and the movement data including gait frequency, gait phase and gait amplitude; step 2, predicting the motion data at the next moment according to the acquired movement data to obtain the motion data prediction value, and acquiring an adaptive oscillator according to the motion data at the current moment and the motion data prediction value at the next moment. feedback error compensation value, the motion data prediction value includes an angular position prediction value and an angular velocity prediction value, and the feedback error compensation value is used as an adjustment amount of an adaptive learning parameter; step three, dynamically adjusting the adaptive learning parameter according to the obtained feedback error compensation value, and inputting the dynamically adjusted adaptive learning parameter into the controller to generate a motion data curve graph, and the motion data curve graph is used to reflect the change of the feedback error compensation value; step four, real-time monitoring the convergence of the feedback error compensation value in the motion data curve graph at the current moment to obtain the feedback error reduction, and the feedback error reduction is used to evaluate the power assist of the power assist motor hip joint.

[0010] Furthermore, the exoskeleton booster encoder detects the motion data of the human body's double-leg lifting at the current moment and performs low-pass filtering, and then also includes updating the sliding data window at the sliding moment according to the motion data after low-pass filtering; the sliding data window is used to store the motion data after low-pass filtering and update it in real time; the specific process of updating the sliding data window at the sliding moment is as follows: obtaining the initial power assist value and output power assist value of the power assist motor hip joint at the current moment and the next moment, the next moment is the total duration of the sliding moment and the current moment, the initial power assist value is the product of the gravity compensation coefficient, the adaptive power assist coefficient at the current moment and the power assist motor phase sine value, the output power assist value is the product of the gravity compensation coefficient, the adaptive power assist coefficient at the next moment and the power assist motor phase sine value; the output power assist value and the initial power assist value are differenced to obtain the real-time torque compensation value, and the sliding data window at the sliding moment is updated in real time according to the acquired real-time torque compensation value, the real-time torque compensation value is used to evaluate the torque balance effect of the power assist motor hip joint at the sliding moment of the sliding data window, and the sliding moment is the duration of the sliding data window sliding forward.

[0011] Furthermore, the real-time torque compensation value is obtained, and then the irregular motion detection value of the power-assisted motor hip joint during irregular motion is obtained according to the obtained real-time torque compensation value; the irregular motion includes non-periodic motion and non-continuous motion; the irregular motion detection value is the sum of the non-periodic state detection value and the motion mutation detection value; the non-periodic state detection value is used to reflect the stability of the power-assisted motor hip joint during irregular motion; the motion mutation detection value is used to reflect the change of gait amplitude of the power-assisted motor hip joint during irregular motion.

[0012] Furthermore, the irregular motion detection value is obtained by the following method: E1, judging whether the non-periodic state detection constant in the power-assisted motor hip joint is not greater than the preset non-periodic state detection constant in the real-time wearable hip joint booster; if so, multiplying the non-periodic state detection constant, the torque attenuation coefficient of the power-assisted motor and the square operation result of the attenuation time constant to obtain the non-periodic state detection value; otherwise, the irregular motion detection value is not calculated, and the torque attenuation coefficient is used to reflect the rate of attenuation of the real-time torque of the power-assisted motor, and the attenuation time constant is used to ensure that the power-assisted motor hip joint quickly releases the real-time torque; E2, subtracting the gait amplitude of the power-assisted motor hip joint at the current moment from the gait amplitude at the previous moment The value operation is performed to obtain the motion mutation detection value, and it is determined whether the obtained motion mutation detection value is not less than the preset motion mutation detection constant in the real-time wearable hip joint booster. If so, the obtained non-periodic state detection value is added to the motion mutation detection value to obtain the irregular motion detection value, otherwise the irregular motion detection value is re-obtained; the specific steps of re-obtaining the irregular motion detection value are: adjusting the gait amplitude of the power-assisted motor hip joint and re-obtaining the motion mutation detection value, until the obtained motion mutation detection value is not less than the preset motion mutation detection constant in the real-time wearable hip joint booster, and the obtained non-periodic state detection value and the re-obtained motion mutation detection value are added to obtain the irregular motion detection value.

[0013] Furthermore, the movement data is obtained according to the motion state of the power-assisted motor hip joint, and then the movement path evaluation value is obtained according to the obtained movement data and the reference movement data; the reference movement data includes a reference gait frequency, a reference gait phase and a reference gait amplitude; the movement path evaluation value is used to evaluate the matching degree between the movement path of the power-assisted motor hip joint at the current moment and the reference movement path; the movement path evaluation value is obtained by the following method: A1, judging whether the angular position of the power-assisted motor hip joint at the current moment is at the reference angular position, if so, directly executing A2, otherwise adjusting the angular position to the reference angular position and then executing A2; A2, judging the angular velocity of the power-assisted motor hip joint at the current moment Whether the degree is within the angular velocity threshold range, if so, execute A3 directly, otherwise adjust the angular velocity to the angular velocity threshold range and then execute A3; A3, obtain the gait evaluation value of the power-assisted motor hip joint at the current moment, and obtain the moving path evaluation value in combination with the logarithmic operation result of the gait evaluation value; the gait evaluation value is the sum of the first gait evaluation value, the second gait evaluation value and the third gait evaluation value; the first gait evaluation value is the ratio of the absolute value of the difference between the gait frequency and the reference gait frequency to the reference gait frequency; the second gait evaluation value is the ratio of the absolute value of the difference between the gait phase and the reference gait phase to the reference gait phase; the third gait evaluation value is the ratio of the absolute value of the difference between the gait amplitude and the reference gait amplitude to the reference gait amplitude.

[0014] Furthermore, the specific limiting expression of the moving path evaluation value is:

[0015]

[0016]

[0017]

[0018]

[0019] Where t is the number of the current moment, t=1,2,...,T, T is the total number of the current moment, e is a natural constant, GU t Y1 represents the estimated value of the moving path of the power assist motor hip joint at the current time t, t represents the first step evaluation value of the power assist motor hip joint at the current time t, Y2 t represents the second gait evaluation value of the power assist motor hip joint at the current time t, Y3 t represents the third gait evaluation value of the power assist motor hip joint at the current time t, B1 t represents the gait frequency of the power assist motor hip joint at the current time t, B10 represents the reference gait frequency, B2 t represents the gait phase of the power assist motor hip joint at the current time t, B20 represents the reference gait phase, B3 t represents the gait amplitude of the power assist motor hip joint at the current time t, B30 represents the reference gait amplitude, W t represents the angular position of the power assist motor hip joint at the current time t, W0 represents the reference angular position, SU t represents the angular velocity of the power assist motor hip joint at the current time t, and ΔSU0 represents the angular velocity threshold range.

[0020] An embodiment of the present application provides a system that applies the real-time wearable hip joint booster adaptive control method as described above, including: a sensor, a controller and a boost motor; the boost motor is composed of a motor driver and a DC brushless motor; the sensor is used to obtain motion data and movement data and store boost information, and transmit the boost information to the controller as a control instruction; the boost information includes a feedback error compensation value, a feedback error reduction amount, an irregular motion detection value and a movement path evaluation value; the controller is used to send the control instruction in the sensor to the boost motor; the motor driver is used to convert the control instruction into a boost signal of the boost motor; and the DC brushless motor is used to drive the boost motor hip joint movement according to the boost signal.

[0021] An embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable medium stores a computer program, and wherein the computer program is executed by a processor to implement the real-time wearable hip joint booster adaptive control method.

[0022] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0023] 1. The motion data of the next moment is predicted by the acquired movement data to obtain the feedback error compensation value, and then the adaptive learning parameters are dynamically adjusted according to the acquired feedback error compensation value to generate a motion data curve graph, and finally the convergence of the feedback error compensation value is monitored in real time to obtain the feedback error reduction amount, thereby achieving more accurate acquisition of the feedback error reduction amount in the adaptive control process, and then achieving improved adaptive control accuracy in the real-time wearable hip joint booster assisting process, effectively solving the problem of low adaptive control accuracy in the real-time wearable hip joint booster assisting process in the prior art.

[0024] 2. By obtaining the initial assistance value and output assistance value of the power-assisted motor hip joint at the current moment and the next moment, and then performing difference calculation between the output assistance value and the initial assistance value to obtain the real-time torque compensation value, the sliding data window at the sliding moment is updated in real time according to the obtained real-time torque compensation value, thereby achieving improved accuracy in obtaining the real-time torque compensation value, and further achieving improved accuracy and efficiency in real-time updating of the sliding data window.

[0025] 3. By judging whether the angular position of the power-assisted motor hip joint at the current moment is at the reference angular position, and judging whether the angular velocity of the power-assisted motor hip joint at the current moment is within the angular velocity threshold range, when the angular position is the reference angular position and the angular velocity is within the angular velocity threshold range, the moving path evaluation value is obtained by combining the logarithmic operation result of the obtained gait evaluation value, thereby achieving more accurate acquisition of the moving path evaluation value, and then achieving more accurate judgment of the moving path of the power-assisted motor hip joint. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A flow chart of a real-time wearable hip joint booster adaptive control method provided in an embodiment of the present application;

[0027] Figure 2 A flowchart for obtaining the adaptive control amount of the power assist motor provided in an embodiment of the present application;

[0028] Figure 3 A schematic diagram of a real-time wearable hip joint booster provided in an embodiment of the present application;

[0029] Figure 4A schematic diagram of the collaborative operation of the controllers provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The embodiments of the present application solve the problem of low accuracy of adaptive control in the assisting process of real-time wearable hip joint booster in the prior art by providing a real-time wearable hip joint booster adaptive control method, system and storage medium. The motion data of the human body's leg lifting at the current moment is detected by the exoskeleton booster encoder and low-pass filtered. At the same time, movement data is obtained according to the motion state of the hip joint of the booster motor. Then, the motion data at the next moment is predicted according to the acquired movement data to obtain the motion data prediction value. At the same time, the feedback error compensation value of the adaptive oscillator is obtained according to the motion data at the current moment and the motion data prediction value at the next moment. Then, the adaptive learning parameters are dynamically adjusted according to the acquired feedback error compensation value. At the same time, the dynamically adjusted adaptive learning parameters are input into the controller to generate a motion data curve graph. Finally, the convergence of the feedback error compensation value in the motion data curve graph at the current moment is monitored in real time to obtain the feedback error reduction amount, thereby improving the accuracy of adaptive control in the assisting process of real-time wearable hip joint booster.

[0031] The technical solution in the embodiment of the present application is to solve the problem of low accuracy of adaptive control during the assisting process of the above-mentioned real-time wearable hip joint booster. The overall idea is as follows:

[0032] By detecting the motion data of the human body's leg lifting at the current moment and performing low-pass filtering to obtain movement data, and at the same time predicting the motion data at the next moment based on the obtained movement data to obtain a feedback error compensation value, then dynamically adjusting the adaptive learning parameters based on the obtained feedback error compensation value to generate a motion data curve graph, and finally monitoring the convergence of the feedback error compensation value in real time to obtain the feedback error reduction, the effect of improving the accuracy of adaptive control during the real-time wearable hip booster assistance process is achieved.

[0033] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0034] like Figure 1As shown, it is a flow chart of a real-time wearable hip joint booster adaptive control method provided by an embodiment of the present application, the method comprising the following steps: step one, detecting the motion data of the human body's double-leg lifting at the current moment through the exoskeleton booster encoder and performing low-pass filtering, and at the same time using the motion data after low-pass filtering as sample data for adaptive learning and obtaining movement data according to the motion state of the booster motor hip joint, the motion data including the angular position and angular velocity of the booster motor hip joint, the low-pass filtering is used to filter out high-frequency noise, and the movement data including gait frequency, gait phase and gait amplitude; step two, predicting the motion data at the next moment according to the acquired movement data to obtain the motion data prediction value, and at the same time using the motion data at the current moment and the motion data at the next moment The motion data prediction value obtains the feedback error compensation value of the adaptive oscillator, the motion data prediction value includes the angular position prediction value and the angular velocity prediction value, the adaptive oscillator is used for periodic motion and frequency detection of nonlinear systems, and the feedback error compensation value is used as the adjustment amount of the adaptive learning parameter; step three, dynamically adjust the adaptive learning parameters according to the obtained feedback error compensation value, and input the dynamically adjusted adaptive learning parameters into the controller to generate a motion data curve graph, and the motion data curve graph is used to reflect the change of the feedback error compensation value; step four, real-time monitoring the convergence of the feedback error compensation value in the motion data curve graph at the current moment to obtain the feedback error reduction, and the feedback error reduction is used to evaluate the power assist of the power assist motor hip joint.

[0035] In this embodiment, the exoskeleton booster encoder is a sensor for measuring and recording human motion data. In the exoskeleton booster encoder, when a human body wears an exoskeleton booster to walk, the exoskeleton booster encoder is usually used to detect in real time the angular position and angular velocity of the human legs during the leg lifting movement, reflecting the real-time state of human motion; low-pass filtering is a signal processing technology used to remove high-frequency noise in the signal. In the exoskeleton booster adaptive control method, low-pass filtering is used to process the motion data detected by the exoskeleton booster encoder. Through low-pass filtering, high-frequency noise caused by sensor noise can be filtered out, thereby improving the accuracy and reliability of motion data and movement data.

[0036] like Figure 2As shown, this is a flow chart for obtaining the adaptive control amount of the power-assisting motor provided in an embodiment of the present application. By real-time collection and analysis of human motion data and combining with an adaptive learning algorithm, this embodiment can accurately predict and adjust the output of the power-assisting motor, provide the hip joint with assistance that adapts to the needs of human motion and reduces the burden on human muscles. Secondly, the method can dynamically adjust the adaptive learning parameters. While quantitatively evaluating the power-assisting effect of the power-assisting motor on the hip joint, it can also adjust the power-assisting strategy in real time according to the user's gait changes to ensure that a stable power-assisting effect can be provided under different walking speeds, terrains and gait modes, thereby improving the accuracy of adaptive control in the process of real-time wearable hip joint booster assistance.

[0037] Furthermore, the motion data of the human body's double-leg lifting at the current moment is detected by the exoskeleton booster encoder and low-pass filtered, and then the sliding data window at the sliding moment is updated according to the motion data after the low-pass filtering; the sliding data window is used to store the motion data after the low-pass filtering and update it in real time; the specific process of updating the sliding data window at the sliding moment is as follows: the initial assist value and the output assist value of the assist motor hip joint at the current moment and the next moment are obtained, the next moment is the total duration of the sliding moment and the current moment, the initial assist value is the gravity compensation coefficient, the adaptive assist coefficient at the current moment and the assist motor The output assist value is the product of the gravity compensation coefficient, the adaptive assist coefficient and the sine value of the assist motor phase at the next moment. The gravity compensation coefficient is used to balance the torque generated by gravity, and the adaptive assist coefficient is used to adjust the output torque of the assist motor hip joint. The output assist value and the initial assist value are differenced to obtain the real-time torque compensation value, and the sliding data window at the sliding moment is updated in real time according to the obtained real-time torque compensation value. The real-time torque compensation value is used to evaluate the torque balancing effect of the assist motor hip joint at the sliding moment of the sliding data window. The sliding moment is the duration of the sliding data window sliding forward.

[0038] Specifically, the specific limiting expression of the real-time torque compensation value is:

[0039] ΔLI Δt =LI t+Δt -LI t ;

[0040]

[0041]

[0042] G0=m*g*s*sinθ;

[0043] Where t is the number of the current moment, t = 1, 2, ..., T, T is the total number of the current moment, Δt is the number of the sliding moment, Δt = Δ1, Δ2, ..., ΔT, ΔT is the total number of sliding moments, t+Δt is the number of the next moment, t+Δt = 1+Δ1, 2+Δ2, ..., T+ΔT, T+ΔT is the total number of the next moment, ΔLI Δt It represents the real-time torque compensation value of the power assist motor hip joint at the sliding moment of Δt, LI t+Δt Indicates the output power value of the power assist motor hip joint at the next moment t+Δt, LI t represents the initial assist value of the power assist motor hip joint at the current time t, G0 represents the gravity compensation coefficient of the power assist motor hip joint, K t+Δt It represents the adaptive power coefficient of the power-assisting motor hip joint at the next moment t+Δt, represents the phase of the booster motor at the next moment t+Δt, K t represents the adaptive power coefficient of the power-assisting motor hip joint at the current time t, represents the phase of the power-assisting motor at the current time t, m represents the mass of the power-assisting motor, g represents the acceleration of gravity, s represents the distance from the center of mass of the hip joint of the power-assisting motor to the center of rotation, and θ represents the angle between the hip joint of the power-assisting motor and the horizontal plane.

[0044] In this embodiment, the adaptive power assist coefficient is the derivative of the sliding window error adaptive rate coefficient; the sliding data window is a dynamic data structure for storing motion data after low-pass filtering, which can update the stored data in real time to reflect the latest state of human movement; at each current moment, the initial power assist value (i.e., the power assist state before this moment) and the output power assist value (i.e., the power assist value output by the controller at this moment) of the power assist motor hip joint are recorded, and the gravity compensation coefficient, the adaptive power assist coefficient and the power assist motor phase are directly obtained through the sensor, and then the initial power assist value at the current moment and the output power assist value at the next moment are obtained, and the power assist motor mass, gravitational acceleration, the distance from the center of mass of the power assist motor hip joint to the center of rotation, and the angle between the power assist motor hip joint and the horizontal plane are all directly obtained from the database.

[0045] It should be understood that the current moment refers to the moment when the encoder detects and records the human leg lifting motion data in real time, and the next moment refers to the moment immediately following the current moment, which is used to predict and update the sliding data window. The sliding moment is a relative concept used to describe the state of the sliding data window at different time points. At each current moment, the sliding data window will be updated according to the latest motion data, thereby forming a dynamic and continuously updated data structure, that is: the low-pass filtered motion data of the current moment is added to the sliding data window, and the earliest data in the window (that is, the historical data in the window) is removed to keep the window size unchanged. In this way, the sliding data window contains the latest motion data.

[0046] In order to more clearly understand the relationship between the sliding time of the sliding data window and the current time motion data, initial power value, and output power value, it is assumed that the size of the sliding data window is 5 current time points (that is, the sliding data window slides forward 5 sliding moments), and the interval between each time point is 1 second. The change statistics of the data at specific time points are shown in Table 1:

[0047] Table 1 Statistics of changes in data at specific time points

[0048]

[0049]

[0050] As shown in Table 1, at time T5, the data in the sliding data window is the motion data after low-pass filtering from time T1 to time T5. At this time, the initial power value of the power-assisted electric hip joint is 11 (N·m) at time T4, and the output power value is 13 (N·m) obtained according to the adaptive learning algorithm at time T5. As time goes by, the sliding data window will be continuously updated to reflect the latest motion data. At the same time, the initial power value and output power value of the power-assisted motor hip joint will also be continuously updated to achieve real-time response and adaptive control of human motion, thereby improving the accuracy of adaptive control during the power-assisted process of the real-time wearable hip joint booster.

[0051] Furthermore, a real-time torque compensation value is obtained, and then the irregular motion detection value of the power-assisted motor hip joint during irregular motion is obtained according to the obtained real-time torque compensation value; irregular motion includes non-periodic motion and non-continuous motion; the irregular motion detection value is the sum of the non-periodic state detection value and the motion mutation detection value; the non-periodic state detection value is used to reflect the stability of the power-assisted motor hip joint during irregular motion; the motion mutation detection value is used to reflect the change in gait amplitude of the power-assisted motor hip joint during irregular motion.

[0052] Among them, the irregular motion detection value is obtained by the following method: E1, judging whether the non-periodic state detection constant in the power-assisted motor hip joint is not greater than the preset non-periodic state detection constant in the real-time wearable hip joint booster. If so, the non-periodic state detection value is obtained by multiplying the non-periodic state detection constant, the torque attenuation coefficient of the power-assisted motor and the square operation result of the attenuation time constant. Otherwise, the irregular motion detection value is not calculated. The torque attenuation coefficient is used to reflect the rate of attenuation of the real-time torque of the power-assisted motor, and the attenuation time constant is used to ensure that the power-assisted motor hip joint quickly releases the real-time torque; E2, performing a difference operation on the gait amplitude of the power-assisted motor hip joint at the current moment and the gait amplitude at the previous moment. To obtain a motion mutation detection value, determine whether the obtained motion mutation detection value is not less than a preset motion mutation detection constant in the real-time wearable hip joint booster, if so, add the obtained non-periodic state detection value to the motion mutation detection value to obtain an irregular motion detection value, otherwise re-obtain the irregular motion detection value; the specific steps for re-obtaining the irregular motion detection value are: adjust the gait amplitude of the power-assisted motor hip joint and re-obtain the motion mutation detection value until the obtained motion mutation detection value is not less than the preset motion mutation detection constant in the real-time wearable hip joint booster, and add the obtained non-periodic state detection value and the re-obtained motion mutation detection value to obtain the irregular motion detection value.

[0053] In this embodiment, the position corresponding to the angle between the power-assisted motor hip joint and the horizontal plane is the angular position of the power-assisted motor hip joint; the non-periodic state detection value is used to reflect the stability of the power-assisted motor hip joint during non-periodic movement (such as standing, stopping). When the human body performs non-periodic movement, the output torque, angular position and angular velocity of the power-assisted motor hip joint will change, and these changes can be quantified by the non-periodic state detection value; the motion mutation detection value is used to reflect the change of gait amplitude of the power-assisted motor hip joint during non-continuous movement (such as jumping). When the human body performs non-continuous movement, the gait amplitude increases, and these changes can be captured by the motion mutation detection value.

[0054] When the irregular motion detection value exceeds the irregular motion detection threshold, it can be considered that the power-assisted motor hip joint is in an unstable state. At this time, corresponding measures need to be taken to adjust or intervene. For example, when the non-periodic state detection value increases, it may indicate that the power-assisted motor hip joint is insufficiently stable when coping with non-periodic motion. At this time, it is necessary to increase the non-periodic state detection constant to improve stability; when the motion mutation detection value increases, it may indicate that the gait amplitude increases, and the output torque of the power-assisted motor needs to be increased to adapt to this change. Among them, the irregular motion detection threshold represents the range corresponding to the maximum and minimum values ​​of the historical irregular motion detection values ​​of the power-assisted motor hip joint in the preset database.

[0055] In summary, by obtaining real-time torque compensation values ​​and calculating irregular motion detection values, real-time monitoring and evaluation of the power-assisted motor hip joint during irregular motion can be achieved, which helps to improve the performance of the power-assisted device and the comfort of the user, ensuring that the power-assisted device can provide stable and effective assistance in various complex motion scenarios, and achieving more accurate detection of the motion state of the human body during irregular motion.

[0056] Specifically, the specific limiting expression of the irregular motion detection value is:

[0057] GUI t =M1 t +M2 t , m1 t ≤m10 and M2 t ≥M20;

[0058]

[0059]

[0060] Where t is the number of the current moment, t = 1, 2, ..., T, T is the total number of the current moment, Δt is the number of the sliding moment, Δt = Δ1, Δ2, ..., ΔT, ΔT is the total number of the sliding moment, t-Δt is the number of the previous moment, t-Δt = 1-Δ1, 2-Δ2, ..., T-ΔT, T-ΔT is the total number of the previous moment, GUI t M1 represents the irregular motion detection value of the power assist motor hip joint at the current time t, t M2 represents the non-periodic state detection value of the power assist motor hip joint at the current time t, t m1 represents the motion mutation detection value of the power assist motor hip joint at the current time t, t represents the non-periodic state detection constant of the power assist motor hip joint at the current time t, m10 represents the preset non-periodic state detection constant, M20 represents the preset motion mutation detection constant, H t represents the torque attenuation coefficient of the power assist motor hip joint at the current time t, Q t represents the decay time constant of the power assist motor hip joint at the current time t, B3 t represents the gait amplitude of the power assist motor hip joint at the current time t, B3 t-Δt represents the gait amplitude of the power-assisted motor hip joint at the previous moment t-Δt, and B3″0 represents the initial gait amplitude before the power-assisted motor hip joint performs irregular motion.

[0061] It should be noted that the irregular motion detection value is calculated only when the non-periodic state detection constant is not greater than the preset non-periodic state detection constant and the motion mutation detection value is not less than the preset motion mutation detection constant, otherwise it is not calculated, wherein the non-periodic state detection constant is obtained from the preset database, the preset non-periodic state detection constant and the preset motion mutation detection constant are set by the preset personnel, and the result of summing and averaging the historical non-periodic state detection constant and the historical motion mutation detection constant in the preset database is expressed, and the torque attenuation coefficient, attenuation time constant, initial gait amplitude and gait amplitude are directly obtained through the sensor.

[0062] Furthermore, movement data is obtained according to the motion state of the power-assisted motor hip joint, and then the movement path evaluation value is obtained according to the obtained movement data and the reference movement data; the reference movement data includes a reference gait frequency, a reference gait phase and a reference gait amplitude; the movement path evaluation value is used to evaluate the degree of matching between the movement path of the power-assisted motor hip joint at the current moment and the reference movement path; the movement path evaluation value is obtained by the following method: A1, judging whether the angular position of the power-assisted motor hip joint at the current moment is at the reference angular position, if so, directly executing A2, otherwise adjusting the angular position to the reference angular position and then executing A2; A2, judging the angular velocity of the power-assisted motor hip joint at the current moment Whether the degree is within the angular velocity threshold range, if so, execute A3 directly, otherwise adjust the angular velocity to the angular velocity threshold range and then execute A3; A3, obtain the gait evaluation value of the power-assisted motor hip joint at the current moment, and obtain the moving path evaluation value by combining the logarithmic operation result of the gait evaluation value; the gait evaluation value is the sum of the first gait evaluation value, the second gait evaluation value and the third gait evaluation value; the first gait evaluation value is the ratio of the absolute value of the difference between the gait frequency and the reference gait frequency to the reference gait frequency; the second gait evaluation value is the ratio of the absolute value of the difference between the gait phase and the reference gait phase to the reference gait phase; the third gait evaluation value is the ratio of the absolute value of the difference between the gait amplitude and the reference gait amplitude to the reference gait amplitude.

[0063] In this embodiment, the reference movement path and the reference angle position are set by a preset personnel, and the angular velocity threshold range represents the range corresponding to the maximum and minimum values ​​of the historical angular velocity of the real-time wearing of the hip joint booster in the preset database; the movement data (including gait frequency, gait phase and gait amplitude) is directly acquired through the sensor, and the reference movement data (including reference gait frequency, reference gait phase and reference gait amplitude) is represented by the sum and average of the historical movement data (including historical gait frequency, historical gait phase and historical gait amplitude) in the historical time period in the preset database.

[0064] Compared with the prior art, this embodiment sets an angular velocity threshold range and monitors the changes in angular velocity in real time to ensure that the power-assist motor responds quickly after receiving an adjustment instruction. Secondly, by acquiring movement data in real time and comparing it with preset reference movement data, an evaluation value of the movement path is calculated. This evaluation value can accurately reflect the degree of match between the actual movement path of the hip joint of the power-assist motor at the current moment and the reference movement path, so that the real-time wearable hip joint booster can better meet the needs of users and achieve more precise control of the movement path of the power-assist motor.

[0065] Furthermore, the specific restricted expression of the moving path evaluation value is:

[0066] W t =W0and SU t ∈ΔSU0;

[0067]

[0068]

[0069]

[0070] Where t is the number of the current moment, t = 1, 2, ..., T, T is the total number of the current moment, e is a natural constant, GU t Y1 represents the estimated value of the moving path of the power assist motor hip joint at the current time t, t represents the first step evaluation value of the power assist motor hip joint at the current time t, Y2 t represents the second gait evaluation value of the power assist motor hip joint at the current time t, Y3 t represents the third gait evaluation value of the power assist motor hip joint at the current time t, B1 t represents the gait frequency of the power assist motor hip joint at the current time t, B10 represents the reference gait frequency, B2 t represents the gait phase of the power assist motor hip joint at the current time t, B20 represents the reference gait phase, B3 t represents the gait amplitude of the power assist motor hip joint at the current time t, B30 represents the reference gait amplitude, W t represents the angular position of the power assist motor hip joint at the current time t, W0 represents the reference angular position, SU t represents the angular velocity of the power assist motor hip joint at the current time t, and ΔSU0 represents the angular velocity threshold range.

[0071] In this embodiment, when B1 t =B10,B2 t =B20 and B3 t=B30, the corresponding first gait evaluation value, second gait evaluation value and third gait evaluation value are all 0 (i.e., moving path evaluation value), indicating that the actual moving path of the power-assisted motor hip joint at the current moment completely matches the reference moving path; it should be noted that the moving path evaluation value is calculated only when the angular position of the power-assisted motor hip joint is the reference angular position and the angular velocity is within the angular velocity threshold range, otherwise it is not calculated. At this time, it is necessary to adjust the angular position to the reference angular position, and adjust the angular velocity to within the angular velocity threshold range before calculating the moving evaluation value.

[0072] It should be understood that the movement path evaluation value increases with the increase of the first gait evaluation value, the second gait evaluation value and the third gait evaluation value, and the gait frequency in the first gait evaluation value, the gait phase in the second gait evaluation value and the gait amplitude in the third gait evaluation value influence each other. Within the same movement distance and time period, when the gait amplitude increases, the corresponding gait frequency will decrease, and the corresponding gait phase will increase. By considering the mutual influence between the gait frequency, gait phase and gait amplitude, it helps to improve the accuracy of the movement path evaluation of the hip joint of the power-assisted motor, thereby achieving the improvement of the accuracy of the adaptive control in the process of real-time wearing of the hip joint booster, and effectively solving the problem of low accuracy of the adaptive control in the process of real-time wearing of the hip joint booster in the prior art.

[0073] Furthermore, a moving path evaluation value is obtained based on the acquired moving data and reference moving data, and then the adaptive learning parameters are dynamically adjusted based on the acquired moving path evaluation value and the feedback error compensation value; the adaptive learning parameters include an adaptive assist coefficient and an adaptive assist response time; the adaptive assist response time is the time interval between the assist motor receiving the adjustment instruction of the adaptive learning parameters and actually providing assist; the feedback error compensation value includes a first predicted deviation value and a second predicted deviation value; the first predicted deviation value is the distance between the angular position predicted value and the angular position; the second predicted deviation value is the difference between the angular velocity predicted value and the angular velocity.

[0074] In this embodiment, when the moving path evaluation value increases and the feedback error compensation value decreases, it is necessary to increase the adaptive assist coefficient and reduce the adaptive assist response time to improve the timeliness of the assist. Otherwise, it is necessary to reduce the adaptive assist coefficient and the adaptive assist response time to avoid excessive assist, thereby improving the stability of the assist. When the moving path evaluation value increases and the feedback error compensation values ​​are all 0, there is no need to adjust the adaptive learning parameters, thereby improving the accuracy and stability of the assist effect of the power-assisted motor on the hip joint.

[0075] Furthermore, the motion data graph includes a first motion data graph and a second motion data graph; the first motion data graph is used to reflect the change of the first predicted deviation value at the current moment; the second motion data graph is used to reflect the change of the second predicted deviation value at the current moment; the specific process of real-time monitoring the convergence of the feedback error compensation value in the motion data graph at the current moment to obtain the feedback error reduction amount is: L1, real-time monitoring of the convergence of the first predicted deviation value on the motion data graph, and judging whether the first predicted deviation value at the current moment is less than the reference distance reduction amount, if so, it indicates that the adaptive control method of the controller is effective and it is considered that the first predicted deviation value at this moment is converged, otherwise it is considered that it is not converged and the angle position prediction value is re-predicted until the first moment. A predicted deviation value is smaller than a reference distance reduction; L2, real-time monitoring of the convergence of the second predicted deviation value on the motion data curve graph, and judging whether the second predicted deviation value at the current moment is smaller than the reference angular velocity reduction. If so, it indicates that the adaptive control method of the controller is effective and the second predicted deviation value at this moment is considered to be converged; otherwise, it is considered not to converge and the angular velocity predicted value is re-predicted until the second predicted deviation value is smaller than the reference angular velocity reduction; L3, performing a difference operation on the first predicted deviation value and the reference distance reduction to obtain a first feedback error reduction, and at the same time performing a difference operation on the second predicted deviation value and the reference angular velocity reduction to obtain a second feedback error reduction, and obtaining the feedback error reduction according to the addition result of the first feedback error reduction and the second feedback error reduction.

[0076] In this embodiment, the reference distance reduction is represented by the sum and average of the historical distance reductions within the historical time period in the preset database, and the reference angular velocity reduction is represented by the sum and average of the historical angular velocity reductions within the historical time period in the preset database; by observing the changes in the feedback error reduction on the motion data curve in real time, the adaptive control effect of the controller can be understood, which improves the user's satisfaction and trust in the real-time wearable hip booster. At the same time, the reference distance reduction and the reference angular velocity reduction are set based on the average value of historical data, which enhances the adaptability and stability of the system, thereby realizing effective evaluation of the adaptive control method of the controller.

[0077] The application provided in the embodiment of the present application is a system for a real-time wearable hip joint booster adaptive control method, including: a sensor, a controller and a boost motor; the boost motor is composed of a motor driver and a DC brushless motor; the sensor is used to obtain motion data and movement data and store the boost information, and transmit the boost information to the controller as a control instruction; the boost information includes a feedback error compensation value, a feedback error reduction amount, an irregular motion detection value and a movement path evaluation value; the controller is used to send the control instruction in the sensor to the boost motor; the motor driver is used to convert the control instruction into a boost signal of the boost motor; the DC brushless motor is used to drive the boost motor hip joint movement according to the boost signal.

[0078] In this embodiment, the controller (using CAN / 485 to communicate with the motor driver and the DC brushless motor) exists in the control module of the real-time wearable hip joint booster, and is used to output control instructions corresponding to the booster motor (installed on the left and right hip joints of the human body on the booster motor in the real-time wearable hip joint booster) (used to adjust the working state of the booster motor and accurately control the DC brushless motor); the motor driver is a bridge connecting the controller and the DC brushless motor, and is used to convert the control instructions of the controller into booster signals that can be understood by the booster motor, thereby driving the booster motor hip joint to work; the DC brushless motor is the power source of the real-time wearable hip joint booster, and is used to convert electrical energy into mechanical energy, thereby driving the booster motor hip joint movement.

[0079] Specifically, the specific control process of the controller is as follows: (1) The controller receives and processes the power assistance information input from the sensor, and predicts the real-time torque compensation value of the power assistance motor hip joint of the left and right legs of the human body at the next moment through an adaptive power assistance algorithm, and sends the real-time torque compensation value as a control instruction to the motor driver and the DC brushless motor; (2) The motor driver receives the control instruction and controls the torque of the DC brushless motor according to the real-time angular position and angular velocity of the power assistance motor hip joint, and follows the movement of the human hip joint in real time to achieve walking assistance.

[0080] Specifically, Figure 3 As shown, it is a schematic diagram of a real-time wearable hip joint booster provided by an embodiment of the present application, such as Figure 4As shown, it is a schematic diagram of the collaborative work of the controller provided in an embodiment of the present application. Most of the human hip joint exoskeleton robots are mainly based on rigid structures. Compared with the prior art, this embodiment effectively realizes the collaboration between the controller (installed on the back waist of the human body on the real-time wearable hip joint booster) (worn on the human body's waist), the motor driver and the DC brushless motor based on the rigid structure and the assist information input by the sensor, which helps to improve the accuracy of adaptive and flexible hip joint assist torque control, so that it can be perfectly coupled to the human hip joint leg and assist the human lower limb movement, reduce the human body's energy metabolism, and realize the improvement of the adaptive control accuracy in the assisting process of the real-time wearable hip joint booster, which effectively solves the problem of adaptive control accuracy in the assisting process of the real-time wearable hip joint booster in the prior art.

[0081] An embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and is characterized in that the computer program is executed by a processor to implement a real-time wearable hip joint booster adaptive control method.

[0082] In this embodiment, when the above-mentioned computer program is executed by a processor (such as a CPU in a computer, a microprocessor, an embedded system, etc.), the storage medium can accurately adjust the adaptive learning parameters and generate corresponding motion data curves through the assist information input by the sensor to reflect the control effect, and then evaluate the assist effect of the power-assisting motor hip joint by real-time monitoring of the motion data curves. Among them, the storage medium can store computer programs for a long time and stably without being affected by environmental factors, and the stored computer programs can be correctly read and executed by the computer system, further ensuring that the exoskeleton booster can provide stable and effective assist, thereby achieving an improvement in the accuracy of adaptive control in the process of real-time wearing of the hip joint booster, and effectively solving the problem of low adaptive control accuracy in the process of real-time wearing of the hip joint booster in the prior art.

[0083] To summarize, the embodiment of the present application predicts the motion data of the next moment through the acquired mobile data to obtain a feedback error compensation value, and then dynamically adjusts the adaptive learning parameters according to the acquired feedback error compensation value to generate a motion data curve graph, and finally monitors the convergence of the feedback error compensation value in real time to obtain the feedback error reduction amount, thereby achieving more accurate acquisition of the feedback error reduction amount in the adaptive control process, and then achieving improved adaptive control accuracy in the real-time wearable hip joint booster assisting process, effectively solving the problem of low adaptive control accuracy in the real-time wearable hip joint booster assisting process in the prior art.

[0084] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0085] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0086] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0087] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0088] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0089] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A real-time wearable hip joint booster adaptive control method, characterized in that: The following steps are involved: Step 1: Detect the motion data of the human body's double-leg lifting at the current moment through the exoskeleton booster encoder and perform low-pass filtering, and obtain movement data according to the motion state of the booster motor hip joint, the motion data includes the angular position and angular velocity of the booster motor hip joint, and the movement data includes gait frequency, gait phase and gait amplitude; Step 2: predicting the motion data at the next moment according to the acquired movement data to obtain a motion data prediction value, and obtaining a feedback error compensation value of the adaptive oscillator according to the motion data at the current moment and the motion data prediction value at the next moment, wherein the motion data prediction value includes an angular position prediction value and an angular velocity prediction value, and the feedback error compensation value is used as an adjustment amount of the adaptive learning parameter; Step 3, dynamically adjusting the adaptive learning parameters according to the obtained feedback error compensation value, and inputting the dynamically adjusted adaptive learning parameters into the controller to generate a motion data curve graph, wherein the motion data curve graph is used to reflect the change of the feedback error compensation value; Step 4: monitor the convergence of the feedback error compensation value in the motion data curve at the current moment in real time to obtain the feedback error reduction amount, and the feedback error reduction amount is used to evaluate the power assist condition of the power assist motor hip joint.

2. The real-time wearable hip joint booster adaptive control method as claimed in claim 1, characterized in that: The exoskeleton booster encoder is used to detect the motion data of the human body's legs lifting at the current moment and perform low-pass filtering, and then the sliding data window at the sliding moment is updated according to the motion data after the low-pass filtering; The sliding data window is used to store the low-pass filtered motion data and update it in real time; The specific process of updating the sliding data window at the sliding moment is as follows: Obtain the initial power assist value and output power assist value of the power assist motor hip joint at the current moment and the next moment, wherein the next moment is the total duration of the sliding moment and the current moment, the initial power assist value is the product of the gravity compensation coefficient, the adaptive power assist coefficient at the current moment and the sine value of the power assist motor phase, and the output power assist value is the product of the gravity compensation coefficient, the adaptive power assist coefficient at the next moment and the sine value of the power assist motor phase; The output assist value and the initial assist value are differentially calculated to obtain a real-time torque compensation value. At the same time, the sliding data window at the sliding moment is updated in real time according to the obtained real-time torque compensation value. The real-time torque compensation value is used to evaluate the torque balance effect of the assist motor hip joint at the sliding moment of the sliding data window. The sliding moment is the duration of the sliding data window sliding forward.

3. The real-time wearable hip joint booster adaptive control method as claimed in claim 2, characterized in that: The step of obtaining the real-time torque compensation value further includes obtaining an irregular motion detection value of the power assist motor hip joint during the irregular motion process according to the obtained real-time torque compensation value; The irregular motion includes non-periodic motion and non-continuous motion; The irregular motion detection value is the sum of the non-periodic state detection value and the motion mutation detection value; The non-periodic state detection value is used to reflect the stability of the power-assisted motor hip joint during irregular motion; The motion mutation detection value is used to reflect the change of the gait amplitude during the irregular motion of the power-assisted motor hip joint.

4. The real-time wearable hip joint booster adaptive control method as claimed in claim 3, characterized in that: The irregular motion detection value is obtained by the following method: E1, determine whether the non-periodic state detection constant in the power-assisted motor hip joint is not greater than the preset non-periodic state detection constant in the real-time wearable hip joint booster. If so, perform multiplication operation on the non-periodic state detection constant, the torque attenuation coefficient of the power-assisted motor and the square operation result of the attenuation time constant to obtain the non-periodic state detection value. Otherwise, do not calculate the irregular motion detection value. The torque attenuation coefficient is used to reflect the rate of attenuation of the real-time torque of the power-assisted motor, and the attenuation time constant is used to ensure that the power-assisted motor hip joint quickly releases the real-time torque. E2, performing a difference operation on the gait amplitude of the power-assisting motor hip joint at the current moment and the gait amplitude at the previous moment to obtain a motion mutation detection value, and judging whether the obtained motion mutation detection value is not less than a preset motion mutation detection constant in the real-time wearable hip joint booster, if so, performing an addition operation on the obtained non-periodic state detection value and the motion mutation detection value to obtain an irregular motion detection value, otherwise re-obtaining the irregular motion detection value; The specific steps of re-obtaining the irregular motion detection value are: The gait amplitude of the power-assisted motor hip joint is adjusted and the motion mutation detection value is reacquired until the acquired motion mutation detection value is not less than the preset motion mutation detection constant in the real-time wearable hip joint booster, and the acquired non-periodic state detection value and the reacquired motion mutation detection value are added to obtain the irregular motion detection value.

5. The real-time wearable hip joint booster adaptive control method as claimed in claim 1, characterized in that: The step of acquiring movement data according to the motion state of the power-assisted motor hip joint further includes acquiring a movement path evaluation value according to the acquired movement data and reference movement data; The reference movement data includes a reference gait frequency, a reference gait phase and a reference gait amplitude; The movement path evaluation value is used to evaluate the matching degree between the movement path of the power assist motor hip joint at the current moment and the reference movement path; The moving path evaluation value is obtained by the following method: A1, determine whether the angle position of the power assist motor hip joint at the current moment is at the reference angle position, if so, directly execute A2, otherwise adjust the angle position to the reference angle position and then execute A2; A2, determining whether the angular velocity of the power assist motor hip joint at the current moment is within the angular velocity threshold range, if so, directly executing A3, otherwise adjusting the angular velocity to the angular velocity threshold range and then executing A3; A3, obtaining the gait evaluation value of the power assist motor hip joint at the current moment, and obtaining the moving path evaluation value by combining the logarithmic operation result of the gait evaluation value; The gait evaluation value is the sum of the first gait evaluation value, the second gait evaluation value and the third gait evaluation value; The first gait evaluation value is the ratio of the absolute value of the difference between the gait frequency and the reference gait frequency to the reference gait frequency; The second gait evaluation value is a ratio of an absolute value of a difference between a gait phase and a reference gait phase to the reference gait phase; The third gait evaluation value is a ratio of an absolute value of a difference between the gait amplitude and the reference gait amplitude to the reference gait amplitude.

6. The real-time wearable hip joint booster adaptive control method as claimed in claim 5, characterized in that: The specific limiting expression of the moving path evaluation value is: Where t is the number of the current moment, t=1,2,...,T, T is the total number of the current moment, e is a natural constant, GU t Y1 represents the estimated value of the moving path of the power assist motor hip joint at the current time t, t represents the first step evaluation value of the power assist motor hip joint at the current time t, Y2 t represents the second gait evaluation value of the power assist motor hip joint at the current time t, Y3 t represents the third gait evaluation value of the power assist motor hip joint at the current time t, B1 t represents the gait frequency of the power assist motor hip joint at the current time t, B10 represents the reference gait frequency, B2 t represents the gait phase of the power assist motor hip joint at the current time t, B20 represents the reference gait phase, B3 t represents the gait amplitude of the power assist motor hip joint at the current time t, B30 represents the reference gait amplitude, W t represents the angular position of the power assist motor hip joint at the current time t, W0 represents the reference angular position, SU t represents the angular velocity of the power assist motor hip joint at the current time t, and ΔSU0 represents the angular velocity threshold range.

7. The real-time wearable hip joint booster adaptive control method as claimed in claim 5, characterized in that: The feedback error compensation value includes a first prediction deviation value and a second prediction deviation value; The first prediction deviation value is the distance between the angular position prediction value and the angular position; The second prediction deviation value is the difference between the angular velocity prediction value and the angular velocity; The step of obtaining a moving path evaluation value according to the obtained moving data and the reference moving data further includes dynamically adjusting the adaptive learning parameters according to the obtained moving path evaluation value and the feedback error compensation value; The adaptive learning parameters include an adaptive power-assistance coefficient and an adaptive power-assistance response duration; The adaptive power-assist response duration is the time interval between the power-assist motor receiving the adjustment instruction of the adaptive learning parameters and actually providing power-assist.

8. The real-time wearable hip joint booster adaptive control method as claimed in claim 1, characterized in that: The motion data graph includes a first motion data graph and a second motion data graph; The first motion data curve graph is used to reflect the change of the first prediction deviation value at the current moment; The second motion data curve graph is used to reflect the change of the second prediction deviation value at the current moment; The specific process of real-time monitoring the convergence of the feedback error compensation value in the motion data curve at the current moment to obtain the feedback error reduction amount is: L1, real-time monitoring of the convergence of the first predicted deviation value on the motion data curve graph, judging whether the first predicted deviation value at the current moment is less than the reference distance reduction amount, if so, it indicates that the adaptive control method of the controller is effective and it is considered that the first predicted deviation value at this moment is converged, otherwise the angle position prediction value is re-predicted until the first predicted deviation value is less than the reference distance reduction amount; L2, real-time monitoring of the convergence of the second predicted deviation value on the motion data curve graph, judging whether the second predicted deviation value at the current moment is less than the reference angular velocity reduction amount, if so, it indicates that the adaptive control method of the controller is effective and the second predicted deviation value at the moment is considered to be converged, otherwise the angular velocity prediction value is re-predicted until the second predicted deviation value is less than the reference angular velocity reduction amount; L3, the first predicted deviation value is subtracted from the reference distance reduction to obtain the first feedback error reduction, and the second predicted deviation value is subtracted from the reference angular velocity reduction to obtain the second feedback error reduction, and the feedback error reduction is obtained according to the addition result of the first feedback error reduction and the second feedback error reduction.

9. A system using the real-time wearable hip joint booster adaptive control method as claimed in any one of claims 1 to 8, characterized in that: include: Sensors, controllers and power assist motors; The booster motor is composed of a motor driver and a brushless DC motor; The sensor is used to obtain motion data and movement data and store power assistance information, and transmit the power assistance information to the controller as a control instruction; The power assistance information includes a feedback error compensation value, a feedback error reduction value, an irregular motion detection value, and a moving path evaluation value; The controller is used to send the control instructions in the sensor to the power-assisting motor; The motor driver is used to convert the control instruction into a power assist signal of the power assist motor; The brushless DC motor is used to drive the power assist motor hip joint movement according to the power assist signal.

10. A computer-readable storage medium storing a computer program, characterized in that: The computer program is executed by the processor as the real-time wearable hip booster adaptive control method as described in any one of claims 1-8.

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