Real-time wearable hip joint assistor adaptive control method and system and storage medium

Abstract

The application discloses a real-time wearing hip joint assistor adaptive control method and system and a storage medium, relates to the technical field of hip joint assistor adaptive control, and discloses a real-time wearing hip joint assistor adaptive control method, which comprises the following steps: acquiring movement data; acquiring a feedback error compensation value; generating a motion data curve graph; and acquiring a feedback error reduction amount. The application predicts the motion data at the next moment by the acquired movement data to acquire the feedback error compensation value, then dynamically adjusts adaptive learning parameters according to the acquired feedback error compensation value to generate the motion data curve graph, and finally monitors the convergence of the feedback error compensation value in real time to acquire the feedback error reduction amount, so that the effect of improving the adaptive control accuracy in the hip joint assistor assisting process of the real-time wearing hip joint assistor is achieved, and the problem of low adaptive control accuracy in the hip joint assistor assisting process of the real-time wearing hip joint assistor in the prior art is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of adaptive control of hip joint assist devices, and in particular to a real-time adaptive control method and system for a hip joint assist device and a storage medium. BACKGROUND

[0002] In recent years, wearable technology and sensor technology have made great progress. Various high-precision and high-sensitivity sensors are widely used in real-time detection of human motion states, 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 assist device or hip joint exoskeleton robot is a robot used for lower limb walking assistance and 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 motion angle position, and obtains the assistance data according to the feedback of the encoder and the output of the gait planning module, controls the motor assistance movement according to the obtained assistance data and the predefined gait trajectory, and realizes precise control of the assistance movement.

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

[0005] For example, the invention patent with the announcement number: CN112548993B announces a lasso-driven hip joint series elastic assistance exoskeleton robot, which includes: a waist connecting mechanism and a leg connecting mechanism, the waist connecting mechanism is provided with the leg connecting mechanism on both sides, the waist connecting mechanism is provided with a motor place series elastic module on both sides, and the leg connecting mechanism is provided with a hip joint place series elastic module at the top, wherein the hip joint place series elastic module and the motor place series elastic module are connected and driven through a lasso transmission mechanism, and the hip joint place series elastic module, the motor place series elastic module and the lasso transmission mechanism are controlled and powered by a driving and sensing system.

[0006] However, in the process of implementing the technical scheme of the present application, the present application finds that the above-mentioned technology at least has the following technical problems:

[0007] In the prior art, the human hip joint can only be passively driven by a power-assisted robot, and the human lower limb hip joint movement and power delay cannot be well coupled, the human lower limb hip joint movement state and intention are not recognized, and there is a lack of flexibility during gait turning and walking and stopping, and there is a problem of low adaptive control accuracy during the power-assisted process of the real-time wearable hip joint power-assisted device. SUMMARY

[0008] The embodiments of the present application provide a real-time wearable hip joint power-assisted device adaptive control method, system and storage medium, solve the problem of low adaptive control accuracy during the power-assisted process of the real-time wearable hip joint power-assisted device in the prior art, and realize the improvement of adaptive control accuracy during the power-assisted process of the real-time wearable hip joint power-assisted device.

[0009] The embodiments of the present application provide a real-time wearable hip joint power-assisted device adaptive control method, which comprises the following steps: step one, detecting the movement data of the human body's double legs lifting at the current time through the exoskeleton power-assisted device encoder and performing low-pass filtering, and acquiring movement data according to the movement state of the power-assisted motor hip joint, the movement data comprising the angle position and angular velocity of the power-assisted motor hip joint, and the movement data comprising gait frequency, gait phase and gait amplitude; step two, predicting the movement data of the next time according to the acquired movement data to obtain a movement data prediction value, and acquiring a feedback error compensation value of an adaptive oscillator according to the movement data of the current time and the movement data prediction value of the next time, the movement data prediction value comprising an angle position prediction value and an angular velocity prediction value, and the feedback error compensation value being used as an adjustment amount of adaptive learning parameters; step three, dynamically adjusting the adaptive learning parameters according to the acquired feedback error compensation value, and inputting the dynamically adjusted adaptive learning parameters into the controller to generate a movement data curve graph, the movement data curve graph being used to reflect the change of the feedback error compensation value; step four, monitoring the convergence of the feedback error compensation value in the movement data curve graph at the current time to obtain a feedback error reduction amount, the feedback error reduction amount being used to evaluate the power-assisted situation of the power-assisted motor hip joint.

[0010] Further, the current time is detected by the exoskeleton assist encoder, and the motion data of the human body lifting the legs is detected and low-pass filtered, and then the sliding data window at the sliding time is updated according to the low-pass filtered motion data; the sliding data window is used to store the low-pass filtered motion data and update in real time; the specific process of updating the sliding data window at the sliding time is as follows: the initial assist value and the output assist value of the assist motor hip joint at the current time and the next time are obtained, the next time is the total time length of the sliding time and the current time, the initial assist value is the product of the gravity compensation coefficient, the adaptive assist coefficient at the current time and the assist motor phase sine value, and the output assist value is the product of the gravity compensation coefficient, the adaptive assist coefficient at the next time and the assist motor phase sine value; the output assist value and the initial assist value are difference operated to obtain the real-time torque compensation value, and the sliding data window at the sliding time 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 time of the sliding data window, and the sliding time is the time length of the sliding data window sliding forward.

[0011] Further, the real-time torque compensation value is obtained, and then the irregular motion detection value of the assist motor hip joint in the irregular motion process 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 assist motor hip joint in the irregular motion process; the motion mutation detection value is used to reflect the change of the gait amplitude of the assist motor hip joint in the irregular motion process.

[0012] Further, the irregular motion detection value is obtained by the following method: E1, judging whether the non-periodic state detection constant in the hip joint of the power-assisted motor is not greater than a preset non-periodic state detection constant in the real-time wearing hip joint power-assisted device, if yes, multiplying the non-periodic state detection constant, the torque decay coefficient of the power-assisted motor and the square operation result of the decay time constant to obtain the non-periodic state detection value, otherwise, not calculating the irregular motion detection value, the torque decay coefficient is used to reflect the rate of real-time torque decay of the power-assisted motor, and the decay time constant is used to ensure that the hip joint of the power-assisted motor releases the real-time torque rapidly; E2, performing difference operation on the gait amplitude of the hip joint of the power-assisted motor at the current time and the gait amplitude at the last time to obtain a motion mutation detection value, judging whether the obtained motion mutation detection value is not less than a preset motion mutation detection constant in the real-time wearing hip joint power-assisted device, if yes, performing addition operation on the obtained non-periodic state detection value and the motion mutation detection value to obtain the irregular motion detection value, otherwise, re-obtaining the irregular motion detection value; the specific steps of re-obtaining the irregular motion detection value are: adjusting the gait amplitude of the hip joint of the power-assisted motor 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 wearing hip joint power-assisted device, and performing addition operation on the obtained non-periodic state detection value and the re-obtained motion mutation detection value to obtain the irregular motion detection value.

[0013] Further, the mobile data is obtained according to the motion state of the hip joint of the power-assisted motor, and then further includes obtaining a mobile path evaluation value according to the obtained mobile data and reference mobile data; the reference mobile data includes a reference gait frequency, a reference gait phase and a reference gait amplitude; the mobile path evaluation value is used to evaluate the matching degree between the mobile path of the hip joint of the power-assisted motor at the current time and the reference mobile path; the mobile path evaluation value is obtained by the following method: A1, judging whether the angle position of the hip joint of the power-assisted motor at the current time is the reference angle position, if yes, directly executing A2, otherwise, adjusting the angle position to the reference angle position and then executing A2; A2, judging whether the angular velocity of the hip joint of the power-assisted motor at the current time is within the angular velocity threshold range, if yes, directly executing A3, otherwise, adjusting the angular velocity to the angular velocity threshold range and then executing A3; A3, obtaining a gait evaluation value of the hip joint of the power-assisted motor at the current time, and obtaining the mobile path evaluation value in combination with the logarithmic operation result of the gait evaluation value; the gait evaluation value is the sum of a first gait evaluation value, a second gait evaluation value and a 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; and 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] Further, the specific limit expression of the movement path evaluation value is:

[0015]

[0016]

[0017]

[0018]

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

[0020] The embodiment of the application provides a system applying the real-time wearable hip joint power-assisted adaptive control method, which comprises a sensor, a controller and a power-assisted motor; the power-assisted motor is composed of a motor driver and a direct-current brushless motor; the sensor is used for acquiring motion data and movement data and storing power-assisted information, and the power-assisted information is transmitted to the controller as a control instruction; the power-assisted information comprises 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 for sending the control instruction in the sensor to the power-assisted motor; the motor driver is used for converting the control instruction into a power-assisted signal of the power-assisted motor; and the direct-current brushless motor is used for driving the power-assisted motor hip joint to move according to the power-assisted signal.

[0021] The embodiment of the application provides a computer readable storage medium, the computer readable medium stores a computer program, characterized in that the computer program is executed by a processor to execute the adaptive control method of the real-time wearable hip joint assistor.

[0022] The one or more technical solutions provided in the embodiment of the application have at least the following technical effects or advantages:

[0023] 1. The feedback error compensation value is obtained by predicting the motion data at the next moment based on the obtained movement data, then the adaptive learning parameter is dynamically adjusted according to the obtained 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 a feedback error reduction amount, so that the feedback error reduction amount in the adaptive control process is more accurately obtained, and the adaptive control accuracy in the hip joint assistor assisting process of the real-time wearable hip joint assistor is improved, thereby effectively solving the problem of low adaptive control accuracy in the hip joint assistor assisting process of the real-time wearable hip joint assistor in the prior art.

[0024] 2. The real-time torque compensation value is obtained by performing difference operation on the output assist value and the initial assist value 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, so that the accuracy of the real-time torque compensation value is improved, and the real-time updating accuracy and efficiency of the sliding data window are improved.

[0025] 3. The moving path evaluation value is obtained by judging whether the angle position of the hip joint of the assist motor at the current moment is the reference angle position, and whether the angular velocity of the hip joint of the assist motor at the current moment is within the angular velocity threshold range, and the logarithmic operation result of the obtained gait evaluation value is combined to obtain the moving path evaluation value, so that the moving path evaluation value is more accurately obtained, and the moving path of the hip joint of the assist motor is more accurately judged. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 The flowchart of the adaptive control method of the real-time wearable hip joint assistor provided in the embodiment of the application is provided.

[0027] Figure 2 The flowchart of the adaptive control method of the real-time wearable hip joint assistor provided in the embodiment of the application is provided.

[0028] Figure 3 The schematic diagram of the real-time wearable hip joint assistor provided in the embodiment of the application is provided.

[0029] Figure 4The schematic diagram of the cooperation of the controller provided by the embodiment of the present application is shown in the following figure. DETAILED DESCRIPTION

[0030] The embodiment of the present application provides a real-time wearable hip joint assistor adaptive control method and system and a storage medium, solves the problem of low adaptive control accuracy in the real-time wearable hip joint assistor assisting process in the prior art, detects the motion data of the human body double legs lifting legs at the current time through an exoskeleton assistor encoder and performs low-pass filtering, simultaneously obtains the movement data according to the motion state of the hip joint of the assistor motor, then predicts the motion data at the next time according to the obtained movement data to obtain a motion data prediction value, simultaneously obtains a feedback error compensation value of an adaptive oscillator according to the motion data at the current time and the motion data prediction value at the next time, then dynamically adjusts the adaptive learning parameters according to the obtained feedback error compensation value, simultaneously inputs the dynamically adjusted adaptive learning parameters into a controller to generate a motion data curve graph, finally monitors the convergence of the feedback error compensation value in the motion data curve graph at the current time to obtain a feedback error reduction amount, and realizes the improvement of the adaptive control accuracy in the real-time wearable hip joint assistor assisting process.

[0031] The technical solution in the embodiment of the present application is to solve the problem of low adaptive control accuracy in the real-time wearable hip joint assistor assisting process, and the general idea is as follows:

[0032] The motion data of the human body double legs lifting legs at the current time is detected and low-pass filtered to obtain movement data, simultaneously the motion data at the next time is predicted according to the obtained movement data to obtain a feedback error compensation value, then the adaptive learning parameters are dynamically adjusted according to the obtained feedback error compensation value to generate a motion data curve graph, finally the convergence of the feedback error compensation value is monitored in real time to obtain a feedback error reduction amount, and the effect of improving the adaptive control accuracy in the real-time wearable hip joint assistor assisting process is achieved.

[0033] In order to better understand the above technical solution, the above technical solution will be described in detail in combination with the description of the drawings and the specific embodiments.

[0034] As Figure 1As shown, the flow chart of the real-time wearable hip joint assist device adaptive control method provided by the embodiment of the application includes the following steps: step one, detecting the motion data of the human body's double legs lifting legs at the current time through the exoskeleton assist device encoder and performing low-pass filtering, at the same time, taking the low-pass filtered motion data as sample data for adaptive learning and obtaining movement data according to the motion state of the assist motor hip joint, the motion data includes the angle position and angular velocity of the assist motor hip joint, and the low-pass filtering is used to filter out high-frequency noise, and the movement data includes gait frequency, gait phase and gait amplitude; step two, predicting the motion data at the next time according to the obtained movement data to obtain the motion data prediction value, at the same time, obtaining the feedback error compensation value of the adaptive oscillator according to the motion data at the current time and the motion data prediction value at the next time, the motion data prediction value includes the angle position prediction value and the angular velocity prediction value, the adaptive oscillator is used for the periodic motion and frequency detection of the nonlinear system, and the feedback error compensation value is used as the adjustment amount of the adaptive learning parameter; step three, dynamically adjusting the adaptive learning parameter according to the obtained feedback error compensation value, at the same time, 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, monitoring the convergence of the feedback error compensation value in the motion data curve graph at the current time to obtain the feedback error reduction amount, and the feedback error reduction amount is used to evaluate the assist situation of the assist motor hip joint.

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

[0036] As Figure 2As shown, the flowchart of the adaptive control amount of the assist motor provided by the embodiment of the application, the embodiment can accurately predict and adjust the output of the assist motor by real-time acquisition and analysis of human motion data and in combination with an adaptive learning algorithm, provide assist force for the hip joint that adapts to the human motion demand, and reduce the muscle burden of the human body. In addition, the method can dynamically adjust the adaptive learning parameters, quantitatively evaluate the hip assist effect of the assist motor, and also adjust the assist strategy in real time according to the gait changes of the user, ensure that stable assist effect can be provided under different walking speeds, terrains and gait modes, and realize the improvement of the adaptive control accuracy in the real-time wearing process of the hip joint assist device.

[0037] Further, the motion data of the human body double legs lifting at the current time is detected by the exoskeleton assist device encoder and low-pass filtered, and then the sliding data window at the sliding time is updated according to the low-pass filtered motion data; the sliding data window is used to store the low-pass filtered motion data and update in real time; the specific process of updating the sliding data window at the sliding time is as follows: the initial assist value and the output assist value of the assist motor hip joint at the current time and the next time are obtained, the next time is the total time length of the sliding time and the current time, the initial assist value is the product of the gravity compensation coefficient, the adaptive assist coefficient at the current time and the phase sine value of the assist motor, the output assist value is the product of the gravity compensation coefficient, the adaptive assist coefficient at the next time and the phase sine value of the assist motor, 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 subjected to difference operation to obtain a real-time torque compensation value, and the sliding data window at the sliding time 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 time of the sliding data window, and the sliding time is the time length of the forward sliding of the sliding data window.

[0038] Specifically, the specific limit 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] In the formula, t is the number of the current time, t = 1, 2,..., T, T is the total number of the current time, Δt is the number of the sliding time, Δt = Δ1, Δ2,..., ΔT, ΔT is the total number of the sliding time, t + Δt is the number of the next time, t + Δt = 1 + Δ1, 2 + Δ2,..., T + ΔT, T + ΔT is the total number of the next time, ΔL I Δt represents the real-time torque compensation value of the hip joint of the assist motor at the Δt-th sliding time, L I t+Δt represents the output assist value of the hip joint of the assist motor at the next time t + Δt, L I t represents the initial assist value of the hip joint of the assist motor at the current time t, G0 represents the gravity compensation coefficient of the hip joint of the assist motor, K t+Δt represents the adaptive assist coefficient of the hip joint of the assist motor at the next time t + Δt, represents the assist motor phase of the assist motor at the next time t + Δt, K t represents the adaptive assist coefficient of the hip joint of the assist motor at the current time t, represents the assist motor phase of the assist motor at the current time t, m represents the mass of the assist motor, g represents the acceleration of gravity, s represents the distance from the center of mass of the hip joint of the assist motor to the rotation center, and θ represents the angle between the hip joint of the assist motor and the horizontal plane.

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

[0045] It needs to be understood that the current time refers to the time when the encoder detects and records the human body double leg lifting movement data in real time, the next time refers to the time immediately after the current time for predicting and updating the sliding data window, the sliding time is a relative concept for describing the state of the sliding data window at different time points, and the sliding data window is updated according to the latest movement data at each current time, thereby forming a dynamic and continuously updated data structure, that is, the low-pass filtered movement data at the current time 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, so that the sliding data window contains the latest movement data.

[0046] In order to more clearly understand the relationship between the sliding time of the sliding data window and the movement data at the current time, the initial assist value and the output assist 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 by 5 sliding times), and each time point interval is 1 second. The specific time point data change statistics table is shown in Table 1:

[0047] Table 1 Specific time point data change statistics table

[0048]

[0049]

[0050] As can be seen from Table 1, at T5 time, the data in the sliding data window is the low-pass filtered movement data at T1 to T5 time, the initial assist value of the assist motor hip joint is 11 (N·m) at T4 time, and the output assist value is 13 (N·m) at T5 time according to the adaptive learning algorithm. With the passage of time, the sliding data window is continuously updated to reflect the latest movement data, and at the same time, the initial assist value and the output assist value of the assist motor hip joint are also continuously updated to realize real-time response and adaptive control of human movement, thereby realizing the improvement of the adaptive control accuracy in the real-time wearing process of the hip joint assist device.

[0051] Further, the real-time torque compensation value is obtained, and then the method further comprises obtaining an irregular motion detection value of the assist motor hip joint in the irregular motion process according to the obtained real-time torque compensation value; the irregular motion comprises non-periodic motion and non-continuous motion; the irregular motion detection value is the sum of a non-periodic state detection value and a motion mutation detection value; the non-periodic state detection value is used to reflect the stability of the assist motor hip joint in the irregular motion process; and the motion mutation detection value is used to reflect the change of the gait amplitude of the assist motor hip joint in the irregular motion process.

[0052] The irregular motion detection value is obtained by the following method: E1, judging whether the aperiodic state detection constant in the hip joint of the power-assisted motor is not greater than a preset aperiodic state detection constant in the real-time worn hip joint power-assisted device, if yes, multiplying the aperiodic state detection constant, the square operation result of the torque decay coefficient and the decay time constant of the power-assisted motor to obtain the aperiodic state detection value, otherwise, not calculating the irregular motion detection value, the torque decay coefficient is used to reflect the rate of real-time torque decay of the power-assisted motor, and the decay time constant is used to ensure that the hip joint of the power-assisted motor quickly releases the real-time torque; E2, performing difference operation on the gait amplitude of the power-assisted motor hip joint at the current time and the gait amplitude at the last time to obtain a motion mutation detection value, judging whether the obtained motion mutation detection value is not less than a preset motion mutation detection constant in the real-time worn hip joint power-assisted device, if yes, performing addition operation on the obtained aperiodic state detection value and the motion mutation detection value to obtain the irregular motion detection value, otherwise, reacquiring the irregular motion detection value; the specific steps of reacquiring the irregular motion detection value are: adjusting the gait amplitude of the power-assisted motor hip joint and reacquiring 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 worn hip joint power-assisted device, and performing addition operation on the obtained aperiodic state detection value and the reacquired motion mutation detection value to obtain the irregular motion detection value.

[0053] In the embodiment, the position corresponding to the angle between the hip joint of the power-assisted motor and the horizontal plane is the angular position of the hip joint of the power-assisted motor; the aperiodic state detection value is used to reflect the stability of the hip joint of the power-assisted motor in the aperiodic motion (such as standing, stopping), when the human body performs aperiodic motion, the output torque, angular position and angular velocity of the hip joint of the power-assisted motor will change, and these changes can be quantified by the aperiodic state detection value; the motion mutation detection value is used to reflect the change of the gait amplitude in the non-continuous motion (such as jumping up) of the hip joint of the power-assisted motor, when the human body performs non-continuous motion, 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 hip joint of the power-assisted motor is in an unstable state, at this time, corresponding measures need to be taken for adjustment or intervention, for example, when the aperiodic state detection value rises, it may indicate that the stability of the hip joint of the power-assisted motor is insufficient when coping with aperiodic motion, at this time, the aperiodic state detection constant needs to be increased to improve the stability; when the motion mutation detection value rises, 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, wherein the irregular motion detection threshold represents the range corresponding to the maximum value and the minimum value of the historical irregular motion detection value of the hip joint of the power-assisted motor 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 hip joint of the assist motor during irregular motion can be achieved, which helps to improve the performance of the assist device and the comfort of the user, ensures that the assist device can provide stable and effective assistance in various complex motion scenarios, and realizes more accurate detection of the motion state of the human body during irregular motion.

[0056] Specifically, the specific limit 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] In the formula, t is the number of the current time, t = 1, 2,..., T, T is the total number of the current time, Δt is the number of the sliding time, Δt = Δ1, Δ2,..., ΔT, ΔT is the total number of the sliding time, t-Δt is the number of the previous time, t-Δt = 1-Δ1, 2-Δ2,..., T-ΔT, T-ΔT is the total number of the previous time, GUI t represents the irregular motion detection value of the hip joint of the assist motor at the current time t, M1 t represents the aperiodic state detection value of the hip joint of the assist motor at the current time t, M2 t represents the motion mutation detection value of the hip joint of the assist motor at the current time t, m1 t represents the aperiodic state detection constant of the hip joint of the assist motor at the current time t, m10 represents the preset aperiodic state detection constant, M20 represents the preset motion mutation detection constant, H t represents the torque decay coefficient of the hip joint of the assist motor at the current time t, Q t represents the decay time constant of the hip joint of the assist motor at the current time t, B3 t represents the gait amplitude of the hip joint of the assist motor at the current time t, B3 t-Δt represents the gait amplitude of the hip joint of the assist motor at the previous time t-Δt, B3"0 represents the initial gait amplitude of the hip joint of the assist motor before 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 a preset non-periodic state detection constant and the motion mutation detection value is not less than a preset motion mutation detection constant, otherwise it is not calculated, wherein the non-periodic state detection constant is obtained from a preset database, and the preset non-periodic state detection constant and the preset motion mutation detection constant are set by a preset person, and are represented by the results of summing and averaging the historical non-periodic state detection constants and the historical motion mutation detection constants in the preset database, respectively. The torque decay coefficient, the decay time constant, the initial gait amplitude, and the gait amplitude are directly obtained by the sensor.

[0062] Further, the movement data is obtained according to the motion state of the hip joint of the assist motor, and then the movement path evaluation value is obtained according to the obtained 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 hip joint of the assist motor at the current time and the reference movement path; the movement path evaluation value is obtained by the following method: A1, judging whether the angle position of the hip joint of the assist motor at the current time is on the reference angle position, if yes, directly executing A2, otherwise adjusting the angle position to the reference angle position and then executing A2; A2, judging whether the angular velocity of the hip joint of the assist motor at the current time is within the angular velocity threshold range, if yes, directly executing A3, otherwise adjusting the angular velocity to the angular velocity threshold range and then executing A3; A3, obtaining a gait evaluation value of the hip joint of the assist motor at the current time, and obtaining the movement path evaluation value combined with the logarithmic operation result of the gait evaluation value; the gait evaluation value is the sum of a first gait evaluation value, a second gait evaluation value, and a 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; and 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 the embodiment, the reference movement path and the reference angle position are set by a preset person, the angular velocity threshold range represents a range corresponding to the maximum value and the minimum value of the historical angular velocity of the hip joint assist device in real time in the preset database; the movement data (including the gait frequency, the gait phase, and the gait amplitude) is directly obtained by the sensor, and the reference movement data (including the reference gait frequency, the reference gait phase, and the reference gait amplitude) is represented by the results of summing and averaging the historical movement data (including the historical gait frequency, the historical gait phase, and the historical gait amplitude) in the historical time period in the preset database, respectively.

[0064] Compared with the prior art, the embodiment can ensure that the assisting motor quickly responds after receiving the adjustment instruction by setting the angular velocity threshold range and monitoring the change of the angular velocity in real time, and secondly, the moving path evaluation value is calculated by acquiring the movement data in real time and comparing with the preset reference movement data, which can accurately reflect the matching degree between the actual moving path of the assisting motor hip joint and the reference moving path at the current time, so that the real-time wearing of the hip joint assistor can better meet the needs of the user, and more accurate control of the moving path of the assisting motor is realized.

[0065] Further, the specific limit expression of the moving path evaluation value is:

[0066] W t = W0and SU t ∈ ΔSU0.

[0067]

[0068]

[0069]

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

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

[0072] It needs to 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, when the gait amplitude increases, the corresponding gait frequency will decrease and the corresponding gait phase will increase in the same movement distance and time period, by considering the mutual influence between the gait frequency, the gait phase and the gait amplitude, it is helpful to improve the movement path evaluation accuracy of the hip joint of the power-assisted motor, and then the adaptive control accuracy in the real-time wearing process of the hip joint power-assisted device is improved, effectively solving the problem of low adaptive control accuracy in the real-time wearing process of the hip joint power-assisted device in the prior art.

[0073] Further, the movement path evaluation value is obtained according to the obtained movement data and reference movement data, and then the adaptive learning parameter is dynamically adjusted according to the obtained movement path evaluation value and feedback error compensation value; the adaptive learning parameter includes an adaptive assistance coefficient and an adaptive assistance response time length; the adaptive assistance response time length is the time interval between the adjustment instruction of the adaptive learning parameter received by the power-assisted motor and the actual assistance provided by the power-assisted motor; 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 angle position prediction value and the angle position; the second prediction deviation value is the difference between the angular velocity prediction value and the angular velocity.

[0074] In this embodiment, when the movement path evaluation value increases and the decrease amplitude of the feedback error compensation value decreases, at this time the adaptive assistance coefficient needs to be increased and the adaptive assistance response time length needs to be decreased to improve the timeliness of the assistance, otherwise the adaptive assistance coefficient and the adaptive assistance response time length need to be decreased to avoid excessive assistance, and then the stability of the assistance is improved; when the movement path evaluation value increases and the feedback error compensation value is 0, the adaptive learning parameter does not need to be adjusted, and the accuracy and stability of the assistance effect of the hip joint of the power-assisted motor are improved.

[0075] Further, 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 prediction deviation value at the current time; the second motion data graph is used to reflect the change of the second prediction deviation value at the current time; the specific process of monitoring the convergence of the feedback error compensation value in the motion data graph at the current time to obtain the feedback error reduction amount is as follows: L1, the convergence of the first prediction deviation value on the motion data graph is monitored in real time, whether the first prediction deviation value at the current time is less than the reference distance reduction amount is judged, if yes, it is considered that the self-adaptive control method of the controller is effective and the first prediction deviation value at the current time is considered to be converged, otherwise it is considered not to be converged and the angle position prediction value is predicted again until the first prediction deviation value is less than the reference distance reduction amount; L2, the convergence of the second prediction deviation value on the motion data graph is monitored in real time, whether the second prediction deviation value at the current time is less than the reference angular velocity reduction amount is judged, if yes, it is considered that the self-adaptive control method of the controller is effective and the second prediction deviation value at the current time is considered to be converged, otherwise it is considered not to be converged and the angular velocity prediction value is predicted again until the second prediction deviation value is less than the reference angular velocity reduction amount; L3, the first prediction deviation value is subtracted from the reference distance reduction amount to obtain the first feedback error reduction amount, and the second prediction deviation value is subtracted from the reference angular velocity reduction amount to obtain the second feedback error reduction amount, and the feedback error reduction amount is obtained according to the addition result of the first feedback error reduction amount and the second feedback error reduction amount.

[0076] In the embodiment, the reference distance reduction amount is represented by the result of summing and averaging the historical distance reduction amounts in the historical time period in the preset database, and the reference angular velocity reduction amount is represented by the result of summing and averaging the historical angular velocity reduction amounts in the historical time period in the preset database; by observing the change of the feedback error reduction amount on the motion data graph in real time, the adaptive control effect of the controller can be understood, the user's satisfaction and trust degree for the real-time wearing of the hip joint assistor are improved, at the same time, the reference distance reduction amount and the reference angular velocity reduction amount are set based on the average value of the historical data, the adaptability and stability of the system are enhanced, and the effective evaluation of the self-adaptive control method of the controller is realized.

[0077] The system provided in this application embodiment, such as the adaptive control method for a real-time wearable hip joint assist device, includes: a sensor, a controller, and an assist motor; the assist motor consists of a motor driver and a brushless DC motor; the sensor is used to acquire motion data and movement data and store assist information, and transmits the assist information as control commands to the controller; the assist information includes feedback error compensation value, feedback error reduction amount, irregular motion detection value, and movement path evaluation value; the controller is used to send the control commands from the sensor to the assist motor; the motor driver is used to convert the control commands into assist signals for the assist motor; the brushless DC motor is used to drive the hip joint movement of the assist motor according to the assist signal.

[0078] In this embodiment, the controller (which communicates with the motor driver and the brushless DC motor via CAN / 485) is located in the control module of the wearable hip assist device. It outputs control commands corresponding to the assist motors (installed on the left and right hip joints of the human body in the assist motor of the wearable hip assist device) to adjust the working state of the assist motors and to precisely control the brushless DC motors. The motor driver is the bridge connecting the controller and the brushless DC motor, which converts the controller's control commands into assist signals that the assist motor can understand, thereby driving the assist motor to work on the hip joint. The brushless DC motor is the power source of the wearable hip assist device, which converts electrical energy into mechanical energy to drive the assist motor to move the hip joint.

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

[0080] Specifically, such as Figure 3 The diagram shown is a schematic of a real-time wearable hip joint assist device provided in an embodiment of this application. Figure 4As shown, the controller provided by the embodiment of the present application is a cooperation diagram. Most of the human hip exoskeleton robots are rigid structures. Compared with the prior art, the embodiment effectively realizes the cooperation between the controller (installed on the human back waist of the real-time wearing hip joint assist device), the motor driver and the direct current brushless motor in the real-time wearing hip joint assist device (worn on the waist of the human body) according to the assistance information input by the sensor on the basis of the rigid structure, which helps to improve the accuracy of adaptive and compliant hip joint assist torque control, perfectly couples the human hip joint leg and assists the human lower limb movement, reduces the energy metabolism of the human body, realizes the improvement of the adaptive control accuracy in the hip joint assist process of the real-time wearing hip joint assist device, and effectively solves the problem of low adaptive control accuracy in the hip joint assist process of the real-time wearing hip joint assist device in the prior art.

[0081] The computer readable storage medium provided by the embodiment of the present application stores a computer program, and the computer program is executed by a processor to realize the adaptive control method of the real-time wearing hip joint assist device.

[0082] In the embodiment, when the computer program is executed by the processor (such as CPU in computer, microprocessor, embedded system, etc.), the storage medium can accurately adjust the adaptive learning parameters and generate the corresponding motion data curve graph through the assistance information input by the sensor to reflect the control effect, and then evaluate the assistance effect of the assistance motor hip joint by monitoring the motion data curve graph in real time. The storage medium can store the computer program for a long time and stably, is not affected by environmental factors, and the stored computer program can be correctly read and executed by the computer system, further ensuring that the exoskeleton assist device can provide stable and effective assistance, and thus realizing the improvement of the adaptive control accuracy in the hip joint assist process of the real-time wearing hip joint assist device, effectively solving the problem of low adaptive control accuracy in the hip joint assist process of the real-time wearing hip joint assist device in the prior art.

[0083] In summary, the embodiment of the present application predicts the motion data of the next moment by the obtained movement data to obtain a feedback error compensation value, dynamically adjusts the adaptive learning parameters according to the obtained 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 a feedback error reduction amount, thereby realizing more accurate acquisition of the feedback error reduction amount in the adaptive control process, and further realizing the improvement of the adaptive control accuracy in the hip joint assist process of the real-time wearing hip joint assist device, effectively solving the problem of low adaptive control accuracy in the hip joint assist process of the real-time wearing hip joint assist device in the prior art.

[0084] Those skilled in the art will appreciate that embodiments of the present application can be devised for a variety of applications. It is therefore intended that the present application cover all such modifications and variations of the application disclosed herein provided they come within the scope of the appended claims and their equivalents. It is intended to

[0085] The present application is described in reference to the drawings using a flowchart and / or a block diagram of the method, apparatus (system) and computer program product according to embodiments of the application. It will be understood that each block of the flowchart and / or block diagram, and combinations of 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, special purpose computer, embedded processing device or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0086] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0087] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0088] While the preferred embodiments of the application have been described, additional variations and modifications can be employed by those skilled in the art. Therefore, the appended claims are intended to cover all such modifications and variations as fall within the scope of the present application.

[0089] Obviously, many modifications and variations of the present application are possible in light of the above teachings. It is, therefore, to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A real-time adaptive control method for a hip exoskeleton, characterized in that, The method comprises the following steps: Step one, detecting the motion data of the human body's legs at the current time through the exoskeleton exoskeleton encoder and performing low-pass filtering, and obtaining movement data according to the movement state of the hip joint of the exoskeleton motor, wherein the movement data includes the angle position and angular velocity of the hip joint of the exoskeleton motor, and the movement data includes the gait frequency, gait phase and gait amplitude; After obtaining the movement data, the movement path evaluation value is obtained according to the obtained movement data and the reference movement data, and the specific limit expression of the movement path evaluation value is: ； ； ； ； where t is a number of a current time point, T is a total number of current time points, and e is a natural constant, represents a movement path evaluation value of the hip joint of the assist motor at the current time point t, represents a first gait evaluation value of the hip joint of the assist motor at the current time point t, represents a second gait evaluation value of the hip joint of the assist motor at the current time point t, represents a third gait evaluation value of the hip joint of the assist motor at the current time point t, represents a gait frequency of the hip joint of the assist motor at the current time point t, represents a reference gait frequency, represents a gait phase of the hip joint of the assist motor at the current time point t, represents a reference gait phase, represents a gait amplitude of the hip joint of the assist motor at the current time point t, represents a reference gait amplitude, represents an angle position of the hip joint of the assist motor at the current time point t, represents a reference angle position, represents an angular velocity of the hip joint of the assist motor at the current time point t, represents an angular velocity threshold range; Step two, predicting the motion data at the next time according to the obtained movement data to obtain a motion data prediction value, and obtaining a feedback error compensation value of an adaptive oscillator according to the motion data at the current time and the motion data prediction value at the next time, wherein the motion data prediction value includes an angle 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; 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 angle position prediction value and the angle position; The second prediction deviation value is the difference between the angular velocity prediction value and the angular velocity; After obtaining the movement path evaluation value and the feedback error compensation value, the adaptive learning parameter is dynamically adjusted according to the obtained movement path evaluation value and the feedback error compensation value; The adaptive learning parameter includes an adaptive assistance coefficient and an adaptive assistance response time length; The adaptive assistance response time length is the time interval between the adjustment instruction of the adaptive learning parameter received by the exoskeleton motor and the actual assistance provided by the exoskeleton motor; 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, wherein the motion data curve graph is used to reflect the change of the feedback error compensation value; The step of dynamically adjusting the adaptive learning parameter according to the obtained feedback error compensation value is: when the movement path evaluation value increases and the feedback error compensation value decreases, the adaptive assistance coefficient needs to be increased and the adaptive assistance response time length needs to be decreased, otherwise, the adaptive assistance coefficient needs to be decreased and the adaptive assistance response time length needs to be decreased, and when the movement path evaluation value increases and the feedback error compensation value is 0, the adaptive learning parameter does not need to be adjusted; Step four, monitoring the convergence of the feedback error compensation value in the motion data curve graph at the current time to obtain a feedback error reduction amount, wherein the feedback error reduction amount is used to evaluate the assistance of the hip joint of the exoskeleton motor.

2. The real-time adaptive control method for a hip joint assistive device worn on the body as claimed in claim 1, wherein After detecting the motion data of the human body's legs at the current time through the exoskeleton exoskeleton encoder and performing low-pass filtering, the sliding data window at the sliding time is updated according to the low-pass filtered motion data; The sliding data window is used to store the low-pass filtered motion data and is updated in real time; The specific process of updating the sliding data window at the sliding time is as follows: Obtaining an initial assistance value and an output assistance value of the hip joint of the assistance motor at a current time and a next time, the next time being a total time length of the sliding time and the current time, the initial assistance value being a product of a gravity compensation coefficient, an adaptive assistance coefficient at the current time and a phase sine value of the assistance motor, and the output assistance value being a product of the gravity compensation coefficient, an adaptive assistance coefficient at the next time and the phase sine value of the assistance motor; Performing difference operation on the output assistance value and the initial assistance value to obtain a real-time torque compensation value, and simultaneously performing real-time update on the sliding data window at the sliding time according to the obtained real-time torque compensation value, the real-time torque compensation value being used to evaluate the torque balance effect of the hip joint of the assistance motor at the sliding time of the sliding data window, and the sliding time being a time length of the forward sliding of the sliding data window.

3. The real-time adaptive control method for a hip joint assistive device worn on the body as claimed in claim 2, wherein The method further comprises obtaining an irregular motion detection value of the hip joint of the assistance motor during irregular motion according to the obtained real-time torque compensation value after obtaining the real-time torque compensation value. The irregular motion comprises non-periodic motion and non-continuous motion. The irregular motion detection value is a sum of a non-periodic state detection value and a motion mutation detection value. The non-periodic state detection value is used to reflect the stability of the hip joint of the assistance motor during the irregular motion. The motion mutation detection value is used to reflect the change of the gait amplitude of the hip joint of the assistance motor during the irregular motion.

4. The real-time adaptive control method for a hip joint assistive device worn on the body as claimed in claim 3, wherein The irregular motion detection value is obtained by the following method: E1, judging whether a non-periodic state detection constant in the hip joint of the assistance motor is not greater than a preset non-periodic state detection constant in the real-time wearable hip joint assistor, if yes, performing multiplication operation on the non-periodic state detection constant, a torque decay coefficient of the assistance motor and a square operation result of a decay time constant to obtain the non-periodic state detection value, otherwise, not calculating the irregular motion detection value, the torque decay coefficient being used to reflect the rate of real-time torque decay of the assistance motor, and the decay time constant being used to ensure that the hip joint of the assistance motor rapidly releases the real-time torque; E2, performing difference operation on the gait amplitude of the hip joint of the assistance motor at the current time and the gait amplitude at the last time to obtain the motion mutation detection value, 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 assistor, if yes, performing addition operation on the obtained non-periodic state detection value and the motion mutation detection value to obtain the irregular motion detection value, otherwise, re-obtaining the irregular motion detection value; The specific steps of re-obtaining the irregular motion detection value are as follows: Adjusting the gait amplitude of the hip joint of the assistance motor 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 assistor, and performing addition operation on the obtained non-periodic state detection value and the re-obtained motion mutation detection value to obtain the irregular motion detection value.

5. The adaptive control method for real-time wearable hip joint assist device according to claim 1, wherein, The method further comprises obtaining a movement path evaluation value according to the obtained movement data and reference movement data after obtaining the movement data according to the motion state of the hip joint of the assistance motor. The reference movement data comprises 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 hip joint of the power-assisted motor at the current time and the reference movement path; The movement path evaluation value is obtained by the following method: A1, judging whether the angle position of the hip joint of the power-assisted motor at the current time is the reference angle position, if yes, directly executing A2, otherwise adjusting the angle position to the reference angle position and then executing A2; A2, judging whether the angular velocity of the hip joint of the power-assisted motor at the current time is within the angular velocity threshold range, if yes, 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 hip joint of the power-assisted motor at the current time, and obtaining the movement path evaluation value by combining the logarithmic operation result of the gait evaluation value; The gait evaluation value is the sum of a first gait evaluation value, a second gait evaluation value and a 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.

6. The real-time adaptive control method for a hip assistive exosuit of claim 1, wherein, The motion data curve graph comprises a first motion data curve graph and a second motion data curve graph; The first motion data curve graph is used to reflect the change of the first prediction deviation value at the current time; The second motion data curve graph is used to reflect the change of the second prediction deviation value at the current time; The specific process of monitoring the convergence of the feedback error compensation value in the motion data curve graph at the current time to obtain the feedback error reduction amount comprises: L1, monitoring the convergence of the first prediction deviation value on the motion data curve graph in real time, judging whether the first prediction deviation value at the current time is less than the reference distance reduction amount, if yes, it is considered that the self-adaptive control method of the controller is effective and the first prediction deviation value at the current time is converged, otherwise the angle position prediction value is re-predicted until the first prediction deviation value is less than the reference distance reduction amount; L2, monitoring the convergence of the second prediction deviation value on the motion data curve graph in real time, judging whether the second prediction deviation value at the current time is less than the reference angular velocity reduction amount, if yes, it is considered that the self-adaptive control method of the controller is effective and the second prediction deviation value at the current time is converged, otherwise the angular velocity prediction value is re-predicted until the second prediction deviation value is less than the reference angular velocity reduction amount; L3, performing difference operation on the first prediction deviation value and the reference distance reduction amount to obtain the first feedback error reduction amount, and performing difference operation on the second prediction deviation value and the reference angular velocity reduction amount to obtain the second feedback error reduction amount, and obtaining the feedback error reduction amount according to the addition operation result of the first feedback error reduction amount and the second feedback error reduction amount.

7. A system for applying the adaptive control method of a real-time wearable hip joint assistive device according to any one of claims 1 to 6, characterized in that, It comprises: a sensor, a controller and a power-assisted motor; The power-assisted motor is composed of a motor driver and a direct-current brushless motor; The sensor is used for acquiring motion data and movement data and storing the assist information, and transmitting the assist information as control instructions to the controller; The assist information includes feedback error compensation value, feedback error reduction value, irregular motion detection value and movement path evaluation value; The controller is used for sending the control instructions in the sensor to the assist motor; The motor driver is used for converting the control instructions into assist signals of the assist motor; The direct current brushless motor is used for driving the assist motor hip joint movement according to the assist signals.

8. A computer readable storage medium, the computer readable medium storing a computer program, characterized in that, The computer program is executed by the processor to perform the real-time wearing hip joint assistive adaptive control method according to any one of claims 1-6.

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