Exoskeleton knee joint assistance control method and device and electronic equipment

Abstract

The invention relates to the technical field of exoskeletons, in particular to an exoskeleton knee joint power-assisted control method and device and electronic equipment. The method comprises the following steps: acquiring motion posture data of lower limbs of a human body from an IMU (Inertial Measurement Unit) inertial measurement system; processing the motion posture data to obtain a corresponding phase angle; performing torque estimation on the phase angle by using a power-assisted planning algorithm corresponding to the phase angle to obtain an expected control torque of the knee joint; and performing power-assisted control on the knee joint according to the expected control torque and a preset force feedback IPD closed-loop control algorithm. The problems that in the prior art, gait recognition accuracy is poor and power assisting control accuracy is low in the complex environment are solved, the smooth power assisting effect on the human body is achieved, and the adaptability of the exoskeleton to the complex environment is improved.

Description

Technical Field

[0001] The present application relates to the field of exoskeleton technology, and more specifically, to an exoskeleton knee joint power-assist control method, device and electronic equipment. Background Art

[0002] The exoskeleton system is a human-machine intelligent system that combines human intelligence with the load capacity of the machine to enhance the human body's capabilities. Human collaborative real-time power-assisted control technology is one of the key technologies of the exoskeleton system, which directly determines the collaborative comfort and power-assisted compliance of the exoskeleton system's human-machine integration. At present, the power-assisted control methods of exoskeletons mainly include: power-assisted control methods based on artificial intelligence, which have problems such as high algorithm complexity, high equipment requirements, and high difficulty in engineering application; the other is to pre-plan the control trajectory through gait recognition, but in the complex outdoor environment, the posture angle of the lower limbs of the human body changes dynamically, which greatly reduces the gait recognition accuracy, power-assisted control accuracy and real-time performance of the exoskeleton system, and the robustness is poor. Summary of the invention

[0003] In view of this, one of the technical problems solved by the embodiments of the present application is to provide an exoskeleton knee joint power-assisted control method, device and electronic equipment, which solves the problems of poor gait recognition accuracy and low power-assisted control precision in complex environments.

[0004] In a first aspect of the embodiments of the present application, a method for controlling an exoskeleton knee joint assistance is disclosed, the method comprising:

[0005] Obtain the motion posture data of the human lower limbs from the IMU inertial measurement system;

[0006] Process the motion posture data to obtain the corresponding phase angle;

[0007] The torque of the phase angle is estimated by using the power assistance planning algorithm corresponding to the phase angle to obtain the expected control torque of the knee joint;

[0008] The knee joint is assisted and controlled based on the preset feedback IPD closed-loop control algorithm and the desired control torque.

[0009] In a second aspect of the embodiment of the present application, an exoskeleton knee joint power assist control device is disclosed, the device comprising:

[0010] The posture data acquisition module is used to obtain the motion posture data of the lower limbs of the human body from the IMU inertial measurement system;

[0011] A phase angle data determination module is used to process the motion posture data to obtain the corresponding phase angle;

[0012] The expected torque determination module is used to estimate the torque of the phase angle by using the power assistance planning algorithm corresponding to the phase angle to obtain the expected control torque of the knee joint;

[0013] The joint assist control module is used to assist the knee joint according to the desired control torque and the preset force feedback IPD closed-loop control algorithm.

[0014] In a third aspect of an embodiment of the present application, an electronic device is disclosed. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above method are implemented.

[0015] In a fourth aspect of the embodiments of the present application, a computer-readable storage medium is disclosed, which stores a computer program. When the computer program is executed by a processor, the steps of the above method are implemented.

[0016] The present application has the following advantages: by processing the motion posture data of the lower limbs of the human body from the IMU inertial measurement system, the processed knee joint posture angle is converted into a phase angle, and the phase angle is estimated by the torque of the power assistance planning algorithm corresponding to the phase angle to obtain the expected control torque of the knee joint, so as to perform power assistance control on the knee joint according to the expected control torque and the preset force feedback IPD closed-loop control algorithm. This method of fusing the posture information and converting it into a phase angle to select the corresponding expected power assistance planning input has the effect of appropriately constraining the power assistance expectations in the knee flexion or extension stage, and realizes the need to identify and classify the gait phase in detail, and to perform power assistance control according to the expected power assistance control torque, thereby achieving the purpose of simulating the muscle work process during movement, ensuring the efficient transfer of load during walking movement, and solving the problems of poor gait recognition accuracy and low power assistance control accuracy in complex environments in the prior art, achieving a soft power assistance effect on the human body, and improving the adaptability of the exoskeleton to complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic flow chart of an exoskeleton knee joint power assist control method provided in one embodiment of the present application;

[0018] Figure 2 A schematic diagram of the installation of an IMU inertial measurement system provided by one embodiment of the present application;

[0019] Figure 3 The motion posture data obtained by the exoskeleton knee joint power-assistance control method provided in one embodiment of the present application;

[0020] Figure 4 A schematic structural diagram of an exoskeleton knee joint power assist control device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0022] It should be noted that although the functional modules are divided in the device schematic and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart.

[0023] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0024] According to one embodiment of the present application, a method for controlling an exoskeleton knee joint assist is provided, such as Figure 1 As shown, the method includes steps S101 to S104.

[0025] Step S101: Acquire motion posture data of the lower limbs of the human body from the IMU inertial measurement system.

[0026] Specifically, the IMU inertial measurement system (such as a sensor) is connected to the terminal via a wired connection or via a wireless communication protocol (such as Bluetooth), so that the terminal can obtain the data monitored in real time by the IMU inertial measurement system. Figure 2 Sensors are installed on the thighs and calves of users wearing the exoskeleton. These sensors can be pre-configured to distinguish the installation position. Specifically, the motion posture data such as Figure 3 The figure includes the thigh angle θ Thigh and the calf angle θ Shank , specifically including θ lThigh ,θ rThigh ,θ lShank ,θ rShank , respectively representing the left thigh angle, right thigh angle, left calf angle, and right calf angle.

[0027] Step S102: Process the motion posture data to obtain the corresponding phase angle.

[0028] In the embodiments of the present application, the phase angle (also called phase angle) is used to characterize the changes in the extension and flexion of the legs during human movement. During human movement, the phase angle of the legs will change with the change of the pace. When the leg takes a step, the phase angle gradually increases; when the leg is retracted, the phase angle gradually decreases.

[0029] Step S103: using the power assistance planning algorithm corresponding to the phase angle to estimate the torque of the phase angle, and obtain the desired control torque of the knee joint.

[0030] Specifically, a power assistance planning algorithm query table can be set in advance, which includes power assistance planning algorithms corresponding to different phase angle ranges. The phase angle calculated in step S102 is queried in the query table to determine the corresponding power assistance planning algorithm, thereby achieving the effect of determining the pre-designed power assistance planning curve based on the calculated phase angle.

[0031] Step S104: performing power-assisted control on the knee joint according to the desired control torque and a preset force feedback IPD closed-loop control algorithm.

[0032] Specifically, the control parameters of the deviation proportion P, integral I and differential D of the force feedback IPD closed-loop control algorithm can be obtained by fitting the experimental data, so as to obtain the output value of the power-assist control according to the expected control torque, and then control according to the output value to achieve the purpose of exoskeleton power assistance.

[0033] The embodiment of the present application processes the motion posture data of the human lower limbs from the IMU inertial measurement system, converts the processed knee joint posture angle into a phase angle, and estimates the phase angle in the power assistance planning algorithm corresponding to the determined phase angle to obtain the expected control torque of the knee joint, thereby performing power assistance control on the knee joint according to the expected control torque and a preset force feedback IPD closed-loop control algorithm. This method of fusing the posture information and converting it into a phase angle to select the corresponding expected power assistance planning input has the effect of appropriately constraining the power assistance expectations in the knee flexion or extension stage, and realizes that power assistance control can be performed according to the expected power assistance control torque without identifying and classifying the gait phase in detail, thereby achieving the purpose of simulating the muscle work process during movement, ensuring the efficient transfer of load during walking movement, and solving the problems of poor gait recognition accuracy and low power assistance control accuracy in complex environments in the prior art, achieving a soft power assistance effect on the human body and improving the adaptability of the exoskeleton to complex environments.

[0034] In some embodiments, step S102 further includes:

[0035] Step S1021 (not shown in the figure): Process the motion posture data to obtain angle information, where the angle information includes a first angle and an angular velocity, wherein the first angle is the sum of the thigh angle and the calf angle on the same side in the motion posture data, and the angular velocity is the product of the sampling frequency of the IMU inertial measurement system and the difference between the first angles at two adjacent sampling time points;

[0036] Step S1022 (not shown in the figure): the first angle and angular velocity are filtered and smoothed and then converted into a phase angle.

[0037] In specific application, the joint angle related information is explained using the left leg and calf data as an example.

[0038] For example, the calculation formula for the first angle of the left leg is:

[0039] θ(t)=θ lThigh (t)+θ lShank (t);

[0040] The calculation formula for the second angle of the left leg is:

[0041] θ 1 (t) = θ lThigh (t)-θ lShank (t);

[0042] Among them, θ lThigh is the posture angle of the left thigh, θ lShank The posture angle of the left calf.

[0043] In some embodiments, step S1022 further includes:

[0044] Based on a preset first low-pass filter and a second low-pass filter, filtering and smoothing the first angle and the angular velocity are performed respectively to obtain a first filtered angle and a first filtered angular velocity;

[0045] An arc tangent process is performed on the first filter angle and the first filter angular velocity, and a negative value of the arc tangent process is used as a phase angle.

[0046] The embodiment of the present application removes the influence of noise on the two parameters by filtering and smoothing the first angle and the angular velocity, so as to improve the accuracy of the phase angle calculated based on the two parameters.

[0047] Specifically, the filter coefficients of the first low-pass filter and the second low-pass filter can be adjusted according to experimental data, so that the filtering effect can be controlled by adjusting the filter coefficients.

[0048] In the embodiment of filtering and smoothing by a low-pass filter, the calculation formula of the first low-pass filter is:

[0049] θ′(t)=a×θ′(t-1)+(1-a)×θ(t);

[0050] Wherein, θ′(t) is the filter output value of the first angle at the current time point, θ(t) is the first angle at the current time point, a is the filter coefficient, and θ′(t-1) is the filter output value of the first angle at the previous time point;

[0051] The calculation formula for the second low-pass filter is:

[0052] ω′(t)=b×ω′(t-1)+(1-b)×ω(t);

[0053] Among them, ω′(t) is the filter output value at the current time point, ω′(t-1) is the filter output value of the angular velocity at the previous time point, ω(t) is the angular velocity at the current time point, and b is the filter coefficient.

[0054] Therefore, the phase angle The calculation formula is:

[0055] Specifically, the filtered output value of the first angle at the previous time point may be stored in a low-pass filter or in a cache.

[0056] In some embodiments, step S103 further includes:

[0057] Step S1031 (not shown in the figure): if the phase angle is greater than 0, the phase angle is calculated according to a preset first power assist algorithm to obtain a first desired control torque;

[0058] Step S1032 (not shown in the figure): if the phase angle is not greater than 0, the phase angle is calculated according to a preset second power assist algorithm to obtain a second desired control torque.

[0059] Specifically, when the phase angle is greater than 0, it indicates that the human body is in the support state period of gait movement, and at the current moment, the knee joint needs to be given a knee extension torque; when the phase angle is not greater than 0, it indicates that the human body is in the swing state period of gait movement, and at the current moment, the knee joint needs to be given a knee flexion torque.

[0060] Specifically, the formula of the first boosting algorithm is:

[0061]

[0062] Among them, T e (t) is the desired control torque, i.e., the torque control input information of the knee joint motor, A is the amplitude of the desired power assist curve generated, B is the frequency, and C is the left-right translation parameter of the desired power assist curve. A, B, and C are constants. By adjusting the three constants A, B, and C, the amplitude, width, and left-right translation of the power assist curve can be adjusted.

[0063] Specifically, the formula of the second boosting algorithm is:

[0064]

[0065] Among them, E is the amplitude of the generated desired power assist curve, F is the frequency, and G is the left and right translation parameter of the desired power assist curve. E, F, and G are constants. By adjusting the three constants E, F, and G, the amplitude, width, and left and right translation of the power assist curve can be adjusted.

[0066] In order to avoid the calculated first expected control torque not exceeding the maximum torque that the human body can withstand, the present application constrains the maximum control torque of the first expected control torque and the second expected control torque by presetting A and E values ​​to judge the first expected control torque, so as to avoid power-assisted control according to the first expected control torque that exceeds the maximum knee extension torque or the second torque of the maximum knee flexion torque that the human body can withstand, which may cause damage to the user wearing the exoskeleton.

[0067] The embodiment of the present application plans the assist curve by judging the value of the phase angle in real time. The calculation is derived from the human body posture. The planned assist curve is updated in real time according to the posture feedback from the human body posture. Real-time dynamic adjustment of the desired assist control torque can be achieved, and then the assist output of the knee joint motor can be adjusted in real time to achieve flexible assist.

[0068] In some embodiments, step S1031 also includes the step of judging and eliminating the shaking state of the knee joint, that is, including:

[0069] When the shaking phenomenon of the knee joint is detected, a first desired control torque at the time point of entering the shaking state is determined by using a first power assist algorithm;

[0070] If the first expected control torque at the time point of entering the shaking state is greater than the preset shaking maximum control torque threshold, the first expected control torque at the time point of entering the shaking state is updated to the preset shaking maximum control torque threshold.

[0071] Specifically, the shaking phenomenon of the knee joint is detected, including: obtaining a second angle obtained by processing the motion posture data, the second angle being the difference between the thigh angle and the calf angle on the same side in the motion posture data; if the second angle at the current time point is less than a preset second angle threshold, and the phase angle at any time point within a predetermined time period before the current time point is greater than a preset first phase angle threshold and less than a preset second phase angle threshold, then it is determined that the knee joint is shaking.

[0072] When applied, if the second angle θ at the current time point t 1 (t)<α and i∈[t-δ,t], then judge according to the following formula:

[0073]

[0074] That is: if a certain time point t within the predetermined time period (t-δ, t) before the current time point t i The second angle satisfies θ1 (t)<α and φ(t i )>β||φ(t i )<γ, it will enter the jitter state at time t i The first desired control torque T e (t i ) and the preset maximum control torque threshold value T max For comparison, if T e (t i )>T max , then T max Update to T e (t i ), otherwise the preset maximum control torque threshold value T max constant.

[0075] In the support state of the motion phase, due to the high dynamic changes in the terrain or the personalized walking characteristics of the wearer, the lower limb posture information may change dynamically, which may cause the knee joint motor output to vibrate, affecting the comfort and safety of wearing. Therefore, it is necessary to perform a vibration judgment, that is, to judge when the second angle θ 1 (t) is less than the predefined parameter α, and the attitude phase angle appears at the current time t and within the previous period t-δ If the value is greater than the predefined parameter β or less than the predefined parameter γ, it is determined to be a shaking state, and the first expected control force at the initial moment of entering the shaking state is defined as the maximum shaking control torque T in this gait cycle. max It can eliminate jitter according to the real-time motion status of the user wearing the exoskeleton.

[0076] In some embodiments, step S104 further includes:

[0077] Obtain the actual feedback torque from the IMU inertial measurement system and calculate the torque control error between the desired control torque and the actual feedback torque;

[0078] Determine the integral of the torque control error and the differential of the torque control error;

[0079] The torque control error, the integral of the torque control error and the difference of the torque control error are input into the feedback IPD closed-loop control algorithm to obtain the torque control output, so as to perform power-assisted control on the knee joint according to the torque control output.

[0080] Specifically, it is calculated by the following formula:

[0081] e u =T e -T act , where T e is the desired control torque, Tact is the actual feedback torque, e u is the torque control error.

[0082] Specifically, the calculation formula of the feedback PID closed-loop control algorithm is:

[0083] u=k p ×e u +k i ×e ui +k d ×e ud ;

[0084] Among them, e ui is the torque control error e u The integral of ud is the torque control error e u The difference of k p , k i , k d is the PID control parameter, which can be obtained by fitting the experimental data.

[0085] An embodiment of the present application provides an exoskeleton knee joint assist control device, such as Figure 4 As shown, the device 40 includes: a posture data acquisition module 401, a phase angle data determination module 402, a desired torque determination module 403, and a joint assist control module 404.

[0086] The posture data acquisition module 401 is used to acquire the motion posture data of the lower limbs of the human body from the IMU inertial measurement system;

[0087] The phase angle data determination module 402 is used to process the motion posture data to obtain the corresponding phase angle;

[0088] The expected torque determination module 403 is used to estimate the torque of the phase angle by using the power assistance planning algorithm corresponding to the phase angle to obtain the expected control torque of the knee joint;

[0089] The joint assist control module 404 is used to perform assist control on the knee joint according to the desired control torque and a preset force feedback IPD closed-loop control algorithm.

[0090] The embodiment of the present application processes the motion posture data of the human lower limbs from the IMU inertial measurement system, converts the processed knee joint posture angle into a phase angle, and estimates the phase angle in the power assistance planning algorithm corresponding to the determined phase angle to obtain the expected control torque of the knee joint, thereby performing power assistance control on the knee joint according to the expected control torque and a preset force feedback IPD closed-loop control algorithm. This method of fusing the posture information and converting it into a phase angle to select the corresponding expected power assistance planning input has the effect of appropriately constraining the power assistance expectations in the knee flexion or extension stage, and realizes that power assistance control can be performed according to the expected power assistance control torque without identifying and classifying the gait phase in detail, thereby achieving the purpose of simulating the muscle work process during movement, ensuring the efficient transfer of load during walking movement, and solving the problems of poor gait recognition accuracy and low power assistance control accuracy in complex environments in the prior art, achieving a soft power assistance effect on the human body and improving the adaptability of the exoskeleton to complex environments.

[0091] Furthermore, the phase angle data determination module includes:

[0092] An angle determination submodule is used to process the motion posture data to obtain angle information, wherein the angle information includes a first angle and an angular velocity, wherein the first angle is the sum of the thigh angle and the calf angle on the same side in the motion posture data, and the angular velocity is the product of the sampling frequency of the IMU inertial measurement system and the difference between the first angles at two adjacent sampling time points;

[0093] The phase angle determination submodule is used to convert the first angle and angular velocity into a phase angle after filtering and smoothing.

[0094] Furthermore, the phase angle determination submodule includes:

[0095] A filtering processing unit, configured to perform filtering and smoothing processing on the first angle and the angular velocity based on a preset low-pass filter, to obtain a first filtering angle and a first filtering angular velocity;

[0096] The phase angle conversion unit is used to perform arc tangent processing on the first filtering angle and the first filtering angular velocity to obtain a phase angle.

[0097] Furthermore, the calculation formula of the first low-pass filter is: θ′(t)=a×θ′(t-1)+(1-a)×θ(t), θ′(t) is the filter output value of the first angle at the current time point, θ(t) is the first angle at the current time point, a is the filter coefficient, and θ′(t-1) is the filter output value of the first angle at the previous time point; the calculation formula of the second low-pass filter is: ω′(t)=b×ω′(t-1)+(1-b)×ω(t), ω′(t) is the filter output value at the current time point, ω′(t-1) is the filter output value of the angular velocity at the previous time point, ω(t) is the angular velocity at the current time point, and b is the filter coefficient.

[0098] Furthermore, the expected torque determination module includes:

[0099] A first determination submodule, configured to calculate the phase angle according to a preset first power assist algorithm to obtain a first desired control torque if the phase angle is greater than 0;

[0100] The second determination submodule is used to calculate the phase angle according to a preset second power assist algorithm to obtain a second desired control torque if the phase angle is not greater than 0.

[0101] Furthermore, the first determining submodule further includes:

[0102] A shaking expected torque determination unit, used to determine a first expected control torque at a time point of entering a shaking state by using a first power-assisting algorithm when a shaking phenomenon of the knee joint is detected;

[0103] The shaking torque maximum value updating unit is used to update the first expected control torque at the time point of entering the shaking state to the preset shaking maximum control torque threshold if the first expected control torque at the time point of entering the shaking state is greater than the preset shaking maximum control torque threshold.

[0104] Furthermore, the jitter expected torque determination unit includes:

[0105] A second angle determination subunit is used to obtain a second angle obtained by processing the motion posture data, where the second angle is a difference between an angle of the thigh and an angle of the calf on the same side in the motion posture data;

[0106] The shaking state determination subunit is used to determine that the knee joint has shaking if the second angle at the current time point is less than the preset second angle threshold, and the phase angle at any time point within the predetermined time period before the current time point is greater than the preset first phase angle threshold and less than the preset second phase angle threshold.

[0107] Furthermore, the joint assist control module includes:

[0108] The torque feedback determination submodule is used to obtain the actual feedback torque from the IMU inertial measurement system and calculate the torque control error between the desired control torque and the actual feedback torque;

[0109] A torque error determination submodule, used to determine the integral of the torque control error and the differential of the torque control error;

[0110] The power-assist control submodule is used to input the torque control error, the integral of the torque control error and the differential of the torque control error into the feedback IPD closed-loop control algorithm to obtain the torque control output so as to perform power-assist control on the knee joint according to the torque control output.

[0111] The above-mentioned device of this embodiment can execute the method shown in the first embodiment of the present application, and its implementation principle is similar, which will not be repeated here.

[0112] Another embodiment of the present application provides a terminal, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the computer program.

[0113] Specifically, the processor may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0114] Specifically, the processor is connected to the memory via a bus, and the bus may include a path for transmitting information. The bus may be a PCI bus or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc.

[0115] The memory can be a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, or an EEPROM, CD-ROM or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to this.

[0116] Optionally, the memory is used to store the code of the computer program for executing the solution of the present application, and the execution is controlled by the processor. The processor is used to execute the application program code stored in the memory to implement the actions of the device provided in the above embodiment.

[0117] Another embodiment of the present application provides a computer-readable storage medium storing computer-executable instructions for executing the above method.

[0118] The device embodiments described above are only illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0119] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0120] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the above-mentioned implementation mode. Technical personnel familiar with the field can also make various equivalent deformations or substitutions without violating the spirit of the present application. These equivalent deformations or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. An exoskeleton knee joint power-assist control method, characterized in that: The method comprises: Obtain the motion posture data of the human lower limbs from the IMU inertial measurement system; Processing the motion posture data to obtain a corresponding phase angle; Using the power assistance planning algorithm corresponding to the phase angle to estimate the torque of the phase angle, and obtain the expected control torque of the knee joint; The knee joint is powered-controlled according to the desired control torque and a preset force feedback IPD closed-loop control algorithm.

2. The method according to claim 1, characterized in that The step of processing the motion posture data to obtain a corresponding phase angle includes: Processing the motion posture data to obtain angle information, the angle information including a first angle and an angular velocity, wherein the first angle is the sum of the thigh angle and the calf angle on the same side in the motion posture data, and the angular velocity is the product of the sampling frequency of the IMU inertial measurement system and the difference between the first angles at two adjacent sampling time points; The first angle and the angular velocity are filtered and smoothed and then converted into a phase angle.

3. The method according to claim 2, characterized in that The step of filtering and smoothing the first angle and the angular velocity and converting them into a phase angle comprises: Based on a preset low-pass filter, filtering and smoothing the first angle and the angular velocity are performed respectively to obtain a first filtered angle and a first filtered angular velocity; An arc tangent process is performed on the first filtering angle and the first filtering angular velocity, and a negative value of the arc tangent process is used as the phase angle.

4. The method according to claim 3, characterized in that The calculation formula of the first low-pass filter is: θ′(t)=a×θ′(t-1)+(1-a)×θ(t), θ′(t) is the filter output value of the first angle at the current time point, θ(t) is the first angle at the current time point, a is the filter coefficient, and θ′(t-1) is the filter output value of the first angle at the previous time point; The calculation formula of the second low-pass filter is: ω′(t)=b×ω′(t-1)+(1-b)×ω(t), ω′(t) is the filter output value at the current time point, ω′(t-1) is the filter output value of the angular velocity at the previous time point, ω(t) is the angular velocity at the current time point, and b is the filter coefficient.

5. The method according to claim 1, characterized in that The step of using the power assistance planning algorithm corresponding to the phase angle to estimate the torque of the phase angle to obtain the desired control torque of the knee joint includes: If the phase angle is greater than 0, the phase angle is calculated according to a preset first power assist algorithm to obtain a first desired control torque; If the phase angle is not greater than 0, the phase angle is calculated according to a preset second power assist algorithm to obtain a second desired control torque.

6. The method according to claim 5, characterized in that The calculating the phase angle according to the preset first power assist algorithm to obtain the first expected control torque also includes: When the shaking phenomenon of the knee joint is detected, the first power assist algorithm is used to determine the first expected control torque at the time point of entering the shaking state; If the first expected control torque at the time point of entering the shaking state is greater than the preset shaking maximum control torque threshold, the first expected control torque at the time point of entering the shaking state is updated to the preset shaking maximum control torque threshold.

7. The method according to claim 6, characterized in that Knee joint vibration is detected, including: Acquire a second angle obtained by processing the motion posture data, where the second angle is a difference between an angle of the thigh and an angle of the calf on the same side in the motion posture data; If the second angle at the current time point is less than the preset second angle threshold, and the phase angle at any time point within the predetermined time period before the current time point is greater than the preset first phase angle threshold and less than the preset second phase angle threshold, it is determined that the knee joint has shaking.

8. The method according to claim 1, characterized in that The power-assisted control of the knee joint according to the preset feedback IPD closed-loop control algorithm and the desired control torque includes: Obtaining an actual feedback torque from the IMU inertial measurement system, and calculating a torque control error between the desired control torque and the actual feedback torque; Determining an integral of the torque control error and a differential of the torque control error; The torque control error, the integral of the torque control error and the difference of the torque control error are input into the feedback IPD closed-loop control algorithm to obtain the torque control output, so as to perform power-assisted control on the knee joint according to the torque control output.

9. An exoskeleton knee joint power assist control device, characterized in that: include: The posture data acquisition module is used to obtain the motion posture data of the lower limbs of the human body from the IMU inertial measurement system; A phase angle data determination module, used for processing the motion posture data to obtain a corresponding phase angle; An expected torque determination module, used to estimate the torque of the phase angle by using the power assistance planning algorithm corresponding to the phase angle, so as to obtain an expected control torque of the knee joint; The joint assist control module is used to perform assist control on the knee joint according to the desired control torque and a preset force feedback IPD closed-loop control algorithm.

10. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-readable instructions, and the processor is used to execute the computer-readable instructions, wherein the computer-readable instructions execute the method according to any one of claims 1 to 8 when executed.

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