Pneumatic soft body actuator, control method and device of pneumatic soft body actuator and electronic equipment

Abstract

The invention provides a pneumatic soft body actuator, a control method and device of the pneumatic soft body actuator and electronic equipment, and belongs to the technical field of mechanical exoskeleton, the pneumatic soft body actuator comprises a pneumatic module, an air pump module, the control equipment and a detection module, the pneumatic module comprises a flexible supporting plate and a plurality of air bags arranged on the flexible supporting plate, and the air pump module is connected with the control equipment. One end of each air bag is arranged on the flexible supporting plate, and the distance between the arrangement positions of every two adjacent air bags is equal to a preset value. The detection module is used for monitoring the actuation condition of the pneumatic module in real time to obtain the current measurement actuation amount. The control device is used for adjusting the gas injection amount of the gas pump module according to the target actuation amount and the current measurement actuation amount so as to adjust the actuation amount of the pneumatic module. In this way, the pneumatic soft actuator has high torque output, and meanwhile portability, comfort and control flexibility are improved. And a control parameter self-adaptive adjustment mechanism is introduced into PID control, so that the control precision is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mechanical exoskeletons, and in particular to a pneumatic soft actuator, a control method and device for the pneumatic soft actuator, and electronic equipment. Background Art

[0002] Wearable robotic exoskeletons are revolutionizing human capabilities, with applications ranging from enhancing strength and endurance to assisting rehabilitation and improving daily activities. These devices are particularly effective by reducing physical fatigue, improving weight-bearing capacity, and helping individuals with physical disabilities regain mobility and independence.

[0003] Wearable robotic exoskeletons are widely used in rehabilitation exoskeleton manipulators due to their advantages such as lightness, low cost and simple drive to assist patients with hand dysfunction in rehabilitation training and daily life. However, the current wearable robotic exoskeletons have poor flexibility, comfort and lightness. Summary of the invention

[0004] In view of this, an object of the present invention is to provide a pneumatic soft actuator, a control method, a device and an electronic device for a pneumatic soft actuator. The pneumatic soft actuator has a smaller volume, greatly improves flexibility, comfort and portability, and realizes automatic control with high precision.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] In a first aspect, the present invention provides a pneumatic soft actuator, comprising a pneumatic module, an air pump module, a control device and a detection module;

[0007] The pneumatic module includes a flexible support plate and a plurality of airbags arranged on the flexible support plate, one end of the airbag is arranged on the flexible support plate, and the distance between the arrangement positions of every two adjacent airbags is equal to a preset value, and each of the airbags is provided with an air hole;

[0008] The air pump module is in communication with the air hole, the detection module is arranged on the pneumatic module, and the control device is respectively in communication connection with the detection module and the air pump module;

[0009] The detection module is used to monitor the actuation status of the pneumatic module in real time, obtain the current measured actuation amount, and report the measured actuation amount to the control device; wherein the measured actuation amount includes angle and torque;

[0010] The control device is used to adjust the gas injection amount of the air pump module according to the target actuation amount and the current measured actuation amount, so as to adjust the actuation amount of the pneumatic module.

[0011] Optionally, the control device is used to adjust the gas injection amount of the air pump module according to the target actuation amount and the current measured actuation amount, comprising:

[0012] Using Kalman filtering to filter the current measured actuation amount to obtain a current denoised actuation amount;

[0013] Calculating an error value between the denoised actuation amount and the target actuation amount;

[0014] Inputting the measured actuation amount into a preset parameter adaptive model, and using the parameter adaptive model to generate a control parameter corresponding to the measured actuation amount;

[0015] The control parameters are used to update the parameters of the PID controller, and the error value is input into the PID controller after the parameters are updated to obtain and output the control amount to the air pump module to adjust the gas injection amount of the air pump module.

[0016] Optionally, the air pump module includes an air pump, an air pipe and a control valve, the air pump is connected to the air hole of the airbag through the air pipe, the control valve is arranged on the air pipe, and the control device is communicatively connected to the air pump and the control valve respectively.

[0017] Optionally, the step of using Kalman filtering to filter the current measured actuation amount to obtain the current denoised actuation amount includes:

[0018] Obtaining state estimation data of the state equation at the previous moment to the current moment; wherein the state estimation data includes an actuation estimation amount and an error covariance;

[0019] Using the observation equation, combined with the actuation estimation amount and the error covariance at the current moment, the measured actuation amount is filtered and denoised to obtain the denoised actuation amount at the current moment;

[0020] By using the state equation, the denoised actuation amount at the next moment is estimated according to the denoised actuation amount at the current moment, so as to obtain the state estimation data at the next moment.

[0021] Optionally, the step of acquiring the parameter adaptive model includes:

[0022] Acquire multiple groups of experimental data of the pneumatic soft actuator; wherein each group of the experimental data includes a state quantity and an optimal control parameter of the PID controller when the pneumatic module is in the state quantity, and the state quantity includes an angle and a torque;

[0023] The state quantity in the experimental data is used as input, and the optimal control parameter in the experimental data is used as output, and a parameter adaptive model is obtained by fitting.

[0024] Optionally, the step of fitting a parameter adaptive model by taking the state quantity in the experimental data as input and the optimal control parameter in the experimental data as output comprises:

[0025] Using a variety of model fitting methods, taking the state quantity in the experimental data as input and the optimal control parameters in the experimental data as output, to fit and obtain a plurality of initial models;

[0026] According to the experimental data, the initial models are evaluated to obtain a performance value of each initial model; wherein the performance value includes accuracy;

[0027] The initial model with the best performance value is used as the parameter adaptive model.

[0028] Optionally, before the step of using Kalman filtering to filter the current measured actuation amount to obtain the current denoised actuation amount, the method further includes:

[0029] Based on at least one measured actuation amount before the current moment, determining whether the current measured actuation amount is abnormal;

[0030] If not, performing the step of using Kalman filtering to filter the current measured actuation amount to obtain the current denoised actuation amount;

[0031] If so, an alarm is issued.

[0032] In a second aspect, the present invention provides a control method for a pneumatic soft actuator, which is applied to the pneumatic soft actuator as described in the first aspect, and the control method comprises:

[0033] Sampling the detection module of the pneumatic soft actuator to obtain the current measured actuation amount of the pneumatic module of the pneumatic soft actuator; wherein the measured actuation amount includes angle and torque;

[0034] According to the target actuation amount and the current measured actuation amount, the gas injection amount of the air pump module in the pneumatic soft actuator is adjusted to adjust the actuation amount of the pneumatic module.

[0035] Optionally, the step of adjusting the gas injection amount of the air pump module in the pneumatic soft actuator according to the target actuation amount and the current measured actuation amount comprises:

[0036] Using Kalman filtering to filter the current measured actuation amount to obtain a current denoised actuation amount;

[0037] Calculating an error value between the denoised actuation amount and the target actuation amount;

[0038] Inputting the measured actuation amount into a preset parameter adaptive model, and using the parameter adaptive model to generate a control parameter corresponding to the measured actuation amount;

[0039] The control parameters are used to update the parameters of the PID controller, and the error value is input into the PID controller after the parameters are updated to obtain and output the control amount to the air pump module to adjust the gas injection amount of the air pump module.

[0040] In a third aspect, the present invention provides a control device for a pneumatic soft actuator, which is applied to the pneumatic soft actuator as described in the first aspect, and the control device includes a detection sampling module and a control module;

[0041] The detection sampling module is used to sample the detection module of the pneumatic soft actuator to obtain the current measured actuation amount of the pneumatic module of the pneumatic soft actuator; wherein the measured actuation amount includes angle and torque;

[0042] The control module is used to adjust the gas injection amount of the air pump module in the pneumatic soft actuator according to the target actuation amount and the current measured actuation amount, so as to adjust the actuation amount of the pneumatic module.

[0043] In a fourth aspect, the present invention provides an electronic device comprising a processor and a memory, wherein the memory stores a computer program executable by the processor, and the processor can execute the computer program to implement the control method of the pneumatic soft actuator as described in the second aspect.

[0044] In a fifth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the control method of the pneumatic soft actuator as described in the second aspect.

[0045] The pneumatic soft actuator, the control method, device and electronic device of the pneumatic soft actuator provided by the embodiments of the present invention have at least the following beneficial effects:

[0046] (1) A pneumatic module is formed by arranging multiple airbags at equal intervals on a flexible support plate, so that the pneumatic module can generate high torque for actuation when inflated, and its volume can be greatly reduced after deflation to occupy only a small space, thereby greatly reducing the volume of the pneumatic soft actuator and improving its portability;

[0047] (2) The control device uses a feedback adjustment method to adjust the gas injection amount of the air pump module according to the real-time measured actuation amount of the pneumatic module, so as to achieve high-precision adjustment of the angle and / or torque of the pneumatic module. At the same time, it can achieve adjustment of any angle or torque, greatly improving the flexibility of the pneumatic module;

[0048] (3) The inflated airbag has low pressure and is flexible, thereby minimizing the wearer's discomfort and improving comfort.

[0049] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0051] Figure 1 A schematic structural diagram of a pneumatic soft actuator provided in an embodiment of the present invention is shown.

[0052] Figure 2 A schematic structural diagram of a pneumatic module provided in an embodiment of the present invention is shown.

[0053] Figure 3 One of the flow charts of the control method of the pneumatic soft actuator provided by the embodiment of the present invention is shown.

[0054] Figure 4 Shows Figure 3 Schematic diagram of the process of some sub-steps of step 12.

[0055] Figure 5 One of the control system architectures of the pneumatic soft actuator provided by an embodiment of the present invention is shown.

[0056] Figure 6 The second control system architecture of the pneumatic soft actuator provided by the embodiment of the present invention is shown.

[0057] Figure 7 The second flowchart of the control method of the pneumatic soft actuator provided by the embodiment of the present invention is shown.

[0058] Figure 8 Shows Figure 7 Schematic diagram of the process flow of some sub-steps of step 23.

[0059] Fig. 9 The third flowchart of the control method of the pneumatic soft actuator provided by the embodiment of the present invention is shown.

[0060] Fig.10 A block diagram of an electronic device provided by an embodiment of the present invention is shown.

[0061] Fig.11 A fourth flow chart of the control method of the pneumatic soft actuator provided in an embodiment of the present invention is shown.

[0062] Fig.12 A block diagram of a control device for a pneumatic soft actuator provided by an embodiment of the present invention is shown.

[0063] Explanation of the reference numerals: 10 - pneumatic soft actuator; 110 - pneumatic module; 1101 - flexible support plate; 1102 - airbag; 120 - air pump module; 1201 - air pump; 1202 - air pipe; 1203 - control valve; 130 - control device; 140 - detection module; 20 - electronic device; 210 - memory; 220 - processor; 230 - communication module; 30 - control device; 310 - detection sampling module; 320 - control module. DETAILED DESCRIPTION

[0064] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0065] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0066] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0067] Referring to the figure, an embodiment of the present invention provides a pneumatic soft actuator 10, referring to Figure 1 , including a pneumatic module 110, an air pump module 120, a control device 130 and a detection module 140.

[0068] Reference Figure 2 The pneumatic module 110 includes a flexible support plate 1101 and a plurality of airbags 1102 arranged on the flexible support plate 1101, one end of the airbag 1102 is arranged on the flexible support plate 1101, and the distance between the setting positions of every two adjacent airbags 1102 is equal to a preset value, and each airbag 1102 is provided with an air hole.

[0069] Please continue to refer to Figure 1 The air pump module 120 is connected to the air hole of the airbag 1102, the detection module 140 is arranged on the pneumatic module 110, and the control device 130 is respectively communicated with the detection module 140 and the air pump module 120.

[0070] The detection module 140 is used to monitor the actuation status of the pneumatic module 110 in real time, obtain the current measured actuation amount, and report the measured actuation amount to the control device 130. The measured actuation amount includes angle and torque.

[0071] The control device 130 is used to adjust the gas injection amount of the air pump module 120 according to the target actuation amount and the current measured actuation amount, so as to adjust the actuation amount of the pneumatic module 110.

[0072] It should be noted that the pneumatic module 110 can be set at any corresponding part that requires exercise assistance (such as the knee joint, ankle joint, elbow joint, etc. of the human body in rehabilitation). The control of the control device 130 is a continuous process, and the air pump module 120 will continuously adjust the gas injection amount of the pneumatic module 110 according to the target actuation amount and the current measured actuation amount, so that the actuation amount of the pneumatic module 110 quickly reaches the target actuation amount and is maintained at the target actuation amount, thereby providing auxiliary motion force for the set part.

[0073] The number of airbags 1102 in the pneumatic module 110 can be flexibly selected according to the needs, for example, it can be 6, 5, or 8. In addition, one end of the multiple airbags 1102 is neatly arranged on the flexible support plate 1101, strictly aligned, and the distance between the setting positions of each two adjacent airbags 1102 is less than the thickness of the airbag 1102 when it is fully expanded. The airbag 1102 can be fixed to the flexible support plate 1101 by sewing or by hot melting, and its fixing method is not limited.

[0074] In addition, all airbags 1102 have the same shape, size and volume, and their shapes can be flexibly selected. For example, in the uninflated state, the airbags 1102 can be Figure 1 The rectangle shown may also be any regular or irregular geometric shape such as a square or a circle.

[0075] Similarly, the shape of the flexible support plate 1101 can be flexibly set, for example, it can be rectangular, circular, or any regular or irregular geometric shape. In addition, the flexible support plate 1101 can be cotton cloth or any fabric that can be folded or bent at will, and the choice of material is not limited.

[0076] In the above-mentioned pneumatic soft actuator 10, a pneumatic module 110 is formed by equidistantly and alignedly arranging a plurality of airbags 1102 on a flexible support plate 1101, so that there is no problem of excessive contact area and small bending angle (i.e., output torque) caused by gaps being filled between adjacent airbags 1102. The interaction force between adjacent aligned airbags 1102 is maximized at any inflation amount, thereby ensuring high torque output of the pneumatic module 110.

[0077] After deflation, the volume of the pneumatic module 110 is greatly reduced and only occupies a small space, thereby greatly reducing the volume of the pneumatic soft actuator 10 and improving portability. In addition, the airbag 1102 after inflation has low pressure and is flexible, thereby minimizing the discomfort of the wearer and improving comfort.

[0078] In addition, the control device 130 adopts a feedback adjustment method to adjust the gas injection amount of the air pump module 120 according to the real-time measured actuation amount of the pneumatic module 110, so as to achieve high-precision adjustment of the angle and / or torque of the pneumatic module 110, and at the same time can achieve adjustment of any angle or torque, greatly improving the flexibility of the pneumatic module 110.

[0079] In order to monitor and obtain the actuation status of the pneumatic module 110 with high precision, the detection module 140 may include a micro inertial sensor and a torque sensor, and the micro inertial sensor and the torque sensor are both arranged on the flexible support plate 1101 of the pneumatic module 110 .

[0080] In the working state, the micro inertial sensor monitors and obtains the angle (ie, the bending angle) of the pneumatic module 110 in real time, and the torque sensor monitors and obtains the torque of the pneumatic module 110 in real time.

[0081] The micro inertial sensor may also be replaced by any sensor capable of measuring an angle.

[0082] In addition, the structure of the air pump module 120 in the above-mentioned pneumatic soft actuator 10 can also be flexibly set. For example, the air pump module 120 can be composed of a solenoid valve and an air duct. The air duct is used to connect the airbag 1102 with the inflation and deflation parts for inflating the airbag 1102. The solenoid valve is arranged on the air duct to control the gas injection amount of the pneumatic module 110, that is, to switch the inflation and deflation of the pneumatic module 110, and to adjust the inflation amount and deflation amount.

[0083] Reference Figure 1 The air pump module 120 may also include an air pump 1201, an air pipe 1202 and a control valve 1203. The air pump 1201 is connected to the air hole of the airbag 1102 through the air pipe 1202. The control valve 1203 is arranged on the air pipe 1202. The control device 130 is respectively communicated with the air pump 1201 and the control valve 1203.

[0084] The control valve 1203 may be a solenoid valve, so that the control device 130 can control the amount of gas injected into the pneumatic module 110 by the air pump module 120 by controlling the opening of the solenoid valve.

[0085] Furthermore, the control valve 1203 may include a vacuum solenoid valve and a pressure solenoid valve, the air outlet of the air pump 1201 is connected to the first branch pipe, the air inlet of the air pump 1201 is connected to the second branch pipe, the first branch pipe and the second branch pipe are both connected to the air pipe 1202, the vacuum solenoid valve is arranged on the second branch pipe, and the pressure solenoid valve is arranged on the first branch pipe. Thus, the air pressure, airflow direction and gas volume injected into the pneumatic module 110 are controlled by the second branch pipe and the pressure solenoid valve, and the pneumatic module 110 is vacuum treated by the first branch pipe and the vacuum solenoid valve.

[0086] Based on the above structure, the control device 130 can flexibly select the method of adjusting the gas injection amount of the air pump module 120 according to the target actuation amount and the current measured actuation amount. For example, a PID controller can be used to determine the control signal (voltage) according to the error value between the target actuation amount and the current measured actuation amount, and the control signal can be output to the control valve 1203, and the control valve 1203 adjusts the opening in response to the control signal, thereby adjusting the gas injection amount of the air pump module 120 to the pneumatic module 110, and then the pneumatic module 110 generates a new actuation amount under the action of the gas injection amount, and the actuation amount is equal to or closer to the target actuation amount. It can also be a preset rule to determine the control signal of the control valve 1203 according to the target actuation amount and the current measured actuation amount, so as to adjust the gas injection amount of the air pump module 120 to the pneumatic module 110. And the above method is only an example, and its implementation method is not limited.

[0087] In order to improve the control accuracy so that the actuation amount of the pneumatic module 110 can reach and maintain the target actuation amount (target angle or target torque) faster, the control device 130 is introduced to filter the measured actuation amount and adaptively adjust the control parameters of the PID controller (i.e., proportional gain, integral gain and differential gain) to adjust the control parameters so that the PID controller generates the control amount of the air pump module 120. Figure 3 , the control device 130 adjusts the gas injection amount of the air pump module 120 through the following steps 12 to 18.

[0088] Step 12: Use Kalman filtering to filter the current measured actuation amount to obtain the current denoised actuation amount.

[0089] Step 14, calculating the error value between the denoised actuation amount and the target actuation amount.

[0090] Step 16: input the measured actuation amount into a preset parameter adaptive model, and use the parameter adaptive model to generate control parameters corresponding to the measured actuation amount.

[0091] Step 18, using the control parameters to update the parameters of the PID controller, and inputting the error value into the PID controller after the parameters are updated, obtaining and outputting the control amount to the air pump module to adjust the gas injection amount of the air pump module.

[0092] Among them, refer to Figure 4 In step 12, the process of filtering and denoising by using Kalman filtering to obtain the current denoised actuation amount includes steps 121 to 125.

[0093] Step 121, obtaining the state estimation data of the state equation at the previous moment to the current moment.

[0094] The state estimation data includes the actuation estimate and the error covariance.

[0095] Step 123 , using the observation equation, combined with the actuation estimation amount and the error covariance at the current moment, the measured actuation amount is filtered and denoised to obtain the denoised actuation amount at the current moment.

[0096] Step 125, using the state equation, estimates the denoised actuation quantity at the next moment according to the denoised actuation quantity at the current moment, and obtains the state estimation data at the next moment. Here, the state estimation data at the next moment is used in the filtering and denoising process of the measured actuation quantity at the next moment.

[0097] In the above steps 123 to 125, the input of the Kalman filter is the measured actuation amount, and the output is the current estimated value after noise elimination (i.e., the denoised actuation amount). For example, when the actuation amount is an angle, the angle estimated at the previous moment is used to predict the angle at the current moment, and the predicted current angle and the currently measured angle are used to estimate the actual current angle. When the actuation amount is torque, the above process is the same and will not be repeated here.

[0098] The state estimation data of the previous moment to the current moment can be expressed by the formula: represents the estimated actuation at time k-1 to time k, P k|k-1 represents the error covariance of the estimate at time k-1 for time k, F k represents the state transfer matrix, Bk represents the control matrix, u k-1 represents the control input, P k-1|k-1 It represents the error covariance of the estimate at time k-1 to time k-1.

[0099] The process of step 124 can be expressed by the formula: P k|k =(IK k H k ) k|k-1 , K k represents the Kalman gain at time k, H k The observation matrix R represents the measured actuation quantity at time k. k represents the observation noise covariance at time k, z k represents the vector of measured actuation at time k, and I is the unit matrix.

[0100] It should be noted that, since the measured actuation amount includes the angle and torque of the pneumatic module 110, the denoised actuation amount obtained after denoising in the above manner is the denoised angle and / or denoised torque. In addition, the above formula is only an example, and the process of filtering and denoising the measured actuation amount to obtain the denoised actuation amount is not limited.

[0101] Through the above steps 121 to 125, a Kalman filter is used to eliminate the noise of the measured actuation amount, so that the denoised actuation amount obtained after filtering is more accurate, thereby further improving the control accuracy.

[0102] After obtaining the current denoised actuation amount, the difference between the target actuation amount and the current actuation amount is calculated in step 14 to obtain the error value. For example, when the target actuation amount in the control command of the control device 130 is the target angle, the difference between the target angle and the denoised angle is the controlled (angle) error value. At this time, the control system architecture of the pneumatic soft actuator 10 is as follows: Figure 5 Similarly, when the target actuation amount in the control command is the target torque, the difference between the target torque and the de-noising torque is the controlled (angle) error value. At this time, the control system architecture of the pneumatic soft actuator 10 is as follows: Figure 6 shown.

[0103] While executing step 14 to obtain the error value, execute step 16 at the same time, using the pre-obtained parameter adaptive model (which characterizes the relationship between the state quantity of the pneumatic module 110 (including but not limited to any of torque, angle, gas pressure, room temperature, load and setting position, etc.) and the optimal control parameter of the corresponding PID controller, the optimal control parameter refers to the control parameter that optimizes the control accuracy of the PID controller under the corresponding state quantity), take the measured actuation quantity as the input of the parameter adaptive model, and obtain the control parameter corresponding to the measured actuation quantity.

[0104] Here, any of the influencing factors such as the current gas pressure, room temperature, load and setting position of the pneumatic module 110, and the measured actuation amount can be used as inputs of the parameter adaptive model, and the control parameters corresponding to the parameter adaptive model are used. In this way, the control parameters are more adapted to the current state of the pneumatic module 110, so that the performance of the PID controller is better, the output control amount is more accurate, and the control accuracy is further improved.

[0105] Furthermore, in step 18, the control device 130 replaces the parameters of the PID controller with the control parameters, and inputs the error value into the PID controller after the control parameters are replaced, and uses the PID controller to generate the control amount of the control valve 1203 of the air pump module 120.

[0106] Through the above steps 12 to 18, the parameter adaptive model is used to adjust the control parameters of the PID controller in real time according to the current state of the pneumatic module 110 to adapt to different external loads, angle positions, control delays and other interference factors, so that the obtained control amount and gas injection amount are more accurate, greatly improving the accuracy of the control amount, and achieving the robustness and stability control of the system. At the same time, the Kalman filter is used to filter and denoise the measured actuation amount to reduce or even eliminate the measurement noise, which helps to further improve the control accuracy.

[0107] The acquisition method of the parameter adaptive model used in the above step 16 can be flexibly set, for example, it can be obtained by using the experimental data of the pneumatic soft actuator 10 as a training sample and performing iterative training of the model, or it can be obtained by performing linear regression on the experimental data of the pneumatic soft actuator 10 according to preset rules. The above two methods are only examples, and their implementation methods are not limited.

[0108] In order to quickly obtain the parameter adaptive model and facilitate regular updating of the parameter adaptive model to ensure accuracy, the control device 130 of the pneumatic soft actuator 10 can be connected to the service device by wired or wireless communication, referring to Figure 7 The service device can obtain the parameter adaptive model by following steps 21 to 23.

[0109] Step 21, obtaining multiple sets of experimental data of the pneumatic soft actuator.

[0110] Among them, each set of experimental data includes state quantities and the optimal control parameters of the PID controller (including proportional gain, integral gain and differential gain) when the pneumatic module is in the state quantity. The state quantity can be but is not limited to: angle, torque, gas pressure, room temperature, load and setting position of the pneumatic module.

[0111] Step 23, taking the state quantity in the experimental data as input and the optimal control parameter in the experimental data as output, a parameter adaptive model is obtained by fitting.

[0112] Here, any data regression analysis method can be selected, with the state quantity in the experimental data as input and the optimal control parameter in the experimental data as output, to perform regression analysis on the experimental data to obtain a parameter adaptive model. Alternatively, the experimental data can be used as a training sample to perform model training to obtain a parameter adaptive model. The above methods are all examples, and their implementation methods are not limited.

[0113] In order to improve the accuracy and calculation speed of the parameter adaptive model, multiple methods are introduced in step 23 to fit the model, obtain the model corresponding to each method, and then select the optimal model from these models as the concept of the parameter adaptive model. Figure 8 The process of obtaining the parameter adaptive model in step 23 includes steps 231 to 235.

[0114] Step 231, adopting a variety of model fitting methods, taking the state quantity in the experimental data as input and taking the optimal control parameters in the experimental data as output, to fit and obtain a plurality of initial models.

[0115] Among them, each model fitting method obtains at least one initial model, and the model fitting methods include linear regression, nonlinear regression, polynomial regression, least squares method, genetic algorithm, particle swarm optimization and iterative training of neural network models.

[0116] Step 233, evaluating the initial models based on the experimental data to obtain the performance value of each initial model.

[0117] The performance value includes at least accuracy and may also include calculation amount and calculation speed.

[0118] Step 235: Use the initial model with the best performance value as the parameter adaptive model.

[0119] The method of determining the parameter adaptive model can be flexibly selected. For example, the parameter adaptive model can be the initial model with the highest accuracy. The parameter adaptive model can also be the initial model with the best performance value among the initial models whose calculation speed reaches the target speed. The implementation method is not limited.

[0120] Through the above steps 231 to 235 , the performance of the parameter adaptive model is ensured, which helps to improve the control accuracy of the pneumatic soft actuator 10 .

[0121] In addition, when the pneumatic soft actuator 10 is used as a rehabilitation auxiliary device for the human body, once an abnormality or failure occurs, such as a sudden increase or decrease in the output torque, it will bring great personal danger. Therefore, in order to reduce the abnormal risk, eliminate the damage to the applicable object and the pneumatic soft actuator 10 caused by the failure, and improve the safety of the pneumatic soft actuator 10, the function of abnormal detection and abnormal response is introduced in the control device 130. Fig. 9 , step 11 may be further included before step 12.

[0122] Step 11, based on at least one measured actuation amount before the current moment, determine whether the current measured actuation amount is abnormal. If not, execute step 12, and if so, execute step 13.

[0123] Step 13, issue an alarm.

[0124] For example, the angle difference between the angle in the measured actuation amount at the current moment and the angle in the measured actuation amount at the previous moment is calculated. If the angle difference exceeds the system response speed (a preset value), the measured angle is determined to be abnormal. Similarly, the torque difference between the torque in the measured actuation amount at the current moment and the torque in the measured actuation amount at the previous moment is calculated. If the torque difference exceeds the system response speed (a preset value), the measured torque is determined to be abnormal. When at least one of the torque and the angle is abnormal, the measured actuation amount is determined to be abnormal.

[0125] When the angle and / or torque in the measured actuation amount returns to zero, or directly jumps to a certain value, and the duration for which the same value is maintained exceeds a time threshold, it is determined that the measured actuation amount is abnormal.

[0126] When the transient change of one value in the measured actuation quantity exceeds the transient threshold value, while the other value does not fluctuate or change, it is determined that the measured actuation quantity is abnormal.

[0127] The alarm issued in step 13 can promptly remind the user to shut down and stop the pneumatic soft actuator 10. In order to further improve safety, the control device 130 can also directly stop the pneumatic soft actuator 10.

[0128] Through the above method, an abnormality detection function is introduced to detect and handle faults in real time, so that faults can be warned and handled in time, which greatly reduces safety risks and improves the safety of the pneumatic soft actuator 10.

[0129] In addition, in the above-mentioned pneumatic soft actuator 10, the control device 130 can be any one of a microprocessor 220, a single chip microcomputer, a programmable logic device, and a computer device. In addition, it can also include a speaker and a light alarm, so that when the data is determined to be abnormal, the speaker and the light alarm are controlled to alarm to warn the user.

[0130] Please refer to Fig.10 , is a block diagram of an electronic device 20, which may be the control device 130 of the pneumatic soft actuator 10. The electronic device 20 includes a memory 210, a processor 220, and a communication module 230. The memory 210, the processor 220, and the communication module 230 are electrically connected to each other directly or indirectly to achieve data transmission or interaction. For example, these components may be electrically connected to each other via one or more communication buses or signal lines.

[0131] The memory 210 is used to store programs or data. The memory 210 may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc.

[0132] The processor 220 is used to read / write the data or program stored in the memory 210 and execute the corresponding functions. For example, in the pneumatic soft actuator 10 provided above, the processor 220 of the control device 130 can execute the computer program stored in the memory 210 to implement the control method of the pneumatic soft actuator 10.

[0133] The communication module 230 is used to establish a communication connection between the control device 130 and other communication terminals through the network, and to send and receive data through the network. For example, in the pneumatic soft actuator 10 provided above, the communication module 230 of the control device 130 sends and receives data with the detection module 140 and the solenoid valve through the network.

[0134] It should be understood that Fig.10 The structure shown is only a schematic diagram of the structure of the electronic device 20. The electronic device 20 may also include Fig.10 More or fewer components as shown, or with Fig.10 Different configurations shown. Fig.10 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0135] Based on the same concept as the pneumatic soft actuator 10 provided above, refer to Fig.11 The embodiment of the present invention further provides a control method for a pneumatic soft body actuator 10, comprising steps 31 to 33. In the pneumatic soft body actuator 10 provided above, the control device 130 is Fig.10 In the structure shown, when the processor 220 executes the computer program stored in the memory 210, steps 31 to 33 are executed.

[0136] Step 31, sampling the detection module of the pneumatic soft actuator to obtain the current measured actuation amount of the pneumatic module of the pneumatic soft actuator.

[0137] The measured actuation quantities include angle and torque.

[0138] Step 33, according to the target actuation amount and the current measured actuation amount, adjust the gas injection amount of the air pump module in the pneumatic soft actuator to adjust the actuation amount of the pneumatic module.

[0139] It should be noted that, in order to improve the control accuracy so that the actuation amount of the pneumatic module 110 reaches and maintains the target actuation amount (target angle or target torque) faster, the measured actuation amount is filtered in step 13, and the control parameters of the PID controller (i.e., proportional gain, integral gain and differential gain) are adaptively adjusted to adjust the control parameters of the PID controller to generate the control amount of the air pump module 120. At this time, the implementation process of step 13 can refer to the specific contents of steps 12 to 18 above, which will not be repeated here.

[0140] In addition, the control method of the pneumatic soft actuator 10 also includes a process of obtaining a parameter adaptive model. The specific content of the process is described in the above steps 21 to 23, which will not be repeated here. It should be noted that the above steps 21 to 23 are completed by the service device. After the service device executes steps 21 to 23 to obtain the parameter adaptive model, the parameter adaptive model is downloaded and deployed to the control device 130.

[0141] The implementation and effect of the control method of the pneumatic soft actuator 10 can be found in the above description of the implementation of the pneumatic soft actuator 10, which will not be repeated here.

[0142] Based on the same concept as the pneumatic soft actuator 10 provided above, refer to Fig.12 The embodiment of the present invention further provides a control device 30 of a pneumatic soft actuator 10 , including a detection sampling module 310 and a control module 320 .

[0143] The detection sampling module 310 is used to sample the detection module of the pneumatic soft actuator to obtain the current measured actuation amount of the pneumatic module of the pneumatic soft actuator.

[0144] The measured actuation quantities include angle and torque.

[0145] The control module 320 is used to adjust the gas injection amount of the air pump module in the pneumatic soft actuator according to the target actuation amount and the current measured actuation amount, so as to adjust the actuation amount of the pneumatic module.

[0146] Furthermore, the control device 30 of the pneumatic soft actuator 10 also includes a model acquisition module, which is used to: obtain multiple sets of experimental data of the pneumatic soft actuator; use the state quantity in the experimental data as input and the optimal control parameters in the experimental data as output to fit a parameter adaptive model.

[0147] The detection sampling module 310 and the control module 320 are applied to the control device 130 of the pneumatic soft actuator 10, and the model acquisition module is applied to the service device.

[0148] For the specific implementation and effect of the control device 30 of the pneumatic soft actuator 10, please refer to the above description of the implementation of the pneumatic soft actuator 10 and its control method. For example, for the specific implementation and effect of the detection sampling module 310, please refer to the above description of the relevant content of step 31, for the specific implementation and effect of the control module 320, please refer to the above description of the relevant content of step 33 and steps 11 to 18, and for the specific implementation and effect of the error model acquisition module, please refer to the above description of the relevant content of steps 21 to 23, which will not be repeated here.

[0149] In addition, each module of the sensorless control device 30 can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor 220 in the electronic device 20 in the form of hardware, or can be stored in the memory 210 of the electronic device 20 in the form of software, so that the processor 220 can call and execute the operations corresponding to each module to implement the sensorless control method of the pneumatic soft actuator 10 provided above.

[0150] An embodiment of the present invention also provides an electronic device 20, including a processor 220 and a memory 210, wherein the memory 210 stores a computer program that can be executed by the processor 220, and the processor 220 can execute the computer program to implement the control method of the pneumatic soft actuator proposed in the embodiment of the present invention.

[0151] The embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by the processor 220, the control method of the pneumatic soft actuator proposed in the embodiment of the present invention is implemented.

[0152] In summary, the pneumatic soft actuator, the control method, device and electronic device of the pneumatic soft actuator provided by the embodiments of the present invention have at least the following beneficial effects:

[0153] (1) Pneumatic soft actuators have high torque output, while being significantly smaller in size, with low air pressure and flexibility, which improves portability, comfort and control flexibility;

[0154] (ii) The PID principle is adopted for control, and an adaptive adjustment mechanism of control parameters is introduced into the PID control, so that the control quantity is more accurate and the control accuracy is greatly improved.

[0155] (III) Kalman filter and anomaly detection mechanism are introduced into the control to improve the problem of fluctuation of sensor data such as angle and torque, ensure the reliability and stability of feedback signals, and further improve control accuracy.

[0156] In several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0157] In addition, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0158] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk.

[0159] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A pneumatic soft actuator, characterized in that: It includes pneumatic module, air pump module, control equipment and detection module; The pneumatic module includes a flexible support plate and a plurality of airbags arranged on the flexible support plate, one end of the airbag is arranged on the flexible support plate, and the distance between the arrangement positions of every two adjacent airbags is equal to a preset value, and each of the airbags is provided with an air hole; The air pump module is in communication with the air hole, the detection module is arranged on the pneumatic module, and the control device is respectively in communication connection with the detection module and the air pump module; The detection module is used to monitor the actuation status of the pneumatic module in real time, obtain the current measured actuation amount, and report the measured actuation amount to the control device; wherein the measured actuation amount includes angle and torque; The control device is used to adjust the gas injection amount of the air pump module according to the target actuation amount and the current measured actuation amount, so as to adjust the actuation amount of the pneumatic module.

2. The pneumatic soft actuator according to claim 1, characterized in that: The step of the control device adjusting the gas injection amount of the air pump module according to the target actuation amount and the current measured actuation amount comprises: Using Kalman filtering to filter the current measured actuation amount to obtain a current denoised actuation amount; Calculating an error value between the denoised actuation amount and the target actuation amount; Inputting the measured actuation amount into a preset parameter adaptive model, and using the parameter adaptive model to generate a control parameter corresponding to the measured actuation amount; The control parameters are used to update the parameters of the PID controller, and the error value is input into the PID controller after the parameters are updated to obtain and output the control amount to the air pump module to adjust the gas injection amount of the air pump module.

3. The pneumatic soft actuator according to claim 1 or 2, characterized in that: The air pump module includes an air pump, an air pipe and a control valve. The air pump is connected to the air hole of the airbag through the air pipe. The control valve is arranged on the air pipe. The control device is respectively connected to the air pump and the control valve for communication.

4. The pneumatic soft actuator according to claim 2, characterized in that: The step of using Kalman filtering to filter the current measured actuation amount to obtain the current denoised actuation amount includes: Obtaining state estimation data of the state equation at the previous moment to the current moment; wherein the state estimation data includes an actuation estimation amount and an error covariance; Using the observation equation, combined with the actuation estimation amount and the error covariance at the current moment, the measured actuation amount is filtered and denoised to obtain the denoised actuation amount at the current moment; By using the state equation, the denoised actuation amount at the next moment is estimated according to the denoised actuation amount at the current moment, so as to obtain the state estimation data at the next moment.

5. The pneumatic soft actuator according to claim 2 or 4, characterized in that: The step of acquiring the parameter adaptive model includes: Acquire multiple groups of experimental data of the pneumatic soft actuator; wherein each group of the experimental data includes a state quantity and an optimal control parameter of the PID controller when the pneumatic module is in the state quantity, and the state quantity includes an angle and a torque; The state quantity in the experimental data is used as input, and the optimal control parameter in the experimental data is used as output, and a parameter adaptive model is obtained by fitting.

6. The pneumatic soft actuator according to claim 5, characterized in that: The step of fitting a parameter adaptive model by taking the state quantity in the experimental data as input and the optimal control parameter in the experimental data as output comprises: Using a variety of model fitting methods, taking the state quantity in the experimental data as input and the optimal control parameters in the experimental data as output, to fit and obtain a plurality of initial models; According to the experimental data, the initial models are evaluated to obtain a performance value of each initial model; wherein the performance value includes accuracy; The initial model with the best performance value is used as the parameter adaptive model.

7. The pneumatic soft actuator according to claim 2, characterized in that: Before the step of using Kalman filtering to filter the current measured actuation amount to obtain the current denoised actuation amount, the method further includes: Based on at least one measured actuation amount before the current moment, determining whether the current measured actuation amount is abnormal; If not, performing the step of using Kalman filtering to filter the current measured actuation amount to obtain the current denoised actuation amount; If so, an alarm is issued.

8. A method for controlling a pneumatic soft actuator, characterized in that: Applied to the pneumatic soft actuator according to any one of claims 1 to 7, the control method comprises: Sampling the detection module of the pneumatic soft actuator to obtain the current measured actuation amount of the pneumatic module of the pneumatic soft actuator; wherein the measured actuation amount includes angle and torque; According to the target actuation amount and the current measured actuation amount, the gas injection amount of the air pump module in the pneumatic soft actuator is adjusted to adjust the actuation amount of the pneumatic module.

9. A control device for a pneumatic soft actuator, characterized in that: Applied to the pneumatic soft actuator according to any one of claims 1 to 7, the control device comprises a detection sampling module and a control module; The detection sampling module is used to sample the detection module of the pneumatic soft actuator to obtain the current measured actuation amount of the pneumatic module of the pneumatic soft actuator; wherein the measured actuation amount includes angle and torque; The control module is used to adjust the gas injection amount of the air pump module in the pneumatic soft actuator according to the target actuation amount and the current measured actuation amount, so as to adjust the actuation amount of the pneumatic module.

10. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores a computer program that can be executed by the processor, and the processor can execute the computer program to implement the control method of the pneumatic soft actuator as described in claim 7.

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