Pneumatic soft actuators, control methods, devices and electronic equipment for pneumatic soft actuators

Abstract

This invention proposes a pneumatic soft actuator, a control method for the pneumatic soft actuator, a device for the pneumatic soft actuator, and an electronic device, belonging to the field of mechanical exoskeleton technology. The pneumatic soft actuator includes a pneumatic module, an air pump module, a control device, and a detection module. The pneumatic module includes a flexible support plate and multiple airbags disposed on the flexible support plate. One end of each airbag is disposed on the flexible support plate, and the spacing between any two adjacent airbags is equal to a preset value. The detection module is used to monitor the actuation of the pneumatic module in real time to obtain the current measured actuation amount. 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, thereby adjusting the actuation amount of the pneumatic module. In this way, the pneumatic soft actuator has high torque output while improving portability, comfort, and control flexibility. Furthermore, an adaptive adjustment mechanism for control parameters is introduced into the PID control, which greatly improves the control accuracy.

Description

Technical Field

[0001] This invention relates to the field of mechanical exoskeleton technology, and more specifically, to a pneumatic soft actuator, a control method for the pneumatic soft actuator, a device for the control of the pneumatic soft actuator, and an electronic device. Background Technology

[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 in reducing physical fatigue, increasing load-bearing capacity, and helping individuals with disabilities regain mobility and live independently.

[0003] Wearable robotic exoskeletons are widely used in rehabilitation exoskeletons due to their advantages such as light weight, low cost, and simple actuation, to assist patients with hand dysfunction in rehabilitation training and daily life. However, current wearable robotic exoskeletons are relatively poor in terms of flexibility, comfort, and portability. Summary of the Invention

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

[0005] To achieve the above objectives, 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 disposed on the flexible support plate. One end of each airbag is disposed on the flexible support plate, and the distance between any two adjacent airbags is equal to a preset value. Each airbag is provided with an air hole.

[0008] The air pump module is connected to the air hole, the detection module is mounted on the pneumatic module, and the control device is communicatively connected to the detection module and the air pump module respectively.

[0009] The detection module is used to monitor the actuation status of the pneumatic module in real time, obtain the current measured actuation quantity, and report the measured actuation quantity to the control device; wherein, the measured actuation quantity 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 step of the control device adjusting the gas injection volume of the air pump module according to the target actuation amount and the current measured actuation amount includes:

[0012] Kalman filtering is used to filter the current measured actuation quantity to obtain the current denoised actuation quantity;

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

[0014] The measured actuation amount is input into a preset parameter adaptive model, and the control parameters corresponding to the measured actuation amount are generated using the parameter adaptive model.

[0015] The parameters of the PID controller are updated using the control parameters, and the error value is input into the updated PID controller to obtain and output a control quantity to the air pump module to adjust the gas injection volume of the air pump module.

[0016] Optionally, the air pump module includes an air pump, an air tube, and a control valve. The air pump is connected to the air vent of the airbag through the air tube, the control valve is disposed on the air tube, 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 quantity to obtain the current denoised actuation quantity includes:

[0018] Obtain the state estimation data of the state equation from the previous time step to the current time step; wherein, the state estimation data includes the actuation estimate and the error covariance;

[0019] Using the observation equation, combined with the actuation estimate and the error covariance at the current time, the measured actuation quantity is filtered and denoised to obtain the denoised actuation quantity at the current time.

[0020] Using the state equation, the denoised actuation amount at the next moment is estimated based on the denoised actuation amount at the current moment, thus obtaining the state estimation data for the next moment.

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

[0022] Multiple sets of experimental data for the pneumatic soft actuator are obtained; wherein each set of experimental data includes a state variable and the optimal control parameters of the PID controller when the pneumatic module is in the state variable, and the state variable includes angle and torque;

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

[0024] Optionally, the step of fitting a parameter adaptive model using the state variables in the experimental data as input and the optimal control parameters in the experimental data as output includes:

[0025] Multiple model fitting methods were used, with the state variables in the experimental data as input and the optimal control parameters in the experimental data as output, to fit and obtain multiple initial models;

[0026] Based on the experimental data, the initial models are evaluated to obtain performance values ​​for each initial model; wherein, the performance values ​​include 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 quantity prior to the current time, determine whether the current measured actuation quantity is abnormal;

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

[0031] If so, an alarm will be triggered.

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

[0033] The detection module of the pneumatic soft actuator is sampled to obtain the current measured actuation quantity of the pneumatic module of the pneumatic soft actuator; wherein, the measured actuation quantity includes angle and torque;

[0034] Based on 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 includes:

[0036] Kalman filtering is used to filter the current measured actuation quantity to obtain the current denoised actuation quantity;

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

[0038] The measured actuation amount is input into a preset parameter adaptive model, and the control parameters corresponding to the measured actuation amount are generated using the parameter adaptive model.

[0039] The parameters of the PID controller are updated using the control parameters, and the error value is input into the updated PID controller to obtain and output a control quantity to the air pump module to adjust the gas injection volume of the air pump module.

[0040] Thirdly, the present invention provides a control device for a pneumatic soft actuator, applied in the pneumatic soft actuator as described in the first aspect, the control device comprising 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 quantity of the pneumatic module of the pneumatic soft actuator; wherein, the measured actuation quantity 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] Fourthly, the present invention provides an electronic device including a processor and a memory, the memory storing a computer program executable by the processor, the processor being able to execute the computer program to implement the control method of the pneumatic soft actuator as described in the second aspect.

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

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

[0046] (1) By setting multiple airbags at equal intervals on a flexible support plate to form a pneumatic module, the pneumatic module can generate high torque to actuate when inflated, and its volume can be greatly reduced after deflation, occupying only a small space, thereby greatly reducing the volume of the pneumatic soft actuator and improving portability.

[0047] (2) The control equipment adopts a feedback regulation method, which adjusts the gas injection amount of the air pump module according to the real-time measurement of the actuation amount of the pneumatic module, so as to achieve high-precision adjustment of the angle and / or torque of the pneumatic module, and at the same time, it can realize the adjustment of any angle or torque, which greatly improves 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] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0050] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

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

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

[0053] Figure 3 This is a schematic flowchart of one of the control methods for a pneumatic soft actuator provided in an embodiment of the present invention.

[0054] Figure 4 It shows Figure 3 A flowchart illustrating some of the sub-steps in step 12.

[0055] Figure 5 This invention illustrates one of the control system architectures for a pneumatic soft actuator provided in an embodiment of the present invention.

[0056] Figure 6 This invention illustrates a second control system architecture for a pneumatic soft actuator provided in an embodiment of the present invention.

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

[0058] Figure 8 It shows Figure 7 A flowchart illustrating some of the sub-steps in step 23.

[0059] Figure 9 The third schematic flowchart of the control method for the pneumatic soft actuator provided in the embodiment of the present invention is shown.

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

[0061] Figure 11 The fourth schematic flowchart illustrates the control method for the pneumatic soft actuator provided in this embodiment of the invention.

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

[0063] Explanation of reference numerals in the attached drawings: 10-Pneumatic soft actuator; 110-Pneumatic module; 1101-Flexible support plate; 1102-Airbag; 120-Air pump module; 1201-Air pump; 1202-Air tube; 1203-Control valve; 130-Control device; 140-Detection module; 20-Electronic device; 210-Memory; 220-Processor; 230-Communication module; 30-Control device; 310-Detection and sampling module; 320-Control module. Detailed Implementation

[0064] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0065] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0066] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0067] Please refer to the figure below. An embodiment of the present invention provides a pneumatic soft actuator 10. Figure 1 It includes 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 disposed on the flexible support plate 1101. One end of each airbag 1102 is disposed on the flexible support plate 1101, and the distance between the positions of any two adjacent airbags 1102 is equal to a preset value. 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 vent of the airbag 1102, the detection module 140 is mounted on the pneumatic module 110, and the control device 130 is communicatively connected to the detection module 140 and the air pump module 120 respectively.

[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 quantity, and report the measured actuation quantity to the control device 130. The measured actuation quantity 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 installed on any corresponding part of the body requiring motion assistance (such as the knee, ankle, or elbow joints during rehabilitation). The control device 130 operates continuously, constantly adjusting the gas injection amount from the air pump module 120 to the pneumatic module 110 based on the target actuation amount and the current measured actuation amount. This ensures that the actuation amount of the pneumatic module 110 quickly reaches and maintains the target actuation amount, thereby providing auxiliary motion force to the installed part.

[0073] The number of airbags 1102 in the pneumatic module 110 can be flexibly selected according to requirements, for example, it can be 6, 5, or 8. Furthermore, one end of each airbag 1102 is neatly arranged and strictly aligned on the flexible support plate 1101, with the spacing between any two adjacent airbags 1102 being less than the thickness of the airbag 1102 when fully inflated. The airbags 1102 can be fixed to the flexible support plate 1101 by sewing or by heat fusion; the fixing method is unrestricted.

[0074] Furthermore, all airbags 1102 have the same shape, size, and volume, and their shape can be flexibly selected. For example, in the uninflated state, the airbag 1102 can be... Figure 1 The rectangle shown can 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. Furthermore, the flexible support plate 1101 can be cotton cloth or any fabric that can be arbitrarily folded or bent, and the choice of material is unrestricted.

[0076] In the aforementioned pneumatic soft actuator 10, a pneumatic module 110 is formed by arranging multiple airbags 1102 at equal intervals and aligned on a flexible support plate 1101. This eliminates the problem of excessive contact area and small bending angle (i.e., output torque) caused by filling gaps between adjacent airbags 1102. As a result, the interaction force between adjacent aligned airbags 1102 is maximized under any inflation volume, ensuring high torque output of the pneumatic module 110.

[0077] After deflation, the pneumatic module 110 is significantly reduced in size, occupying only a small space, thereby greatly reducing the size of the pneumatic soft actuator 10 and improving portability. Furthermore, the inflated airbag 1102 has low pressure and is flexible, thus minimizing discomfort for the wearer and improving comfort.

[0078] In addition, the control device 130 adopts a feedback regulation method to adjust the gas injection volume 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, it can realize the adjustment of any angle or torque, which greatly improves the flexibility of the pneumatic module 110.

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

[0080] In operation, the miniature inertial sensor monitors and acquires the angle (i.e., bending angle) of the pneumatic module 110 in real time, and the torque sensor monitors and acquires the torque of the pneumatic module 110 in real time.

[0081] The miniature inertial sensor can also be replaced with any sensor capable of measuring angles.

[0082] Furthermore, the structure of the air pump module 120 in the aforementioned pneumatic soft actuator 10 can also be flexibly configured. For example, the air pump module 120 can be composed of a solenoid valve and an air guide tube. The air guide tube is used to connect the airbag 1102 with the inflation and deflation device for inflating the airbag 1102. The solenoid valve is located on the air guide tube and is used 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 and deflation amounts.

[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 set on the air pipe 1202. The control device 130 is communicatively connected to the air pump 1201 and the control valve 1203 respectively.

[0084] Among them, the control valve 1203 can be a solenoid valve, so that the control device 130 can control the amount of gas injected into the pneumatic module 110 by controlling the opening degree of the solenoid valve.

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

[0086] Based on the above structure, the control device 130 can flexibly select the method to adjust 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) based on the error value between the target actuation amount and the current measured actuation amount, and output the control signal to the control valve 1203. The control valve 1203 responds to the control signal and adjusts its opening, thereby adjusting the gas injection amount of the air pump module 120 to the pneumatic module 110. Under the action of this gas injection amount, the pneumatic module 110 generates a new actuation amount, which is equal to or closer to the target actuation amount. Alternatively, a preset rule can be used to determine the control signal of the control valve 1203 based on the target actuation amount and the current measured actuation amount to adjust the gas injection amount of the air pump module 120 to the pneumatic module 110. The above methods are merely examples, and their implementation is not limited.

[0087] To improve control accuracy and enable the actuation quantity of the pneumatic module 110 to reach and maintain the target actuation quantity (target angle or target torque) more quickly, a function is introduced in the control device 130 to filter the measured actuation quantity and adaptively adjust the control parameters (i.e., proportional gain, integral gain, and derivative gain) of the PID controller. This adjusted PID controller then generates the control quantity for the air pump module 120. (Refer to...) Figure 3 The control device 130 adjusts the gas injection volume of the air pump module 120 through steps 12 to 18.

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

[0089] Step 14: Calculate the error between the denoised actuation amount and the target actuation amount.

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

[0091] Step 18: Update the parameters of the PID controller using control parameters, input the error value into the updated PID controller, obtain and output the control quantity to the air pump module to adjust the gas injection volume of the air pump module.

[0092] Among them, reference Figure 4 Step 12 involves using Kalman filtering to denoise and obtain the current denoised actuator, which includes steps 121 to 125.

[0093] Step 121: Obtain the state estimation data of the state equation at the previous time step for the current time step.

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

[0095] Step 123: Using the observation equation, combined with the current actuation estimate and error covariance, the measured actuation quantity is filtered and denoised to obtain the denoised actuation quantity at the current time.

[0096] Step 125: Using the state equation, estimate the denoised actuator at the next time step based on the denoised actuator at the current time step, obtaining the state estimation data for the next time step. Here, the state estimation data for the next time step is used in the filtering and denoising process of the measured actuator at the next time step.

[0097] In steps 123 to 125 above, the input to the Kalman filter is the measured actuation amount, and the output is the current estimated value after noise removal (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 process is the same as above, and will not be repeated here.

[0098] The state estimate of the current time from the previous time step can be expressed by the formula: P represents the actuation estimator at time k-1 with respect to time k. k|k-1 F represents the covariance of the error estimated at time k-1 with respect to time k. k B represents the state transition matrix.k Represents the control matrix, u k-1 P represents the control input. k-1|k-1 This represents the error covariance of the estimate at time k-1.

[0099] Step 124 can be expressed by the following formula: P k|k =(IK k H k )P k|k-1 K k H represents the Kalman gain at time k. k R represents the observation matrix of the measured actuation at time k. k Let z represent the observation noise covariance at time k. k Let I be the vector representing the measured actuation quantity at time k, and let I be the identity matrix.

[0100] It should be noted that since the measured actuation quantity includes the angle and torque of the pneumatic module 110, the denoised actuation quantity obtained after denoising using the above method is the denoised angle and / or denoised torque. Furthermore, the above formula is merely an example; the process of filtering and denoising the measured actuation quantity to obtain the denoised actuation quantity is not limited.

[0101] Through steps 121 to 125 above, a Kalman filter is used to eliminate noise in the measurement of the actuation quantity, thereby making the filtered and denoised actuation quantity more accurate, and thus further improving the control precision.

[0102] After obtaining the current denoised actuation amount, step 14 calculates the difference between the target actuation amount and the current actuation amount 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 control (angle) error value. At this time, the control system architecture of the pneumatic soft actuator 10 is as follows: Figure 5 As shown. Similarly, when the target actuation quantity in the control command is the target torque, the difference between the target torque and the noise-reducing torque is the control (angle) error value. At this time, the control system architecture of the pneumatic soft actuator 10 is as follows: Figure 6 As shown.

[0103] While performing step 14 to obtain the error value, step 16 is also performed. Using the pre-obtained parameter adaptive model (which characterizes the relationship between the state variables of the pneumatic module 110 (including but not limited to any of the following: torque, angle, gas pressure, room temperature, load, and setting position) and the optimal control parameters of the corresponding PID controller, the optimal control parameters refer to the control parameters that make the control accuracy of the PID controller optimal under the corresponding state variables), the measured actuation quantity is used as the input of the parameter adaptive model to obtain the control parameters 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, along with the measured actuation quantity, can be used as inputs to the parameter adaptive model, utilizing the control parameters corresponding to the parameter adaptive model. This makes the control parameters more closely matched to the current state of the pneumatic module 110, thereby improving the performance of the PID controller, resulting in more precise output control quantities, and further enhancing control accuracy.

[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 replacing the control parameters, and uses the PID controller to generate the control quantity for the control valve 1203 of the air pump module 120.

[0106] Through steps 12 to 18 above, an adaptive parameter model is used to adjust the control parameters of the PID controller in real time based on the current state of the pneumatic module 110. This adapts to different external loads, angular positions, and control delays, resulting in higher accuracy of the obtained control quantity and gas injection quantity, significantly improving the accuracy of the control quantity and achieving robust and stable control of the system. Simultaneously, Kalman filtering is used to filter and denoise the measured actuation quantity, reducing or even eliminating measurement noise, which further enhances control accuracy.

[0107] The method for obtaining the parameter adaptive model used in step 16 above can be flexibly set. For example, it can be obtained by using the experimental data of the pneumatic soft actuator 10 as training samples for 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. Moreover, the above two methods are just examples, and their implementation is not limited.

[0108] To quickly obtain the parameter adaptive model and facilitate regular updates to ensure accuracy, the control device 130 of the pneumatic soft actuator 10 can communicate with the service device via wired or wireless means, as shown in the reference. Figure 7 The service equipment can obtain the parameter adaptive model through the following steps 21 to 23.

[0109] Step 21: Obtain multiple sets of experimental data for the pneumatic soft actuator.

[0110] Each set of experimental data includes state variables and the optimal control parameters (including proportional gain, integral gain, and derivative gain) of the PID controller when the pneumatic module is in the state variable state. The state variables can be, but are not limited to, the pneumatic module's angle, torque, gas pressure, room temperature, load, and setting position.

[0111] Step 23: Using the state variables in the experimental data as input and the optimal control parameters in the experimental data as output, fit the parameter adaptive model.

[0112] Here, any data regression analysis method can be chosen, using the state variables in the experimental data as input and the optimal control parameters 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 training samples to train the model and obtain a parameter adaptive model. The above methods are merely examples, and their implementation is not limited.

[0113] To improve the accuracy and computational speed of the parameter adaptive model, step 23 introduces a method for model fitting, obtaining models for each method, and then selecting the optimal model from these models as the parameter adaptive model concept. (Refer to...) Figure 8 The process of obtaining the parameter adaptive model in step 23 includes steps 231 to 235.

[0114] Step 231: Using multiple model fitting methods, with the state variables in the experimental data as input and the optimal control parameters in the experimental data as output, multiple initial models are fitted to obtain them.

[0115] Each model fitting method yields 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: Based on the experimental data, evaluate the initial models and obtain the performance values ​​of each initial model.

[0117] The performance values ​​include at least accuracy, and may also include computational load and computational speed.

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

[0119] The method for determining the parameter adaptive model can be flexibly chosen. For example, the parameter adaptive model can be the initial model with the highest accuracy. Alternatively, it can be the initial model with the best performance among all initial models whose computational speed reaches the target speed. Its implementation is unrestricted.

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

[0121] Furthermore, when the pneumatic soft actuator 10 is used as a rehabilitation aid, any abnormality or malfunction, such as a sudden increase or decrease in output torque, could pose a significant personal danger. Therefore, to reduce the risk of abnormalities, eliminate the damage to the user and the pneumatic soft actuator 10 caused by malfunctions, and improve the safety of the pneumatic soft actuator 10, anomaly detection and response functions are introduced into the control device 130. (Refer to...) Figure 9 Step 11 may be included before step 12.

[0122] Step 11: Based on at least one measured actuation quantity prior to the current time, determine whether the current measured actuation quantity is abnormal. If not, proceed to step 12; if yes, proceed to step 13.

[0123] Step 13: Issue an alarm.

[0124] For example, the angle difference between the current measured actuation value and the angle measured 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 current measured actuation value and the torque measured 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 angle is abnormal, the measured actuation value is determined to be abnormal.

[0125] If the angle and / or torque in the measured actuation quantity return to zero, or jump directly to a certain value, and the duration of maintaining the same value reaches a time threshold, then the measured actuation quantity is determined to be abnormal.

[0126] If the transient change in one value of the measured actuator exceeds the transient threshold, while the other value shows no fluctuation or change, the measured actuator is determined to be abnormal.

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

[0128] By introducing an anomaly detection function in the above manner to detect and handle faults in real time, it is possible to provide timely warnings and handle faults, which greatly reduces safety risks and improves the safety of the pneumatic soft actuator 10.

[0129] Furthermore, in the aforementioned pneumatic soft actuator 10, the control device 130 can be any of a microprocessor 220, a microcontroller, a programmable logic device, or a computer device. Additionally, it may include a speaker and a light warning device, thereby controlling the speaker and light warning device to sound an alarm when abnormal data is detected, to alert the user.

[0130] Please refer to Figure 10 This is a block diagram of electronic device 20, which can be the control device 130 of the aforementioned pneumatic soft actuator 10. Electronic device 20 includes a memory 210, a processor 220, and a communication module 230. The memory 210, processor 220, and communication module 230 are electrically connected directly or indirectly to each other to achieve data transmission or interaction. For example, these components can be electrically connected to each other through 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, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0132] The processor 220 is used to read / write data or programs stored in the memory 210 and perform 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 via a network, and to send and receive data via 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 via a network.

[0134] It should be understood that, Figure 10 The structure shown is only a schematic diagram of the electronic device 20. The electronic device 20 may also include components that are larger than those shown. Figure 10 The more or fewer components shown, or having the same Figure 10 The different configurations shown. Figure 10 The components shown can be implemented using hardware, software, or a combination thereof.

[0135] Based on the same concept as the pneumatic soft actuator 10 provided above, referring to Figure 11 This invention also provides a control method for a pneumatic soft actuator 10, including steps 31 to 33. This ensures that, in the pneumatic soft actuator 10 provided above, the control device 130... Figure 10 The structure shown executes steps 31 to 33 when the processor 220 executes the computer program stored in the memory 210.

[0136] Step 31: 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.

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

[0138] Step 33: 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.

[0139] It should be noted that, in order to improve control accuracy and enable the actuation quantity of the pneumatic module 110 to reach and maintain the target actuation quantity (target angle or target torque) more quickly, step 13 introduces the concept of filtering the measured actuation quantity and adaptively adjusting the control parameters (i.e., proportional gain, integral gain, and derivative gain) of the PID controller, so that the PID controller with adjusted control parameters generates the control quantity of the air pump module 120. The implementation process of step 13 can be found in the details of steps 12 to 18 above, and will not be repeated here.

[0140] Furthermore, the control method for the pneumatic soft actuator 10 also includes a process for acquiring a parameter adaptive model. The specific details of this process are described in steps 21 to 23 above and will not be repeated here. It should be noted that steps 21 to 23 are performed by the service device. After obtaining the parameter adaptive model through steps 21 to 23, the service device downloads and deploys the parameter adaptive model to the control device 130.

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

[0142] Based on the same concept as the pneumatic soft actuator 10 provided above, referring to Figure 12 The present invention also provides a control device 30 for 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 actuation quantities being measured 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, used to: acquire multiple sets of experimental data of the pneumatic soft actuator; and fit a parameter adaptive model by taking the state variables in the experimental data as input and the optimal control parameters in the experimental data as output.

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

[0148] For details on the implementation and effects of the control device 30 for 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 implementation and effects of the detection sampling module 310, please refer to the description of the relevant content in step 31 above. For the implementation and effects of the control module 320, please refer to the description of the relevant content in steps 33 and steps 11 to 18 above. For the implementation and effects of the error model acquisition module, please refer to the description of the relevant content in steps 21 to 23 above. These details will not be repeated here.

[0149] Furthermore, each module of the aforementioned sensorless control device 30 can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor 220 within the electronic device 20, or stored in the memory 210 of the electronic device 20 as software, so that the processor 220 can call and execute the corresponding operations of each module to achieve the sensorless control method of the pneumatic soft actuator 10 provided above.

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

[0151] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by processor 220, implements the control method for the pneumatic soft actuator as proposed in this invention.

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

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

[0154] (ii) The PID principle is used for control, and an adaptive adjustment mechanism for control parameters is introduced into the PID control, which makes the control quantity more accurate and greatly improves the control precision.

[0155] (III) The introduction of Kalman filters and anomaly detection mechanisms in the control improves the problem of fluctuations in sensor data such as angle and torque, ensures the reliability and stability of feedback signals, and further improves control accuracy.

[0156] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

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

[0158] If the aforementioned functions are implemented as software functional 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 this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0159] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A pneumatic soft actuator, characterized in that, Includes pneumatic modules, air pump modules, control equipment, and detection modules; The pneumatic module includes a flexible support plate and multiple airbags disposed on the flexible support plate. One end of each airbag is disposed on the flexible support plate. The ends of the multiple airbags are neatly arranged and strictly aligned on the flexible support plate. The distance between the positions of any two adjacent airbags is equal to a preset value, which is less than the thickness of the airbag when fully inflated. Each airbag is provided with air holes, thus eliminating the problem of excessive contact area and small bending angle caused by gaps between adjacent airbags. The air pump module is connected to the air hole, the detection module is mounted on the pneumatic module, and the control device is communicatively connected to the detection module and the air pump module respectively. The detection module is used to monitor the actuation status of the pneumatic module in real time, obtain the current measured actuation quantity, and report the measured actuation quantity to the control device; wherein, the measured actuation quantity 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 volume of the air pump module according to the target actuation amount and the current measured actuation amount includes: Kalman filtering is used to filter the current measured actuation quantity to obtain the current denoised actuation quantity; Calculate the error value between the denoised actuation amount and the target actuation amount; The measured actuation amount is input into a preset parameter adaptive model, and the control parameters corresponding to the measured actuation amount are generated using the parameter adaptive model. The parameters of the PID controller are updated using the control parameters, and the error value is input into the updated PID controller to obtain and output a control quantity to the air pump module to adjust the gas injection volume 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 vent of the airbag through the air pipe. The control valve is located on the air pipe. The control device is communicatively connected to the air pump and the control valve.

4. The pneumatic soft actuator according to claim 2, characterized in that, The step of using Kalman filtering to filter the current measured actuation quantity to obtain the current denoised actuation quantity includes: Obtain the state estimation data of the state equation from the previous time step to the current time step; wherein, the state estimation data includes the actuation estimate and the error covariance; Using the observation equation, combined with the actuation estimate and the error covariance at the current time, the measured actuation quantity is filtered and denoised to obtain the denoised actuation quantity at the current time. Using the state equation, the denoised actuation amount at the next moment is estimated based on the denoised actuation amount at the current moment, thus obtaining the state estimation data for the next moment.

5. The pneumatic soft actuator according to claim 2 or 4, characterized in that, The steps for obtaining the parameter adaptive model include: Multiple sets of experimental data for the pneumatic soft actuator are obtained; wherein each set of experimental data includes a state variable and the optimal control parameters of the PID controller when the pneumatic module is in the state variable, and the state variable includes angle and torque; Using the state variables in the experimental data as input and the optimal control parameters in the experimental data as output, a parameter adaptive model is obtained by fitting.

6. The pneumatic soft actuator according to claim 5, characterized in that, The step of fitting an adaptive parameter model using the state variables in the experimental data as input and the optimal control parameters in the experimental data as output includes: Multiple model fitting methods were used, with the state variables in the experimental data as input and the optimal control parameters in the experimental data as output, to fit multiple initial models. Based on the experimental data, the initial models are evaluated to obtain performance values ​​for each initial model; wherein, the performance values ​​include 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 applying Kalman filtering to filter the current measured actuation quantity to obtain the current denoised actuation quantity, the method further includes: Based on at least one measured actuation quantity prior to the current time, determine whether the current measured actuation quantity is abnormal; If not, then perform the step of using Kalman filtering to filter the current measured actuation quantity to obtain the current denoised actuation quantity; If so, an alarm will be triggered.

8. A control method for a pneumatic soft actuator, characterized in that, The control method, applied in any one of claims 1 to 7, comprises: The detection module of the pneumatic soft actuator is sampled to obtain the current measured actuation quantity of the pneumatic module of the pneumatic soft actuator; wherein, the measured actuation quantity includes angle and torque; Based on 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, The control device, used in any one of claims 1 to 7, 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 quantity of the pneumatic module of the pneumatic soft actuator; wherein, the measured actuation quantity 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 includes a processor and a memory, the memory storing a computer program that can be executed by the processor to implement the control method of the pneumatic soft actuator as described in claim 7.

Images