Bionic tendon control method based on PID, test method and test platform

Abstract

The present application relates to the technical field of bionic tendon, especially to a PID-based bionic tendon control method, a test method and a test platform, and discloses a PID-based bionic tendon control method, which comprises the following steps: S1: adopting a Lagrange dynamics modeling method to establish a dynamics model for a planar two-joint collaborative robot, S2: determining an error function according to an uncertainty boundary and a diagonal matrix, and obtaining a control model according to the error function, S3: obtaining angle data of a robot joint and air pressure data of a pneumatic muscle, and inputting the data into the control model to obtain control parameters. The present application determines an error function through an uncertainty boundary and a diagonal matrix, and obtains a control model according to the error function. The control model obtained in this way can obtain stable control parameters, can satisfy the strategy generation of single-arm or double-arm control, and has good effectiveness and robustness.

Description

Technical Field

[0001] This invention relates to the field of bionic tendon technology, and in particular to a PID-based bionic tendon control method, testing method, and testing platform. Background Technology

[0002] In biomimetic robots, biomimetic arms that mimic the human skeletal-muscle structure have been extensively studied due to their structural advantages. Traditional motor structures cannot simulate the skeletal-muscle structure of the arm, while the emergence of biomimetic tendons has effectively simulated the role of muscles in the skeletal muscle system.

[0003] Existing platforms and control strategies cannot apply relevant controls, especially PID control, to single-arm or dual-arm experiments while taking into account system uncertainties, and thus cannot obtain stable control methods and strategies. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a PID-based bionic tendon control method, testing method, and testing platform. The specific technical solution is as follows:

[0005] The PID-based bionic tendon control method includes the following steps:

[0006] S1: A dynamic model of the two planar joints of the collaborative robot is established using the Lagrange dynamics modeling method.

[0007] S2: Determine the error function based on the uncertainty boundary and the diagonal matrix, and obtain the control model based on the error function.

[0008] S3: Acquire the angle data of the robot joints and the air pressure data of the pneumatic muscles, and input the data into the control model to obtain control parameters.

[0009] As an improvement to the above technical solution, the dynamic model described in step S1 is as follows:

[0010]

[0011] Where (q) is the inertia matrix, Let G(q) be the centrifugal force and Coriolis force vector, G(q) be the gravity vector, and q be the matrix formed by the actual displacements. The derivative of q is the matrix formed by the actual velocities. The matrix is ​​composed of actual accelerations.

[0012] in, x and θ are the rotation angle and length of the bionic tendon, respectively, and the values ​​of x and θ are obtained by measurement.

[0013] As an improvement to the above technical solution, step S2 includes the following steps:

[0014] S21: Obtain the uncertainty boundary based on the theoretical inertia matrix, theoretical centrifugal force and Coriolis force vector matrix, and gravity vector matrix, and determine the diagonal matrix composed of constants.

[0015] S22: Determine the error function based on the diagonal matrix and the uncertainty boundary.

[0016] S23: Obtain the control model based on the error function and uncertainty boundary.

[0017] As an improvement to the above technical solution, the acquisition of the uncertainty boundary depends on the following formula:

[0018]

[0019] in, The theoretical inertia matrix, The theoretical centrifugal force and Coriolis force vector matrix, Here is the gravity vector matrix, and e is the displacement error. Let t be the velocity error and t be the time in the joint system. For the desired speed, Let be the desired acceleration, and s be a diagonal matrix consisting of constants.

[0020] The error function is obtained based on the following equation:

[0021]

[0022] Where S is a diagonal matrix composed of constants. Uncertainty boundary.

[0023] The control model includes the following formula:

[0024]

[0025] in, Let be the error function. Let ∈ be the uncertainty boundary, ∈ be a very small positive real number, and t be the time in the joint system.

[0026] The PID-based bionic tendon testing method includes the following steps:

[0027] S01: Adjust the air pressure inside the pneumatic tendon through the drive system.

[0028] S02: Repeat step S01 until the relationship between air pressure and force, and the relationship between air pressure and rotation angle of the pneumatic tendon are obtained.

[0029] S03: The air pressure inside the pneumatic tendons of the upper arm and forearm is adjusted by the drive system.

[0030] S04: Repeat step S03 until the static and dynamic characteristics of the two-bar pneumatic tendon are obtained.

[0031] S05: Using PID parameters as input parameters, execute the PID-based bionic tendon control method as described in any of the foregoing technical solutions, and perform corresponding actions based on the calculated control parameters.

[0032] S06: Repeat step S05 until the optimal dynamic characteristics of the two-bar linkage pneumatic tendon under PID control are obtained.

[0033] The PID-based bionic tendon testing platform is operated using the PID-based bionic tendon testing method described in the aforementioned technical solution, and includes: a mounting frame, a bionic arm fixed to the mounting frame, and a drive system connected to the bionic arm.

[0034] Specifically, the bionic arm includes an upper pneumatic tendon and a lower pneumatic tendon. The drive system is used to adjust the air pressure inside the upper and lower pneumatic tendons. A baffle is provided on one side of the upper and lower pneumatic tendons to restrict the contraction of the upper and lower pneumatic tendons in the vertical direction.

[0035] As an improvement to the above technical solution, the bionic arm includes a support plate, which is fixed to the mounting frame. A measuring component and an upper and lower pneumatic tendon are provided on one side of the support plate. At least two sets of threaded pins are provided on one side of the support plate. A fisheye bearing is interference-fitted to the outside of the threaded pin. The interior of one set of fisheye bearings is threadedly connected to the upper pneumatic tendon, and the interior of the other set of fisheye bearings is threadedly connected to the lower pneumatic tendon.

[0036] As an improvement to the above technical solution, the measuring component includes an encoder, which is sleeved on the end of a threaded pin.

[0037] As an improvement to the above technical solution, the drive system includes an air compressor, the connecting end of which is connected to a vent pipe, the other end of which is connected to the upper pneumatic tendon and the lower pneumatic tendon, and the middle of the vent pipe is provided with several pressure regulating valves.

[0038] As an improvement to the above technical solution, the lower ends of the lower pneumatic tendon and the upper pneumatic tendon are provided with eye bolts for connecting the load.

[0039] The beneficial effects of this invention are:

[0040] The error function is determined by using the uncertainty boundary and the diagonal matrix, and the control model is obtained based on the error function. The control model obtained in this way can obtain stable control parameters, can meet the strategy generation requirements of single-arm or dual-arm control, and has good effectiveness and robustness.

[0041] By combining testing methods and testing platforms, we can not only conduct dynamic tests with a single arm, but also stably run dynamic tests with two arms, ensuring the coordination between the bionic arms. Attached Figure Description

[0042] Figure 1 This is a three-dimensional structural diagram of the PID-based bionic tendon testing platform of the present invention.

[0043] Figure 2 This is a front view of the PID-based bionic tendon testing platform of the present invention.

[0044] Reference numerals: 10, mounting frame; 20, bionic arm; 21, support plate; 22, threaded pin; 23, encoder; 24, fisheye bearing; 25, upper pneumatic tendon; 26, lower pneumatic tendon; 27, baffle; 28, eye bolt; 30, drive system; 31, air compressor; 32, pressure regulating valve; 33, vent pipe. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0046] Existing platforms and control strategies cannot apply relevant controls, especially PID control, to single-arm or dual-arm experiments while taking into account system uncertainties, and thus cannot obtain stable control methods and strategies.

[0047] To address the above problems, the following three sets of implementation methods are provided:

[0048] Example 1

[0049] A PID-based bionic tendon control method is provided, comprising the following steps:

[0050] S1: A dynamic model of the two planar joints of the collaborative robot is established using the Lagrange dynamics modeling method.

[0051] Specifically, the dynamic model in step S1 is as follows:

[0052]

[0053] Where (q) is the inertia matrix, Let G(q) be the centrifugal force and Coriolis force vector, G(q) be the gravity vector, and q be the matrix formed by the actual displacements. The derivative of q is the matrix formed by the actual velocities. The matrix is ​​composed of actual accelerations.

[0054] in, x and θ are the rotation angle and length of the bionic tendon, respectively, and the values ​​of x and θ are obtained by measurement.

[0055] S2: Determine the error function based on the uncertainty boundary and the diagonal matrix, and obtain the control model based on the error function.

[0056] Step S2 includes the following steps:

[0057] S21: Obtain the uncertainty boundary based on the theoretical inertia matrix, theoretical centrifugal force and Coriolis force vector matrix, and gravity vector matrix, and determine the diagonal matrix composed of constants.

[0058] When obtaining the uncertainty boundary, it is necessary to consider the actual values ​​of displacement and velocity. Based on this, it is also necessary to know the matrices formed by the actual displacements, the matrices formed by the actual velocities, and the matrices formed by the actual accelerations. Specifically:

[0059] The matrix formed by the actual displacements is obtained by the following equation:

[0060]

[0061] q = e + q d

[0062] Where, q d (t) represents the desired displacement, e represents the displacement error, and x and θ represent the rotation angle and length of the bionic tendon, respectively.

[0063] The matrix representing the actual velocities is obtained based on the following equation:

[0064]

[0065]

[0066] in, Let be the desired velocity, e be the velocity error, and x and θ be the rotation angle and length of the bionic tendon, respectively.

[0067] The matrix of actual accelerations is obtained by the following equation:

[0068]

[0069]

[0070] in, Let e ​​be the desired acceleration, e be the acceleration error, and x and θ be the rotation angle and length of the bionic tendon, respectively.

[0071] The diagonal matrix is ​​obtained according to the following formula:

[0072] S = diag{[s i ] n×n}

[0073] S is a diagonal matrix composed of constants, s i is a constant greater than 0, and diag represents the diagonal matrix symbol.

[0074] The determination of the uncertainty boundary depends on the following equation:

[0075]

[0076] in, The theoretical inertia matrix, The theoretical centrifugal force and Coriolis force vector matrix, Here is the gravity vector matrix, and e is the displacement error. Let t be the velocity error and t be the time in the joint system. For the desired speed, Let be the desired acceleration, and s be a diagonal matrix consisting of constants.

[0077] After determining the diagonal matrix and the uncertainty boundary, the error function can be obtained, specifically in step S22.

[0078] S22: Determine the error function based on the diagonal matrix and the uncertainty boundary.

[0079] The error function is obtained by the following equation:

[0080]

[0081] Where S is a diagonal matrix composed of constants. Uncertainty boundary.

[0082] S23: Obtain the control model based on the error function and uncertainty boundary.

[0083] The control model includes the following equation:

[0084]

[0085] in, Let be the error function. Let ∈ be the uncertainty boundary, ∈ be a very small positive real number, and t be the time in the joint system.

[0086] S3: Acquire the angle data of the robot joints and the air pressure data of the pneumatic muscles, and input the data into the control model to obtain control parameters.

[0087] After obtaining the control parameters, the PID control mode can be switched on the host computer's operating interface to directly use the calculated parameters to perform PID control tests on the bionic tendon.

[0088] Based on the PID control method described above, appropriate PID parameters, including proportional, integral, derivative, and ideal parameters, are input. The real-time acquisition curve of the operating platform angle is observed. The state variable data of joint angle and muscle length are compared with the ideal reference trajectory in real time, thereby updating and optimizing the control parameters in the automatic control system, analyzing the optimal PID parameter control, and ensuring the effectiveness and stability of the control strategy.

[0089] Example 2

[0090] To complement Embodiment 1, a PID-based bionic tendon testing method is provided, comprising the following steps:

[0091] S01: Adjust the air pressure inside the pneumatic tendon through the drive system.

[0092] After step S01 is executed, the air pressure and displacement values ​​of the pneumatic tendon will be output, and the images drawn during the test will be saved and displayed in real time by the host computer.

[0093] S02: Repeat step S01 until the relationship between air pressure and force, and the relationship between air pressure and rotation angle of the pneumatic tendon are obtained.

[0094] S03: The air pressure inside the pneumatic tendons of the upper arm and forearm is adjusted by the drive system.

[0095] After step S03 is executed, the air pressure and rotation angle values ​​of the pneumatic tendon will be output, and the images drawn during the test will be saved and displayed in real time by the host computer.

[0096] S04: Repeat step S03 until the static and dynamic characteristics of the two-bar pneumatic tendon are obtained.

[0097] In step S03, the results are different depending on the number of bionic arms. When a single arm is tested, the static and dynamic characteristics of the two-bar single-arm pneumatic tendon are obtained.

[0098] When testing two arms, the results revealed issues such as the static and dynamic characteristics of the two-bar linkage dual-arm robot during movement.

[0099] S05: Using PID parameters as input parameters, execute any of the PID-based bionic tendon control methods described in Embodiment 1, and perform corresponding actions based on the calculated control parameters.

[0100] The input parameters here are the proportional term parameters, integral term parameters, and derivative term parameters recorded in Example 1. After calculation based on these parameters in Example 1, the output control parameters are obtained, and the action control is then performed based on the output control parameters.

[0101] If step S05 tests both arms, then when step S05 is executed, it outputs the pneumatic tendon pressure of the upper left or right arm, the pneumatic tendon pressure of the front left or right arm, the rotation angle value of the pneumatic tendon of the upper left or right arm, and the rotation angle value of the pneumatic tendon of the front left or right arm, and uses the host computer to save and display the images drawn during the test in real time.

[0102] S06: Repeat step S05 until the optimal dynamic characteristics of the two-bar linkage pneumatic tendon under PID control are obtained.

[0103] The optimal dynamic characteristics of the two-bar linkage double-arm activating tendon can be compared and verified with actual conditions to determine the effectiveness and robustness of the control method in Example 1.

[0104] Example 3

[0105] For reference in conjunction with Embodiment 2, please refer to Figure 1 and Figure 2 A PID-based bionic tendon testing platform is provided, which is operated using the PID-based bionic tendon testing method as described in Embodiment 2. The platform includes: a mounting frame 10, a bionic arm 20 fixed on the mounting frame 10, and a drive system 30 connected to the bionic arm 20.

[0106] Specifically, the bionic arm 20 includes an upper pneumatic tendon 25 and a lower pneumatic tendon 26. The drive system 30 is used to adjust the air pressure inside the upper pneumatic tendon 25 and the lower pneumatic tendon 26. A baffle 27 is provided on one side of the upper pneumatic tendon 25 and the lower pneumatic tendon 26. The baffle 27 is used to restrict the contraction of the upper pneumatic tendon 25 and the lower pneumatic tendon 26 in the vertical direction.

[0107] By restricting the contraction of the upper pneumatic tendon 25 and the lower pneumatic tendon 26 in the vertical direction by setting a baffle 27 on one side of the bionic arm 20, the upper pneumatic tendon 25 and the lower pneumatic tendon 26 can rotate according to their corresponding positions.

[0108] Furthermore, when single-arm testing is required, the number of bionic arms 20 is one set, including only one set of upper pneumatic tendons 25 and lower pneumatic tendons 26. However, when dual-arm testing is required, such as... Figure 2As shown, the number of bionic arms 20 is two sets, including two sets of upper pneumatic tendons 25 and lower pneumatic tendons 26.

[0109] To ensure steering stability of the upper pneumatic tendon 25 and lower pneumatic tendon 26 at the turning point, please refer to... Figure 2 Specifically, the bionic arm 20 includes a support plate 21, which is fixed to the mounting frame 10. A measuring component and an upper pneumatic tendon 25 and a lower pneumatic tendon 26 are provided on one side of the support plate 21. At least two sets of threaded pins 22 are provided on one side of the support plate 21. A fisheye bearing 24 is interference-fitted on the outer side of the threaded pin 22. The interior of one set of fisheye bearings 24 is threadedly connected to the upper pneumatic tendon 25, and the interior of the other set of fisheye bearings 24 is threadedly connected to the lower pneumatic tendon 26.

[0110] Specifically, when testing the movement of a single pneumatic tendon:

[0111] The baffle 27 restricts the vertical displacement of the upper pneumatic tendon 25 and the lower pneumatic tendon 26, allowing the upper pneumatic tendon 25 or the lower pneumatic tendon 26 to rotate around the threaded pin 4 via the fisheye bearing 16, thus enabling the measurement of the rotational angle characteristics of a single pneumatic tendon.

[0112] When testing the entire bi-link tendon, the rotation of the upper pneumatic tendon 25 and the lower pneumatic tendon 26 at the fisheye bearing 16 can be simulated using the same structure to measure the rotation angle characteristics of a single arm.

[0113] Furthermore, when it is necessary to test the bi-arm tendons, the rotation of the upper pneumatic tendon 25 and the lower pneumatic tendon 26 at the fisheye bearing 16 can be simulated using the same structure, which can simulate and measure the rotation angle characteristics of the bi-arm.

[0114] In one embodiment, see Figure 2 The measuring component includes an encoder 23, which is fitted onto the end of a threaded pin 22.

[0115] In addition to detecting the angle change curve by the encoder, a data acquisition board can be installed on the support plate 21 to acquire the signal of the encoder 23 and transmit the signal to the host computer.

[0116] In one embodiment, see Figure 2 The drive system 30 includes an air compressor 31. The air compressor 31 is connected to a vent pipe 33 at one end. The other end of the vent pipe 33 is connected to the upper pneumatic tendon 25 and the lower pneumatic tendon 26. Several pressure regulating valves 32 are provided in the middle of the vent pipe 33.

[0117] The main air pressure control is provided by the air compressor 31, the air pressure is adjusted by the pressure regulating valve 32 to ensure the accuracy of control, and the airflow is input to the upper pneumatic tendon 25 and the lower pneumatic tendon 26 through the air pipe 33.

[0118] The host computer can monitor the angle change curve detected by the encoder and the outlet pressure change curve of the electric pressure regulating valve in real time, and automatically save the collected data in real time. In addition, the encoder can be a photoelectric incremental encoder.

[0119] In one embodiment, see Figure 2 The lower pneumatic tendon 26 and the lower end of the upper pneumatic tendon 25 are provided with eye bolts 28 for connecting loads.

[0120] When measuring a single pneumatic tendon and a two-bar pneumatic tendon, the eye bolt 28 can provide a counterweight function to fix the load as required.

[0121] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A PID-based bionic tendon control method, characterized in that, Includes the following steps: S1: A dynamic model of the two planar joints of the collaborative robot is established using the Lagrange dynamics modeling method; S2: Determine the error function based on the uncertainty boundary and the diagonal matrix, and obtain the control model based on the error function; S3: Acquire the angle data of the robot joints and the air pressure data of the pneumatic muscles, and input the data into the control model to obtain control parameters; The determination of the uncertainty boundary depends on the following equation: in, The theoretical inertia matrix, The theoretical centrifugal force and Coriolis force vector matrix, Here is the gravity vector matrix, and e is the displacement error. Let t be the velocity error and t be the time in the joint system. For the desired speed, Let be the desired acceleration, and s be a diagonal matrix composed of constants; The error function is obtained based on the following equation: in, Uncertainty boundary.

2. The PID-based bionic tendon control method according to claim 1, characterized in that: The dynamic model mentioned in step S1 is: Where H(q) is the inertia matrix, For centrifugal force and Coriolis force vectors, It is the gravity vector. The matrix formed by the actual displacements. for The derivative of is the matrix formed by the actual velocities. A matrix composed of actual accelerations; in, , x、 The rotation angle and length of the bionic tendon are respectively measured, and x and y are obtained by measurement. The value of .

3. The PID-based bionic tendon control method according to claim 1, characterized in that: Step S2 includes the following steps: S21: Obtain the uncertainty boundary based on the theoretical inertia matrix, theoretical centrifugal force and Coriolis force vector matrix, and gravity vector matrix, and determine the diagonal matrix composed of constants; S22: Determine the error function based on the diagonal matrix and the uncertainty boundary; S23: Obtain the control model based on the error function and uncertainty boundary.

4. The PID-based bionic tendon control method according to claim 3, characterized in that: The control model includes the following formula: in, Let be the error function. For the uncertainty boundary, It is a very small positive real number. For time within the joint system.

5. A PID-based bionic tendon testing method, characterized in that, Includes the following steps: S01: Adjust the air pressure inside the pneumatic tendon through the drive system; S02: Repeat step S01 until the relationship between air pressure and force, and the relationship between air pressure and rotation angle of the pneumatic tendon are obtained. S03: The air pressure values ​​inside the pneumatic tendons of the upper arm and forearm are adjusted separately through the drive system; S04: Repeat step S03 until the static and dynamic characteristics of the two-bar pneumatic tendon are obtained; S05: Using PID parameters as input parameters, execute the PID-based bionic tendon control method as described in any one of claims 1-4, and perform corresponding actions according to the calculated control parameters; S06: Repeat step S05 until the optimal dynamic characteristics of the two-bar linkage pneumatic tendon under PID control are obtained.

6. A PID-based bionic tendon testing platform, operated using the PID-based bionic tendon testing method as described in claim 5, characterized in that... include: Mounting frame (10); A bionic arm (20) fixed to a mounting frame (10), the bionic arm (20) comprising an upper pneumatic tendon (25) and a lower pneumatic tendon (26); and A drive system (30) connected to the bionic arm (20) is used to adjust the air pressure values ​​inside the upper pneumatic tendon (25) and the lower pneumatic tendon (26); A baffle (27) is provided on one side of the upper pneumatic tendon (25) and the lower pneumatic tendon (26), and the baffle (27) is used to restrict the contraction of the upper pneumatic tendon (25) and the lower pneumatic tendon (26) in the vertical direction.

7. The PID-based bionic tendon testing platform according to claim 6, characterized in that: The bionic arm (20) includes a support plate (21), which is fixed to the mounting frame (10). A measuring component and an upper pneumatic tendon (25) and a lower pneumatic tendon (26) are provided on one side of the support plate (21). At least two sets of threaded pins (22) are provided on one side of the support plate (21). A fish-eye bearing (24) is interference-fitted on the outer side of the threaded pin (22). The interior of one set of fish-eye bearings (24) is threadedly connected to the upper pneumatic tendon (25), and the interior of the other set of fish-eye bearings (24) is threadedly connected to the lower pneumatic tendon (26).

8. The PID-based bionic tendon testing platform according to claim 7, characterized in that: The measuring component includes an encoder (23) which is fitted onto the end of a threaded pin (22).

9. The PID-based bionic tendon testing platform according to any one of claims 6-8, characterized in that: The drive system (30) includes an air compressor (31), the air compressor (31) is connected to a vent pipe (33), the other end of the vent pipe (33) is connected to the upper pneumatic tendon (25) and the lower pneumatic tendon (26), and the middle part of the vent pipe (33) is provided with several pressure regulating valves (32).

10. The PID-based bionic tendon testing platform according to claim 9, characterized in that: The lower pneumatic tendon (26) and the lower pneumatic tendon (25) are provided with eye bolts (28) for connecting loads.

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