Robot joint control method and device and electronic equipment
By calculating the error convergence characterization value and gain normalization factor of the robot joint to determine the equivalent control parameters, the problem of low control accuracy caused by unknown disturbances in robot joint motion is solved, and efficient and stable joint control is achieved.
Patent Information
- Application Number
- CN202511211665.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-27
- Publication Date
- 2025-11-14
AI Technical Summary
During robot joint movement, unknown disturbances such as friction can cause the actual motion state to deviate from the expected motion state, resulting in low control accuracy.
The motion error is determined based on the actual and expected motion parameters of the robot joints. Equivalent control parameters, including proportional control parameters, are calculated using the error convergence characterization value and gain normalization factor to control the joint motion so that the error remains in a convergent state or approaches a convergent state.
It improves the control precision of robot joints, reduces the uncertainty introduced by unknown disturbances such as friction, and balances the convergence speed of motion errors and the degree of chattering, thus achieving efficient control and stability.
Smart Images
Figure CN120941394A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of robotics, and in particular to a method, apparatus, and electronic device for controlling robot joints. Background Technology
[0002] During robot joint movement, unknown disturbances, such as friction, often cause the actual joint movement to deviate from the expected movement. Specifically, friction is inevitably generated between mechanical structures, and its magnitude varies with factors such as joint movement speed, temperature, humidity, and the degree of wear of parts, making it difficult to predict accurately. In addition, there are other unknown disturbances in the external environment, such as airflow disturbances.
[0003] The aforementioned unknown disturbances introduce uncertainty into the movement of the robot joints, causing the actual movement state of the joints to differ from the expected movement state, thus resulting in lower control precision of the joints. Summary of the Invention
[0004] The purpose of this invention is to provide a robot joint control method, device, and electronic device to improve the control accuracy of robot joints. The specific technical solution is as follows:
[0005] In a first aspect, embodiments of the present invention provide a robot joint control method, the method comprising:
[0006] Based on the actual first motion parameters and the expected second motion parameters of each joint of the robot, the motion error of each joint is determined.
[0007] Based on the position error and velocity error in the motion error, the error convergence characterization value is obtained;
[0008] Based on the error convergence characterization value and the gain normalization factor, equivalent control parameters are determined to keep the motion error of each joint in a convergent state or to approach a convergent state. The equivalent control parameters include: a proportional term control parameter, which is obtained based on the difference between the first motion parameter and the second motion parameter, and the value of the gain normalization factor is determined based on the absolute value of the error convergence characterization value.
[0009] Based on the first motion parameters and the equivalent control parameters, the joint control parameters are determined;
[0010] The movement of each joint is controlled according to the joint control parameters.
[0011] In a second aspect, embodiments of the present invention provide a robot joint control device, the device comprising:
[0012] The error determination module is used to determine the motion error of each joint based on the actual first motion parameters and the expected second motion parameters of each joint of the robot.
[0013] The convergence characterization value acquisition module is used to obtain the error convergence characterization value based on the position error and velocity error in the motion error;
[0014] An equivalent control parameter determination module is used to determine, based on the error convergence characterization value and the gain normalization factor, equivalent control parameters that keep the motion error of each joint in a convergent state or approach a convergent state. The equivalent control parameters include: a proportional term control parameter, which is obtained based on the difference between the first motion parameter and the second motion parameter; and the value of the gain normalization factor is determined based on the absolute value of the error convergence characterization value.
[0015] A joint control parameter determination module is used to determine joint control parameters based on the first motion parameter and the equivalent control parameter;
[0016] The joint control module is used to control the movement of each joint according to the joint control parameters.
[0017] Thirdly, embodiments of the present invention provide an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;
[0018] Memory, used to store computer programs;
[0019] When a processor executes a program stored in memory, it implements the steps of the method described in the first aspect.
[0020] It should be noted that the aforementioned electronic devices can be either back-end control devices or robots.
[0021] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method described in the first aspect.
[0022] Fifthly, embodiments of the present invention provide a computer program product containing instructions that, when run on a computer, cause the computer to perform the steps of the method described in the first aspect.
[0023] As can be seen from the above, in the process of controlling robot joints using the scheme provided in the embodiments of the present invention, the motion error of each joint is obtained based on the actual motion parameters and expected motion parameters of each joint. An error convergence characterization value is determined based on the motion error, and then equivalent control parameters are obtained based on the error convergence characterization value to keep the motion error of each joint in a convergent state or to approach a convergent state. Based on the equivalent control parameters, joint control parameters can be obtained. Furthermore, the movement of each joint can be controlled according to the joint control parameters to keep the motion error of each joint in a convergent state or to approach a convergent state, thereby making the actual motion of each joint approach the expected motion, reducing the uncertainty introduced by unknown disturbances such as friction to the robot joint motion, and improving the control accuracy of the robot joints.
[0024] Furthermore, in determining the equivalent control parameters, not only were the error convergence characterization values of each joint considered, but also the gain normalization factor used to normalize these values. The value of the gain normalization factor was determined based on the absolute value of the error convergence characterization value. This allows the magnitude of the determined equivalent control parameters to be adaptively adjusted according to the magnitude of the error convergence characterization value. In other words, the control force can be adaptively adjusted according to the magnitude of the robot joint motion error. Therefore, when eliminating joint motion errors based on the determined parameters, both the convergence speed and chattering degree of the motion error can be considered. This achieves efficient and rapid control of the robot joints while ensuring control accuracy and stability.
[0025] Of course, implementing any product or method of the present invention does not necessarily require achieving all of the advantages described above at the same time. Attached Figure Description
[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings.
[0027] Figure 1 A flowchart illustrating the first robot joint control method provided in an embodiment of the present invention;
[0028] Figure 2 A flowchart illustrating the second robot joint control method provided in an embodiment of the present invention;
[0029] Figure 3 This is a schematic diagram of the structure of a robot joint control device provided in an embodiment of the present invention;
[0030] Figure 4This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation
[0031] 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art based on the present invention are within the scope of protection of the present invention.
[0032] First, some concepts involved in the solutions provided in the embodiments of the present invention will be introduced.
[0033] 1. Sliding mode variables:
[0034] Sliding mode variables are pre-designed variables, usually constructed based on the motion error of robot joints. Their specific values indicate whether the motion error of robot joints is in a convergent state.
[0035] 2. Sliding surface:
[0036] When the value of the sliding mode variable indicates that the motion error of the robot joint is in a convergent state, it can be said that the system state is at the sliding surface. The aforementioned system state refers to the overall motion state of all joints of the robot. In this case, the motion error of the joint will tend to 0 within a finite time, that is, the actual motion of each joint will gradually approach the expected motion.
[0037] When the value of the sliding mode variable indicates that the joint motion error is not in a convergent state, it can be said that the system state is not on the sliding surface. In this case, the joint motion error will not tend to 0 in a finite time. If no intervention is taken, the actual motion of each joint will differ from the expected motion.
[0038] 3. Sliding mode control law:
[0039] Sliding mode control laws are key to sliding mode control. They are used to generate joint control parameters, and controlling the robot's joint movements according to these parameters ensures that the system state remains on the sliding surface. Specifically:
[0040] If the system state is already on the sliding surface, the generated joint control parameters can be used to keep the system state on the sliding surface; if the system state is not on the sliding surface, the generated joint control parameters can be used to move the system state toward the sliding surface.
[0041] In this way, the actual movement of each robot joint can gradually approach the expected movement.
[0042] 4. Booming phenomenon:
[0043] As can be seen from the above introduction to sliding mode control law, sliding mode control law is used to control the system state to be on the sliding surface.
[0044] In the case of a system not being at the sliding surface, the process of controlling the system state to approach the sliding surface based on the sliding mode control law is a process of multiple attempts and continuous iterations. This leads to the system state repeatedly fluctuating near the sliding surface during the above process, a phenomenon known as chattering. Macroscopically, chattering manifests as high-frequency vibration in the joints when the joint control parameters generated by the sliding mode control law control the robot's joint movements. This exacerbates joint wear and reduces the stability of joint movements.
[0045] The implementing entity of the solution provided in the embodiments of the present invention will be introduced next.
[0046] The execution subject of the solution provided in this embodiment of the invention can be any electronic device with data processing, storage, communication and other functions, specifically a background control device or the robot itself.
[0047] In one scenario, the aforementioned robot could be an embodied artificial intelligence robot.
[0048] Embossed intelligent robots can be understood as intelligent systems that perceive and act based on a physical body. They acquire information, understand problems, make decisions, and take actions through interaction with the environment, thereby generating intelligent behavior and adaptability. The core of embodied intelligent robots is that the intelligent agent possesses the ability to interact and perceive the environment, as well as the ability to autonomously plan, make decisions, and execute a series of behaviors based on the perceived tasks and environment.
[0049] It should be noted that embodied intelligent robots do not only refer to humanoid robots; any tangible intelligent machine that can move in space can be understood as a form of embodied intelligent robot.
[0050] The solutions provided by the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
[0051] See Figure 1 The above is a flowchart of the first robot joint control method provided in the embodiment of the present invention. The method includes the following steps S101-S105.
[0052] Step S101: Determine the motion error of each joint based on the actual first motion parameters and the expected second motion parameters of each joint of the robot.
[0053] The aforementioned joints can be various joints of a robot, and the embodiments of the present invention do not limit them. The following is an example.
[0054] For example, it may include upper limb joints for grasping, carrying and other functions, such as shoulder joints, elbow joints, wrist joints and hand joints; or lower limb joints for supporting the robot's body weight and enabling movement, such as hip joints, knee joints and foot joints; or trunk joints for connecting different parts of the robot's body and enabling bending, such as waist joints.
[0055] In one scenario, staff can pre-determine joints prone to motion errors as the joints for which control parameters need to be determined, based on actual needs and / or experience.
[0056] The aforementioned first motion parameter refers to the actual motion parameters of each joint obtained by sensors, which may specifically include the joint's position, velocity, and acceleration.
[0057] The aforementioned second motion parameter can be the motion parameter that the staff sets according to actual needs and expects each joint to have, and can also include the position, velocity and acceleration of the joint.
[0058] Since joint movement is generally achieved through rotation, the position, velocity, and acceleration of a joint can also be referred to as the joint's angle, angular velocity, and angular acceleration.
[0059] In this process, staff can pre-set the second motion parameters that they expect each joint to have at various times. When the electronic device performs this step, it can obtain the actual first motion parameters of each joint at the current time and determine the second motion parameters corresponding to the current time.
[0060] After obtaining the actual first motion parameters and the expected second motion parameters for each joint, the motion error can be determined based on the difference between the two motion parameters. Specifically, the motion error includes the positional and velocity differences between the two motion parameters, and may also include the acceleration differences between the two motion parameters.
[0061] Step S102: Based on the position error and velocity error in the motion error, obtain the error convergence characterization value.
[0062] The aforementioned error convergence characterization values are used to characterize the convergence status of motion errors. The convergence status includes: motion errors being in a convergent state and motion errors not being in a convergent state.
[0063] In one scenario, the motion error is in a convergent state, corresponding to the aforementioned system state being on the synovial surface. In this case, although there is a certain error between the actual motion of each joint and the expected motion, the motion error will tend to 0 within a finite time, meaning that the actual motion of each joint will gradually approach the expected motion. In another scenario, the motion error is in a non-convergent state, corresponding to the aforementioned system state not being on the synovial surface. In this case, the motion error is difficult to tend to 0 within a finite time, and without intervention, the actual motion of each joint will differ from the expected motion.
[0064] It should be noted that joint control parameters need to be determined regardless of whether the motion error is in a convergent state. The difference is as follows: if the motion error is in a convergent state, joint control parameters can be determined to keep the motion error in a convergent state, so that the actual motion of each joint gradually approaches the expected motion. If the motion error is not in a convergent state, joint control parameters can be determined to control the motion error to approach a convergent state, so that the motion error gradually approaches a convergent state, and thus, after it is in a convergent state, the actual motion of each joint gradually approaches the expected motion.
[0065] Specifically, the position error and velocity error can be substituted into the expression for the set sliding mode variable to obtain the value of the sliding mode variable, which serves as the aforementioned error convergence characterization value. The expression for the sliding mode variable can be as follows:
[0066]
[0067] Where q and q represents the actual position and actual velocity in the first motion parameter, respectively. d and Let represent the expected position and expected velocity in the second motion parameters, respectively, and λ represent the set convergence coefficient. It can be seen that... The above speed error is represented by qq. d This indicates the aforementioned positional error.
[0068] In this case, if s = 0, it means that the motion error is in a convergent state; otherwise, it means that the motion error is not in a convergent state.
[0069] Step S103: Based on the error convergence characterization value and the gain normalization factor, determine the equivalent control parameters that make the motion error of each joint remain in a convergent state or approach a convergent state.
[0070] The aforementioned equivalent control parameters include proportional control parameters, which are obtained based on the difference between the first motion parameter and the second motion parameter, and are used to control the robot's joints to quickly approach the expected motion state.
[0071] Specifically, the above proportional term control parameters can be determined based on the set power of the error convergence characterization value and the gain normalization factor. The set power can be any power greater than 0.
[0072] Since the set power of the error convergence characterization value is positively correlated with the magnitude of the error convergence characterization value, when determining the proportional term control parameter based on the set power of the error convergence characterization value, the magnitude of the proportional term control parameter is positively correlated with the magnitude of the error convergence characterization value. Thus, when the error convergence characterization value is large, the determined proportional term control parameter is also large, which can improve the convergence speed of motion error. In addition, the error convergence characterization value is normalized based on the gain normalization factor, that is, the magnitude of the determined proportional term control parameter is adaptively adjusted according to the magnitude of the error convergence characterization value, thereby achieving a balance between the convergence speed of motion error and the degree of system state chattering.
[0073] In one possible implementation, the power set above can be any power greater than 0 and less than 1.
[0074] In this case, when the proportional control parameter is determined based on the set power of the error convergence characterization value, the proportional control parameter and the error convergence characterization value are not simply linearly related. This can slow down the rate at which the proportional control parameter changes with the magnitude of the error convergence characterization value, which is beneficial to reduce the jitter of the system state near the sliding surface and improve the stability of the control motion error convergence process.
[0075] In one possible implementation, the above-mentioned power is set to 1 / 2. In this case, the proportional term control parameter p1 can be determined according to the following expression:
[0076]
[0077] Where s represents the error convergence characterization value, k1 represents the set first control gain coefficient, k1>0, N s This represents the gain normalization factor mentioned above.
[0078] When the proportional control parameters are determined by power of 1 / 2 of the error convergence characterization value, the determined proportional control parameters can take into account both the speed and smoothness of the robot's joints approaching the expected motion state, thus improving the rationality of the scheme.
[0079] The value of the above gain normalization factor is determined based on the absolute value of the error convergence characterization value, and is used to normalize the error convergence characterization value.
[0080] This embodiment of the invention does not limit the specific setting method of the above-mentioned gain normalization factor, but only needs to ensure that the first ratio can be limited within a set range based on the gain normalization factor. The above-mentioned first ratio is: the ratio of the error convergence characterization value to the gain normalization factor.
[0081] For example, the value of the gain normalization factor is determined in the following manner:
[0082] If the absolute value of the error convergence characterization value is greater than or equal to the set upper threshold, then the value of the gain normalization factor is determined as the upper threshold; and / or,
[0083] If the error convergence characteristic value is less than or equal to the set lower threshold, then the value of the gain normalization factor is determined as the lower threshold; and / or,
[0084] If the error convergence characteristic value is greater than the lower limit but less than the upper threshold, then the value of the gain normalization factor is determined to be an absolute value.
[0085] That is, the gain normalization factor N s The following expression can be used to represent it:
[0086]
[0087] Where UB represents the upper limit threshold, DB represents the lower limit threshold, and UB > DB.
[0088] Thus, when the magnitude of the error convergence characterization value is greater than or equal to the upper threshold, the gain normalization factor is taken as the upper threshold, so the ratio of the magnitude of the error convergence characterization value to the gain normalization factor is greater than or equal to 1. That is, when the motion error is large, the determined proportional control parameter is large, which can improve the convergence speed of the motion error. When the magnitude of the error convergence characterization value is less than or equal to the lower threshold, the gain normalization factor is taken as the lower threshold, so the ratio of the magnitude of the proportional control parameter to the gain normalization factor is less than or equal to 1. That is, when the motion error is small, the determined joint control parameter is small, which can reduce the chattering phenomenon generated by the system state near the sliding surface. In other cases, the gain normalization factor is taken as the magnitude of the error convergence characterization value, so the ratio of the magnitude of the error convergence characterization value to the gain normalization factor is equal to 1. That is, when the motion error is moderate, the determined proportional control parameter is also moderate, taking into account the convergence speed and chattering degree, so that the sliding control law has a certain linear adjustment capability.
[0089] Step S104: Determine the joint control parameters based on the first motion parameters and the equivalent control parameters.
[0090] After obtaining the equivalent control parameters, the joint control parameters used to control the motion error to maintain or approach a convergent state can be determined by combining the actual first motion parameters of each joint of the robot. Specifically, these joint control parameters can be the control torque vectors used to drive the motion of each joint.
[0091] The purpose of this step is to determine the joint control parameters that keep the joint motion error in a convergent state or approach a convergent state. Specifically, the relevant parameters can be substituted into a pre-constructed sliding mode control law to obtain the joint control parameters.
[0092] For example, in the process of constructing the sliding mode control law, the joint dynamics of the robot can be considered to obtain the inertial characterization values, gravity characterization values, and Coriolis and centrifugal force characterization values of each joint. Thus, when determining the joint control parameters using the sliding mode control law, the joint control parameters are determined based on the obtained characterization values and the aforementioned related parameters. Specific methods are described in subsequent embodiments and will not be detailed here.
[0093] Step S105: Control the movement of each joint according to the joint control parameters.
[0094] As can be seen from the above, in the process of controlling robot joints using the scheme provided in the embodiments of the present invention, the motion error of each joint is obtained based on the actual motion parameters and expected motion parameters of each joint. An error convergence characterization value is determined based on the motion error, and then equivalent control parameters are obtained based on the error convergence characterization value to keep the motion error of each joint in a convergent state or to approach a convergent state. Based on the equivalent control parameters, joint control parameters can be obtained. Furthermore, the movement of each joint can be controlled according to the joint control parameters to keep the motion error of each joint in a convergent state or to approach a convergent state, thereby making the actual motion of each joint approach the expected motion, reducing the uncertainty introduced by unknown disturbances such as friction to the robot joint motion, and improving the control accuracy of the robot joints.
[0095] Furthermore, in determining the equivalent control parameters, not only were the error convergence characterization values of each joint considered, but also the gain normalization factor used to normalize these values. The value of the gain normalization factor was determined based on the absolute value of the error convergence characterization value. This allows the magnitude of the determined equivalent control parameters to be adaptively adjusted according to the magnitude of the error convergence characterization value. In other words, the control force can be adaptively adjusted according to the magnitude of the robot joint motion error. Therefore, when eliminating joint motion errors based on the determined parameters, both the convergence speed and chattering degree of the motion error can be considered. This achieves efficient and rapid control of the robot joints while ensuring control accuracy and stability.
[0096] In one possible implementation, the determined equivalent control parameters also include integral term control parameters.
[0097] The integral term control parameter is obtained based on the cumulative difference between the first motion parameter and the second motion parameter from the start of the robot's movement to the current moment, and is used to eliminate steady-state error.
[0098] The steady-state error mentioned above will be described below.
[0099] In some cases, once the system is in a sliding mode, the actual motion state of the robot's joints no longer changes. However, the robot may still be subject to continuous external disturbances, such as friction or load changes. These continuous external disturbances will cause a deviation between the actual motion state and the desired motion state of the robot's joints. This deviation is called the steady-state error. Therefore, the aforementioned integral term control parameters can be used to eliminate the steady-state error.
[0100] Since the integral term control parameters are obtained based on the cumulative difference between the first motion parameter and the second motion parameter from the start of the robot's movement to the current moment, as long as there is a steady-state error, the integral term control parameters can be continuously determined to eliminate the error. This allows for long-term correction of the steady-state error in the system, improving the accuracy and robustness of the robot's joint control.
[0101] Specifically, the integral term control parameters can be determined based on the set second control gain coefficient, gain normalization factor, and the integral of the error convergence characterization value over time.
[0102] For example, the integral term control parameter p2 can be determined according to the following expression:
[0103]
[0104] Where k2 represents the set second control gain coefficient, k2>0, and t represents time.
[0105] In this case, on the one hand, the magnitude of the integral term control parameter is positively correlated with the value when the sliding mode variable is not zero, that is, it is positively correlated with the duration when the system state is not located on the sliding mode surface. This allows the determined integral term control parameter to better reflect the cumulative error from the start of movement of each joint of the robot to the current moment, thus improving the accuracy of correcting the steady-state error of the system based on the integral term control parameter. On the other hand, the error convergence characterization value is also normalized based on the gain normalization factor, that is, the magnitude of the determined integral term control parameter is further adaptively adjusted according to the magnitude of the error convergence characterization value, thus achieving a balance between the convergence speed of motion error and the degree of system state chattering.
[0106] In one possible implementation, the first control gain coefficient k1 is greater than the second control gain coefficient k2. For example, k1 can be 1 and k2 can be 0.5. This balances the response speed of motion state regulation with the accuracy of motion control.
[0107] In one possible implementation, the determined equivalent control parameters also include: feedforward control parameters and derivative control parameters.
[0108] The feedforward control parameters are derived from the expected acceleration in the second motion parameters. This ensures that the actual acceleration of each joint of the robot approaches the desired acceleration. Since acceleration reflects the rate of position change, this allows for faster movement of each joint to the target position.
[0109] The differential control parameters are derived from the velocity error in the second motion parameters and are used to suppress overshoot when controlling robot joint motion based on joint control parameters. Overshoot refers to the actual motion parameters of the joint being greater than or less than the desired motion parameters.
[0110] For example, as mentioned above, the integral term control parameter is used to eliminate steady-state error. It can be understood as correcting the motion state of the joint. However, it is easy for the correction force to be too large or too small, so that the motion state of the joint after correction still does not match the expectation.
[0111] Therefore, the differential control parameters can be determined based on the velocity error in the second motion parameter, so that the actual motion parameters of the joint approximate the desired motion parameters, thereby suppressing the influence of overshoot on the system state.
[0112] exist Figure 1 Based on the illustrated embodiment, when determining joint control parameters, the robot's joint dynamics can be considered to obtain the inertial characterization values, gravity characterization values, and Coriolis and centrifugal force characterization values of each joint. Based on these characterization values, the joint control parameters are determined. In view of the above, this embodiment of the invention provides a second robot joint control method.
[0113] See Figure 2 The above is a flowchart illustrating the second robot joint control method provided in the embodiment of the present invention. The method includes the following steps S201-S207.
[0114] Step S201: Determine the motion error of each joint based on the actual first motion parameters and the expected second motion parameters of each joint of the robot.
[0115] Step S202: Based on the position error and velocity error in the motion error, obtain the error convergence characterization value.
[0116] Step S203: Based on the error convergence characterization value and the gain normalization factor, determine the equivalent control parameters that make the motion error of each joint remain in a convergent state or approach a convergent state.
[0117] Steps S201-S203 above are the same as those mentioned above. Figure 1 In the illustrated embodiment, steps S101-S103 are the same and will not be repeated here.
[0118] Step S204: Based on the actual position in the first motion parameters, obtain the first characterization value representing the inertia of each joint and the second characterization value representing the gravity of each joint.
[0119] The first characterization value can be an inertia matrix (also called a mass matrix) that depends on the actual position of each joint, reflecting the dynamic characteristics of each joint. Specifically, it can be determined based on the position of each joint using the Newton-Euler algorithm.
[0120] The second characterization value can be a gravity matrix that depends on the actual position of each joint, representing the torque effect of gravity on each joint under different postures. Specifically, it can be calculated based on the joint mass and joint position.
[0121] Step S205: Based on the actual position and the actual velocity in the first motion parameter, obtain the third characterization value representing the Coriolis and centrifugal force acting on each joint.
[0122] The third characterization value can be a Coriolis force and centrifugal force matrix determined based on the position and velocity of each joint. It is used to characterize the coupling effect between joints due to relative motion. Specifically, the components of centrifugal motion and entrainment motion can be determined based on the position and velocity of each joint, and then the two can be superimposed to obtain the Coriolis force and centrifugal force matrix.
[0123] Step S206: Determine the joint control parameters based on the first characterization value, the second characterization value, the third characterization value, and the equivalent control parameters.
[0124] Specifically, the product of the third characterization value and the equivalent control parameter can be calculated, and then the sum of the resulting product and the first and second characterization values can be calculated. The sum can then be used as the joint control parameter.
[0125] Step S207: Control the movement of each joint according to the joint control parameters.
[0126] As can be seen from the above, when determining the joint control parameters, a joint dynamic model is constructed based on the position of each joint. Based on the joint dynamic model, joint adjustments can be made to each joint while satisfying dynamic constraints, achieving efficient coordinated adjustment of multiple joints and improving the adaptive response capability to disturbance changes in multi-degree-of-freedom coupled systems.
[0127] Based on the foregoing embodiments, the sliding mode control law provided by the embodiments of the present invention will be described below through specific examples.
[0128] For example, an embodiment of the present invention provides a sliding mode control law that can be expressed by the following expression:
[0129]
[0130] Where τ represents the control torque of each joint, and q represents the actual position in the first motion parameter. Let M(q) represent the actual velocity in the second motion parameter, M(q) represent the inertia matrix of each joint, and G(q) represent the gravity matrix of each joint. Let represent the Coriolis and centrifugal force matrices acting on each joint, and s represent the error convergence characterization value. e and Let e = qq represent the position error and velocity error, respectively. d , q d This indicates the expected position in the second motion parameter. The second motion parameter represents the expected velocity, where n represents the set power exponent, with a value greater than 0 and less than 1, k1 and k2 represent the set first and second control gain coefficients respectively, with values greater than 0, t represents time, and N represents the time parameter. s N represents the gain normalization factor. s The value of is determined based on the absolute value of s. It should be noted that the parameter within the square brackets following M(q) corresponds to the aforementioned equivalent control parameter. Corresponding to the proportional control parameters mentioned earlier, This corresponds to the integral term control parameter mentioned earlier.
[0131] It can be seen that the above sliding mode control law retains the error feedback structure of sliding mode control in its design, through sliding mode variables. The value of is chosen to adaptively adjust the system state, ensuring it remains on or continuously approaches the sliding surface. This gradually eliminates the discrepancy between the actual and expected motion of the robot joints, exhibiting good robustness and effectively handling unknown disturbances. Furthermore, by introducing the derivative of the sliding variable and the integral channel, the aforementioned second-order sliding mode control law is implemented. When controlling the robot joints based on this sliding mode control law, the system state achieves dual convergence on the sliding surface and its changing trend, thereby improving convergence speed and stability.
[0132] Furthermore, the aforementioned sliding mode control law incorporates a sliding mode gain normalization factor N in its design. s It is used to normalize the values of sliding mode variables, thereby enabling dynamic adjustment of the nonlinear action intensity when determining joint control parameters based on the values of sliding mode variables, and realizing dynamic adjustment of sliding mode gain.
[0133] Specifically, when the sliding mode variable is large, the gain normalization factor is set to the upper limit threshold, resulting in a large ratio between the magnitude of the sliding mode variable and the gain normalization factor. This leads to larger joint control parameters, providing sufficient control force for the robot joint and enabling rapid adjustment. Conversely, when the sliding mode variable is small, the gain normalization factor is set to the lower limit threshold, resulting in a smaller ratio between the magnitude of the sliding mode variable and the gain normalization factor. This leads to smaller joint control parameters, requiring less adjustment force on the robot joint. This reduces the probability of frequent switching of adjustment direction near the sliding surface due to excessive adjustment force, effectively suppressing chattering caused by frequent switching of adjustment direction. Furthermore, when the sliding mode variable is in the middle range, the ratio between the magnitude of the sliding mode variable and the gain normalization factor is 1. In this case, the joint control parameters are determined linearly based on the actual magnitude of the sliding mode variable. This ensures that the determined joint control parameters are linearly related to the value of the sliding mode variable, giving the sliding mode control law a certain degree of linear control capability, thus balancing improved convergence speed and reduced chattering. It can be seen that the above sliding mode control law comprehensively improves the stability and dynamic performance of robot joint control, and is particularly suitable for lower limb joint control systems with high requirements for response speed and stability.
[0134] For example, another sliding mode control law provided by embodiments of the present invention can be expressed by the following expression:
[0135]
[0136] The difference between this example and the sliding mode control law in the previous example is that the equivalent control parameters also include... and
[0137] in, This represents the expected acceleration in the second motion parameter, which corresponds to the feedforward control parameter mentioned earlier, and is used to further improve the speed at which each joint is driven to reach the target position. Corresponding to the differential term control parameters mentioned earlier, where λ represents the set convergence coefficient, and the differential control parameter is used to suppress overshoot caused by the integral term in the process of eliminating steady-state error. Thus, based on the above sliding mode control law, accurate joint control parameters can be determined more efficiently and reasonably, further improving the accuracy and stability of robot control.
[0138] The following examples illustrate the application of the embodiments of the present invention in specific scenarios.
[0139] Example 1: In the field of medical rehabilitation robots, the robots lack stability when assisting patients in performing rehabilitation movements such as standing, slowly squatting, and swinging from side to side.
[0140] In the solution provided by this invention, a second-order sliding mode control structure is constructed through the derivative and integral channels of the sliding mode variables, and a gain normalization factor is introduced. This enables the system state to achieve dual convergence on the sliding surface and its changing trend, thereby improving the convergence speed and stability. Thus, based on the determined joint control parameters, the joint movements of the medical rehabilitation robot become more stable and precise, effectively suppressing high-frequency chattering. This allows the medical rehabilitation robot to more stably assist patients in rehabilitation movements, significantly improving safety.
[0141] Example 2: In the case of the aforementioned robot being an industrial humanoid robot, the robot needs to perform tasks such as collaborative assembly, cargo handling, and loading / unloading. Therefore, the robot needs to possess stable adjustment capabilities under high-intensity load changes. Currently, in related technologies, the joint stability of robots performing these tasks is relatively poor, and mechanical damage caused by joint vibration can easily lead to task interruption.
[0142] In the solution provided by this invention, a second-order sliding mode control structure is constructed through the derivative and integral channels of the sliding mode variables, and a gain normalization factor is introduced. This enables the system state to achieve dual convergence on the sliding surface and its changing trend, thereby improving the convergence speed and stability. Thus, based on the determined joint control parameters, high-precision and high-stability joint control can be achieved, ensuring that the robot maintains balance and coordination when performing tasks such as bending to pick up objects and rotating to assemble, thereby improving the robot's task execution success rate.
[0143] Corresponding to the above-described robot joint control method, this embodiment of the invention also provides a robot joint control device.
[0144] See Figure 3The above is a schematic diagram of a robot joint control device provided in an embodiment of the present invention. The device includes the following modules:
[0145] Error determination module 301 is used to determine the motion error of each joint based on the actual first motion parameters and the expected second motion parameters of each joint of the robot.
[0146] The convergence characterization value acquisition module 302 is used to obtain the error convergence characterization value based on the position error and velocity error in the motion error;
[0147] The equivalent control parameter determination module 303 is used to determine, based on the error convergence characterization value and the gain normalization factor, the equivalent control parameters that make the motion error of each joint remain in a convergent state or approach a convergent state. The equivalent control parameters include: a proportional term control parameter, which is obtained based on the difference between the first motion parameter and the second motion parameter, and the value of the gain normalization factor is determined based on the absolute value of the error convergence characterization value.
[0148] The joint control parameter determination module 304 is used to determine joint control parameters based on the first motion parameters and the equivalent control parameters;
[0149] The joint control module 305 is used to control the movement of each joint according to the joint control parameters.
[0150] As can be seen from the above, in the process of controlling robot joints using the scheme provided in the embodiments of the present invention, the motion error of each joint is obtained based on the actual motion parameters and expected motion parameters of each joint. An error convergence characterization value is determined based on the motion error, and then equivalent control parameters are obtained based on the error convergence characterization value to keep the motion error of each joint in a convergent state or to approach a convergent state. Based on the equivalent control parameters, joint control parameters can be obtained. Furthermore, the movement of each joint can be controlled according to the joint control parameters to keep the motion error of each joint in a convergent state or to approach a convergent state, thereby making the actual motion of each joint approach the expected motion, reducing the uncertainty introduced by unknown disturbances such as friction to the robot joint motion, and improving the control accuracy of the robot joints.
[0151] Furthermore, in determining the equivalent control parameters, not only were the error convergence characterization values of each joint considered, but also the gain normalization factor used to normalize these values. The value of the gain normalization factor was determined based on the absolute value of the error convergence characterization value. This allows the magnitude of the determined equivalent control parameters to be adaptively adjusted according to the magnitude of the error convergence characterization value. In other words, the control force can be adaptively adjusted according to the magnitude of the robot joint motion error. Therefore, when eliminating joint motion errors based on the determined parameters, both the convergence speed and chattering degree of the motion error can be considered. This achieves efficient and rapid control of the robot joints while ensuring control accuracy and stability.
[0152] In one possible implementation, the joint control parameter determination module 304 is specifically used to obtain a first characterization value characterizing the inertia of each joint and a second characterization value characterizing the gravity of each joint based on the actual position in the first motion parameters.
[0153] Based on the actual position and the actual velocity in the first motion parameter, a third characterization value is obtained to characterize the Coriolis and centrifugal forces acting on each joint.
[0154] Based on the first characterization value, the second characterization value, the third characterization value, and the equivalent control parameter, the joint control parameter is determined.
[0155] As can be seen from the above, when determining the joint control parameters, a joint dynamic model is constructed based on the position of each joint. Based on the joint dynamic model, joint adjustments can be made to each joint while satisfying dynamic constraints, achieving efficient coordinated adjustment of multiple joints and improving the adaptive response capability to disturbance changes in multi-degree-of-freedom coupled systems.
[0156] In one possible implementation, the equivalent control parameters further include: integral term control parameters, wherein the integral term control parameters are obtained based on the cumulative difference between the first motion parameter and the second motion parameter from the start of the robot's movement to the current moment.
[0157] Since the integral term control parameters are obtained based on the cumulative difference between the first motion parameter and the second motion parameter from the start of the robot's movement to the current moment, as long as there is a steady-state error, the integral term control parameters can be continuously determined to eliminate the error. This allows for long-term correction of the steady-state error in the system, improving the accuracy and robustness of the robot's joint control.
[0158] In one possible implementation, the equivalent control parameter determination module 303 is specifically used to determine the proportional term control parameter based on the set first control gain coefficient, the gain normalization factor, and the set power of the error convergence characterization value; and to determine the integral term control parameter based on the set second control gain coefficient, the gain normalization factor, and the integral of the error convergence characterization value over time; and to obtain the equivalent control parameters that make the motion error of each joint remain in a convergent state or approach a convergent state based on the proportional term control parameter and the integral term control parameter.
[0159] Since the set power of the error convergence characterization value is positively correlated with the magnitude of the error convergence characterization value, when determining the proportional term control parameter based on the set power of the error convergence characterization value, the magnitude of the proportional term control parameter is positively correlated with the magnitude of the error convergence characterization value. Thus, when the error convergence characterization value is large, the determined proportional term control parameter is also large, which can improve the convergence speed of motion error. In addition, the error convergence characterization value is normalized based on the gain normalization factor, that is, the magnitude of the determined proportional term control parameter is adaptively adjusted according to the magnitude of the error convergence characterization value, thereby achieving a balance between the convergence speed of motion error and the degree of system state chattering. Regarding the integral term control parameters, on the one hand, the magnitude of the integral term control parameters is positively correlated with the value when the sliding mode variable is not zero, that is, it is positively correlated with the duration when the system state is not located on the sliding mode surface. This allows the determined integral term control parameters to better reflect the cumulative error of each joint of the robot from the start of movement to the current moment, improving the accuracy of correcting the steady-state error of the system based on the integral term control parameters. On the other hand, the error convergence characterization value is also normalized based on the gain normalization factor, that is, the magnitude of the determined integral term control parameters is further adaptively adjusted according to the magnitude of the error convergence characterization value, achieving a balance between the convergence speed of motion error and the degree of system state chattering.
[0160] In one possible implementation, the joint control parameter τ is determined according to the following expression:
[0161]
[0162] Where q represents the actual position in the first motion parameter. Let M(q) represent the actual velocity in the second motion parameter, M(q) represent the inertia matrix of each joint, and G(q) represent the gravity matrix of each joint. Let represent the Coriolis and centrifugal force matrices acting on each joint, and s represent the error convergence characterization value. e and Let e = qq represent the position error and the velocity error, respectively. d q dThis indicates the expected position in the second motion parameter. The second motion parameter represents the expected velocity, n represents the set power exponent, with a value greater than 0 and less than 1, k1 and k2 represent the first control gain coefficient and the second control gain coefficient respectively, with values greater than 0, t represents time, and N represents the expected velocity. s This represents the gain normalization factor.
[0163] It can be seen that the sliding mode control law retains the error feedback structure of sliding mode control in its design, through sliding mode variables. This system achieves good robustness in regulating the system state and effectively copes with unknown disturbances. Furthermore, by introducing the sliding mode variable derivative and integral path, the aforementioned second-order sliding mode control structure is realized. When controlling the robot joints based on this sliding mode control law, the system state can achieve dual convergence on the sliding surface and its changing trend, thereby improving convergence speed and stability. Moreover, the design of the aforementioned sliding mode control law incorporates a sliding mode gain normalization factor N. s This normalization factor is used to normalize the values of sliding mode variables, thereby dynamically adjusting the nonlinear intensity of joint control parameters based on the values of the sliding mode variables, achieving dynamic adjustment of the sliding mode gain. Specifically, when the value of the sliding mode variable is large, the gain normalization factor is set to the upper limit threshold, resulting in a larger ratio between the magnitude of the sliding mode variable and the gain normalization factor. Consequently, the determined joint control parameters are larger, providing sufficient control force for the robot joint and enabling rapid adjustment. When the value of the sliding mode variable is small, the gain normalization factor is set to the lower limit threshold, resulting in a smaller ratio between the magnitude of the sliding mode variable and the gain normalization factor. Consequently, the determined joint control parameters are smaller, requiring less adjustment force for the robot joint. This reduces the probability of frequent switching of adjustment direction near the sliding surface due to excessive adjustment force, effectively suppressing chattering caused by frequent switching of adjustment direction. Furthermore, when the sliding mode variable's value is within the intermediate range, the ratio of the sliding mode variable's magnitude to the gain normalization factor is 1. In this case, the joint control parameters are determined linearly based on the actual magnitude of the sliding mode variable. This means the determined joint control parameters are linearly related to the value of the sliding mode variable, giving the sliding mode control law a certain degree of linear control capability, thus balancing improved convergence speed with reduced chattering. It can be seen that the above sliding mode control law comprehensively improves the stability and dynamic performance of robot joint control, making it particularly suitable for lower limb joint control systems with high requirements for response speed and stability.
[0164] In one possible implementation, the equivalent control parameters further include: feedforward control parameters and derivative control parameters, wherein the feedforward control parameters are obtained based on the expected acceleration in the second motion parameters, and the derivative control parameters are obtained based on the velocity error in the second motion parameters.
[0165] In this way, the determined feedforward control parameters can make the actual acceleration of each joint of the robot approach the desired acceleration. Since acceleration reflects the rate of change of position, this allows the joints to reach the target position more quickly. Furthermore, based on the velocity error in the second motion parameter, the differential control parameters can be determined to make the actual motion parameters of the joints approximate the desired motion parameters, thereby suppressing the influence of overshoot on the system state.
[0166] In one possible implementation, the value of the gain normalization factor is determined as follows:
[0167] If the absolute value of the error convergence characterization value is greater than or equal to a set upper threshold, then the value of the gain normalization factor is determined as the upper threshold; and / or, if the error convergence characterization value is less than or equal to a set lower threshold, then the value of the gain normalization factor is determined as the lower threshold; and / or, if the error convergence characterization value is greater than the lower threshold and less than the upper threshold, then the value of the gain normalization factor is determined as the absolute value; the upper threshold is greater than the lower threshold.
[0168] Thus, when the magnitude of the error convergence characterization value is greater than or equal to the upper threshold, the gain normalization factor is taken as the upper threshold, so the ratio of the magnitude of the error convergence characterization value to the gain normalization factor is greater than or equal to 1. That is, when the motion error is large, the determined proportional control parameter is large, which can improve the convergence speed of the motion error. When the magnitude of the error convergence characterization value is less than or equal to the lower threshold, the gain normalization factor is taken as the lower threshold, so the ratio of the magnitude of the proportional control parameter to the gain normalization factor is less than or equal to 1. That is, when the motion error is small, the determined joint control parameter is small, which can reduce the chattering phenomenon generated by the system state near the sliding surface. In other cases, the gain normalization factor is taken as the magnitude of the error convergence characterization value, so the ratio of the magnitude of the error convergence characterization value to the gain normalization factor is equal to 1. That is, when the motion error is moderate, the determined proportional control parameter is also moderate, taking into account the convergence speed and chattering degree, so that the sliding control law has a certain linear adjustment capability.
[0169] This invention also provides an electronic device, such as... Figure 4As shown, it includes a processor 401, a communication interface 402, a memory 403, and a communication bus 404, wherein the processor 401, the communication interface 402, and the memory 403 communicate with each other through the communication bus 404.
[0170] Memory 403 is used to store computer programs;
[0171] The processor 401, when executing the program stored in the memory 403, implements any of the aforementioned robot joint control methods. The aforementioned electronic device can be a background control device or a robot.
[0172] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.
[0173] The communication interface is used for communication between the aforementioned electronic devices and other devices.
[0174] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.
[0175] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.
[0176] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described robot joint control methods.
[0177] The present invention also provides a computer program product containing instructions that, when run on a computer, cause the computer to execute any of the above-described robot joint control methods.
[0178] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk (SSD)).
[0179] It should be noted that, in this document, relational terms such as "first" and "second" are used only 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.
[0180] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, electronic devices, and storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.
[0181] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.
Claims
1. A robot joint control method, characterized in that, The method includes: Based on the actual first motion parameters and the expected second motion parameters of each joint of the robot, the motion error of each joint is determined. Based on the position error and velocity error in the motion error, the error convergence characterization value is obtained; Based on the error convergence characterization value and the gain normalization factor, equivalent control parameters are determined to keep the motion error of each joint in a convergent state or to approach a convergent state. The equivalent control parameters include: a proportional term control parameter, which is obtained based on the difference between the first motion parameter and the second motion parameter, and the value of the gain normalization factor is determined based on the absolute value of the error convergence characterization value. Based on the first motion parameters and the equivalent control parameters, the joint control parameters are determined; The movement of each joint is controlled according to the joint control parameters.
2. The method according to claim 1, characterized in that, Determining the joint control parameters based on the first motion parameters and the equivalent control parameters includes: Based on the actual position in the first motion parameters, a first characterization value representing the inertia of each joint and a second characterization value representing the gravity of each joint are obtained. Based on the actual position and the actual velocity in the first motion parameter, a third characterization value is obtained to characterize the Coriolis and centrifugal forces acting on each joint. Based on the first characterization value, the second characterization value, the third characterization value, and the equivalent control parameter, the joint control parameter is determined.
3. The method according to claim 1 or 2, characterized in that, The equivalent control parameters further include: integral term control parameters, wherein the integral term control parameters are obtained based on the cumulative difference between the first motion parameter and the second motion parameter from the start of the robot's movement to the current moment.
4. The method according to claim 3, characterized in that, The determination of equivalent control parameters based on the error convergence characterization value and the gain normalization factor to keep the motion error of each joint in a convergent state or to approach a convergent state includes: Based on the set first control gain coefficient, the gain normalization factor, and the set power of the error convergence characterization value, the proportional term control parameters are determined, and based on the set second control gain coefficient, the gain normalization factor, and the integral of the error convergence characterization value over time, the integral term control parameters are determined. Based on the proportional term control parameters and the integral term control parameters, equivalent control parameters are obtained that enable the motion error of each joint to remain in a convergent state or approach a convergent state.
5. The method according to claim 4, characterized in that, The joint control parameter τ is determined according to the following expression: Where q represents the actual position in the first motion parameter. Let M(q) represent the actual velocity in the second motion parameter, M(q) represent the inertia matrix of each joint, and G(q) represent the gravity matrix of each joint. Let represent the Coriolis and centrifugal force matrices acting on each joint, and s represent the error convergence characterization value. e and Let e = qq represent the position error and the velocity error, respectively. d , q d This indicates the expected position in the second motion parameter. The second motion parameter represents the expected velocity, n represents the set power exponent, with a value greater than 0 and less than 1, k1 and k2 represent the first control gain coefficient and the second control gain coefficient respectively, with values greater than 0, t represents time, and N represents the expected velocity. s This represents the gain normalization factor.
6. The method according to any one of claims 1 to 5, characterized in that, The equivalent control parameters further include: feedforward control parameters and derivative control parameters, wherein the feedforward control parameters are obtained based on the expected acceleration in the second motion parameters, and the derivative control parameters are obtained based on the velocity error in the second motion parameters.
7. The method according to any one of claims 1 to 6, characterized in that, The value of the gain normalization factor is determined in the following manner: If the absolute value of the error convergence characterization value is greater than or equal to the set upper limit threshold, then the value of the gain normalization factor is determined as the upper limit threshold. And / or, if the error convergence characterization value is less than or equal to the set lower threshold, then the value of the gain normalization factor is determined as the lower threshold. And / or, if the error convergence characterization value is greater than the lower limit value and less than the upper limit threshold, then the value of the gain normalization factor is determined to be the absolute value; The upper limit threshold is greater than the lower limit threshold.
8. A robot joint control device, characterized in that, The device includes: The error determination module is used to determine the motion error of each joint based on the actual first motion parameters and the expected second motion parameters of each joint of the robot. The convergence characterization value acquisition module is used to obtain the error convergence characterization value based on the position error and velocity error in the motion error; An equivalent control parameter determination module is used to determine, based on the error convergence characterization value and the gain normalization factor, equivalent control parameters that keep the motion error of each joint in a convergent state or approach a convergent state. The equivalent control parameters include: a proportional term control parameter, which is obtained based on the difference between the first motion parameter and the second motion parameter; and the value of the gain normalization factor is determined based on the absolute value of the error convergence characterization value. A joint control parameter determination module is used to determine joint control parameters based on the first motion parameter and the equivalent control parameter; The joint control module is used to control the movement of each joint according to the joint control parameters.
9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the steps of the method described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method described in any one of claims 1 to 7.