Mechanical arm speed control method and device, computer device and storage medium

Abstract

The application relates to the field of mechanical arm control, and discloses a mechanical arm speed control method and device, computer equipment and a storage medium, the method comprising the following steps: acquiring an actual position of a robot end flange at a current moment, and acquiring a desired position of the end flange of the mechanical arm at a next moment according to path planning of the mechanical arm; calculating a control speed of the mechanical arm at the current moment according to the actual position, the desired position and a preset trajectory tracking model; performing inverse Jacobian transformation on the control speed to obtain corresponding motor output force, and performing corresponding control according to the motor output force. The movement of the mechanical arm can be controlled in a speed control mode, the control speed and real-time performance are improved, and control errors are reduced.

Description

Technical Field

[0001] This invention relates to the field of robotic arm control, and more particularly to a robotic arm speed control method, device, computer equipment, and storage medium. Background Technology

[0002] In actual clinical practice of dental implant robotic arm projects, the robotic arm is generally controlled primarily by position control. This involves controlling the movement of the robotic arm to actively follow the patient's movements, enabling functions such as follow-up drilling and follow-up to target points. Traditional PID control requires manual tuning of multiple parameters. During parameter tuning, inappropriate parameter selection can lead to increased tracking errors in the controller, resulting in unstable control performance. Furthermore, the position response is generally too slow, with poor real-time performance, which may have adverse consequences in actual clinical practice. Summary of the Invention

[0003] In a first aspect, this application provides a method for controlling the speed of a robotic arm, comprising:

[0004] Obtain the actual position of the robot's end flange at the current moment, and based on the path planning of the robotic arm, obtain the expected position of the robotic arm's end flange at the next moment;

[0005] The control speed of the robotic arm at the current moment is calculated based on the actual position, the desired position, and the preset trajectory tracking model.

[0006] The control speed is subjected to an inverse Jacobian transformation to obtain the corresponding motor output force, and the motor output force is controlled accordingly.

[0007] Furthermore, the method for establishing the trajectory tracking model includes:

[0008] Obtain the state parameters of the robotic arm, establish the tracking error equation, and establish the motion equation of the robotic arm based on the state parameters;

[0009] Based on the Lyapunov stability criteria, the stability condition that makes the equation of motion converge to 0 in the tracking error equation is determined, and the trajectory tracking model is determined based on the stability condition.

[0010] Furthermore, obtaining the state parameters of the robotic arm includes:

[0011] Determine whether the state parameter has an exact value;

[0012] If the state parameter has a definite value, then the motion equation of the robotic arm can be directly established based on the state parameter;

[0013] If the state parameter does not have a definite value, then an estimation equation relationship for the state parameter is established, and the state parameter is parameterized and estimated based on the estimation equation relationship.

[0014] Furthermore, the estimated equation relationship is as follows:

[0015]

[0016]

[0017]

[0018] In the formula, To estimate the error, 'a' is the state parameter. For the estimated state parameters, The time derivative of the estimation error is... The derivative of the state parameter with respect to time. The derivative of the estimated state parameters with respect to time is given.

[0019] Furthermore, the trajectory tracking model expression is as follows:

[0020]

[0021] In the formula, u represents the control speed. Let x be the derivative of the desired position with respect to time, e be the tracking error, and K be the velocity convergence parameter obtained by tuning to adjust the convergence speed.

[0022] Furthermore, the method for tuning the velocity convergence parameter includes:

[0023] The velocity convergence parameters to be tuned are input into the tracking model in ascending order, and the tracking error corresponding to each velocity convergence parameter is recorded in real time.

[0024] The velocity convergence parameter range that allows the tracking error to be within a preset error range is selected as the tuned velocity convergence parameter.

[0025] Furthermore, obtaining the actual position of the robot's end flange at the current moment, and obtaining the expected position of the robot arm's end flange at the next moment based on the robot arm's path planning, includes:

[0026] The desired position of the end flange of the robotic arm is determined by parsing external input commands;

[0027] The state parameters of each joint of the robotic arm are obtained by a joint encoder, and the forward kinematics analysis of the state parameters of each joint is performed to determine the actual position of the end flange of the robotic arm.

[0028] Secondly, this application also provides a robotic arm speed control device, comprising:

[0029] The trajectory calculation module is used to obtain the actual position of the robot's end flange at the current moment, and to obtain the expected position of the robot arm's end flange at the next moment based on the path planning of the robot arm.

[0030] The tracking calculation module is used to calculate the control speed of the robotic arm at the current moment based on the actual position, the desired position, and a preset trajectory tracking model.

[0031] The control module is used to perform an inverse Jacobian transformation on the control speed to obtain the corresponding motor output force, and to perform corresponding control based on the motor output force.

[0032] Thirdly, this application also provides a computer device, including a processor and a memory, wherein the memory stores a computer program, and the computer program executes the robotic arm speed control method when it is run on the processor.

[0033] Fourthly, this application also provides a readable storage medium storing a computer program that executes the robotic arm speed control method when run on a processor.

[0034] This invention discloses a method, device, computer equipment, and storage medium for controlling the speed of a robotic arm. The method includes: acquiring the actual position of the robot's end flange at the current moment, and acquiring the desired position of the end flange of the robotic arm at the next moment based on the path planning of the robotic arm; calculating the control speed of the robotic arm at the current moment based on the actual position, the desired position, and a preset trajectory tracking model; performing an inverse Jacobian transform on the control speed to obtain the corresponding motor output force, and performing corresponding control based on the motor output force. This speed control method improves the control speed and real-time performance while reducing control errors. In clinical settings, this allows the robotic arm to respond to commands more quickly, providing doctors with better surgical control feedback. Attached Figure Description

[0035] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope of protection of the present invention. In the various drawings, similar components are numbered similarly.

[0036] Figure 1 A schematic flowchart of a robotic arm speed control method according to an embodiment of this application is shown;

[0037] Figure 2 A schematic diagram of the robotic arm control feedback process according to an embodiment of this application is shown;

[0038] Figure 3 This paper illustrates a schematic diagram of tracking error fluctuations during the control process of a robotic arm, according to an embodiment of this application.

[0039] Figure 4 A schematic diagram of a robotic arm speed control device according to an embodiment of this application is shown. Detailed Implementation

[0040] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0041] The components of the embodiments of the invention described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0042] In the following, the terms “comprising,” “having,” and their cognates, which may be used in various embodiments of the invention, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as excluding, firstly, the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations thereof.

[0043] Furthermore, the terms "first," "second," and "third" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0044] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of the invention pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be interpreted as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of the invention.

[0045] This application is applied to the control process of robotic arms, particularly in controlling the movement of robotic arms by setting a speed. This application establishes a tracking control model to determine the appropriate control speed at the current moment based on the current position of the robotic arm and the target position. By using the Lyapunov stability criterion, the control error of the robotic arm is made to converge as close to zero as possible. This results in stable and efficient control of the robotic arm. Furthermore, because speed control is used, the speed parameters can be modified in real time, leading to faster control response and better real-time performance.

[0046] Example 1

[0047] like Figure 1 As shown, the robotic arm speed control method in this embodiment includes:

[0048] Step S100: Obtain the actual position of the robot's end flange at the current moment, and obtain the expected position of the robot's end flange at the next moment according to the path planning of the robot arm.

[0049] In practical robotic arm control, a pre-planned motion path is typically used to control the arm's movement. Alternatively, a destination can be set, and a planning algorithm can devise a path for the arm to reach it. Therefore, based on the planned path, the desired position of the arm along the planned path at the next moment can be determined. Furthermore, the actual position of the end effector flange can be obtained using the parameters of the arm's joints. It can be understood that the output of the arm's motors at the current moment can be calculated based on this actual and desired position.

[0050] The actual position of the end effector flange of the robotic arm is its spatial coordinates at the current moment. Compared to the desired position, this actual position can be considered the starting point. The robotic arm needs to calculate the amount of force required at this actual position to stably and accurately move to the desired position in the next moment. In this embodiment, the timeframe is related to the robot's own sampling period.

[0051] The desired position can be obtained through known path planning. The planned path is typically a continuous curve. Therefore, based on the current actual position of the robotic arm's end flange and the planned path, the desired position of the robotic arm at the next moment can be determined. For example, the desired position can be determined by taking the spatial coordinates closest to the current end flange along the planned trajectory. Alternatively, the desired position can be estimated based on the planned motion time.

[0052] Step S200: Calculate the control speed of the robotic arm at the current moment based on the actual position, the desired position, and the preset trajectory tracking model.

[0053] In this embodiment, the robotic arm is controlled by speed control. That is, using a preset trajectory tracking model, the desired position and actual position obtained in step S100 are used to calculate the appropriate speed at which the end flange of the robotic arm should move. This calculated speed is the control speed. It can be understood that speed control is faster and more intuitive than position control. Furthermore, after obtaining the speed, it is easy to obtain the corresponding motor output through inverse kinematics, making the control of the robotic arm more real-time.

[0054] The trajectory tracking model is established based on the Lyapunov stability criterion.

[0055] Specifically, the expression for this trajectory tracking model is as follows:

[0056]

[0057] In the formula, u represents the control speed. Let x be the derivative of the desired position with respect to time, e be the tracking error, and K be the velocity convergence parameter obtained through tuning to adjust the convergence speed. From the above equation, it can be seen that as long as the desired position is used as input and the value of K is tuned, the corresponding control speed can be obtained.

[0058] The trajectory tracking model described above can converge the tracking error e to a very small range, that is, e approaches 0, so that the output of u is stable and will not fluctuate greatly.

[0059] The acquisition and stability proof of the above estimation tracking model are as follows.

[0060] It should be noted that when using trajectory tracking models in robotic arms, there will always be various systematic errors for each robotic arm. These systematic errors can be obtained directly from the manufacturer as a state parameter. However, due to the long service time of the machine, the recorded state parameters may be inaccurate, or the manufacturer may not provide such state parameters. Therefore, we will discuss different cases of this state parameter.

[0061] First, when the state parameter 'a' has a definite value, the velocity equation and the tracking error equation can be established, and their expressions are as follows:

[0062]

[0063] e = x d -x;

[0064] In the formula This represents the movement speed of the robotic arm.

[0065] From the above velocity state expression, it can be seen that when e approaches 0, the overall error of the robotic arm system is small. The small fluctuations make the robotic arm system stable. Therefore, the stability of the robotic arm system can be proved by using the Lyapunov stability criteria, thus obtaining the above trajectory tracking model.

[0066] First, we can design the Lyapunov equations. It can be deduced Appropriate It is positive definite. From this, the stability condition can be obtained, and the velocity equation for the motor output speed can be constructed based on it.

[0067] In this embodiment, the constructed velocity equation is as follows: Based on the constructed velocity equation, we can derive the following: Then at this time It is negative definite. Substitute the obtained u into From the derivation, we can obtain achievable Negative definite. Therefore, the velocity equations constructed above satisfy the Lyapunov asymptotic stability criteria.

[0068] Therefore, it can be concluded that when the systematic error 'a' has a definite value, Lyapunov satisfies condition (1). V (e) = 0, (2) V (e) is positive definite, (3) When the value is negative constant, the velocity equations constructed above are stable.

[0069] When the state parameter 'a' has no definite value, the stability is proven as follows:

[0070] When 'a' is not fixed or is unknown, parameterized estimation of 'a' is required. An adaptive controller is proposed. Because this state parameter is constant, preconditions exist. The estimated state parameters are expressed as Then we can establish an estimation equation, the relationship of which is:

[0071]

[0072]

[0073] because

[0074] In the formula, To estimate the error, 'a' is the state parameter. For the estimated state parameters, The time derivative of the estimation error is... The time derivative of the state parameter. The time derivative of the estimated state parameters. Because Therefore, we can obtain

[0075] Based on this, the Lyapunov equation can be established.

[0076] in, It is a positive definite system.

[0077] Meanwhile, let the velocity equation be: Combining the above two equations, we get:

[0078]

[0079] in If the system is positive definite, then in this case, it exists. The velocity equation can be set as follows:

[0080] Then it can be deduced that At this point, design can be performed. Then it exists The function is semi-negative definite, which guarantees convergence and stability. Therefore, according to Lyapunov's lemma, the system asymptotically converges when the following three conditions are met.

[0081] (1) V(e)≥0;

[0082] (2)

[0083] (3) If If it is bounded and continuous, then it can be deduced that...

[0084] Combining the two proofs above, and according to Lyapunov's stability principle, we know that g(t) = Ke 2 ,in Then according to the formula Since K≠0, in In this case, e approaches 0. Therefore, the entire robotic arm system is stable, and the error approaches 0. Because Therefore, we can obtain Substitute into the formula

[0085] Finally, the trajectory tracking model can be obtained:

[0086]

[0087] In this embodiment, by establishing corresponding velocity equations and error equations for the motion state of the robotic arm, and then constructing relevant Lyapunov equations, it is determined that the motion of the robotic arm can reach a stable state and that the error converges to 0. Then, based on this, velocity equations are constructed, and conditions for the velocity equations to be stable are sought, and finally the above trajectory tracking model is obtained. This illustrates the process of obtaining the trajectory tracking model and the proof of its stability.

[0088] Among them, the velocity convergence parameter k can be adjusted according to the actual situation to determine the final value. The value of k is mainly used to control the convergence speed, without changing the convergence trend of the entire model. Therefore, the velocity convergence parameters to be tuned can be input into the tracking model in ascending order, and the tracking error corresponding to each velocity convergence parameter can be recorded in real time. The velocity convergence parameter interval corresponding to the tracking error within the preset interval is selected as the tuned velocity convergence parameter interval. The velocity convergence parameter is determined from the velocity convergence parameter interval according to the actual working environment.

[0089] By substituting the desired position and actual position determined in step S100 into the trajectory tracking model described above, the control speed can be obtained. Since the trajectory tracking model has been proven stable and allows the error e to converge towards zero, the resulting control speed will be stable and minimize the motion error of the robotic arm. Simultaneously, according to... Since x and e exhibit periodic and convergent changes, a will also converge as a parameter, achieving parameter self-adaptation. Therefore, the movement of the robotic arm can be stably controlled.

[0090] like Figure 3 As shown, the reference signal at the desired position is selected as r. k =A(-cos(2πfT) s k)+1). Here, the parameter A is set to 50, the frequency f = 0.18Hz, and the sampling period T is... s =15ms, K=45. Under the above conditions, after conducting simulation experiments using the trajectory tracking model, the obtained tracking error curve shows that during the periodic tracking period, the error range between the expected tracking curve and the actual curve is less than 0.5mm, and it exhibits continuous function characteristics, reflecting stability and error convergence.

[0091] Step S300: Perform an inverse Jacobian transformation on the control speed to obtain the corresponding motor output force, and perform corresponding control based on the motor output force.

[0092] The obtained control speed is the control speed of the end flange of the robotic arm. Specifically, as to how much force each motor of the robotic arm should output, the Jacobian inverse transformation needs to be performed on this control speed to obtain the corresponding motor output force. After obtaining the motor output force, the motor can perform corresponding control according to this output force.

[0093] Specifically, the motor output force can be sent to the torque controller, and then the torque controller controls the motors of the robotic arm to move.

[0094] It can be understood that the movement process of the robotic arm is a continuous process, and the position of the end flange of the robotic arm changes in real time during the movement process. Therefore, it can be known that the control speed and output force at the current moment do not necessarily represent the control speed and output force at the next moment. Therefore, after calculating the control speed and the robotic arm performs the corresponding movement, the actual position of the end flange of the robotic arm needs to be updated in real time. Therefore, the joint encoder of the robotic arm will also perform forward kinematics analysis on the loading parameters of each joint on the current robotic arm to determine the position of the current end flange of the robotic arm, and then use the newly confirmed position as the current position and re-enter it as the input to continue calculating the control speed at the next moment. These state parameters include the angle, direction, etc. of the joints.

[0095] Specifically, the entire process is as Figure 2 shown. In the figure, the host computer 100 is a computer device for controlling the movement of the robotic arm 200. Specifically, it can be a PC or a host control machine supporting the robotic arm 200. The host computer 100 is used to receive the instructions issued by the user, obtain the expected position according to these instructions, and will receive the current position of the robotic arm fed back from the robotic arm in real time. After calculating the control speed based on these data, the host computer 100 sends the control speed to the robotic arm 200. The robotic arm will perform the Jacobian inverse transformation on this control speed to determine the output force of each joint motor specifically. It can be specifically controlled by the torque controller. At the same time, the actual force exerted by the joint motor will be recognized by the joint encoder, and then through forward kinematic processing, to update the actual position of the end flange of the robotic arm in real time, and then transmit it to the host computer, thereby realizing the tracking control of the robotic arm.

[0096] After the motor of the robotic arm outputs force, it drives the end flange of the robotic arm to move along the planned path. The time of movement according to this output force is related to the above feedback time. After moving for this feedback time, the host computer re-obtains the actual position of the end flange of the robotic arm, thereby updating the expected position at the next moment, and then according to the new actual position and the expected position, calculates the output speed at the current moment to perform the control at the next moment, forming a cycle in this way until the robotic arm moves to the target position.

[0097] It can be seen that when the robotic arm moves, it is a real-time feedback motion process. By setting the feedback frequency, the frequency of collecting the current position of the robotic arm is adjusted, so that the change frequency of the output force can be adjusted, and then whether the motion process of the robotic arm is compliant and smooth can be changed. It can be understood that the higher the feedback frequency, the more compliant and smooth the motion of the robotic arm, and the greater the same computational burden. The lower the feedback frequency, the smaller the computational burden, and the motion process of the robotic arm is not as smooth as when the feedback frequency is high.

[0098] The robotic arm speed control method of this embodiment takes the actual position of the end of the robotic arm flange and the preset desired position as inputs, and uses a preset trajectory tracking model to generate the control speed corresponding to the current position of the robotic arm in real time, so that the control speed can be quickly calculated to quickly control the robotic arm. And this trajectory tracking model can output a stable control speed, and make the overall error of the robotic arm approach 0. In the entire control process of the robotic arm, in addition to being able to respond quickly, it can also reflect sufficient stability, which can improve the working efficiency during actual work.

[0099] Embodiment 2

[0100] As Figure 4 shown, the present application also provides a robotic arm speed control device, including:

[0101] A trajectory calculation module 10, configured to obtain the actual position of the end flange of the robot at the current moment, and obtain the desired position of the end flange of the robotic arm at the next moment according to the path planning of the robotic arm;

[0102] A tracking calculation module 20, configured to calculate the control speed of the robotic arm at the current moment according to the actual position, the desired position, and a preset trajectory tracking model;

[0103] A control module 30, configured to perform a Jacobian inverse transformation on the control speed to obtain a corresponding motor output force, and perform corresponding control according to the motor output force.

[0104] The present application also provides a computer device, including a processor and a memory. The memory stores a computer program, and when the computer program runs on the processor, it executes the robotic arm speed control method. This computer device is a host computer for controlling the motion of the robotic arm, which can be a PC or a console配套 with the robotic arm, or even a server. That is to say, as long as it is a computer device that can establish a control connection with the robotic arm, it is within the protection scope of the present application.

[0105] This application also provides a readable storage medium storing a computer program that, when run on a processor, executes the described robotic arm speed control method. The method includes: obtaining the actual position of the robot's end flange at the current moment, and obtaining the desired position of the end flange of the robotic arm at the next moment based on the path planning of the robotic arm; calculating the control speed of the robotic arm at the current moment based on the actual position, the desired position, and a preset trajectory tracking model; performing an inverse Jacobian transform on the control speed to obtain the corresponding motor output force, and performing corresponding control based on the motor output force. This speed control method improves the control speed and real-time performance, and reduces control errors.

[0106] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, as an alternative implementation, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0107] In addition, the functional modules or units in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0108] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0109] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for controlling the speed of a robotic arm, characterized in that, include: Obtain the actual position of the robot's end flange at the current moment, and based on the path planning of the robotic arm, obtain the expected position of the robotic arm's end flange at the next moment; Based on the actual position, the desired position, and the preset trajectory tracking model, the control speed of the robotic arm at the current moment is calculated. Perform an inverse Jacobian transformation on the control speed to obtain the corresponding motor output force, and perform corresponding control based on the motor output force; The method for establishing the trajectory tracking model includes: Obtain the state parameters of the robotic arm, establish the tracking error equation, and establish the motion equation of the robotic arm based on the state parameters; Based on the Lyapunov stability criteria, the stability condition that makes the motion equation converge to 0 in the tracking error equation is determined, and the trajectory tracking model is determined based on the stability condition. The method for obtaining the state parameters of the robotic arm includes: Determine whether the state parameter has an exact value; If the state parameter has a definite value, then the motion equation of the robotic arm is established based on the definite value of the state parameter; If the state parameter does not have a definite value, then an estimation equation relation for the state parameter is established. Based on the estimation equation relation, the state parameter is parametrically estimated, and the motion equation is established based on the parametrically estimated state parameter.

2. The robotic arm speed control method according to claim 1, characterized in that, The estimated equation is as follows: ； ； ； In the formula, To estimate the error, The state parameter, For the estimated state parameters, The time derivative of the estimation error is... The derivative of the state parameter with respect to time. The derivative of the estimated state parameters with respect to time is given.

3. The robotic arm speed control method according to claim 1, characterized in that, The trajectory tracking model expression is: ； In the formula, u represents the control speed. Let x be the derivative of the desired position with respect to time, e be the tracking error, and K be the velocity convergence parameter obtained by tuning to adjust the convergence speed.

4. The robotic arm speed control method according to claim 3, characterized in that, The method for tuning the velocity convergence parameter includes: The velocity convergence parameters to be tuned are input into the tracking model in ascending order, and the tracking error corresponding to each velocity convergence parameter is recorded in real time. The velocity convergence parameter range that allows the tracking error to be within a preset error range is selected as the tuned velocity convergence parameter.

5. The robotic arm speed control method according to claim 1, characterized in that, The process of obtaining the actual position of the robot's end flange at the current moment and obtaining the expected position of the robot arm's end flange at the next moment based on the robot arm's path planning includes: By parsing external input commands, the path planning of the robotic arm is determined, and the desired position of the end flange of the robotic arm is determined based on the path planning; The state parameters of each joint of the robotic arm are obtained by using a joint encoder, and the actual position of the end flange of the robotic arm is obtained by performing forward kinematic analysis on the state parameters of each joint.

6. A speed control device for a robotic arm, characterized in that, include: The trajectory calculation module is used to obtain the actual position of the robot's end flange at the current moment, and to obtain the expected position of the robot arm's end flange at the next moment based on the path planning of the robot arm. The tracking calculation module is used to calculate the control speed of the robotic arm at the current moment based on the actual position, the desired position, and a preset trajectory tracking model. The control module is used to perform an inverse Jacobian transformation on the control speed to obtain the corresponding motor output force, and to perform corresponding control based on the motor output force; The method for establishing the trajectory tracking model includes: Obtain the state parameters of the robotic arm, establish the tracking error equation, and establish the motion equation of the robotic arm based on the state parameters; Based on the Lyapunov stability criteria, the stability condition that makes the motion equation converge to 0 in the tracking error equation is determined, and the trajectory tracking model is determined based on the stability condition. The method for obtaining the state parameters of the robotic arm includes: Determine whether the state parameter has an exact value; If the state parameter has a definite value, then the motion equation of the robotic arm is established based on the definite value of the state parameter; If the state parameter does not have a definite value, then an estimation equation relation for the state parameter is established. Based on the estimation equation relation, the state parameter is parametrically estimated, and the motion equation is established based on the parametrically estimated state parameter.

7. A computer device, characterized in that, It includes a processor and a memory, the memory storing a computer program that, when executed on the processor, performs the robotic arm speed control method according to any one of claims 1 to 5.

8. A readable storage medium, characterized in that, It stores a computer program that, when run on a processor, executes the robotic arm speed control method according to any one of claims 1 to 5.

Images