A robot joint adaptive super-spiral observation method based on barrier function

Abstract

The application discloses a kind of robot joint adaptive super-spiral observation methods based on barrier function, belong to robot joint drive and motor non-inductive control technical field.This method faces the robot joint module using permanent magnet synchronous motor, constructs super-spiral observer, and designs the segmented adaptive gain law based on barrier function, on-line adjustment is carried out to observer parameter, so that position, speed estimation error can quickly converge to preset area in finite time, and disturbance upper limit does not need to be known in advance;Wherein, in the stage of greater error, observation gain is improved to speed up convergence speed, and gain is adaptively reduced after error enters boundary region to suppress chattering, so that rapidity and smoothness are considered.This method can reduce the dependence of robot joint module on position sensor, improve the compactness and reliability of module structure, reduce observation chattering, and improve the position and speed estimation accuracy under the condition of sudden load and parameter perturbation.

Description

Technical Field

[0001] This invention relates to the technical fields of robot joint drive and sensorless motor control, specifically to a robot joint adaptive superspiral observation method based on obstacle function. Background Technology

[0002] Robot joint modules typically integrate motors, drivers, reducers, controllers, and sensing units, serving as the core execution units for motion output in industrial robots, collaborative robots, humanoid robots, and service robots. As robot systems evolve towards higher integration, miniaturization, lightweight design, high power density, and high dynamic response, joint modules place higher demands on drive motor control systems. These systems not only require high-precision position and speed feedback capabilities but also a compact structure, strong anti-interference capabilities, and reliable operation.

[0003] Permanent magnet synchronous motors (PMSMs) are widely used in robot joint modules due to their advantages such as high efficiency, high power density, simple structure, and fast response speed. In existing technologies, robot joint modules typically obtain rotor position and speed information through encoders, resolvers, or Hall effect devices to achieve closed-loop control. However, these sensors increase the size, wiring complexity, and manufacturing cost of the joint module. Furthermore, they pose a risk of failure under complex operating conditions such as impact vibration, temperature rise, oil contamination, and electromagnetic interference, thus affecting the overall reliability and lifespan of the robot joint module.

[0004] To reduce reliance on mechanical position sensors, sensorless control methods based on motor mathematical models and voltage / current sampling signals for position and speed estimation are gaining increasing attention. Existing sensorless observation methods include neural network observers, Luenberger observers, and sliding mode observers. Among these, sliding mode observers are widely used due to their strong robustness. However, traditional sliding mode observers typically rely on a fixed observation gain, which often requires selecting a large gain to resist unknown disturbances. This can easily lead to severe chattering, affecting the stability, low-noise characteristics, and position and velocity estimation accuracy of the robot's joint modules.

[0005] The superhelical algorithm, as a second-order sliding mode algorithm, can reduce chattering and improve observation performance to a certain extent. However, existing observers based on the superhelical algorithm still generally suffer from the following problems: First, the design of the observation gain usually requires prior knowledge of the upper bound of the disturbance, but in the actual working conditions of robot joint modules, load changes, parameter perturbations, and friction disturbances are complex and varied, making it difficult to accurately obtain the upper bound of the disturbance; Second, when using monotonically increasing or fixed large gains, although error convergence can be guaranteed, it is easy to cause the steady-state gain to be too large, making it difficult to further suppress chattering; Third, under the conditions of frequent start-stop, rapid acceleration and deceleration, or sudden load application of joint modules, traditional methods are difficult to balance fast convergence and low chattering.

[0006] Therefore, how to provide a non-intrusive observation method suitable for robot joint modules, so that the position and rotation speed estimation errors can be quickly converged to a preset area within a finite time without prior knowledge of the upper limit of the disturbance, while taking into account fast dynamic response and low steady-state chattering, has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0007] The purpose of this invention is to provide an adaptive superspiral observation method for robot joints based on an obstacle function, to solve the problems of high dependence on position sensors in robot joint modules, large chattering in traditional sliding mode observers, easy overestimation of observation gain, and insufficient accuracy in position and rotational speed estimation under complex working conditions in existing technologies. The method introduces an obstacle function into the online gain adjustment process of the superspiral observer, enabling the estimation error to converge quickly to a preset region within a finite time. After the error enters the preset region, the observation gain is automatically reduced, thus balancing speed, robustness, and smoothness.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a robot joint adaptive superspiral observation method based on an obstacle function, the method comprising the following steps: S1. Collect the stator phase current of the permanent magnet synchronous motor and the inverter output voltage in the robot joint module, and convert them to obtain the α-axis current, β-axis current, α-axis voltage and β-axis voltage. S2. Based on the current state equation of the permanent magnet synchronous motor in the αβ stationary coordinate system, a super-spiral observer is constructed to obtain the α-axis current estimate and the β-axis current estimate. S3. Using the current estimation error as a sliding mode variable, a piecewise adaptive gain law based on the barrier function is constructed to adjust the observation gain of the superspiral observer online. S4. Obtain the α-axis back EMF estimate and the β-axis back EMF estimate based on the observer output, and calculate the rotor position and speed based on the α-axis back EMF estimate and the β-axis back EMF estimate; S5. The rotor position and rotation speed are used as feedback quantities for the robot joint module controller for position control, speed control and / or current control.

[0009] Furthermore, in step S2, the current state equation of the permanent magnet synchronous motor in the αβ stationary coordinate system is:

[0010] in, , These are the α-axis current and the β-axis current, respectively. , These are the α-axis voltage and the β-axis voltage, respectively. , These are the back electromotive forces along the α-axis and the back electromotive forces along the β-axis, respectively. L is the stator resistance, and L is the stator inductance.

[0011] Furthermore, the α-axis back EMF and β-axis back EMF satisfy:

[0012] in, For mechanical angular velocity, For rotor position, It is a permanent magnet flux linkage.

[0013] Furthermore, in step S2, the constructed superhelical observer is as follows:

[0014] in, , , These are the α-axis current estimates and the β-axis current estimates, respectively. , These represent the α-axis current estimation error and the β-axis current estimation error, respectively. , For observation gain.

[0015] Further, in step S3, the calculation formula for the piecewise adaptive gain law is:

[0016] in, For piecewise adaptive gain law, For sliding mode variables, This represents the initial value of the sliding mode variable. For sliding mode variables from initial values Departure and first arrival at the preset error boundary The moment; The gain coefficient is close to the stage gain coefficient; Here is the estimated motor speed at time t; This is the barrier function, i.e., the adaptive law; The formula for the barrier function is:

[0017] in, To preset the error boundary, This is the lower bound parameter of the barrier function; after the error enters the preset region, the barrier function will... The gain is adaptively adjusted to adapt to changes.

[0018] Furthermore, in step S3, the observation gain satisfies:

[0019] in, This is a piecewise adaptive gain law.

[0020] Further, in step S4, the formulas for calculating the estimated values ​​of the α-axis back electromotive force and the β-axis back electromotive force are as follows:

[0021] in, This is the estimated value of the back electromotive force along the α axis. This is the estimated value of the back electromotive force along the β axis; The formulas for calculating rotor position and speed are:

[0022] in, To estimate the rotor position, To estimate the rotational speed.

[0023] Secondly, embodiments of the present invention also provide an electronic device, including a processor and a memory, wherein the memory stores a computer program that can be executed by the processor, and the processor can execute the above-described method when executing the program.

[0024] As can be seen from the above technical solution, this invention discloses a robot joint adaptive superspiral observation method based on obstacle function, which has the following advantages compared with the prior art: 1. This invention utilizes a barrier function to construct an adaptive gain law, enabling online adjustment of observer parameters without prior acquisition of the upper bound of the perturbation.

[0025] 2. The observation gain can be increased when the error is large, which helps to improve the convergence speed of the estimation error and enable the position and rotation speed estimation errors to quickly enter the preset area within a limited time.

[0026] 3. It can automatically reduce the observation gain after the error enters the preset region, reduce steady-state chattering, and improve estimation smoothness.

[0027] 4. This invention is applicable to scenarios such as sudden load changes, parameter perturbations, start-stop operations, and frequent switching of operating conditions in robot joint modules, and can improve the system's anti-disturbance capability, estimation accuracy, and operational reliability.

[0028] 5. It can replace or partially replace position sensor feedback, reducing the structural complexity and cost of robot joint modules and improving module integration.

[0029] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0030] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of this application 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 some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0033] Figure 1 This is a schematic diagram of the adaptive superspiral observation method based on obstacle function for permanent magnet synchronous motors in robot joint modules provided in an embodiment of the present invention.

[0034] Figure 2 This is a schematic diagram of the segmented adaptive gain law switching of the present invention.

[0035] Figure 3 This is a schematic diagram of the electronic device structure provided by the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 some embodiments of the present invention, but not all embodiments.

[0037] In the description of this invention, it should be noted that some processes described in this application specification and drawings include multiple operations that appear in a specific order. However, it should be clearly understood that these operations may be performed in any order or in parallel. Furthermore, various numbers are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0038] 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.

[0039] See Figure 1 As shown in the figure, this invention discloses a robot joint adaptive superspiral observation method based on obstacle function, which mainly includes the following steps: S1. Collect the stator phase current of the permanent magnet synchronous motor and the inverter output voltage in the robot joint module, and convert them to obtain the α-axis current, β-axis current, α-axis voltage and β-axis voltage. S2. Based on the current state equation of the permanent magnet synchronous motor in the αβ stationary coordinate system, a super-spiral observer is constructed to obtain the α-axis current estimate and the β-axis current estimate. S3. Using the current estimation error as a sliding mode variable, a piecewise adaptive gain law based on the barrier function is constructed to adjust the observation gain of the superspiral observer online. S4. Obtain the estimated values ​​of α-axis back EMF and β-axis back EMF based on the observer output, and calculate the rotor position and speed based on the estimated values ​​of α-axis back EMF and β-axis back EMF. S5. Use the rotor position and speed as feedback quantities for the robot joint module controller for position control, speed control and / or current control.

[0040] The specific embodiments of the present invention will be described in detail below: In one specific implementation, in step S2, the current state equation of the permanent magnet synchronous motor in the αβ stationary coordinate system is:

[0041] in, , These are the α-axis current and the β-axis current, respectively. , These are the α-axis voltage and the β-axis voltage, respectively. , These are the back electromotive forces along the α-axis and the back electromotive forces along the β-axis, respectively. L is the stator resistance, and L is the stator inductance.

[0042] In one specific implementation, the α-axis back electromotive force and the β-axis back electromotive force satisfy:

[0043] in, For mechanical angular velocity, For rotor position, It is a permanent magnet flux linkage.

[0044] In one specific implementation, the superhelical observer constructed in step S2 is:

[0045] in, , , These are the α-axis current estimates and the β-axis current estimates, respectively. , These represent the α-axis current estimation error and the β-axis current estimation error, respectively. , For observation gain.

[0046] In one specific implementation, the calculation formula for the piecewise adaptive gain law in step S3 is as follows:

[0047] in, It is a piecewise adaptive gain law; For sliding mode variables, That is, the current estimation error; This represents the initial value of the sliding mode variable, indicating the error state at the initial moment. For sliding mode variables from initial values Departure and first arrival at the preset boundary area boundary The moment, that is, the first At that moment. This is the gain coefficient for the approximate stage. Let t be the estimated motor speed at time t. The barrier function, i.e., the adaptive law, is used to adjust the sliding mode variable. Approaching the preset boundary At that time, by increasing the observation gain to force error convergence, finite-time convergence under perturbationless upper bound prior information can be achieved.

[0048] The formula for the barrier function is:

[0049] in, To preset the error boundary, This is the lower bound parameter of the barrier function; after the error enters the preset region, the barrier function will... Adaptive gain adjustment when changes occur: Reduce gain when it is small to suppress chattering, when Increase the gain rapidly as it approaches the boundary to pull the error back to the preset area.

[0050] In one specific implementation, in step S3, the observation gain satisfies:

[0051] in, This is a piecewise adaptive gain law.

[0052] In one specific implementation, the formulas for calculating the α-axis back electromotive force estimate and the β-axis back electromotive force estimate in step S4 are as follows:

[0053] in, This is the estimated value of the back electromotive force along the α axis. This is the estimated value of the back electromotive force along the β axis; The formulas for calculating rotor position and speed are:

[0054] in, To estimate the rotor position, To estimate the rotational speed.

[0055] As can be seen from the description of the above embodiments, the present invention provides a robot joint adaptive superspiral observation method based on obstacle function, which has the following technical advantages: First, by using the barrier function to construct an adaptive gain law, the observer parameters can be adjusted online without prior acquisition of the upper bound of the perturbation; Second, increasing the observation gain during periods of large error can help improve the convergence speed of the estimation error, enabling the position and rotational speed estimation errors to quickly enter the preset area within a limited time. Third, the observation gain is automatically reduced after the error enters the preset region to reduce steady-state chattering and improve estimation smoothness. Fourth, it is applicable to scenarios such as sudden load changes, parameter perturbations, start-stop and frequent working condition switching in robot joint modules, which can improve the system's anti-disturbance capability, estimation accuracy and operational reliability; Fifth, it can replace or partially replace position sensor feedback, reducing the structural complexity and cost of robot joint modules and improving module integration.

[0056] Additionally, refer to Figure 3 As shown, this embodiment of the invention also provides an electronic device that can perform the above-described method. The electronic device may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may also include a computer program stored in the memory 11 and capable of running on the processor 10.

[0057] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 and calls data stored in the memory 11 to perform various functions of the electronic device and process data.

[0058] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, electronic devices, or computer program products, etc. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0059] It should be noted that the word "comprising" does not exclude the presence of components or steps not listed in the claims. The words "a" or "an" preceding a component do not exclude the presence of a plurality of such components. This invention can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer.

[0060] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0061] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. 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 variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A robot joint adaptive superspiral observation method based on obstacle function, characterized in that, The method includes the following steps: S1. Collect the stator phase current of the permanent magnet synchronous motor and the inverter output voltage in the robot joint module, and convert them to obtain the α-axis current, β-axis current, α-axis voltage and β-axis voltage. S2. Based on the current state equation of the permanent magnet synchronous motor in the αβ stationary coordinate system, a super-spiral observer is constructed to obtain the α-axis current estimate and the β-axis current estimate. S3. Using the current estimation error as a sliding mode variable, a piecewise adaptive gain law based on the barrier function is constructed to adjust the observation gain of the superspiral observer online. S4. Obtain the α-axis back EMF estimate and the β-axis back EMF estimate based on the observer output, and calculate the rotor position and speed based on the α-axis back EMF estimate and the β-axis back EMF estimate; S5. The rotor position and rotation speed are used as feedback quantities for the robot joint module controller for position control, speed control and / or current control.

2. The method according to claim 1, characterized in that, In step S2, the current state equation of the permanent magnet synchronous motor in the αβ stationary coordinate system is: in, , These are the α-axis current and the β-axis current, respectively. , These are the α-axis voltage and the β-axis voltage, respectively. , These are the back electromotive forces along the α-axis and the back electromotive forces along the β-axis, respectively. L is the stator resistance, and L is the stator inductance.

3. The method according to claim 2, characterized in that, The α-axis back electromotive force and the β-axis back electromotive force satisfy: in, For mechanical angular velocity, For rotor position, It is a permanent magnet flux linkage.

4. The method according to claim 3, characterized in that, In step S2, the constructed superhelical observer is as follows: in, , , These are the α-axis current estimates and the β-axis current estimates, respectively. , These represent the α-axis current estimation error and the β-axis current estimation error, respectively. , For observation gain.

5. The method according to claim 4, characterized in that, In step S3, the calculation formula for the piecewise adaptive gain law is as follows: in, For piecewise adaptive gain law, For sliding mode variables, This represents the initial value of the sliding mode variable. For sliding mode variables from initial values Departure and first arrival at the preset error boundary The moment; The gain coefficient is close to the stage gain coefficient; Let be the estimated motor speed at time t; This is the barrier function, i.e., the adaptive law; The formula for the barrier function is: in, To preset the error boundary, This is the lower bound parameter of the barrier function; after the error enters the preset region, the barrier function will... The gain is adaptively adjusted to adapt to changes.

6. The method according to claim 5, characterized in that, In step S3, the observation gain satisfies: in, This is a piecewise adaptive gain law.

7. The method according to claim 6, characterized in that, In step S4, the formulas for calculating the estimated values ​​of the α-axis back electromotive force and the β-axis back electromotive force are as follows: in, This is the estimated value of the back electromotive force along the α axis. This is the estimated value of the back electromotive force along the β axis; The formulas for calculating rotor position and speed are: in, To estimate the rotor position, To estimate the rotational speed.

8. An electronic device, characterized in that, It includes a processor and a memory, the memory storing a computer program that can be executed by the processor, the processor executing the program to implement the method as described in any one of claims 1 to 7.

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