Asynchronous sliding mode control method for multi-mode mechanical arm

Abstract

The invention relates to the technical field of multi-mode mechanical arms, and discloses a multi-mode mechanical arm asynchronous sliding mode control method which comprises the steps that a mechanical arm control system is started, the current working state of a mechanical arm is monitored in real time through a sensor, and a working mode is determined; establishing a model on the basis of fuzzy logic, inputting a working mode and state information of the mechanical arm, outputting adjustment values of key parameters of a controller, and updating configuration of the controller by utilizing the adjusted parameters; a high-order sliding mode surface is designed, and the chattering phenomenon when the sliding mode surface is close is reduced in combination with a discontinuous item smoothing technology. According to the asynchronous sliding mode control method of the multi-mode mechanical arm, the current working state data of the mechanical arm are collected in real time through the position sensor, the speed sensor and the torque sensor which are installed on the mechanical arm, accurate monitoring of the state of the mechanical arm is achieved, and real-time monitoring is beneficial to improving the response speed and accuracy of the system; and it is ensured that the mechanical arm can efficiently operate in different working modes.

Description

Technical Field

[0001] The invention relates to the technical field of multi-modal mechanical arms, and in particular to an asynchronous sliding mode control method of a multi-modal mechanical arm. Background Art

[0002] A multimodal robotic arm is a robotic arm system that can operate in multiple working modes. However, existing technologies (such as traditional PID control) have low positioning accuracy in tasks with high precision requirements and cannot meet the needs of certain precision operations. Traditional control methods are prone to jitter when approaching the sliding surface, causing the robotic arm to be unstable during movement, affecting the accuracy and reliability of the operation. At the same time, the existing control methods have a long response time and cannot respond quickly to command changes, reducing the real-time performance and work efficiency of the system. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] To achieve the above object, the present invention provides the following technical solutions: An asynchronous sliding mode control method for a multi-mode manipulator comprises: starting a manipulator control system, monitoring the current working state of the manipulator in real time through a sensor, and determining the working mode; establishing a model based on fuzzy logic, inputting the working mode and the state information of the manipulator, outputting the adjustment values ​​of key parameters of the controller, and updating the controller configuration using the adjusted parameters; designing a high-order sliding mode surface, combining a discontinuous term smoothing technology to reduce the chattering phenomenon when approaching the sliding mode surface; using a predictive control algorithm to plan path conversion in advance, adjusting the controller parameters in the period before entering a new mode, and obtaining a new path; the controller works according to the obtained configuration and path, and stores the data during the operation for subsequent optimization.

[0005] As a further solution of the present invention: the robot arm control system is started, and the current working status of the robot arm is monitored in real time through sensors, and the working mode is determined. The specific steps are: after starting the robot arm control system, the current working status data of the robot arm is collected in real time through the position sensor, speed sensor and torque sensor installed on the robot arm; the sensor data includes but is not limited to joint angle, position and speed of the end effector, and joint torque information; the data collected by the sensor is analyzed using a fuzzy logic model, and the working mode is determined.

[0006] As a further solution of the present invention: the model is established based on fuzzy logic, and the working mode and the state information of the robot are input, the adjustment value of the key parameters of the controller is output, and the controller configuration is updated using the adjusted parameters. The specific steps are: dynamically adjusting the key parameters of the controller using a fuzzy logic model; the input is the current working mode and the state information of the robot, and the output is the adjustment value of the key parameters of the controller, and the expression is: ; in: is the adjustment value of the key parameters of the controller, For the The weight of a fuzzy rule, For the The membership function of the fuzzy rules represents the working mode and status Corresponding to The degree of fuzzy rules, For the The parameter adjustment function corresponding to the fuzzy rule is is the number of fuzzy rules.

[0007] As a further solution of the present invention: the design of a high-order sliding surface, combined with the discontinuous term smoothing technology to reduce the chattering phenomenon when approaching the sliding surface, the specific steps are: designing a high-order sliding surface, and combining the discontinuous term smoothing technology to reduce the chattering phenomenon when approaching the sliding surface, the expression is: ; in: is the sliding surface, is the state vector of the robot arm, is the first-order derivative of the state vector, is the second-order derivative of the state vector, and is a positive constant used to adjust the shape and dynamic characteristics of the sliding surface; a discontinuous term smoothing function is introduced to further reduce the chattering phenomenon, and the expression is: ; in: is a positive definite constant, is a small positive number, is a sign function; the above sliding surface and smoothing function are combined into the sliding mode control law to obtain the final control input, which is expressed as: ; in: is the final control input, is an equivalent control item, is a positive definite matrix used to adjust the control gain, is the sliding surface, is the discontinuous term smoothing function.

[0008] As a further solution of the present invention: the predictive control algorithm is used to plan the path conversion in advance, adjust the controller parameters in the period before entering the new mode, and obtain a new path. The specific steps are: define a prediction model, which predicts the future system state based on the current state and control input; use a linear time-invariant system model; define a cost function to optimize the control input to minimize the error and control energy in the future time period; use a quadratic programming method to minimize the above cost function; obtain the optimal control input sequence by solving the upper and lower limits of the control input, and then apply the first control input to the actual system, and repeat the above process in the next sampling period.

[0009] As a further solution of the present invention: the controller works according to the obtained configuration and path, and stores the data during the operation for subsequent optimization. The specific steps are: the controller generates a specific control signal based on the latest control parameters and path information and sends it to each joint driver of the robotic arm; the control signal includes position, speed and torque commands to ensure that the robotic arm moves accurately along the predetermined path; during the execution process, the actual state information of the robotic arm is continuously collected through sensors and compared with the expected state; when it is detected that the deviation exceeds the preset threshold, feedback adjustment is immediately performed; during operation, the controller will regularly record all relevant data.

[0010] As a further solution of the present invention: when it is detected that the deviation exceeds the preset threshold, feedback adjustment is immediately performed, and the expression is: ; Among them, u is the final control input, is an equivalent control item, is a positive definite matrix, is the sliding surface, is the discontinuous term smoothing function, It is the feedback adjustment item.

[0011] As a further solution of the present invention: when it is detected that the deviation exceeds the preset threshold, feedback adjustment is immediately performed, which also includes defining a performance indicator to evaluate the overall performance of the system to obtain a minimized performance indicator; by minimizing the performance indicator, the controller parameters and other related parameters are continuously adjusted.

[0012] The present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the asynchronous sliding mode control method for a multi-modal robotic arm as described in the first aspect of the present invention is implemented.

[0013] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the asynchronous sliding mode control method for a multi-modal robotic arm as described in the first aspect of the present invention is implemented.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention collects the current working status data of the robotic arm in real time through the position sensor, speed sensor and torque sensor installed on the robotic arm, thereby realizing accurate monitoring of the state of the robotic arm. Real-time monitoring helps to improve the response speed and accuracy of the system, and ensures that the robotic arm can operate efficiently in different working modes. A fuzzy logic model is used to dynamically adjust the key parameters of the controller, thereby realizing adaptive adjustment of the controller parameters, improving the robustness and flexibility of the system, and making the operation of the robotic arm in different working modes more stable and efficient. A high-order sliding surface is designed, and the discontinuous term smoothing technology is combined to reduce the chattering phenomenon when approaching the sliding surface, thereby realizing the optimization of the sliding mode control. The high-order sliding surface can better handle the dynamic characteristics of the system, improve the robustness and response speed of the system, and the discontinuous term smoothing technology effectively reduces the chattering problem in the traditional sliding mode control, so that the robotic arm can operate more smoothly when approaching the sliding surface. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The figure is a flow chart of an asynchronous sliding mode control method for a multi-modal manipulator. DETAILED DESCRIPTION

[0016] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and understandable, the specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings.

[0017] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0018] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0019] Example 1 See also Figure 1 , which is the first embodiment of the present invention, and which provides an asynchronous sliding mode control method for a multi-modal manipulator, comprising: S1. Start the robot control system, monitor the current working status of the robot in real time through sensors, and determine the working mode.

[0020] Specifically, the robot control system is started, and the current working status of the robot is monitored in real time through sensors, and the working mode is determined. The specific steps are: after starting the robot control system, the current working status data of the robot is collected in real time through the position sensor, speed sensor and torque sensor installed on the robot; the sensor data includes but is not limited to the joint angle, the position and speed of the end effector, and the joint torque information; the data collected by the sensor is analyzed using a fuzzy logic model, and the working mode is determined.

[0021] It should be noted that by using a variety of sensors to monitor the status of the robotic arm in real time, it is ensured that the system can accurately obtain the current working status of the robotic arm. By using the fuzzy logic model to analyze the sensor data, the current working mode of the robotic arm can be effectively identified and determined, providing a reliable foundation for subsequent control strategies, improving the response speed and accuracy of the system, and ensuring that the robotic arm can operate efficiently in different working modes.

[0022] S2. Establish a model based on fuzzy logic, input the working mode and the state information of the robot arm, output the adjustment values ​​of the key parameters of the controller, and use the adjusted parameters to update the controller configuration.

[0023] Specifically, a model is established based on fuzzy logic, and the working mode and the state information of the robot are input, the adjustment values ​​of the key parameters of the controller are output, and the controller configuration is updated using the adjusted parameters. The specific steps are: the key parameters of the controller are dynamically adjusted using a fuzzy logic model; the input is the current working mode and the state information of the robot, and the output is the adjustment value of the key parameters of the controller. The expression is: ; in: is the adjustment value of the key parameters of the controller, For the The weight of a fuzzy rule, For the The membership function of the fuzzy rules represents the working mode and status Corresponding to The degree of fuzzy rules, For the The parameter adjustment function corresponding to the fuzzy rule is is the number of fuzzy rules.

[0024] It should be noted that the key parameters of the controller are dynamically adjusted based on the fuzzy logic model, so that the controller can be adaptively adjusted according to the current working mode and status information. The fuzzy logic model can handle uncertainty and adapt to different working environments by adjusting the membership function and weights, thereby improving the robustness and flexibility of the system and ensuring that the controller parameters are always in the optimal state, thereby improving the overall control performance and making the operation of the robot arm in different working modes more stable and efficient.

[0025] S3. Design a high-order sliding surface and combine it with discontinuous term smoothing technology to reduce the chattering phenomenon when approaching the sliding surface.

[0026] Specifically, a high-order sliding surface is designed, and the non-continuous term smoothing technology is combined to reduce the chattering phenomenon when approaching the sliding surface. The specific steps are: design a high-order sliding surface, and combine the non-continuous term smoothing technology to reduce the chattering phenomenon when approaching the sliding surface. The expression is: ; in: is the sliding surface, is the state vector of the robot arm, is the first-order derivative of the state vector, is the second-order derivative of the state vector, and is a positive constant used to adjust the shape and dynamic characteristics of the sliding surface; a discontinuous term smoothing function is introduced to further reduce the chattering phenomenon, and the expression is: ; in: is a positive definite constant, is a small positive number, is a sign function; the above sliding surface and smoothing function are combined into the sliding mode control law to obtain the final control input, which is expressed as: ; in: is the final control input, is an equivalent control item, is a positive definite matrix used to adjust the control gain, is the sliding surface, is the discontinuous term smoothing function.

[0027] It should be noted that the design of high-order sliding surfaces can better handle the dynamic characteristics of the system, improve the robustness and response speed of the system, and the discontinuous term smoothing technology effectively reduces the chattering problem in traditional sliding mode control, so that the robot arm can run more smoothly when approaching the sliding surface, significantly improving the control accuracy and stability of the system, reducing the vibration of the robot arm during movement, extending the service life of the robot arm, and improving the reliability of task execution.

[0028] S4. Use the predictive control algorithm to plan the path conversion in advance, adjust the controller parameters in the period before entering the new mode, and obtain the new path.

[0029] Specifically, the predictive control algorithm is used to plan the path transition in advance, adjust the controller parameters in the period before entering the new mode, and obtain the new path. The specific steps are: define a prediction model, which predicts the future system state based on the current state and control input; use a linear time-invariant system model; define a cost function to optimize the control input to minimize the error and control energy in the future time period; use a quadratic programming method to minimize the above cost function; obtain the optimal control input sequence by solving the upper and lower limits of the control input, and then apply the first control input to the actual system, and repeat the above process in the next sampling period.

[0030] It should be noted that the path transition is planned in advance through the predictive model, the future behavior of the system is considered, the control input at the current moment is optimized, the cost function is defined and the quadratic programming method is used to minimize the cost function, ensuring that the control input achieves a smooth transition while minimizing the error and control energy, thereby improving the system's foresight and robustness, allowing the robotic arm to seamlessly switch between different working modes, reducing impact and instability. The predictive control method can also improve the response speed and accuracy of the system, ensuring that the robotic arm operates efficiently in complex environments.

[0031] S5. The controller works according to the obtained configuration and path, and stores the data during operation for subsequent optimization.

[0032] Specifically, the controller works according to the obtained configuration and path, and stores the data during operation for subsequent optimization. The specific steps are: the controller generates a specific control signal based on the latest control parameters and path information and sends it to each joint driver of the robot arm; the control signal includes position, speed and torque commands to ensure that the robot arm moves accurately along the predetermined path; during the execution process, the actual state information of the robot arm is continuously collected through sensors and compared with the expected state; when the deviation is detected to exceed the preset threshold, feedback adjustment is immediately performed; during the operation, the controller will regularly record all relevant data; when the deviation is detected to exceed the preset threshold, feedback adjustment is immediately performed, and the expression is: ; Among them, u is the final control input, is an equivalent control item, is a positive definite matrix, is the sliding surface, is the discontinuous term smoothing function, It is a feedback adjustment item; when it is detected that the deviation exceeds the preset threshold, feedback adjustment is performed immediately, which also includes defining a performance indicator to evaluate the overall performance of the system and obtaining a minimized performance indicator; by minimizing the performance indicator, the controller parameters and other related parameters are continuously adjusted.

[0033] It should be noted that the controller generates specific control signals based on the latest control parameters and path information to ensure that the robot arm moves accurately along the predetermined path. The actual state information of the robot arm is continuously collected through sensors and compared with the expected state. When the deviation exceeds the preset threshold, feedback adjustment is immediately performed to ensure the stability and accuracy of the system. The controller will regularly record all relevant data, including actual state, control input, expected state, etc. These data are used for subsequent performance evaluation and optimization. Through real-time monitoring and feedback adjustment, the accuracy and stability of the robot arm when performing tasks are ensured. Data recording and subsequent optimization further improve the long-term performance and reliability of the system.

[0034] This embodiment also provides a computer device, which is suitable for an asynchronous sliding mode control method of a multi-modal robotic arm, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement an asynchronous sliding mode control method of a multi-modal robotic arm as proposed in the above embodiment.

[0035] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0036] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, an asynchronous sliding mode control method for a multi-modal robotic arm is implemented as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage device, a flash memory, a disk or an optical disk.

[0037] In summary, the position sensor, speed sensor and torque sensor installed on the robot arm are used to collect the current working status data of the robot arm in real time, so as to realize accurate monitoring of the state of the robot arm. Real-time monitoring helps to improve the response speed and accuracy of the system, and ensure that the robot arm can operate efficiently in different working modes. The fuzzy logic model is used to dynamically adjust the key parameters of the controller, and the adaptive adjustment of the controller parameters is realized, which improves the robustness and flexibility of the system, making the operation of the robot arm in different working modes more stable and efficient. A high-order sliding surface is designed, and the discontinuous term smoothing technology is combined to reduce the chattering phenomenon when approaching the sliding surface, so as to realize the optimization of the sliding mode control. The high-order sliding surface can better handle the dynamic characteristics of the system and improve the robustness and response speed of the system. The discontinuous term smoothing technology effectively reduces the chattering problem in the traditional sliding mode control, so that the robot arm can operate more smoothly when approaching the sliding surface.

[0038] Example 2 Please refer to Table 1, which is the second embodiment of the present invention. In order to further verify the technical solution of the present invention, experimental simulation data of the asynchronous sliding mode control method of the multi-modal manipulator are provided.

[0039] In order to verify the effectiveness and superiority of the asynchronous sliding mode control method of the multi-modal robotic arm proposed in the present invention, a series of experiments were carried out. The experimental object was a 6-DOF industrial robotic arm, and its working environment simulated a variety of typical application scenarios, including assembly, handling and precision operation. In the experiment, two control methods were adopted: the prior art (traditional PID control) and the asynchronous sliding mode control method proposed in the present invention.

[0040] Position sensors, speed sensors and torque sensors are installed at the key joints of the robotic arm to collect real-time working status data of the robotic arm, including joint angles, position and speed of the end effector, and joint torque information. The sensor data is analyzed through a fuzzy logic model to determine the current working mode. For example, in assembly tasks, the system recognizes that the robotic arm is in fine positioning mode; in handling tasks, the system recognizes that the robotic arm is in fast moving mode.

[0041] A fuzzy logic model is used to dynamically adjust the key parameters of the controller. The input is the current working mode and the state information of the robot arm, and the output is the adjustment value of the key parameters of the controller.

[0042] A high-order sliding surface is designed and combined with the discontinuous term smoothing technology to reduce the chattering phenomenon when approaching the sliding surface. A discontinuous term smoothing function is introduced to further reduce the chattering phenomenon. The above sliding surface and smoothing function are combined into the sliding mode control law to obtain the final control input.

[0043] Define a prediction model that predicts the future system state based on the current state and control input. Use a linear time-invariant system model and define a cost function to optimize the control input to minimize the error and control energy in the future time period. Use a quadratic programming method to minimize the above cost function. By solving the upper and lower limits of the control input, the optimal control input sequence is obtained. Then the first control input is applied to the actual system and the above process is repeated in the next sampling period.

[0044] Based on the latest control parameters and path information, the controller generates specific control signals and sends them to the joint drivers of the robot arm. The control signals include position, speed and torque commands to ensure that the robot arm moves accurately along the predetermined path. During the execution process, the actual state information of the robot arm is continuously collected through sensors and compared with the expected state. When the deviation is detected to exceed the preset threshold, feedback adjustment is performed immediately.

[0045] During operation, the controller will regularly record all relevant data, including actual status, control input, expected status, etc. These data are used for subsequent performance evaluation and optimization. By defining performance indicators to evaluate the overall performance of the system, the controller parameters and other related parameters are continuously adjusted to minimize the performance indicators.

[0046] Tabular data analysis: The table shows the performance comparison of the robot arm in several typical tasks under different control methods: Table 1: Performance comparison of multi-modal robotic arm control methods Test subjects Parameter name unit Prior Art (PID) The present invention (asynchronous sliding mode control) Assembly tasks Positioning accuracy mm 0.5 0.2 Jitter Amplitude mm 0.3 0.1 Average response time s 0.8 0.5 Controlling energy consumption J 120 90 Transport tasks Movement accuracy mm 2.0 1.0 Jitter Amplitude mm 0.8 0.3 Average response time s 1.2 0.8 Controlling energy consumption J 150 110 Precision Operation Positioning accuracy μm 10 5 Jitter Amplitude μm 5 2 Average response time s 1.0 0.7 Controlling energy consumption J 200 150 It can be seen from the above table that the asynchronous sliding mode control method of the multi-modal manipulator proposed in the present invention is significantly superior to the existing PID control method in many aspects, as follows: In assembly tasks, the positioning accuracy of the present invention is 0.2mm, while the positioning accuracy of the prior art is 0.5mm, an increase of 60%. In precision operations, the positioning accuracy of the present invention reaches 5μm, while the positioning accuracy of the prior art is 10μm, an increase of 50%, indicating that the present invention has obvious advantages in tasks requiring high precision and can provide more accurate positioning capabilities.

[0047] In assembly tasks, the jitter amplitude of the present invention is 0.1mm, while the jitter amplitude of the prior art is 0.3mm, a reduction of 67%. In precision operations, the jitter amplitude of the present invention is 2μm, while the jitter amplitude of the prior art is 5μm, a reduction of 60%. The significant reduction in the jitter amplitude means that the robot arm is more stable during movement, which helps to improve the stability and reliability of the operation.

[0048] In assembly tasks, the average response time of the present invention is 0.5 seconds, while the average response time of the prior art is 0.8 seconds, which is shortened by 37.5%. In handling tasks, the average response time of the present invention is 0.8 seconds, while the average response time of the prior art is 1.2 seconds, which is shortened by 33.3%. Shorter response time means that the robot arm can respond to instructions faster and improve work efficiency.

[0049] In assembly tasks, the control energy consumption of the present invention is 90J, while the control energy consumption of the prior art is 120J, which is reduced by 25%. In handling tasks, the control energy consumption of the present invention is 110J, while the control energy consumption of the prior art is 150J, which is reduced by 26.7%. Low energy consumption not only helps to save energy, but also extends the service life of the robotic arm and reduces operating costs.

[0050] In summary, the asynchronous sliding mode control method of the multi-modal robotic arm proposed in the present invention shows significant advantages in many aspects such as positioning accuracy, jitter amplitude, response time and control energy consumption. By introducing fuzzy logic models, high-order sliding surface design, discontinuous term smoothing technology and predictive control algorithms, the present invention effectively solves the problems existing in traditional control methods and improves the control performance and stability of the robotic arm.

[0051] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An asynchronous sliding mode control method for a multi-modal manipulator, characterized in that: include, Start the robot arm control system, monitor the current working status of the robot arm in real time through sensors, and determine the working mode; A model is established based on fuzzy logic, and the working mode and state information of the robot are input. The adjusted values ​​of the key parameters of the controller are output, and the controller configuration is updated using the adjusted parameters. Design a high-order sliding surface and combine it with discontinuous term smoothing technology to reduce chattering when approaching the sliding surface; Use the predictive control algorithm to plan the path transition in advance, adjust the controller parameters in the period before entering the new mode, and obtain the new path; The controller works according to the obtained configuration and path, and stores the data during operation for subsequent optimization.

2. The asynchronous sliding mode control method of a multi-modal manipulator according to claim 1, characterized in that: The robot control system is started to monitor the current working state of the robot in real time through sensors and determine the working mode. The specific steps are: After starting the robotic arm control system, the position sensor, speed sensor and torque sensor installed on the robotic arm are used to collect the current working status data of the robotic arm in real time; The sensor data includes, but is not limited to, joint angles, end effector position and velocity, and joint torque information; A fuzzy logic model is used to analyze the data collected by the sensor and determine the working mode.

3. The asynchronous sliding mode control method of a multi-modal manipulator according to claim 1, characterized in that: The model is established based on fuzzy logic, and the working mode and state information of the robot are input, the adjustment values ​​of the key parameters of the controller are output, and the controller configuration is updated using the adjusted parameters. The specific steps are: Using fuzzy logic model to dynamically adjust the key parameters of the controller; The input is the current working mode and the state information of the robot arm, and the output is the adjustment value of the key parameters of the controller. The expression is: ; in: is the adjustment value of the key parameters of the controller, For the The weight of a fuzzy rule, For the The membership function of the fuzzy rules represents the working mode and status Corresponding to The degree of fuzzy rules, For the The parameter adjustment function corresponding to the fuzzy rule is is the number of fuzzy rules.

4. The asynchronous sliding mode control method of a multi-modal manipulator according to claim 1, characterized in that: The design of the high-order sliding surface, combined with the discontinuous term smoothing technology to reduce the chattering phenomenon when approaching the sliding surface, has the following specific steps: A high-order sliding surface is designed and combined with the discontinuous term smoothing technology to reduce the chattering phenomenon when approaching the sliding surface. The expression is: ； in: is the sliding surface, is the state vector of the robot arm, is the first-order derivative of the state vector, is the second-order derivative of the state vector, and is a positive constant used to adjust the shape and dynamic characteristics of the sliding surface; A discontinuous term smoothing function is introduced to further reduce the chattering phenomenon. The expression is: ; in: is a positive definite constant, is a small positive number, is a symbolic function; Combining the above sliding surface and smoothing function into the sliding mode control law, the final control input is obtained, which is expressed as: ； in: is the final control input, is an equivalent control item, is a positive definite matrix used to adjust the control gain, is the sliding surface, is the discontinuous term smoothing function.

5. The asynchronous sliding mode control method of a multi-modal manipulator according to claim 1, characterized in that: The predictive control algorithm is used to plan the path conversion in advance, adjust the controller parameters in the period before entering the new mode, and obtain the new path. The specific steps are: Define a predictive model that predicts future system states based on current states and control inputs; Use a linear time-invariant system model; Define a cost function to optimize the control input to minimize the error and control energy in the future time period; Use quadratic programming method to minimize the above cost function; By solving the upper and lower limits of the control input, the optimal control input sequence is obtained, and then the first control input is applied to the actual system and the above process is repeated in the next sampling period.

6. The asynchronous sliding mode control method of a multi-modal manipulator according to claim 1, characterized in that: The controller works according to the obtained configuration and path, and stores the data during operation for subsequent optimization. The specific steps are: The controller generates specific control signals based on the latest control parameters and path information and sends them to the various joint drivers of the robot arm; The control signals include position, speed and torque commands to ensure that the robot moves accurately along the predetermined path; During the execution process, the actual state information of the robot arm is continuously collected through sensors and compared with the expected state; When the deviation is detected to exceed the preset threshold, feedback adjustment is immediately performed; During operation, the controller regularly records all relevant data.

7. The asynchronous sliding mode control method of a multi-modal manipulator according to claim 6, characterized in that: When the deviation is detected to exceed the preset threshold, feedback adjustment is performed immediately, and the expression is: ； Among them, u is the final control input, is an equivalent control item, is a positive definite matrix, is the sliding surface, is the discontinuous term smoothing function, It is the feedback adjustment item.

8. The asynchronous sliding mode control method of a multi-modal manipulator according to claim 7, characterized in that: When the deviation is detected to exceed the preset threshold, feedback adjustment is immediately performed, and also includes: Define a performance indicator to evaluate the overall performance of the system and obtain the minimized performance indicator; By minimizing the performance indicators, the controller parameters and other related parameters are continuously adjusted.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the asynchronous sliding mode control method of a multi-modal robotic arm as described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the asynchronous sliding mode control method of a multi-modal robotic arm according to any one of claims 1 to 8 are implemented.

Images