Multi-working-condition self-adaptive mechanical arm motor control system

Abstract

The invention relates to a multi-working-condition self-adaptive mechanical arm motor control system, and belongs to the technical field of mechanical arms, the multi-working-condition self-adaptive mechanical arm motor control system comprises a control module, a mechanical arm body, an auxiliary module and a storage module, the control module and the mechanical arm body are in two-way signal connection through network communication, and the mechanical arm body and the auxiliary module are in two-way signal connection through network communication; the output end of the mechanical arm body is electrically connected with the input end of the auxiliary module, and the control module is in two-way signal connection with the storage module through network communication. The multi-working-condition self-adaptive mechanical arm motor control system comprises a control module, a mechanical arm body, an auxiliary module and a storage module, the control module and the mechanical arm body are in two-way signal connection through network communication, and the mechanical arm body and the auxiliary module are in two-way signal connection through network communication; the output end of the mechanical arm body is electrically connected with the input end of the auxiliary module, and the control module is in two-way signal connection with the storage module through network communication.

Description

Technical Field

[0001] The present invention relates to the technical field of robotic arms, and in particular to a multi-working-condition adaptive robotic arm motor control system. Background Art

[0002] The robotic arm motor control system is the core device that drives and coordinates the movement of the robotic arm. It is mainly used to accurately control the position and speed of each joint of the robotic arm, enabling it to complete complex tasks such as grasping, handling, and assembly. The system drives the joint movement of the robotic arm through the motor, thereby realizing the stable operation of the robotic arm in scenarios such as industrial automation.

[0003] For example, a Chinese patent (publication number: CN105437227B) discloses a planar articulated robot and its control system, wherein the planar articulated robot includes a frame, a first robotic arm arranged on the frame, a first motor arranged between the frame and the first robotic arm, and a robotic arm connected to the first robotic arm. The first motor is embedded in the first robotic arm and directly drives the first robotic arm to rotate. The planar articulated robot and its control system provided by this invention have higher working efficiency, higher assembly accuracy and lower cost.

[0004] However, the system has poor adaptability and lacks a feedback structure, which makes it difficult for the robotic arm to adapt to different situations in actual use. For example, when clamping objects made of different materials, the required force varies, and the robotic arm finds it difficult to clamp different objects and ensure the integrity of the objects. Therefore, a multi-working condition adaptive robotic arm motor control system is proposed to solve the above problems. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention provides a multi-working condition adaptive robotic arm motor control system with advantages such as strong adaptability, which solves the problem of weak adaptability of existing motor control systems.

[0006] To achieve the above objectives, the present invention provides the following technical solutions: a multi-working-condition adaptive manipulator motor control system, comprising a control module, a manipulator body, an auxiliary module, and a storage module, wherein the control module and the manipulator body are connected via a bidirectional network communication signal, the manipulator body is connected via a bidirectional network communication signal, an output end of the manipulator body is electrically connected to an input end of the auxiliary module, and the control module is connected via a bidirectional network communication signal;

[0007] The control module includes a hardware unit and a software unit;

[0008] The robot arm body includes multiple motors, induction units and harmonic reducers.

[0009] Furthermore, the hardware unit includes a host computer, a power distribution cabinet and a servo controller, and the output end of the power distribution cabinet is electrically connected to the input end of the host computer.

[0010] Furthermore, the software unit includes a task planning layer, an adaptive decision layer and a joint control layer. The task planning layer includes path planning and a human-computer interaction interface.

[0011] Furthermore, the auxiliary module includes a camera unit, a conveyor belt unit and a cooling unit. The camera unit is connected to the host computer through a two-way signal of network communication, the output end of the host computer is connected to the input end signal of the conveyor belt unit, and the cooling unit is connected to the host computer through a two-way signal of network communication.

[0012] Furthermore, the sensing unit includes an absolute encoder, a torque sensor and a Hall current sensor.

[0013] Furthermore, the storage module includes a server and a virtual machine interface, and the server is connected to the control module via a bidirectional signal of network communication.

[0014] Furthermore, the joint control layer includes model reference adaptive control (MRAC), the output end of the absolute encoder is connected to the input end of the host computer for signal connection, and the output end of the Hall sensor is connected to the input end of the host computer for signal connection.

[0015] Furthermore, the output terminal of the servo controller is signal-connected to the input terminal of the motor.

[0016] Furthermore, the adaptive decision layer includes RBF neural network compensation and adaptive impedance control.

[0017] Furthermore, the output end of the torque sensor is signal-connected to the output end of the host computer, and the output end of the motor is signal-connected to the input end of the torque sensor.

[0018] Compared with the prior art, the present invention provides a multi-working-condition adaptive manipulator motor control system with the following beneficial effects:

[0019] 1. The multi-working condition adaptive robotic arm motor control system comprises a control module, a robotic arm body, an auxiliary module and a storage module. The control module and the robotic arm body are connected via a two-way network communication signal, the robotic arm body is connected to the auxiliary module via a two-way network communication signal, the output end of the robotic arm body is electrically connected to the input end of the auxiliary module, and the control module is connected to the storage module via a two-way network communication signal. 2. The multi-working condition adaptive robotic arm motor control system adjusts the posture of the robotic arm body through the control module and can adapt to a variety of usage environments. At the same time, it cooperates with the sensing unit to effectively monitor the actual operation of the motor, thereby performing adaptive adjustments to a certain extent, and cooperates with the control module to further improve the adaptability to different working conditions.

[0020] 3. The multi-condition adaptive robotic arm motor control system stores the specific operations of the control module through the server of the storage module, which is convenient for subsequent error correction of the staff's operations and can improve the safety of production activities. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a system diagram of the present invention;

[0022] Figure 2 is a system diagram of the hardware units of the present invention;

[0023] Figure 3 is a system diagram of the software unit of the present invention;

[0024] Figure 4 A system diagram of the task planning layer of the present invention;

[0025] Figure 5 is a system diagram of the sensing unit of the present invention;

[0026] Figure 6 Flowchart of the operation of the system of the present invention. DETAILED DESCRIPTION

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0028] See also Figures 1 to 6In this embodiment, a multi-working condition adaptive robotic arm motor control system includes a control module, a robotic arm body, an auxiliary module and a storage module. The control module and the robotic arm body are connected through a two-way network communication signal, the robotic arm body and the auxiliary module are connected through a two-way network communication signal, the output end of the robotic arm body is electrically connected to the input end of the auxiliary module, and the control module and the storage module are connected through a two-way network communication signal.

[0029] In addition, the control module includes a hardware unit and a software unit. The hardware unit includes a host computer, a power distribution cabinet and a servo controller. The output end of the power distribution cabinet is electrically connected to the input end of the host computer, and the output end of the power distribution cabinet is electrically connected to the input end of the robotic arm body. In addition to outputting electric energy to the host computer, the power distribution cabinet also needs to output electric energy to the robotic arm body. The software unit includes a task planning layer, an adaptive decision layer and a joint control layer. The task planning layer includes path planning and a human-computer interaction interface. The human-computer interaction interface can help users control the robotic arm more conveniently. At the same time, the operating status of the auxiliary module will also be displayed through the human-computer interaction interface.

[0030] In addition, the robotic arm body includes multiple motors, induction units and harmonic reducers. The motors are specifically servo motors, and the multiple servo motors are controlled by a servo controller. The harmonic reducer can be used to reduce the output speed to achieve the effect of expanding the torque, and the harmonic reducer has a higher transmission ratio, and the transmission accuracy and stability are better. Since the teeth inside the harmonic reducer are in surface contact, combined with multi-tooth meshing to disperse the load, the pressure per unit area is small, and the carrying capacity can be effectively improved. The induction unit includes an absolute encoder, a torque sensor and a Hall current sensor. The absolute encoder can directly read the absolute position without a reference point. Even if the robotic arm is accidentally powered off or restarted, the precise position data can still be retained to avoid recalibration, and can meet the sub-millimeter positioning requirements of the robotic arm joints. The digital signal output is resistant to electromagnetic interference and can still operate stably in a strong electromagnetic environment.

[0031] It can be understood that the output end of the Hall sensor is connected to the input end signal of the host computer, the Hall sensor can monitor the current in real time, and the Hall sensor adopts non-contact current sensing to reduce consumption.

[0032] It can be known that the output end of the torque sensor is connected to the output end signal of the host computer, and the output end of the motor is connected to the input end signal of the torque sensor. After detecting the torque borne by the motor, the torque sensor transmits the information to the host computer. At this time, the size of the motor output torque can be adjusted by manual control. The output end of the servo controller is connected to the input end signal of the motor, and the servo controller is used to control multiple motors.

[0033] It should be further explained that the joint control layer includes model reference adaptive control (MRAC), the output end of the absolute encoder is connected to the input end signal of the host computer, and the absolute encoder will transmit the detection result to the host computer to help the control module make reasonable decisions. The adaptive decision layer includes RBF neural network compensation and adaptive impedance control. The RBF neural network can update the weight online to achieve continuous optimization of the disturbance torque compensation.

[0034] In this embodiment, by adopting model reference adaptive control (MRAC) to adjust parameters in real time and combining it with RBF neural network disturbance compensation, the anti-interference ability of the system can be effectively improved. The sensing unit transmits various data of the motor to the control module to improve the adaptability to various working conditions.

[0035] Please refer again Figure 1 and Figure 6 In order to further improve the adaptability, the auxiliary module in this embodiment includes a camera unit, a conveyor belt unit and a cooling unit. The camera unit is connected to the host computer through a two-way signal network communication. The camera unit will continuously observe the position of the object and transmit the picture to the host computer in real time. This structure can better meet the situation where the robot arm body and the host computer are not in the same position, and realize remote control. The output end of the host computer is connected to the input end signal of the conveyor belt unit, and the cooling unit is connected to the host computer through a two-way signal network communication. The cooling unit will effectively cool down the robot arm body. A temperature sensor is also provided on the robot arm body. The temperature sensor is used to detect the cooling effect of the cooling unit. The control module can also be used to adjust the working power of the cooling unit to meet the requirements of different working conditions.

[0036] Furthermore, the storage module includes a server and a virtual machine interface. The server and the control module are connected through a two-way network communication signal. The virtual machine interface can improve the scalability and ecological integration capabilities of the system. The virtual machine interface supports MATLAB / Simulink virtual prototype simulation, and the control parameter cloud optimization iteration efficiency is effectively improved.

[0037] In this embodiment, in addition to recording the user's operation process, the server can also effectively record the camera unit and the captured video, which is convenient for subsequent analysis of the operation process in combination with the image. The virtual machine interface can improve the overall scalability.

[0038] It can be understood that by coordinating the task planning layer, adaptive decision layer and joint control layer on the control module, the system's adaptability to various working conditions can be significantly improved.

[0039] The electrical components in the text are all electrically connected with the controller and the power supply, the control mode of the application is controlled by the controller, the control circuit of the controller can be realized by simple programming of the person skilled in the art, the power supply also belongs to the common knowledge in the art, and the application is mainly used for protecting mechanical devices, therefore, the control mode and circuit connection of the application will not be explained in detail.

[0040] The working principle of the above embodiment is:

[0041] When in use, first, open the man-machine interaction interface on the plane of the upper computer, then click on the path planning, and the motion of the mechanical arm can be planned, at the same time, the path is split by the control layer in the upper computer, and multiple motors are independently controlled to run in sequence, at the same time, the absolute encoder on the sensing unit can record the number of revolutions of the single motor at any time, and the torque sensor detects the torque received by the motor, when the mechanical arm needs to clamp and transport heavy objects, through the cooperation of the absolute encoder and the torque sensor, it can check whether the mechanical arm is normally and stably running, at the same time, the adaptive decision layer can analyze the structure of the absolute encoder and the torque sensor, and further control the motor to supplement the deviation according to the torque size and position error, so as to improve the adaptability, at the same time, the user can also manually control through the upper computer, further improving the adaptability of the system.

[0042] It should be noted that, in this text, relational terms such as first and second are used merely to distinguish one entity or action from another, without necessarily requiring or implying any such actual relationship or order between or among the entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without more limitations, the element defined by the statement "comprises a" does not exclude the existence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0043] Although embodiments of the application have been shown and described, it is to be understood that the application is not limited to the details of the above-described embodiments, since various modifications, alternative constructions, and equivalent arrangements can be made thereunto without departing from the spirit and scope of the application.

Claims

1. A multi-working-condition adaptive manipulator motor control system, characterized in that: The system comprises a control module, a robot body, an auxiliary module and a storage module, wherein the control module and the robot body are connected via a network communication bidirectional signal, the robot body and the auxiliary module are connected via a network communication bidirectional signal, the output end of the robot body is electrically connected to the input end of the auxiliary module, and the control module and the storage module are connected via a network communication bidirectional signal; The control module includes a hardware unit and a software unit; The robot arm body includes multiple motors, induction units and harmonic reducers.

2. The multi-mode adaptive manipulator motor control system according to claim 1, characterized in that: The hardware unit includes a host computer, a power distribution cabinet and a servo controller, and the output end of the power distribution cabinet is electrically connected to the input end of the host computer.

3. The multi-mode adaptive manipulator motor control system according to claim 1, characterized in that: The software unit includes a task planning layer, an adaptive decision layer and a joint control layer. The task planning layer includes path planning and a human-computer interaction interface.

4. The multi-mode adaptive manipulator motor control system according to claim 2, characterized in that: The auxiliary module includes a camera unit, a conveyor belt unit and a cooling unit. The camera unit is connected to the host computer via a two-way network communication signal. The output end of the host computer is connected to the input end signal of the conveyor belt unit. The cooling unit is connected to the host computer via a two-way network communication signal.

5. The multi-working-mode adaptive manipulator motor control system according to claim 1, characterized in that: The sensing unit includes an absolute encoder, a torque sensor and a Hall current sensor.

6. The multi-mode adaptive manipulator motor control system according to claim 5, characterized in that: The storage module includes a server and a virtual machine interface, and the server is connected to the control module via a network communication bidirectional signal.

7. The multi-mode adaptive manipulator motor control system according to claim 1, characterized in that: The joint control layer includes a model reference adaptive control (MRAC), the output end of the absolute encoder is connected to the input end of the host computer for signal connection, and the output end of the Hall sensor is connected to the input end of the host computer for signal connection.

8. The multi-mode adaptive manipulator motor control system according to claim 1, characterized in that: The output end of the servo controller is connected to the input end signal of the motor.

9. The multi-working-mode adaptive manipulator motor control system according to claim 3, characterized in that: The adaptive decision layer includes RBF neural network compensation and adaptive impedance control.

10. The multi-working-mode adaptive manipulator motor control system according to claim 1, characterized in that: The output end of the torque sensor is connected to the output end signal of the host computer, and the output end of the motor is connected to the input end signal of the torque sensor.

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