Multi-time scale adaptive field-oriented control method for robot joint motor

Abstract

The application discloses a kind of multi-time scale adaptive magnetic field oriented control methods of robot joint motor, it is related to motor control technical field, by constructing electrical time scale current observer and mechanical time scale speed observer, realize the on-line adaptation of electrical parameter and mechanical parameter change.System acquires motor three-phase current and rotor position signal, after coordinate transformation, using the estimated value of the output of observer to generate compensation signal, correct the reference input of current loop and speed loop, and through Lyapunov stability theory design adaptive law, ensure that observation error converges, finally through SVPWM generates PWM signal to drive motor.The method improves the precision, dynamic response and robustness of robot joint control.

Description

Technical Field

[0001] This invention relates to the field of motor control technology, and more specifically to a multi-timescale adaptive magnetic field orientation control method for robot joint motors. Background Technology

[0002] As robotics technology rapidly advances towards higher precision, greater flexibility, and lighter weight, the joint motor module, as a core component for robot motion execution, directly determines the robot's motion accuracy, dynamic response speed, and operational stability through its control performance. Field-Oriented Control (FOC), due to its ability to decouple motor torque from flux linkage, offers advantages such as high precision and high efficiency, and has become the mainstream control strategy for robot joint motor modules. The GD32 series microcontroller, with its high cost-effectiveness, high computing efficiency, and abundant peripheral resources, is widely used in the hardware development of mid-to-high-end robot joint drive controllers, and the FOC control scheme based on the GD32 has become an important technical route in the field of robot joint drive.

[0003] However, existing robot joint motor control methods based on GD32-FOC still face many technical bottlenecks in practical applications:

[0004] First, robot joint motor modules face complex dynamic conditions during operation, such as sudden load changes, joint start-stop shocks, and external disturbances. Under different conditions, the electrical parameters (stator resistance, inductance) and mechanical parameters (moment of inertia, damping coefficient) of the motor will change dynamically. Traditional FOC control uses PI regulators or observers with fixed parameters, which are difficult to adapt to the time-varying characteristics of parameters, resulting in decreased control accuracy, lag in dynamic response, and even system oscillation.

[0005] Secondly, robot joint motion exhibits multi-timescale characteristics: the dynamic processes of the electrical timescale (microsecond level, involving current loop adjustment and flux linkage observation) and the mechanical timescale (millisecond level, involving velocity loop, position loop adjustment, and load disturbance compensation) are coupled with each other. Existing control methods do not differentiate the dynamic characteristics of different timescales and often use a single control bandwidth and observation period, which makes it impossible to suppress rapid disturbances at the electrical level in a timely manner and difficult to accurately compensate for slow disturbances at the mechanical level, thus restricting the overall control performance of the system.

[0006] Furthermore, existing adaptive observation methods often rely on complex mathematical models and substantial computational resources. Under the limited computing power of the GD32 microcontroller, it is difficult to achieve a balance between real-time performance and adaptive performance—either simplifying the observation algorithm leads to insufficient adaptive accuracy, or excessively pursuing accuracy increases computational latency, failing to meet the dual requirements of real-time response and dynamic adaptation for robot joint motor modules. Simultaneously, existing methods lack robustness to motor parameter perturbations and external disturbances, easily leading to inaccurate observations and control instability under extreme conditions, affecting the robot's operational safety and reliability.

[0007] Therefore, in view of the technical problems of existing robot joint motor control methods based on GD32-FOC, such as insufficient adaptive capability, poor multi-timescale coupling processing, and difficulty in balancing real-time performance and accuracy, it is urgent to propose a method that can adapt to the computing power characteristics of GD32 microcontrollers and realize dynamic adaptive observation and precise control at multiple time scales. This will improve the control accuracy, dynamic response speed and robustness of robot joint motor modules and meet the application requirements of various robots for high-performance joint drives. Summary of the Invention

[0008] In view of the above problems, the present invention is proposed to provide a multi-timescale adaptive magnetic field orientation control method for robot joint motors that overcomes or at least partially solves the above problems.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] In a first aspect, embodiments of the present invention provide a multi-timescale adaptive magnetic field orientation control method for robot joint motors, including:

[0011] Configure motor parameters;

[0012] Real-time acquisition of motor three-phase current signals, rotor position signals, and motor speed;

[0013] Based on the rotor position, the coordinate transformation of the motor's three-phase current signal is performed, and FOC current loop control is executed to output the dq-axis current component and dq-axis voltage reference value.

[0014] An electrical time-scale current observer is constructed based on motor parameters, motor speed, dq-axis current components, and dq-axis voltage reference values ​​to obtain dq-axis current estimates.

[0015] A mechanical time-scale speed observer is constructed based on motor parameters, motor speed, and dq-axis current components to obtain speed estimates.

[0016] The control reference input is compensated and corrected based on the estimated values ​​of dq-axis current and speed to obtain the compensated dq-axis current and compensated speed.

[0017] The compensated dq-axis current is used to generate a PWM drive signal through space vector pulse width modulation.

[0018] Preferably, the motor parameters include: stator resistance. dq axis inductance Permanent magnet flux The pole pair number p, the moment of inertia J, and the coefficient of friction B.

[0019] Preferably, the three-phase current signal of the motor is subjected to coordinate transformation based on the rotor position, and FOC current loop control is executed to output the dq-axis current component and dq-axis voltage reference value, including:

[0020] Using Clark transform and Park transform:

[0021]

[0022]

[0023] in, This indicates the three-phase current of the motor. Represents the dq-axis current components. Indicates the rotor position;

[0024] According to the current reference value Calculate the reference values ​​for the dq-axis voltages using FOC current loop control:

[0025]

[0026] in, These represent the reference values ​​for the d-q axis voltages, respectively. These are the proportional and integral coefficients of the PI controller for the d-axis and q-axis current loops, respectively.

[0027] Preferably, based on the voltage equation of a permanent magnet synchronous motor:

[0028]

[0029] in, Indicates the motor speed;

[0030] Constructing an electrical time-scale current observer:

[0031]

[0032] in, For the system matrix, The observer gain matrix is ​​updated online using an adaptive law. These are the estimated values ​​of the dq-axis currents, respectively.

[0033] Preferably, based on the equation of motion:

[0034]

[0035]

[0036] in, For load torque, It is electromagnetic torque.

[0037] Constructing a mechanical timescale rotational speed observer:

[0038]

[0039] in, The observer gain is updated online via an adaptive mechanism. This represents the estimated rotational speed. This represents the estimated load disturbance value.

[0040] Preferably, the control reference input is compensated and corrected based on the dq-axis current estimate and speed estimate, including:

[0041] Based on the deviation between the estimated and measured dq-axis current values, a d-axis current compensation signal is generated. and q-axis current compensation signal :

[0042]

[0043] Based on the deviation between the estimated and measured speed values, a speed compensation signal is generated. :

[0044]

[0045] in, These represent the d-axis current compensation signal, q-axis current compensation signal, and speed compensation signal, respectively.

[0046] The control reference input is compensated and corrected based on the d-axis current compensation signal, q-axis current compensation signal, and speed compensation signal.

[0047]

[0048] in, These represent the d-axis reference current, q-axis reference current, and reference rotational speed, respectively. These represent the compensated dq-axis current and the compensated rotational speed, respectively.

[0049] Preferably, it also includes: designing an adaptive law for parameters based on Lyapunov stability theory to update the parameters in the electrical time-scale current observer and the mechanical time-scale rotational speed observer.

[0050] Preferably, the parameter adaptive law is designed based on Lyapunov stability theory, specifically as follows:

[0051]

[0052] in, Positive adaptive gain It is a positive definite matrix.

[0053] When the stator resistance or dq-axis inductance changes, the current estimation error... Change, adaptive law dynamic adjustment This enables the observer to quickly track the actual current, generating d-axis and q-axis current compensation signals to correct the d-axis and q-axis reference currents and suppress current pulsations caused by parameter changes; when the moment of inertia, friction coefficient, or load torque changes... Speed ​​estimation error during sudden changes Change, adaptive law dynamic adjustment It tracks the estimated speed and load disturbance in real time, generates a speed compensation signal, corrects the reference speed, and realizes feedforward compensation for sudden load changes and external disturbances.

[0054] Secondly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a multi-timescale adaptive magnetic field orientation control method for robot joint motors.

[0055] Thirdly, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a multi-timescale adaptive magnetic field orientation control method for robot joint motors.

[0056] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a multi-time-scale adaptive magnetic field orientation control method for robot joint motors, which has the following effects:

[0057] By combining GD32-FOC control with a multi-timescale adaptive observer, the control challenges of robot joint motors under complex working conditions are effectively solved. By constructing an electrical timescale current observer and a mechanical timescale speed observer, multi-timescale hierarchical observation and online adaptive compensation of electrical and mechanical parameters are achieved. An adaptive law designed based on Lyapunov stability theory ensures convergence of observation errors under time-varying parameters and external disturbances, significantly enhancing system robustness. A feedforward compensation mechanism effectively suppresses current ripples and speed fluctuations, improving dynamic response speed and control accuracy. Simultaneously, the algorithm optimizes computational load while maintaining stability, adapting to the computing power of embedded platforms and making it suitable for robot joint drive scenarios with high precision and high dynamic requirements. Overall, it achieves a synergistic improvement in control accuracy, response speed, and anti-interference capability. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0059] Figure 1 This is a flowchart of the multi-time-scale adaptive magnetic field orientation control method for robot joint motors provided in this embodiment of the invention;

[0060] Figure 2 This is a schematic diagram of the observer structure provided in an embodiment of the present invention;

[0061] Figure 3 This is the current loop adaptive update process provided in the embodiments of the present invention;

[0062] Figure 4 The velocity loop adaptive update process provided in this embodiment of the invention. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] This invention discloses a multi-time-scale adaptive magnetic field orientation control method for robot joint motors, such as... Figure 1 As shown, it includes:

[0065] Configure motor parameters;

[0066] Real-time acquisition of motor three-phase current signals, rotor position, and motor speed;

[0067] Based on the rotor position, the coordinate transformation of the motor's three-phase current signal is performed, and FOC current loop control is executed to output the dq-axis current component and dq-axis voltage reference value.

[0068] An electrical time-scale current observer is constructed based on motor parameters, motor speed, dq-axis current components, and dq-axis voltage reference values ​​to obtain dq-axis current estimates.

[0069] A mechanical time-scale speed observer is constructed based on motor parameters, motor speed, and dq-axis current components to obtain speed estimates and load disturbance estimates.

[0070] The control reference input is compensated and corrected based on the estimated values ​​of dq-axis current and speed to obtain the compensated dq-axis current and compensated speed.

[0071] The compensated dq-axis current is used to generate a PWM drive signal through space vector pulse width modulation, and the compensated speed is used to adjust load disturbance and speed estimation error.

[0072] Furthermore, it also includes designing parameter adaptive laws based on Lyapunov stability theory to update the parameters in the electrical time-scale current observer and the mechanical time-scale rotational speed observer.

[0073] This invention achieves online adaptive response to changes in electrical and mechanical parameters by constructing an electrical time-scale current observer and a mechanical time-scale speed observer. The system acquires three-phase current and rotor position signals from the motor. After coordinate transformation, it uses the estimated values ​​output by the observers to generate compensation signals, correcting the reference inputs of the current and speed loops. An adaptive law is designed using Lyapunov stability theory to ensure convergence of observation errors. Finally, a pulse width modulation (PWM) signal is generated using space vector pulse width modulation (SVPWM) to drive the motor. This method improves the accuracy, dynamic response, and robustness of robot joint control.

[0074] The implementation process of this invention is described in detail below.

[0075] In this embodiment, the hardware modules required for FOC control are initialized and the motor parameters are configured in the GD32 microcontroller;

[0076] The electrical parameters of the permanent magnet motor are as follows: dq axis inductance is The stator resistance is The rotor flux is The number of permanent magnets is Rated speed is The control cycle is The moment of inertia of the motor is J=0.0001. The coefficient of friction, B, is 0.0022. The process of implementing the multi-time-scale adaptive observer control method is as follows:

[0077] 1) System initialization and parameter configuration

[0078] Initialize the hardware modules required for FOC control in the GD32 microcontroller and configure motor parameters: stator resistance. dq axis inductance Permanent magnet magnetic flux , number of pole pairs p, moment of inertia J, coefficient of friction B;

[0079] 2) Acquisition of current and position signals

[0080] Acquire the three-phase current of the motor using an ADC. And obtain the rotor position through the encoder. With motor speed .

[0081] 3) Coordinate transformation and FOC current loop control

[0082] Using Clark transform and Park transform:

[0083]

[0084]

[0085] in, This indicates the three-phase current of the motor. Represents the dq-axis current components. Indicates the rotor position;

[0086] Based on the dq axis current reference value The reference values ​​for the dq-axis voltage are calculated using a PI controller.

[0087]

[0088] in, These represent the reference values ​​for the d-q axis voltages, respectively. These are the proportional and integral coefficients of the PI controller for the d-axis and q-axis current loops, respectively.

[0089] 4) Design of electrical time-scale current observers, such as Figure 2 As shown.

[0090] Based on the voltage equation of a permanent magnet synchronous motor:

[0091]

[0092] Constructing an electrical time-scale current observer:

[0093]

[0094] in For the system matrix, The observer gain matrix is ​​adjusted online using an adaptive law. These are the estimated values ​​of the dq-axis currents, respectively. This indicates the motor speed.

[0095] 5) Design of a mechanical time-scale speed and load torque observer

[0096] Based on the equation of motion:

[0097]

[0098] in, For load torque, Electromagnetic torque;

[0099] Design a mechanical timescale rotational speed observer:

[0100]

[0101] in The observer gain is updated online via an adaptive mechanism to accommodate changes in J and B. This represents the estimated rotational speed. This represents the estimated load disturbance value.

[0102] 6) State compensation and reference value correction

[0103] The control reference input is compensated and corrected based on the dq-axis current estimate and speed estimate, including:

[0104] Based on the deviation between the estimated and measured dq-axis current values, a d-axis current compensation signal is generated. and q-axis current compensation signal :

[0105]

[0106] Based on the deviation between the estimated and measured speed values, a speed compensation signal is generated. :

[0107]

[0108] in, These represent the d-axis current compensation signal, q-axis current compensation signal, and speed compensation signal, respectively.

[0109] The control reference input is compensated and corrected based on the d-axis current compensation signal, q-axis current compensation signal, and speed compensation signal.

[0110]

[0111] in, These represent the d-axis reference current, q-axis reference current, and reference rotational speed, respectively. These represent the compensated dq-axis current and the compensated rotational speed, respectively.

[0112] 7) Adaptive parameter update

[0113] Based on Lyapunov stability theory, an adaptive law for parameters is designed to ensure convergence of observation errors:

[0114]

[0115] in, Positive adaptive gain It is a positive definite matrix.

[0116] when When one of them changes, the current estimation error Change, adaptive law dynamic adjustment This enables the observer to quickly track the actual current and generate a compensation signal. Correct current reference value To suppress current ripples caused by parameter changes, such as Figure 3 As shown; when the moment of inertia or coefficient of friction changes or the load torque... Speed ​​estimation error during sudden changes Change, adaptive law dynamic adjustment Real-time tracking of speed estimate With load disturbance estimate Generate speed compensation signal Correcting the reference speed This enables feedforward compensation for load fluctuations and external disturbances, such as... Figure 4 As shown.

[0117] 8) Space Vector Modulation and PWM Generation

[0118] The compensated dq-axis current is used as the corrected... The corrected dq-axis voltage reference value is calculated using the formula in 3), and then converted to the corrected dq-axis voltage reference value through inverse Park transformation. The coordinate system voltage is then used to generate six PWM signals to drive the inverter using the SVPWM algorithm.

[0119] 9) Loop Execution and Control Optimization

[0120] Each current loop cycle executes steps 2) to 4) and 6) to 7); each speed loop cycle executes steps 5) and 8). The system monitors the operating status in real time, and online optimization can be achieved by adjusting the observer parameters and PI parameters through the host computer.

[0121] 10) When the motor has been running stably for 0.2 seconds, a step load disturbance is applied, and the stator resistance d / q axis inductance parameter perturbation is introduced at the same time. The traditional FOC control method and the method of the present invention are compared and tested.

[0122] The test data is as follows:

[0123]

[0124] Experimental data show that, compared with traditional FOC control methods, this invention, through hierarchical adaptive compensation on both electrical and mechanical time scales, combined with a Lyapunov-stable adaptive law, successfully achieves accurate online identification and suppression of time-varying motor parameters and external disturbances. This invention significantly reduces steady-state speed error and speed drop under load disturbances, substantially reduces current and torque ripple, and shortens disturbance recovery time by more than 80%.

[0125] This embodiment provides a computer device, including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements a multi-timescale adaptive magnetic field orientation control method for robot joint motors.

[0126] This embodiment provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements a multi-timescale adaptive magnetic field orientation control method for robot joint motors.

[0127] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0128] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0129] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-time-scale adaptive magnetic field orientation control method for robot joint motors, characterized in that, include: Configure motor parameters; Real-time acquisition of motor three-phase current signals, rotor position, and motor speed; Based on the rotor position, the coordinate transformation of the motor's three-phase current signal is performed, and FOC current loop control is executed to output the dq-axis current component and dq-axis voltage reference value. Based on motor parameters, motor speed, dq-axis current components and dq-axis voltage reference values, and by constructing an electrical time-scale current observer through the voltage equation of permanent magnet synchronous motor, the estimated value of dq-axis current is obtained. Based on motor parameters, motor speed and dq axis current components, a mechanical time-scale speed observer is constructed through the motion equation to obtain the speed estimate; The control reference input is compensated and corrected based on the estimated dq-axis current and speed to obtain the compensated dq-axis current and speed; this includes generating a d-axis current compensation signal based on the deviation between the estimated and measured dq-axis current values. and q-axis current compensation signal Based on the deviation between the estimated and measured speed values, a speed compensation signal is generated. The control reference input is compensated and corrected based on the d-axis current compensation signal, q-axis current compensation signal, and speed compensation signal. in, These represent the d-axis reference current, q-axis reference current, and reference rotational speed, respectively. These represent the compensated dq-axis current and the compensated rotational speed, respectively. The PWM drive signal is generated based on the compensated dq axis current through space vector pulse width modulation. It also includes: designing parameter adaptive laws based on Lyapunov stability theory to update the parameters in the electrical time-scale current observer and the mechanical time-scale rotational speed observer, specifically: in, Positive adaptive gain It is a positive definite matrix; When the stator resistance or dq-axis inductance changes, the current estimation error... Change, adaptive law dynamic adjustment This enables the observer to quickly track the actual current, generating d-axis and q-axis current compensation signals to correct the d-axis and q-axis reference currents and suppress current pulsations caused by parameter changes; when the moment of inertia, friction coefficient, or load torque changes... Speed ​​estimation error during sudden changes Change, adaptive law dynamic adjustment It tracks the estimated speed and load disturbance in real time, generates a speed compensation signal, corrects the reference speed, and realizes feedforward compensation for sudden load changes and external disturbances.

2. The method as described in claim 1, characterized in that, Motor parameters include: stator resistance dq axis inductance Permanent magnet flux The pole pair number p, the moment of inertia J, and the coefficient of friction B.

3. The method as described in claim 2, characterized in that, Based on the rotor position, coordinate transformation is performed on the three-phase current signal of the motor, and FOC current loop control is executed to output the dq-axis current component and dq-axis voltage reference values, including: Using Clark transform and Park transform: in, This indicates the three-phase current of the motor. Represents the dq-axis current components. Indicates the rotor position; Based on the dq axis current reference value Calculate the reference values ​​for the dq-axis voltages using FOC current loop control: in, These represent the reference values ​​for the d-q axis voltages, respectively. These are the proportional and integral coefficients of the PI controller for the d-axis and q-axis current loops, respectively.

4. The method as described in claim 3, characterized in that, Based on the voltage equation of a permanent magnet synchronous motor: in, Indicates the motor speed; Constructing an electrical time-scale current observer: in, For the system matrix, The observer gain matrix is ​​updated online using an adaptive law. These are the estimated values ​​of the dq-axis currents, respectively.

5. The method as described in claim 4, characterized in that, Based on the equation of motion: in, For load torque, Electromagnetic torque; Constructing a mechanical timescale rotational speed observer: in, The observer gain is updated online via an adaptive mechanism. This represents the estimated rotational speed. This represents the estimated load disturbance value.

6. The method as described in claim 5, characterized in that, Based on the deviation between the estimated and measured dq-axis current values, a d-axis current compensation signal is generated. and q-axis current compensation signal : Based on the deviation between the estimated and measured speed values, a speed compensation signal is generated. : in, These represent the d-axis current compensation signal, q-axis current compensation signal, and speed compensation signal, respectively.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the multi-timescale adaptive magnetic field orientation control method for robot joint motors as described in any one of claims 1 to 6.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the multi-timescale adaptive magnetic field orientation control method for robot joint motors as described in any one of claims 1 to 6.

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