High-precision adaptive brushless motor control method for turntable confocal microscope

Abstract

The invention discloses a high-precision adaptive brushless motor control method for a turntable confocal microscope. The method comprises a cascade PID control structure, a vector control (FOC) algorithm, a low-pass filter, an adaptive parameter adjustment algorithm and an interference suppression technology. And the cascade PID control structure adopts a three-stage control structure for respectively controlling the current, the speed and the position of the motor. A vector control (FOC) algorithm separates magnetic field control and torque control of the motor by performing vector decomposition on stator current of the motor so as to realize accurate control of the motor. And the low-pass filter is used for filtering current and position feedback signals to ensure smoothness and stability of the feedback signals. According to the adaptive parameter adjustment, through an adjustment algorithm, the system can automatically adjust PID parameters and FOC parameters according to the running state of the motor and the environment change, and the optimal performance of the system under different working conditions is ensured. The interference suppression technology adopts various interference suppression means, such as electromagnetic interference suppression, voltage fluctuation suppression, temperature change compensation and mechanical vibration suppression. According to the brushless motor control technical scheme, through a series of innovative design and optimization, high-precision, quick-response, strong-anti-interference and high-adaptability control over the brushless motor is achieved.

Description

Technical Field

[0001] The present application belongs to the field of motor control, and specifically relates to a high-precision adaptive brushless motor control method for a spinning disk confocal microscope. Background Art

[0002] In the field of motor control, stepper motors and DC motors were once widely used motor types. Stepper motors are widely used in various precision equipment, such as microscopes, for their precise control performance. Its working principle is to control the rotation angle of the motor by receiving pulse signals. Each pulse signal makes the motor rotate a fixed angle, thereby achieving precise position control. DC motors, with their simple structure and easy-to-control speed characteristics, occupy an important position in many industrial applications. The speed of the motor can be adjusted by changing the voltage at both ends of the motor.

[0003] However, with the development of technology, brushless motors have gradually come to the fore. Brushless motors have many significant advantages over stepper motors and DC motors. First, there are no brushes and commutators in the structure of brushless motors, which greatly reduces mechanical wear and significantly improves the service life and reliability of the motor. Secondly, brushless motors have higher efficiency and lower noise, and can output greater torque at the same input power, or consume less power at the same output power. In addition, brushless motors are relatively small in size and have high torque, which makes them very suitable for use in equipment with high space and performance requirements, especially high-end microscopes.

[0004] In terms of brushless motor control, traditional control methods mainly rely on simple open-loop control or control based on voltage / frequency ratio. Although these methods can realize the operation of the motor to a certain extent, they have many shortcomings in terms of precise control, dynamic response and anti-interference ability. For example, in open-loop control, the operating status of the motor cannot be fed back to the control system in real time. Once the motor load changes or is subject to external interference, the motor's operating performance will drop significantly, and may even cause the motor to lose control. In the control based on voltage / frequency ratio, although the motor speed can be controlled by adjusting the ratio of voltage and frequency, the control of dynamic processes such as motor starting, acceleration, and deceleration is not accurate enough, and it is sensitive to changes in motor parameters, making it difficult to adapt to brushless motors of different models and specifications.

[0005] In recent years, with the continuous development of control theory and microelectronics technology, some advanced control algorithms and control strategies have begun to be applied to the control of brushless motors, such as vector control (FOC) and PID control. Vector control separates the magnetic field and torque control of the motor by vector decomposing the stator current of the motor, thereby achieving precise control of the motor. PID control is a classic feedback control method that can effectively improve the stability and dynamic response performance of the system by adjusting the three parameters of proportion, integration and differentiation. However, these control methods still face some challenges in practical applications, such as difficulty in parameter setting, high dependence on the motor model, and limited anti-interference ability.

[0006] In the existing brushless motor control technology, although there are some applications and research results, most of the applications are mainly focused on improving the hardware structure of the motor or optimizing a single control algorithm. There are relatively few studies on how to organically combine multiple control algorithms to achieve all-round and precise control of the brushless motor, and how to improve the anti-interference ability and adaptability of the system. For example, some applications proposed a new brushless motor stator structure, which improved the efficiency and output torque of the motor by changing the slot type and winding method of the stator, but did not conduct in-depth research on the control strategy of the motor. Other applications focused on improving the vector control algorithm and proposed a vector control method based on fuzzy logic. Although it improved the dynamic response performance of the system to a certain extent, its adaptability to motor parameter changes and external interference is still not ideal.

[0007] In summary, although the existing brushless motor control technology has made certain progress, it still has many shortcomings in terms of precise control, dynamic response, anti-interference ability and adaptability, which require further research and improvement.

[0008] Application Contents

[0009] In view of the many deficiencies of existing brushless motor control technology in terms of precise control, dynamic response, anti-interference ability and adaptability, this application aims to propose a high-precision adaptive brushless motor control method for a spinning disk confocal microscope, so as to overcome the defects of the prior art, solve the problems of insufficient precise control, slow dynamic response, weak anti-interference ability, poor adaptability, etc. in the control technology of existing brushless motors in the application of spinning disk confocal microscopes, and provide a high-performance and high-reliability brushless motor control technology to meet the strict requirements of microscopes, especially spinning disk confocal microscopes, for motor control. This technical solution achieves high-precision, fast response, strong anti-interference and high adaptability control of brushless motors through innovative control strategies and algorithm optimization. The specific technical solutions and features are as follows:

[0010] A high-precision adaptive brushless motor control method for a rotating disk confocal microscope includes a cascade PID control structure, a vector control (FOC) algorithm, a low-pass filter, an adaptive parameter adjustment algorithm, and interference suppression technology. The cascade PID control structure adopts a three-level cascade PID control structure to control the current, speed, and position of the motor respectively. The innermost loop is the current loop, the middle loop is the speed loop, and the outermost loop is the position loop, which are used to improve the control accuracy and dynamic response performance of the system. The vector control (FOC) algorithm separates the magnetic field and torque control of the motor by vector decomposing the stator current of the motor, and then introduces an inverse Park transform to convert the input U q and U d The electrical angle corresponding to the dq coordinate system is obtained through the inverse Park transform, so as to realize precise control of the motor and improve the efficiency and dynamic performance of the motor. The vector control (FOC) algorithm ensures that the motor does not exceed its maximum torque limit during operation by limiting the maximum torque, thereby protecting the motor from overload damage. The vector control (FOC) algorithm calculates the current waveform percentage according to the sector where the motor rotor is located, and brings the percentage into the PWM generator of the MCU to generate the UVW three-phase waveform, control the corresponding MOS tube bridge, and realize precise control of the motor. The low-pass filter includes a current filter and a position filter, which are used to filter out abnormal data in discrete values ​​to ensure the smoothness and stability of the feedback signal. The adaptive parameter adjustment algorithm monitors the current, speed and position feedback signals of the motor in real time, and automatically identifies the operating status and environmental changes of the motor, and automatically adjusts the PID parameters and FOC parameters according to the transfer function to ensure the optimal performance of the system under different working conditions. The interference suppression technology includes electromagnetic interference suppression, voltage fluctuation suppression, temperature change compensation and mechanical vibration suppression. The interference suppression technology described above can effectively suppress external interferences such as electromagnetic interference, voltage fluctuations, temperature changes and mechanical vibrations by optimizing sensor design, improving signal transmission paths and enhancing the anti-interference algorithm of the controller.

[0011] Furthermore, the current loop obtains the current value of the motor in real time through the current sensor, and compares it with the preset current target value to calculate the error signal. The PID parameters of the current loop are P=0.35, I=0, D=0.00001. The output of the current loop is used to control the current of the motor to ensure the stability of the current of the motor during startup and operation.

[0012] Furthermore, the speed loop obtains the speed value of the motor in real time through the speed sensor, compares it with the preset speed target value, and calculates the error signal. The PID parameters of the speed loop are P=0.35, I=0.2, D=0. The output of the speed loop is used to control the speed of the motor to ensure smooth changes in the speed of the motor during acceleration and deceleration.

[0013] Furthermore, the position loop obtains the rotor position of the motor in real time through the position sensor, compares it with the preset position target value, and calculates the error signal. The PID parameters of the position loop are P=7, I=1.5, D=0.05. The output of the position loop is used to control the position of the motor to ensure accurate control of the position of the motor during the positioning process.

[0014] Furthermore, the inverse Park transform converts the input U q and U d The electrical angle corresponding to the dq coordinate system is obtained by the inverse Park transform. In scenarios that require ultra-low latency response speed, traditional inverse Park transforms may introduce certain calculation delays. In order to reduce this delay, the present application adopts an optimized inverse Park transform form to reduce the amount of real-time calculations by pre-calculation and table lookup. The specific method is as follows: S1. During the system initialization phase, pre-calculate and store a series of commonly used inverse Park transform matrices corresponding to electrical angles. S2. During the real-time control process, quickly obtain the required inverse Park transform matrix by table lookup, thereby reducing calculation time. Optimized inverse Park transform formula:

[0015]

[0016] Among them, θ is the electrical angle of the motor rotor, which is obtained in real time by the position sensor. In order to reduce calculation delay, the values ​​of cos(θ) and sin(θ) are obtained by table lookup.

[0017] Furthermore, the specific method of limiting the maximum torque by the vector control (FOC) algorithm is as follows:

[0018] S1. Torque calculation. First, calculate the current torque T of the motor according to the formula:

[0019]

[0020] Among them, i q is the q-axis current, is the magnetic flux per pole of the motor. For a given motor, is a constant.

[0021] S2, torque limit. Set the maximum torque T max , usually determined based on the rated torque and safety margin of the motor. The torque is limited by the following formula:

[0022]

[0023] Among them, sat(x, a, b) is a saturation function that ensures that the value of x is in the interval [a, b].

[0024] Furthermore, the specific method of the vector control (FOC) algorithm controlling the motor through the waveform is as follows:

[0025] S1. Calculate the waveform percentage: According to the i after torque limit q value, calculate the current waveform percentage. According to the formula:

[0026]

[0027] in, It is the maximum value of the q-axis current and is usually determined based on the rated current of the motor and the safety margin.

[0028] S2. Generate UVW three-phase waveform: Generate UVW three-phase waveform according to the waveform percentage and the electrical angle θ of the motor rotor. According to the formula:

[0029]

[0030] S3, output UVW three-phase waveform through the PWM module of MCU, control the switch of MOSFET, and drive the motor to run.

[0031] Furthermore, in the low-pass filter, the current filter parameter is T f =0.02,y prev =0, timestamp prev = xTaskGetTickCount(). The position filter parameter is T f =0.01,y prev =0, timestamp prev =xTaskGetTickCount(). Through these parameters, the filter can effectively filter out high-frequency noise in the position signal to ensure the accuracy of the position feedback signal.

[0032] Furthermore, the specific adjustment process of the adaptive parameter adjustment algorithm is as follows:

[0033] S1. Parameter identification. By running the motor back and forth in open-loop mode, the coordinates of the current waveform point I are recorded. API and the rotation angle coordinate A API , and bring these coordinates into the simulation to obtain the transfer function G(s) of the motor.

[0034] S2, open loop operation: In the system initialization stage, the motor is placed in open loop mode, the motor is reciprocated, and the current waveform point coordinates I are recorded. API and the rotation angle coordinate A API The specific steps are as follows:

[0035] (a) Set the motor to run in open-loop mode and input a sinusoidal current signal with the frequency gradually increasing from low to high.

[0036] (b) Record the current waveform I at each frequency point API and the corresponding motor rotor position A API .

[0037] (c) When the motor's rotation amplitude comes to a stop, stop recording data.

[0038] S3, data processing: record the current waveform point coordinates I API and the rotation angle coordinate A API Substitute it into the simulation and get the transfer function G(s) of the motor. The specific steps are as follows:

[0039] (a) Use the system identification toolbox of the simulation software and input the I API and A API Data, fitting the transfer function G(s) described by the motor.

[0040] The general form of the transfer function G(s) described in (b) is:

[0041]

[0042] Where K is the gain and τ is the time constant.

[0043] S4, parameter adjustment: According to the transfer function G(s), the system automatically adjusts the PID parameters and FOC parameters using the Ziegler-Nichols method or the automatic parameter adjustment tool of the simulation software to ensure the stable operation of the motor under different load and environmental conditions. The specific process is as follows:

[0044] (a) PID parameter adjustment: According to the transfer function G(s), the PID parameters are automatically adjusted using the Ziegler-Nichols method or the automatic parameter adjustment tool of the simulation software.

[0045] The specific steps are as follows:

[0046] (i) Ziegler-Nichols method: First, gradually increase the proportional gain P of the PID controller until the system exhibits continuous equal-amplitude oscillations, and record the critical gain K at this time. u and the oscillation period T u According to the formula of the Ziegler-Nichols method, the PID parameters are calculated:

[0047] P=0.6·K u

[0048]

[0049] (ii) Automatic parameter adjustment using simulation software: Use the automatic parameter adjustment tool of the simulation software to input the transfer function G(s) and control performance indicators (such as overshoot, adjustment time, etc.) to automatically optimize the PID parameters.

[0050] (b) FOC parameter adjustment: According to the transfer function G(s), adjust the parameters in the FOC algorithm to ensure the stable operation of the motor under different load and environmental conditions. The specific steps are as follows:

[0051] (i) Adjust the current loop parameters: Calculate the bandwidth ω of the current loop based on the inductance L and resistance R of the motor. c :

[0052]

[0053] According to the bandwidth ω c , adjust the PID parameters of the current loop:

[0054] P curreent =ω c

[0055]

[0056] (ii) Adjust the speed loop parameters: According to the mechanical time constant τ of the motor m , calculate the bandwidth ω of the velocity loop v :

[0057]

[0058] According to the bandwidth ω v , adjust the PID parameters of the speed loop:

[0059] P velocity =ω v

[0060]

[0061] D velocity =0

[0062] (iii) Adjust the position loop parameters: According to the mechanical time constant τ of the motor m And the performance requirements of the position loop, adjust the PID parameters of the position loop:

[0063]

[0064] (c) Real-time parameter adjustment: During the operation of the motor, the current, speed and position feedback signals of the motor are monitored in real time, and the current current, speed and position data are recorded at regular intervals. These data are used to refit the transfer function G(s) of the motor. According to the new transfer function G(s), the Ziegler-Nichols method or the automatic parameter adjustment tool of the simulation software is used to dynamically adjust the PID parameters and the current loop, speed loop and position loop parameters in the FOC algorithm.

[0065] Furthermore, the electromagnetic interference suppression adopts shielded cables and filters. The shielding layer of the shielded cable is grounded at the controller end to ensure that the electromagnetic interference is effectively guided out through the grounding path. The filter is installed on the power line and the signal line. The cutoff frequency of the filter is lower than the highest frequency of the signal to ensure that the high-frequency noise is effectively filtered out and reduce the impact of electromagnetic interference on the control signal. At the same time, by adopting star grounding, it is ensured that all ground wires are connected to a common ground reference point, reducing ground wire noise and reducing the coupling effect of electromagnetic interference.

[0066] Furthermore, the voltage fluctuation suppression adopts a voltage stabilization circuit and a voltage compensation algorithm to ensure that the motor can still operate stably when the voltage fluctuates. The voltage stabilization circuit can effectively filter out high-frequency noise in the voltage, and the voltage compensation algorithm can automatically adjust the control parameters according to the voltage change to ensure the stability of the output torque and speed of the motor. The voltage stabilization circuit includes a linear regulator and a switching regulator. The regulator is installed at the power input end to ensure the stability of the output voltage. The output voltage V out By input voltage V in and the regulator voltage drop V drop Decide:

[0067] V out =V in -V drop

[0068] The output voltage of the switching regulator V out By input voltage V in And the duty cycle D determines:

[0069] V out =D·V in

[0070] Furthermore, the voltage compensation algorithm monitors the power supply voltage V in real time. supply , and adjust the output of the PID controller according to the voltage change to compensate for the impact of voltage fluctuations on motor performance. Voltage compensation coefficient K comp Calculated by the following formula:

[0071]

[0072] Adjust the output u of the PID controller comp :

[0073] u comp =u original ·K comp

[0074] Among them, V nominal is the rated voltage of the motor, u original is the uncompensated PID controller output.

[0075] Furthermore, the temperature change compensation monitors the temperature of the motor in real time through a temperature sensor, and automatically adjusts the control parameters according to the temperature change to compensate for the impact of temperature on the motor performance. The temperature change compensation can adjust the current and voltage of the motor according to the temperature change to ensure the stable operation of the motor under different temperature conditions. The specific process is as follows:

[0076] S1. Real-time monitoring of the motor temperature T. According to the temperature change, adjust the motor current and voltage to compensate for the impact of temperature on motor performance. Temperature compensation coefficient K temp Calculated by the following formula:

[0077] K temp =1+α·(TT nominal )

[0078] S2, adjust the motor current I comp and voltage V comp :

[0079] I comp =I original ·K temp

[0080] V comp =V original ·K temp

[0081] Where α is the temperature coefficient, T nominal is the rated temperature of the motor, I original and V original are the uncompensated currents and voltages.

[0082] Furthermore, the mechanical vibration suppression adopts a damping device and a dynamic balancing algorithm to reduce the impact of mechanical vibration on motor performance. The damping device uses a rubber pad or spring to absorb mechanical vibration, and the rubber pad or spring is installed between the motor and the mechanical structure to reduce the transmission of mechanical vibration. The dynamic balancing algorithm monitors the vibration signal V of the motor in real time. vibrationAccording to the vibration signal, the control parameters of the motor are adjusted to reduce the impact of vibration on the motor performance. The vibration compensation coefficient K of the dynamic balancing algorithm is vib Calculated by the following formula:

[0083] K vib =1-β·V vibration

[0084] Adjust the motor control parameters u vib :

[0085] u vib =u original ·K vib

[0086] Where β is the vibration coefficient, u original is the uncompensated control parameter.

[0087] In summary, this application achieves high-precision, fast-response, strong anti-interference and high-adaptability control of brushless motors through innovative cascade PID control structure, vector control (FOC) algorithm, low-pass filter, adaptive parameter adjustment and interference suppression technology, overcomes the defects of the prior art, and improves the overall performance of the brushless motor control system. The brushless motor control technology proposed in this application has many significant advantages and positive effects, and the beneficial effects achieved are as follows:

[0088] 1. High-precision control. Cascade PID control structure: A three-level cascade PID control structure is used to control the current, speed and position of the motor respectively. This structure can refine the control accuracy step by step. The innermost current loop ensures the stability of the motor current, the middle ring speed loop ensures the smooth change of the motor speed, and the outermost position loop ensures the precise control of the motor position. By accurately adjusting the PID parameters of each link, the system can maintain high-precision control under various working conditions. Vector control (FOC) algorithm: Through the vector control (FOC) algorithm, the stator current of the motor is vector-decomposed, and the magnetic field and torque control of the motor are separated to achieve precise control of the motor. The FOC algorithm can effectively improve the efficiency and dynamic performance of the motor, ensure that the motor can be adjusted smoothly and quickly during startup, acceleration and deceleration, and reduce overshoot and oscillation. For example, in the field of high-end microscopy, especially the spinning disk confocal microscope, the motor needs to switch lenses quickly according to demand, which puts forward high requirements on the speed, torque and stability of the motor.

[0089] 2. Fast dynamic response. Fast response control mechanism: The combination of cascade PID control structure and vector control (FOC) algorithm enables the brushless motor to respond quickly and accurately to control instructions and load changes. The fast response of the current loop ensures the stability of the motor current during startup and acceleration, the fast response of the speed loop ensures the smooth change of the motor speed, and the fast response of the position loop ensures the precise control of the motor position. Low-pass filter: The current and position feedback signals are filtered through the low-pass filter to ensure the smoothness and stability of the feedback signal. The low-pass filter can effectively filter out abnormal data in the discrete value, reduce the noise of the feedback signal, and improve the dynamic response performance of the system.

[0090] 3. Strong anti-interference ability. Low-pass filter: The current and position feedback signals are filtered through a low-pass filter to ensure the smoothness and stability of the feedback signal. The low-pass filter can effectively filter out abnormal data in discrete values, reduce the noise of the feedback signal, and improve the anti-interference ability of the system. For example, in the strong electromagnetic interference environment of the industrial site, the motor control system can operate stably, reduce misoperation and abnormal operation, and ensure the continuity and safety of production. Interference suppression technology: A variety of interference suppression technologies are adopted, such as electromagnetic interference suppression, voltage fluctuation suppression, temperature change compensation and mechanical vibration suppression. By optimizing the sensor design, improving the signal transmission path and enhancing the anti-interference algorithm of the controller, the system can effectively suppress external interference and ensure the reliable operation of the motor in complex environments.

[0091] 4. High adaptability. Adaptive parameter adjustment: Through the adaptive parameter adjustment algorithm, the system can automatically adjust the PID parameters and FOC parameters according to the motor's operating status and environmental changes to ensure the best performance of the system under different working conditions. By real-time monitoring of the motor's current, speed and position feedback signals, the system can automatically identify the motor's transfer function and automatically adjust the control parameters according to the transfer function to adapt to brushless motors of different models and specifications, as well as the changes in motor parameters under different loads and environmental conditions. Parameter identification: Run the motor back and forth in open-loop mode and record the current waveform point coordinates I API and the rotation angle coordinate A API , and bring these coordinates into the simulation software for simulation to obtain the transfer function G(s) of the motor. According to the transfer function G(s), the system automatically adjusts the PID parameters and FOC parameters to ensure the stable operation of the motor under different load and environmental conditions.

[0092] 5. Simplify parameter setting. Automatic parameter identification and optimization algorithm: Through the automatic parameter identification and optimization algorithm, the system can automatically obtain the transfer function and related parameters of the motor during the operation of the motor, and automatically adjust the control strategy according to these parameters. Users do not need to have deep control theory knowledge and rich debugging experience, but can quickly achieve precise control of brushless motors, greatly reducing the complexity and time cost of parameter adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] Figure 1 This is the control flow chart of the entire application;

[0094] Figure 2 This is the logic diagram of the cascade PID controller for this application;

[0095] Figure 3 This is the FOC control logic diagram for this application; DETAILED DESCRIPTION

[0096] The word "embodiment" used here as an "exemplary" does not necessarily mean that any embodiment described is superior to or better than other embodiments. Unless otherwise specified, the performance index tests in the embodiments of this application are performed using conventional test methods in the art. It should be understood that the terms described in this application are only used to describe specific implementation methods and are not used to limit the content disclosed in this application.

[0097] Unless otherwise specified, the technical and scientific terms used in this document have the same meanings as commonly understood by ordinary technicians in the technical field to which this application belongs; other experimental methods and technical means not specifically specified in this application refer to experimental methods and technical means commonly used by ordinary technicians in this field.

[0098] The terms "substantially" and "approximately" used herein are used to describe small fluctuations. For example, they can refer to less than or equal to ±5%, such as less than or equal to ±2%, such as less than or equal to ±1%, such as less than or equal to ±0.5%, such as less than or equal to ±0.2%, such as less than or equal to ±0.1%, such as less than or equal to ±0.05%. The numerical data represented or presented in the range format herein are used only for convenience and brevity, and should therefore be flexibly interpreted as including not only the values ​​clearly listed as the limits of the range, but also all independent values ​​or sub-ranges contained in the range. For example, the numerical range of "1-5%" should be interpreted as including not only the clearly listed values ​​of 1% to 5%, but also the independent values ​​and sub-ranges within the range shown. Therefore, independent values ​​such as 2%, 3.5% and 4% and sub-ranges such as 1%-3%, 2%-4% and 3%-5% are included in this numerical range. This principle also applies to the range of only one numerical value. In addition, such an interpretation applies regardless of the width of the range or the characteristics described.

[0099] In this document, including in the claims, transitional words such as "comprises," "includes," "with," "having," "containing," "involving," "accommodating," etc. are understood to be open-ended, i.e., meaning "including but not limited to." Only the transitional words "consisting of" and "composed of" are closed transitional words.

[0100] In order to better illustrate the content of the present application, numerous specific details are provided in the specific examples below. It should be understood by those skilled in the art that the present application can also be implemented without certain specific details. In the embodiments, some methods, means, instruments, equipment, etc. well known to those skilled in the art are not described in detail in order to highlight the main purpose of the present application.

[0101] Under the premise of no conflict, the technical features disclosed in the embodiments of the present application can be arbitrarily combined, and the obtained technical solutions belong to the contents disclosed in the embodiments of the present application. It should be noted that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like mentioned in the present application indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, which is only for the convenience of describing the technical features and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application, unless it conflicts with the context. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance, unless it conflicts with the context.

[0102] The specific implementation methods of the present application are described below to facilitate technical personnel in this technical field to understand the present application, but it should be clear that the present application is not limited to the scope of the specific implementation methods. For ordinary technical personnel in this technical field, as long as various changes are within the spirit and scope of the present application defined and determined by the attached claims, these changes are obvious, and all application creations using the concept of this application are protected.

[0103] Example

[0104] See also Figures 1 to 3 In order to realize the high-precision adaptive brushless motor control method for a spinning disk confocal microscope proposed in this application, this application provides an embodiment, and the specific implementation method is as follows:

[0105] 1. Hardware design.

[0106] 1. Motor selection:

[0107] Choose a high-performance brushless motor with the following main parameters: rated voltage: 48V; rated current: 20A; rated power: 1000W; rated speed: 3000RPM; maximum torque: 5N / m. The motor uses a three-phase winding, with a resistance of 0.5Ω and an inductance of 1mH for each phase.

[0108] 2. Sensor design:

[0109] (1) Current sensor: A Hall effect current sensor, model ACS712, is used with a measurement range of ±20A and an accuracy of 1%. The current sensor is installed on the three-phase winding of the motor to monitor the current value of the motor in real time.

[0110] (2) Speed ​​sensor: It uses an incremental encoder, model E6B2, with a resolution of 1000 pulses / rev and an accuracy of 0.1%. The encoder is installed on the motor shaft to monitor the motor speed in real time.

[0111] (3) Position sensor: It uses an absolute encoder, model S5800, with a resolution of 16 bits and an accuracy of 0.01 degrees. The encoder is installed on the motor shaft to monitor the motor rotor position in real time.

[0112] 3. Controller design:

[0113] (1) The microcontroller (MCU) uses STM32F407, which operates at a frequency of 168 MHz and has a rich set of peripheral interfaces, including ADC, PWM, SPI, I2C, etc. The MCU is responsible for receiving sensor signals, executing control algorithms, and outputting PWM waveforms to control the motor.

[0114] (2) Drive circuit: A three-phase full-bridge drive circuit is used, with two N-channel MOSFETs (model IRLZ44N) used in each phase. The on-resistance of the MOSFET is 0.02Ω, and it can withstand a maximum current of 30A. The drive circuit uses an isolated drive chip (model HCPL-3120) to ensure stable transmission of the control signal.

[0115] 2. Software design.

[0116] 1. Initialization:

[0117] (1) Initialize the MCU's peripheral interfaces, including ADC, PWM, SPI, I2C, etc.

[0118] (2) Initialize the current sensor, speed sensor and position sensor, and calibrate the zero point and range of the sensor.

[0119] (3) Initialize the cascade PID control parameters and FOC algorithm parameters, and set the initial values ​​as follows:

[0120] Current loop: P = 0.35, I = 0, D = 0.00001;

[0121] Speed ​​loop: P = 0.35, I = 0.2, D = 0;

[0122] Position loop: P=7, I=1.5, D=0.05.

[0123] 2. Data collection:

[0124] (1) The output signal of the current sensor is collected through ADC and converted into a current value.

[0125] (2) Read the output signal of the speed sensor through the SPI interface and convert it into a rotational speed value.

[0126] (3) Read the output signal of the position sensor through the SPI interface and convert it into the rotor position value.

[0127] (4) The acquisition frequency is 10kHz to ensure the real-time and accuracy of the data.

[0128] 3. Cascade PID control:

[0129] (1) Current loop:

[0130] Calculate the current error: e current =I target -I actual

[0131] Calculate the PID output:

[0132] Limit output value: u current =sat(u current , -20, 20)

[0133] (2) Speed ​​loop:

[0134] Calculate the speed error: e velocity =V target -V actual

[0135] Calculate the PID output:

[0136] Limit output value: u velocity =sat(u velocity , -6000, 6000)

[0137] (3) Position loop:

[0138] Calculate the position error: e angle =θ target -θactual

[0139] Calculate the PID output: Limit output value: u angle =sat(u angle , 0, 65535)

[0140] 4. Vector control (FOC) algorithm:

[0141] (1) Inverse Park transform:

[0142] Calculate the current value in the dq coordinate system:

[0143] (2) Torque limitation:

[0144] Limit maximum torque: T max =5N / m

[0145] Calculate the torque: T = i q ·φ

[0146] Limiting torque: T = sat(T, -T max , T max )

[0147] (3) Waveform generation:

[0148] Calculate waveform percentage:

[0149] Generate UVW three-phase waveform:

[0150] Output PWM waveform: Output UVW three-phase waveform through the PWM module of MCU, control the switch of MOSFET, and drive the motor to run.

[0151] 5. Low pass filter:

[0152] (1) Current filter:

[0153] Filter parameters: T f =0.02,y prev =0, timestamp prev =xTaskGetTickCount()

[0154] Filter formula: y = y prev +T f ·(xy prev )

[0155] Update parameter: y prev =y,timestamp prev =xTaskGetTickCount()

[0156] (2) Position filter:

[0157] Filter parameters: T f =0.01,y prev =0, timestamp prev =xTaskGetTickCount()

[0158] Filter formula: y = y prev +T f ·(xy prev )

[0159] Update parameter: y prev =y,timestamp prev =xTaskGetTickCount()

[0160] 6. Adaptive parameter adjustment:

[0161] (1) Parameter identification: Run the motor back and forth in open-loop mode and record the current waveform point coordinates I every 1 second. API and the rotation angle coordinate A API These coordinates are then substituted into MATLAB for simulation to obtain the transfer function G(s) of the motor.

[0162] (2) Parameter adjustment: According to the transfer function G(s), the PID parameters and FOC parameters are automatically adjusted to ensure stable operation of the motor under different load and environmental conditions.

[0163] 7. Interference suppression:

[0164] (1) Electromagnetic interference suppression: Use shielded cables and filters. The shielding layer of the shielded cables is grounded at the controller end to ensure that the electromagnetic interference is effectively guided out through the grounding path. The filters are installed on the power lines and signal lines. The cutoff frequency of the filters is lower than the highest frequency of the signal to ensure that high-frequency noise is effectively filtered out and reduce the impact of electromagnetic interference on the control signal. At the same time, by using star grounding, ensure that all ground wires are connected to a common ground reference point, reduce ground wire noise, and reduce the coupling effect of electromagnetic interference.

[0165] (2) Voltage fluctuation suppression: Use a voltage stabilization circuit and a voltage compensation algorithm to ensure that the motor can still operate stably when the voltage fluctuates. The voltage stabilization circuit can effectively filter out high-frequency noise in the voltage, and the voltage compensation algorithm can automatically adjust the control parameters according to the voltage changes to ensure the stability of the motor's output torque and speed. Select a suitable voltage regulator, such as LM7805 (linear voltage regulator) or MP2315 (switching voltage regulator). This embodiment uses LM7805 (linear voltage regulator). Install the linear voltage regulator at the power input to ensure the stability of the output voltage. The output voltage V out By input voltage V in and the regulator voltage drop V drop Decide:

[0166] V out =V in -V drop

[0167] The voltage compensation algorithm described above monitors the power supply voltage V in real time. supply , and adjust the output of the PID controller according to the voltage change to compensate for the impact of voltage fluctuations on motor performance. Voltage compensation coefficient K comp Calculated by the following formula:

[0168]

[0169] Adjust the output u of the PID controller comp :

[0170] u comp =u original ·K comp

[0171] Among them, V nominal is the rated voltage of the motor, u original is the uncompensated PID controller output.

[0172] (3) Temperature change compensation: The temperature of the motor is monitored in real time through a temperature sensor, and the control parameters are automatically adjusted according to the temperature change to compensate for the impact of temperature on the motor performance. The temperature change compensation can adjust the current and voltage of the motor according to the temperature change to ensure the stable operation of the motor under different temperature conditions. The specific process is as follows:

[0173] S1. Real-time monitoring of the motor temperature T. According to the temperature change, adjust the motor current and voltage to compensate for the impact of temperature on motor performance. Temperature compensation coefficient K temp Calculated by the following formula:

[0174] K temp =1+α·(TT nominal )

[0175] S2, adjust the motor current I comp and voltage V comp :

[0176] I comp =I original ·K temp

[0177] V comp =V original ·K temp

[0178] Where α is the temperature coefficient, T nominal is the rated temperature of the motor, I original and V original are the uncompensated currents and voltages.

[0179] (4) Mechanical vibration suppression: Use a shock absorber and a dynamic balancing algorithm to reduce the impact of mechanical vibration on motor performance. The shock absorber uses a rubber pad or spring to absorb mechanical vibration. The rubber pad or spring is installed between the motor and the mechanical structure to reduce the transmission of mechanical vibration. The dynamic balancing algorithm monitors the vibration signal V of the motor in real time. vibration According to the vibration signal, the control parameters of the motor are adjusted to reduce the impact of vibration on the motor performance. The vibration compensation coefficient K of the dynamic balancing algorithm is vib Calculated by the following formula:

[0180] K vib =1-β·V vibration

[0181] Adjust the motor control parameters u vib :

[0182] u vib =u original ·K vib

[0183] Where β is the vibration coefficient, u original is the uncompensated control parameter.

[0184] In summary, the present application achieves high-precision, fast response, strong anti-interference and high adaptability control of brushless motors through innovative cascade PID control structure, vector control (FOC) algorithm, low-pass filter, adaptive parameter adjustment and interference suppression technology, overcomes the defects of the prior art, and improves the overall performance of the brushless motor control system, especially the overall performance of the brushless motor control system in microscope applications.

[0185] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

[0186] The technical solutions disclosed in this application and the technical details disclosed in the embodiments are merely illustrative of the application concept of this application and do not constitute a limitation on the technical solutions of this application. Any conventional changes, replacements or combinations of the technical details disclosed in the embodiments of this application have the same application concept as this application and are within the protection scope of the claims of this application.

Claims

1. A high-precision adaptive brushless motor control method for a rotating disk confocal microscope, comprising a cascade PID control structure, a vector control (FOC) algorithm, a low-pass filter, an adaptive parameter adjustment algorithm and interference suppression technology. The cascade PID control structure adopts a three-level cascade PID control structure to control the current, speed and position of the motor respectively. The innermost loop is the current loop, the middle loop is the speed loop, and the outermost loop is the position loop. The vector control (FOC) algorithm separates the magnetic field and torque control of the motor by vector decomposing the stator current of the motor, and then introduces an inverse Park transform to convert the input U q and U d The electrical angle of the corresponding dq coordinate system is obtained by inverse Park transformation. The vector control (FOC) algorithm ensures that the motor does not exceed its maximum torque limit during operation by limiting the maximum torque. The vector control (FOC) algorithm calculates the current waveform percentage according to the sector in which the motor rotor is located, and brings the percentage into the PWM generator of the MCU to generate a UVW three-phase waveform to control the corresponding MOS tube bridge. The low-pass filter includes a current filter and a position filter for filtering out abnormal data in discrete values. The adaptive parameter adjustment algorithm monitors the current, speed and position feedback signals of the motor in real time, and automatically identifies the operating status and environmental changes of the motor, and automatically adjusts the PID parameters and FOC parameters according to the transfer function. The interference suppression technology includes electromagnetic interference suppression, voltage fluctuation suppression, temperature change compensation and mechanical vibration suppression.

2. The high-precision adaptive brushless motor control method for a spinning disk confocal microscope according to claim 1, characterized in that: The PID parameters of the current loop are P=0.35, I=0, D=0.00001, the PID parameters of the speed loop are P=0.35, I=0.2, D=0, and the PID parameters of the position loop are P=7, I=1.5, D=0.

05.

3. The high-precision adaptive brushless motor control method for a spinning disk confocal microscope according to claim 1, characterized in that: The inverse Park transform reduces the amount of real-time calculation by pre-calculation and table lookup. The specific method is as follows: S1. In the system initialization phase, pre-calculate and store a series of commonly used inverse Park transform matrices corresponding to electrical angles. S2. In the real-time control process, quickly obtain the required inverse Park transform matrix by table lookup, thereby reducing the calculation time. Optimized inverse Park transform formula: Among them, θ is the electrical angle of the motor rotor, which is obtained in real time through the position sensor. The values ​​of cos(θ) and sin(θ) are obtained by looking up the table.

4. The high-precision adaptive brushless motor control method for a spinning disk confocal microscope according to claim 1, characterized in that: The specific method of limiting the maximum torque by the vector control (FOC) algorithm is as follows: S1. Torque calculation. First, calculate the current torque T of the motor according to the formula: Among them, i q is the q-axis current, is the magnetic flux per pole of the motor. S2, torque limit. Set the maximum torque T max , usually determined based on the rated torque and safety margin of the motor. The torque is limited by the following formula: Among them, sat(x, a, b) is a saturation function that ensures that the value of x is in the interval [a, b].

5. The high-precision adaptive brushless motor control method for a spinning disk confocal microscope according to claim 1, characterized in that: The specific method of the vector control (FOC) algorithm controlling the motor through the waveform is as follows: S1. Calculate the waveform percentage: According to the i after torque limit q value, calculate the current waveform percentage. According to the formula: in, is the maximum value of the q-axis current. S2. Generate UVW three-phase waveform: Generate UVW three-phase waveform according to the waveform percentage and the electrical angle θ of the motor rotor. According to the formula: S3, output UVW three-phase waveforms through the PWM module of the MCU, control the switching of MOSFET, and drive the motor to run.

6. The high-precision adaptive brushless motor control method for a spinning disk confocal microscope according to claim 1, characterized in that: In the low-pass filter, the current filter parameter is T f =0.02,y prev =0, timestamp prev = xTaskGetTickCount(). The position filter parameter is T f =0.01,y prev =0, timestamp prev =xTaskGetTickCount().

7. The high-precision adaptive brushless motor control method for a spinning disk confocal microscope according to claim 1, characterized in that: The specific adjustment process of the adaptive parameter adjustment algorithm is as follows: S1. Parameter identification. By running the motor back and forth in open-loop mode, the coordinates of the current waveform point I are recorded. API and the rotation angle coordinate A API , and bring these coordinates into the simulation to obtain the transfer function G(s) of the motor. S2, open loop operation: In the system initialization stage, the motor is placed in open loop mode, the motor is reciprocated, and the current waveform point coordinates I are recorded. API and the rotation angle coordinate A API The specific steps are as follows: (a) Set the motor to run in open-loop mode and input a sinusoidal current signal with the frequency gradually increasing from low to high. (b) Record the current waveform I at each frequency point API and the corresponding motor rotor position A API . (c) When the motor's rotation amplitude comes to a stop, stop recording data. S3, data processing: record the current waveform point coordinates I API and the rotation angle coordinate A API Substitute it into the simulation and get the transfer function G(s) of the motor. The specific steps are as follows: (a) Use the system identification toolbox of the simulation software and input the I API and A API Data, fitting the transfer function G(s) described by the motor. The general form of the transfer function G(s) described in (b) is: Where K is the gain and τ is the time constant. S4. Parameter adjustment: According to the transfer function G(s), the system automatically adjusts the PID parameters and FOC parameters using the Ziegler-Nichols method or the automatic parameter adjustment tool of the simulation software to ensure stable operation of the motor under different load and environmental conditions.

8. The high-precision adaptive brushless motor control method for a spinning disk confocal microscope according to claim 1 or 7, characterized in that: The specific process of S4 and parameter adjustment is as follows: (a) PID parameter adjustment: According to the transfer function G(s), use the Ziegler-Nichols method or the automatic parameter adjustment tool of the simulation software to automatically adjust the PID parameters. The specific steps are as follows: (i) Ziegler-Nichols method: First, gradually increase the proportional gain P of the PID controller until the system exhibits continuous equal-amplitude oscillations, and record the critical gain K at this time. u and the oscillation period T u According to the formula of the Ziegler-Nichols method, the PID parameters are calculated: P=0.6·K u (ii) Automatic parameter adjustment using simulation software: Use the automatic parameter adjustment tool of the simulation software to input the transfer function G(s) and control performance indicators (such as overshoot, adjustment time, etc.) to automatically optimize the PID parameters. (b) FOC parameter adjustment: According to the transfer function G(s), adjust the parameters in the FOC algorithm to ensure the stable operation of the motor under different load and environmental conditions. The specific steps are as follows: (i) Adjust the current loop parameters: Calculate the bandwidth ω of the current loop based on the inductance L and resistance R of the motor. c : According to the bandwidth ω c , adjust the PID parameters of the current loop: P current =ω c (ii) Adjust the speed loop parameters: According to the mechanical time constant τ of the motor m , calculate the bandwidth ω of the velocity loop v : According to the bandwidth ω v , adjust the PID parameters of the speed loop: P velocity =ω v D velocity ＝0 (iii) Adjust the position loop parameters: According to the mechanical time constant τ of the motor m And the performance requirements of the position loop, adjust the PID parameters of the position loop: (c) Real-time parameter adjustment: During the operation of the motor, the current, speed and position feedback signals of the motor are monitored in real time, and the current current, speed and position data are recorded at regular intervals. These data are used to refit the transfer function G(s) of the motor. According to the new transfer function G(s), the Ziegler-Nichols method or the automatic parameter adjustment tool of the simulation software is used to dynamically adjust the PID parameters and the current loop, speed loop and position loop parameters in the FOC algorithm.

9. The high-precision adaptive brushless motor control method for a spinning disk confocal microscope according to claim 1, characterized in that: The electromagnetic interference suppression adopts shielded cable and filter, the shielding layer of the shielded cable is grounded at the controller end, the filter is installed on the power line and the signal line, the cutoff frequency of the filter is lower than the highest frequency of the signal, and the shielded cable and filter are star-grounded.

10. The high-precision adaptive brushless motor control method for a spinning disk confocal microscope according to claim 1, characterized in that: The voltage fluctuation suppression adopts a voltage stabilizing circuit and a voltage compensation algorithm. The voltage stabilizing circuit filters out high-frequency noise in the voltage, and the voltage compensation algorithm automatically adjusts control parameters according to voltage changes.

11. The high-precision adaptive brushless motor control method for a spinning disk confocal microscope according to claim 1 or 10, characterized in that: The voltage stabilization circuit includes a linear voltage stabilizer and a switching voltage stabilizer, and the voltage stabilizer is installed at the power input end. The output voltage V out By input voltage V in and the voltage drop of the regulator V drop Decide: V out =V in -V drop The output voltage of the switching regulator V out By input voltage V in And the duty cycle D determines: V out =D·V in 12. The high-precision adaptive brushless motor control method for a spinning disk confocal microscope according to claim 1 or 10, characterized in that: The voltage compensation algorithm described above monitors the power supply voltage V in real time. supply , and adjust the output of the PID controller according to the voltage change to compensate for the impact of voltage fluctuations on motor performance. Voltage compensation coefficient K comp Calculated by the following formula: Adjust the output u of the PID controller comp : in comp =in original ·K comp Among them, V nominal is the rated voltage of the motor, u original is the uncompensated PID controller output.

13. The high-precision adaptive brushless motor control method for a spinning disk confocal microscope according to claim 1, characterized in that: The temperature change compensation monitors the temperature of the motor in real time through a temperature sensor, and automatically adjusts the control parameters according to the temperature change to compensate for the impact of temperature on the motor performance. The temperature change compensation adjusts the current and voltage of the motor according to the temperature change. The specific process is as follows: S1. Real-time monitoring of the motor temperature T. According to the temperature change, adjust the motor current and voltage to compensate for the impact of temperature on motor performance. Temperature compensation coefficient K temp Calculated by the following formula: K temp =1+α·(T-T nominal ) S2, adjust the motor current I comp and voltage V comp : I comp =I original ·K temp V comp =V original ·K temp Where α is the temperature coefficient, T nominal is the rated temperature of the motor, I original and V original are the uncompensated currents and voltages.

14. The high-precision adaptive brushless motor control method for a spinning disk confocal microscope according to claim 1, characterized in that: The mechanical vibration suppression adopts a shock absorbing device and a dynamic balancing algorithm. The shock absorbing device uses a rubber pad or a spring, and the rubber pad or the spring is installed between the motor and the mechanical structure.

15. The high-precision adaptive brushless motor control method for a spinning disk confocal microscope according to claim 1 or 14, characterized in that: The dynamic balancing algorithm monitors the vibration signal V of the motor in real time. vibration According to the vibration signal, the control parameters of the motor are adjusted. The vibration compensation coefficient K of the dynamic balancing algorithm is vib Calculated by the following formula: K vib =1-β·V vibration Adjust the motor control parameters u vib : in vib =in original ·K vib Where β is the vibration coefficient, u original is the uncompensated control parameter.

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