Track control method and system of transformer substation oil sample collection robot

Abstract

The invention relates to the field of automation control, in particular to a track control method and system for a transformer substation oil sample collection robot. The method comprises the step of realizing accurate control through combination of a brushless direct current motor vector control technology and a fuzzy self-adaptive PID (Proportion Integration Differentiation) control technology. The method comprises the following steps: acquiring the actual rotating speed of a motor by using a position sensor, comparing the actual rotating speed with a target rotating speed to generate an error signal, and inputting a speed outer ring PI regulator to calculate a quadrature-axis current reference quantity; a three-phase current is obtained through a current sensor, a current component is obtained through conversion, and a voltage reference quantity is generated through a current loop PI regulator; and finally, a PWM signal is generated through inverse transformation and space vector modulation to drive a motor. The problems that in the prior art, parameter setting is difficult, and nonlinear adaptability is poor are effectively solved, and the movement performance and stability of the tracked robot are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of automation control, and in particular to a crawler control method and system for an oil sample collection robot in a transformer substation. Background Art

[0002] In the motion control of the oil sampling robot in the substation, the stability and accuracy of the track drive system directly affect the reliability of the robot's operation in complex terrain (such as oily ground and slopes). Brushless DC motors (BLDCMs) are widely used due to their high efficiency and low maintenance characteristics, but traditional control methods have significant limitations. The current mainstream control strategies are mostly based on dual closed-loop PI control combined with field-oriented control (FOC). Taking the patent CN218387323U, a BLDC motor speed loop and current loop dual closed-loop control system, as an example, it discloses a dual closed-loop control scheme based on the speed outer loop and the current inner loop, which adjusts the motor speed by fixing the PI parameters. However, its core problem is:

[0003] Fixed parameters lack adaptability: Traditional PI controllers have fixed parameters, making it difficult to quickly adjust the current loop output when the load suddenly changes or the ground friction coefficient changes, causing track speed fluctuations or even instability. For example, when a robot encounters oil and slip, fixed PI parameters cannot compensate for torque deviations in a timely manner, causing speed tracking lag or overshoot.

[0004] Insufficient magnetic field decoupling: Some solutions fail to fully implement independent control of the direct-axis (d-axis) and quadrature-axis (q-axis) currents, resulting in coupling of electromagnetic torque and flux linkage, affecting torque linearity. Especially during high-speed field-weakening control, direct-axis current interference can exacerbate torque ripple and reduce control accuracy.

[0005] Weak anti-interference capability: Existing methods lack a dynamic parameter adjustment mechanism. When motor parameters drift due to temperature changes (such as increased winding resistance), control performance degrades significantly. For example, traditional FOC is prone to current loop saturation when parameters drift, resulting in inverter output waveform distortion.

[0006] These problems are particularly prominent in substation inspection scenarios, where robots need to frequently respond to sudden load disturbances and environmental interference. Existing control strategies are unable to meet the requirements of high precision and strong robustness. Summary of the Invention

[0007] The present invention provides a crawler control method and system for a substation oil sampling robot, aiming to solve the problems of difficult parameter setting, poor adaptability to nonlinear and time-varying systems, and sensitivity to integral saturation and differential noise in existing crawler robot control methods.

[0008] To achieve the above objectives, the following technical solutions are adopted.

[0009] A crawler control method for a substation oil sample collection robot comprises the following steps:

[0010] S1: The actual speed of the brushless DC motor is collected in real time through the position sensor, and compared with the given target speed to generate a speed error signal; S2: The speed error signal is input into the speed outer loop PI regulator to calculate the quadrature-axis current reference iq*; S3: The stator three-phase current is obtained through the three-phase stator winding current sensor, and the direct-axis current component id and the quadrature-axis current component iq are obtained through Clarke transformation and Park transformation; S4: The difference between the quadrature-axis current reference iq* and the quadrature-axis current component iq is input into the quadrature-axis current loop PI regulator to generate a quadrature-axis voltage reference uq; S5: The direct-axis current reference id* is set to 0, and the difference between the direct-axis current component id and the direct-axis current component id is input into the direct-axis current loop PI regulator to generate a direct-axis voltage reference ud; S6: The quadrature-axis voltage reference uq and the direct-axis voltage reference ud are converted into α-axis voltage u α and β-axis voltage u β through Park inverse transformation; S7: u α and u β is input to the space vector modulation module to generate a PWM signal to drive the three-phase inverter and output the three-phase voltage to the stator winding of the brushless DC motor.

[0011] Optionally, step S8 is also included: dynamically adjusting the PI parameters of the quadrature-axis current loop and the direct-axis current loop through a fuzzy adaptive PID controller, wherein the input of the fuzzy adaptive PID controller is the speed error e and its change rate ec, and the output is the correction amount of the PID parameters.

[0012] Optionally, the fuzzy adaptive PID controller adopts a two-dimensional structure, the input of the fuzzy adaptive PID controller is the speed error e and the error change rate ec, and the output is the correction amount of the proportional coefficient ΔKp, the integral coefficient ΔKi and the differential coefficient ΔKd, and satisfies the following relationship:

[0013] The fuzzy control rule is based on the membership function of the error and the error change rate, and the membership function adopts a symmetrically distributed triangular function;

[0014] The fuzzy inference results are defuzzified by the center of gravity method to generate continuous correction quantities, which are superimposed on the initial values ​​of the PID parameters.

[0015] Optionally, the direct-axis current reference id* is set to 0, so that the brushless DC motor operates in a field-oriented control mode, thereby achieving decoupling control of the electromagnetic torque and the direct-axis current.

[0016] Optionally, the input of the speed outer loop PI regulator is a speed error signal, and the output is a quadrature-axis current reference iq*, and iq is linearly related to the electromagnetic torque, and closed-loop tracking of the motor speed is achieved by adjusting iq.

[0017] Optionally, the fuzzy control rule includes the following logic:

[0018] When the error e is positive and the error change rate ec is positive, a positive correction value of ΔKp is output;

[0019] When the error e is negative and the error change rate ec approaches zero, a negative correction value of ΔKi is output to reduce the integral accumulation;

[0020] The fuzzy control rules cover all possible combinations of errors and error change rates and are adjusted by weighted averaging continuous parameters.

[0021] Optionally, the space vector modulation module converts the α-axis voltage u α and the β-axis voltage u β into six PWM signals based on the voltage space vector pulse width modulation SVPWM algorithm, and drives the three-phase inverter to generate a sinusoidal equivalent voltage waveform.

[0022] Optionally, when the quadrature-axis current reference iq* exceeds a preset threshold, its output range is limited by a limiting module to avoid control failure caused by integrator saturation.

[0023] A crawler control system for a substation oil sample collection robot, comprising:

[0024] Position sensor, used to collect motor speed in real time;

[0025] Three-phase current sensor, used to obtain the three-phase current of the stator;

[0026] A processor, configured to execute a track control method for a substation oil sample collection robot according to any one of claims 1 to 8;

[0027] The three-phase inverter drives the brushless DC motor according to the PWM signal.

[0028] Optionally, the processor has a built-in memory for storing a fuzzy rule table, initial values ​​of PID parameters and a defuzzification algorithm to support real-time online adjustment of control parameters.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] This invention proposes a track control method for a substation oil sampling robot. By combining brushless DC motor vector control technology with fuzzy adaptive PID control technology, it effectively solves the problems existing in the existing technology. The control method realizes decoupling control of electromagnetic torque and direct axis current through vector control, so that the motor speed can accurately follow the given target speed, thereby significantly improving the motion performance and stability of the tracked robot. In addition,

[0031] The fuzzy adaptive PID controller of this application can dynamically adjust the PI parameters and optimize the control parameters in real time according to the speed error and its rate of change, further improving the system's anti-interference ability and control accuracy.

[0032] This application optimizes the control strategy, such as setting the direct-axis current reference to 0, so that the motor operates in a magnetic field-oriented control mode, further improving the performance of the control system.

[0033] This application provides protection at the hardware level, avoiding integrator saturation through the limiting module and ensuring the reliability of the control system.

[0034] This application applies the control method to a specific crawler control system, so that the technical solution can be actually applied to the oil sample collection robot in the substation, which has high practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The brushless DC motor vector control structure diagram provided in this application;

[0036] Figure 2 This is the structural type diagram of the fuzzy controller for this application;

[0037] Figure 3 This is the structural diagram of the fuzzy adaptive PID controller provided in this application. DETAILED DESCRIPTION

[0038] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0039] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise indicated, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0040] Example 1

[0041] like Figure 1-Figure 3 As shown, the present invention provides a track control method for a substation oil sample collection robot. The method combines brushless DC motor vector control technology with fuzzy adaptive PID control technology to achieve precise control of the motion state of the tracked robot.

[0042] The core of the present invention is to achieve precise control of the motion state of the crawler robot through brushless DC motor vector control technology. Specifically, the control method includes the following steps:

[0043] First, a position sensor is used to collect the actual speed of the brushless DC motor in real time. This position sensor can be a high-precision encoder that accurately measures the motor's real-time speed. The actual speed is compared with the given target speed to generate a speed error signal. This speed error signal forms the basis of the subsequent control algorithm, and its accuracy directly affects the control effect.

[0044] Next, the speed error signal is input into the speed outer loop PI regulator. The PI regulator is a common control algorithm that adjusts the error through two parameters: proportional and integral. In the present invention, the PI regulator calculates the quadrature-axis current reference iq* based on the speed error signal. The quadrature-axis current reference iq* is linearly related to the electromagnetic torque, and closed-loop tracking of the motor speed can be achieved by adjusting iq. This process is a key step in achieving precise control of the motor speed, ensuring that the motor can operate stably at a given target speed.

[0045] The three-phase stator current is then acquired using a three-phase stator winding current sensor. This current sensor monitors the motor's current status in real time, providing data support for subsequent control. The acquired three-phase stator current undergoes Clarke and Park transforms to obtain the direct-axis current component id and the quadrature-axis current component iq. Clarke and Park transforms are commonly used coordinate transformation methods in motor control, converting the three-phase current into controllable direct-axis and quadrature-axis current components.

[0046] After obtaining the direct-axis current component id and the quadrature-axis current component iq, the difference between the quadrature-axis current reference iq and the quadrature-axis current component iq is input into the quadrature-axis current loop PI regulator to generate the quadrature-axis voltage reference uq. Simultaneously, the direct-axis current reference id is set to 0, and the difference between the direct-axis current reference id and the direct-axis current component id is input into the direct-axis current loop PI regulator to generate the direct-axis voltage reference ud. Setting the direct-axis current reference id* to 0 is a key technical feature of the present invention, enabling the brushless DC motor to operate in a field-oriented control mode, achieving decoupled control of the electromagnetic torque and the direct-axis current. This decoupling control method can effectively improve the motor's control accuracy and dynamic performance.

[0047] Finally, the quadrature-axis voltage reference uq and the direct-axis voltage reference ud are converted into the α-axis voltage uα and the β-axis voltage uβ via an inverse Park transform. These uα and uβ are then input into the space vector modulation module, generating PWM signals to drive the three-phase inverter, which then outputs three-phase voltages to the stator windings of the brushless DC motor. Based on the voltage space vector pulse width modulation (SVPWM) algorithm, the space vector modulation module converts the α-axis voltage uα and the β-axis voltage uβ into six PWM signals, which drive the three-phase inverter to generate a sinusoidal equivalent voltage waveform. This process enables precise control and drive of the motor, ensuring stable operation under complex operating conditions.

[0048] Another important technical feature of the present invention is the introduction of fuzzy adaptive PID control technology. The fuzzy adaptive PID controller can dynamically adjust the PI parameters of the quadrature-axis current loop and the direct-axis current loop, thereby further improving the performance of the control system.

[0049] The fuzzy adaptive PID controller adopts a two-dimensional structure. Its inputs are the speed error e and the error rate of change ec, and its outputs are corrections for the proportional coefficient ΔKp, the integral coefficient ΔKi, and the differential coefficient ΔKd. The fuzzy control rules are based on the membership functions of the error and the error rate of change, which use symmetrically distributed triangular functions. The fuzzy inference results are defuzzified using the centroid method to generate continuous corrections that are added to the initial values ​​of the PID parameters.

[0050] Specifically, the fuzzy control rules include the following logic: When the error e is positive and the error rate of change ec is positive, a positive correction of ΔKp is output; when the error e is negative and the error rate of change ec approaches zero, a negative correction of ΔKi is output to reduce integral accumulation. The fuzzy control rules cover all possible combinations of errors and error rates and are adjusted through continuous parameter adjustments using weighted averaging. This fuzzy adaptive PID control approach effectively addresses performance limitations in scenarios with sudden motor load changes or strongly coupled multivariables, improving the system's interference tolerance and control accuracy.

[0051] In practical applications, the parameter adjustment process of the fuzzy adaptive PID controller is performed in real time. The controller dynamically adjusts the PID parameters based on the real-time speed error and its rate of change, ensuring that the control system always operates optimally. This dynamic adjustment method effectively avoids the difficulties of parameter tuning in traditional PID control, reliance on experience or trial and error, and improves the adaptability and robustness of the control system.

[0052] The present invention also provides a track control system for a substation oil sampling robot. The system includes a position sensor, a three-phase current sensor, a processor, and a three-phase inverter. The position sensor is used to collect real-time motor speed, the three-phase current sensor is used to obtain the three-phase stator current, the processor executes the control method described above, and the three-phase inverter drives the brushless DC motor according to the PWM signal.

[0053] The processor's built-in memory stores the fuzzy rule table, initial PID parameter values, and defuzzification algorithms, enabling real-time online adjustment of control parameters. This system architecture ensures effective implementation of control methods and improves the tracked robot's kinematic performance and stability.

[0054] In practical applications, the system can be flexibly configured to suit different operating conditions and requirements. For example, the target speed and control parameters can be adjusted to achieve optimal control based on the robot's specific tasks within the substation. Furthermore, the system can integrate other sensors and modules, such as temperature and vibration sensors, to further enhance the robot's intelligence and reliability.

[0055] In summary, this invention combines brushless DC motor vector control technology with fuzzy adaptive PID control technology to achieve precise control of the track motion of a substation oil sampling robot. This control method and system are highly practical and innovative, effectively resolving existing challenges and improving the robot's performance and stability.

[0056] Example 2

[0057] The algorithm identification target involved in the present invention is a crawler-type substation oil sample automatic collection robot.

[0058] In BLDCM vector control technology, the three-phase stator current of the AC motor is decomposed into DC axis current id and quadrature axis current iq based on coordinate transformation theory. By controlling the direct axis voltage ud and quadrature axis voltage uq at the stator end, id and iq are controlled to achieve decoupling of the magnetic field and torque current, and obtain dynamic control of the electromagnetic torque.

[0059] When the direct-axis current of the BLDCM is zero, its torque expression in the dq coordinate system is:

[0060]

[0061] Where p is the number of pole pairs, ψf is the rotor flux, and iq is the torque component of the stator current. According to the motor control equation, the motor torque is proportional to the component of the stator current vector on the q-axis. Therefore, to control the motor torque, it is only necessary to maintain ψf constant. By controlling the q-axis stator current vector component, the motor torque output can be precisely controlled.

[0062] The brushless DC motor vector control designed by the present invention is based on the method of id=0, and its structure is shown in the figure below. Figure 1 As shown, the rotation speed of the brushless DC motor rotor can accurately follow the given target rotation speed, thereby achieving efficient and stable operation.

[0063] In the motor control system, the position sensor can measure the error between the actual speed and the given target speed, and then calculate and adjust it through the speed outer loop PI regulator to convert the error into a reference value of the q-axis current.

[0064] After two coordinate transformations, the quadrature-axis current reference iq* and the motor's three-phase stator winding current obtain the errors of the excitation component id and torque component iq in the dq coordinate system as input to complete the adjustment of the dq-axis current components and output the quadrature-axis voltage reference uq.

[0065] At the same time, the error between the direct-axis current reference id* and the motor three-phase current after two coordinate transformations to obtain the actual direct-axis current id is used as the input of the direct-axis current loop PI regulator, and the direct-axis voltage ud is output;

[0066] Finally, the quadrature-axis voltage uq and the direct-axis voltage ud are transformed by Park inverse transformation to output u α and u β. The PWM signal generated by the space vector modulation module is then input into the three-phase inverter, and finally the three-phase voltage is output to the stator winding of the brushless DC motor to achieve precise control and drive of the motor.

[0067] Figure 2 These are two commonly used dimensional types of fuzzy controllers. The control dimension is determined by the input quantity. The more dimensions there are, the better the control effect and the better the dynamic performance will be. However, the higher the dimension, the more complex the corresponding control algorithm will be. Therefore, the dimension of the fuzzy controller generally does not exceed three dimensions.

[0068] In actual production applications, obtaining an accurate mathematical model of the control system is difficult due to difficult-to-estimate issues, or the actual problem is too complex to analyze. PID parameters need to be adjusted through extensive experimentation, and this adjustment process is complex and difficult to adapt to changes in the parameters of the controlled object. Therefore, using PID for adjustment is not ideal. Furthermore, BLDCM is a complex, nonlinear, time-varying system with strong coupling between its multiple variables, making it difficult to achieve high-precision control using traditional PID control. This is where the concepts of fuzzy control and adaptive control can be introduced to combine with PID control.

[0069] Fuzzy adaptive PID control is an intelligent and efficient regulation technology that performs well in brushless DC motor servo control systems. It has excellent regulation capabilities, can effectively improve the anti-interference ability of brushless DC motors, and can adaptively track and accurately control the dynamic characteristics of the system, thereby improving the stability and accuracy of the system and enabling the system to quickly and stably reach the set value. The input of the fuzzy adaptive PID controller is the speed error e and the error conversion rate ec, which can meet the requirements of e and ec for PID parameter self-tuning at any time. Therefore, it is essentially a second-order structure type fuzzy controller, and the output of the controller ΔK p ΔK i ΔK d are the correction values ​​after PID parameter adjustment.

[0070] 4. Use two-dimensional fuzzy control structure, use deviation and deviation change rate as input to achieve corresponding control, and use K in PID control p , K i , K d Adjustment amount, ΔK p ΔK i ΔK d is the output quantity.

[0071]

[0072] Where K p0 , K i0 , K d0 is the original PID parameter K p , K i , K d The initial value, plus the adjustment value ΔK p ΔK i ΔK d , and obtain the final coefficients.

[0073] Perform fuzzy processing on the input and output to determine ΔK p ΔK i ΔK d The error, error rate, and output of the domain are calculated, and the membership is obtained through the membership function. According to the control accuracy requirements of the brushless DC motor, the input error domain of the controller is set to [-3, 3], corresponding to the speed error of [-0.5, 0.5] in the actual debugging process, and the error rate domain is set to [-3, 3], corresponding to the speed error rate of [-0.5, 0.5] in the actual debugging process. The input and output membership functions form a symmetrically distributed triangular membership function. This membership function is simple and practical, and is suitable for online parameter adjustment. ΔK p ΔK i ΔK d The domains are [-0.3, 0.3], [-0.3, 0.3], and [-0.3, 0.3], corresponding to the actual domains during debugging: [-2, 2], [-0.2, 0.2], and [-0.045, 0.045]. The quantization factor and the proportional factor are determined based on the system's dynamic response. By leveraging the controller's output error and its changing trend, along with relevant knowledge and experience, fuzzy conditional statements can be established, and thus a fuzzy control rule table can be constructed.

[0074] After obtaining the fuzzy rule table, it is also necessary to defuzzify the membership output after fuzzy reasoning. The brushless DC motor servo control system based on fuzzy adaptive PID control uses the deviation of the two sampling values ​​obtained at any time to make inferences and judgments, and the inferred output is a fuzzy set. This output cannot be directly applied to system control. It is necessary to use a proportional factor to defuzzify the output and convert it into an output in the actual domain, and finally apply it to system control. The maximum membership method only considers the output membership function to obtain a value of the maximum membership, without comprehensively considering the shape of the entire output membership function, so it is easy to cause information loss. In order to defuzzify more accurately, after consideration, the present invention adopts the center of gravity method, and its expression is:

[0075]

[0076] In the above formula, ν0 represents the output value after defuzzification, v k Indicates the value within the fuzzy control input range; (μ v )v k v k After defuzzification, the accurate correction parameters of the PID controller can be obtained.

[0077] It is understood from common technical knowledge that the present invention may be implemented by other embodiments that do not depart from its spirit or essential features. Therefore, the embodiments disclosed above are, in all respects, merely illustrative and not exclusive. All modifications within the scope of the present invention or equivalent to the scope of the present invention are intended to be encompassed by the present invention.

Claims

1. A crawler control method for a substation oil sampling robot, characterized in that: The following steps are involved: S1: The actual speed of the brushless DC motor is collected in real time through a position sensor and compared with a given target speed to generate a speed error signal. S2: The speed error signal is input into the speed outer loop PI regulator to calculate the quadrature-axis current reference iq*. S3: The stator three-phase current is obtained through a three-phase stator winding current sensor, and the direct-axis current component id and the quadrature-axis current component iq are obtained through Clarke transform and Park transform. S4: Input the difference between the quadrature-axis current reference iq* and the quadrature-axis current component iq into the quadrature-axis current loop PI regulator to generate a quadrature-axis voltage reference uq; S5: Set the direct-axis current reference id* to 0, and input the difference between the direct-axis current component id into the direct-axis current loop PI regulator to generate a direct-axis voltage reference ud; S6: Convert the quadrature-axis voltage reference uq and the direct-axis voltage reference ud into an α-axis voltage uα and a β-axis voltage uβ through an inverse Park transform; S7: Input uα and uβ into the space vector modulation module to generate a PWM signal to drive the three-phase inverter and output the three-phase voltage to the stator winding of the brushless DC motor.

2. The crawler control method of a substation oil sampling robot according to claim 1, characterized in that: The method further includes step S8: dynamically adjusting the PI parameters of the quadrature-axis current loop and the direct-axis current loop by a fuzzy adaptive PID controller, wherein the input of the fuzzy adaptive PID controller is the speed error e and its change rate ec, and the output is the correction amount of the PID parameters.

3. The crawler control method of a substation oil sampling robot according to claim 1, characterized in that: The fuzzy adaptive PID controller adopts a two-dimensional structure. The input of the fuzzy adaptive PID controller is the speed error e and the error change rate ec, and the output is the correction amount of the proportional coefficient ΔKp, the integral coefficient ΔKi and the differential coefficient ΔKd, and satisfies the following relationship: The fuzzy control rule is based on the membership function of the error and the error change rate, and the membership function adopts a symmetrically distributed triangular function; The fuzzy inference results are defuzzified by the center of gravity method to generate continuous correction quantities, which are superimposed on the initial values ​​of the PID parameters.

4. The crawler control method of a substation oil sampling robot according to claim 1, characterized in that: The direct-axis current reference id* is set to 0, so that the brushless DC motor operates in a field-oriented control mode, achieving decoupling control of the electromagnetic torque and the direct-axis current.

5. The crawler control method of a substation oil sampling robot according to claim 1, characterized in that: The input of the speed outer loop PI regulator is the speed error signal, and the output is the quadrature axis current reference iq*, and iq is linearly related to the electromagnetic torque. By adjusting iq, closed-loop tracking of the motor speed is achieved.

6. The crawler control method of a substation oil sampling robot according to claim 3, characterized in that: The fuzzy control rules include the following logic: When the error e is positive and the error change rate ec is positive, a positive correction value of ΔKp is output; When the error e is negative and the error change rate ec approaches zero, a negative correction value of ΔKi is output to reduce the integral accumulation; The fuzzy control rules cover all possible combinations of errors and error change rates and are adjusted by weighted averaging continuous parameters.

7. The crawler control method of a substation oil sampling robot according to claim 1, characterized in that: The space vector modulation module converts the α-axis voltage uα and the β-axis voltage uβ into six PWM signals based on the voltage space vector pulse width modulation (SVPWM) algorithm, and drives the three-phase inverter to generate a sinusoidal equivalent voltage waveform.

8. The crawler control method of a substation oil sampling robot according to claim 1, characterized in that: When the quadrature-axis current reference iq* exceeds a preset threshold, its output range is limited by a limiting module to avoid control failure caused by integrator saturation.

9. A crawler control system for a substation oil sample collection robot, characterized in that: include: Position sensor, used to collect motor speed in real time; Three-phase current sensor, used to obtain the three-phase current of the stator; A processor, configured to execute a track control method for a substation oil sample collection robot according to any one of claims 1 to 8; The three-phase inverter drives the brushless DC motor according to the PWM signal.

10. The crawler control system of the oil sampling robot for a substation according to claim 9, characterized in that: The processor has a built-in memory for storing a fuzzy rule table, initial values ​​of PID parameters and a defuzzification algorithm, so as to support real-time online adjustment of control parameters.

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