Servo power control system and method for automatic driving

Abstract

The invention discloses a servo power control system and method for automatic driving, and the system comprises a temperature sensor which is used for collecting the real-time temperature of a road surface; the first data processor is connected with the temperature sensor and determines an expected vehicle distance according to the real-time temperature of the road surface; the positioning module is used for acquiring real-time position information; the second data processor is connected with the positioning module and the first data processor and determines expected position information according to the expected vehicle distance and the real-time position information; the position PI D controller is connected with the second data processor and is used for adjusting the position of the vehicle according to the expected position information; the angle sensor is used for acquiring a real-time steering angle; a servo motor; and the angle PI D controller is connected with the angle sensor and the servo motor and is used for controlling the servo motor according to the control instruction and the real-time steering angle. According to the method, the problems that the vehicle distance is influenced by temperature change and the motor back electromotive force and the load torque are disturbed in practical application are solved.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving technology, and in particular to a servo power control system and method for autonomous driving. Background Art

[0002] Although servo control in hot-in-place autopilot technology has many advantages, it also has some disadvantages, including:

[0003] 1) Complexity: The control of the autonomous driving servo system involves complex algorithms and software and hardware integration, which makes the design, debugging and maintenance of the system relatively complex. At present, some solutions also use extended state observers to observe load torque or back EMF disturbances. It should be said that the solution of using extended state observers to replace traditional disturbance observers to estimate back EMF or (and) load torque takes into account the influence of unmodeled dynamics and changes in transmission system parameters, and can estimate the changing back EMF or (and) load torque. However, to date, no solution has been found to use two extended state observers to estimate load torque and back EMF disturbances separately.

[0004] 2) Environmental adaptability: Environmental adaptability is crucial for autonomous driving servo systems, especially when the on-site thermal heating unit faces actual scenario problems. For example, large amounts of smoke on site and high temperatures in the core heater may challenge sensor performance. In this case, the system requires more robust and adaptable algorithms and sensors to ensure reliable operation under severe meteorological conditions. To address visual and thermal sensitivity challenges, the system needs to adopt highly adaptive algorithms, including optimizing image processing technology to deal with low visibility, and thermal image processing to overcome the impact of high temperature environments. In addition, dynamic adjustment of road conditions is also part of environmental adaptability, which requires advanced vehicle stability control and real-time road condition perception technology. In summary, in complex and changing environments, the servo system must have strong adaptability, especially in actual scenarios, to ensure the safety and reliability of the autonomous driving system. Summary of the invention

[0005] The purpose of the present invention is to provide a servo power control system and method for autonomous driving, which provides an innovative solution to the challenges in the field of precision industry, and is designed to solve the impact of real-time temperature changes on vehicle distance, as well as the back electromotive force and load torque disturbances of the motor at various speeds. The system uses a cascade control structure to efficiently coordinate the disturbance processing of the electrical and mechanical parts, providing accurate estimates and immediate control responses for these disturbances. The inner-loop control uses an extended state observer to quickly identify and compensate for electrical disturbances to ensure the sensitivity and accuracy of the motor response. The outer-loop control focuses on the changes in load torque, and realizes real-time adjustment and suppression of mechanical disturbances through advanced algorithms and sensor systems. The implementation of this strategy not only optimizes the dynamic performance of the system, but also ensures high stability and excellent performance under complex working conditions, bringing significant technological progress to the field of high-precision control.

[0006] In order to achieve the above purpose, the technical solutions adopted are as follows:

[0007] According to a first aspect of the present invention, a servo power control system for automatic driving is provided, the system comprising:

[0008] Temperature sensor, used to collect real-time road surface temperature;

[0009] a first data processor connected to the temperature sensor and configured to determine the desired vehicle distance according to the real-time temperature of the road surface;

[0010] Positioning module, used to obtain real-time location information;

[0011] a second data processor, connected to the positioning module and the first data processor, and configured to determine expected position information according to the expected vehicle distance and the real-time position information;

[0012] a position PID controller, connected to the second data processor, for adjusting the position of the vehicle according to the desired position information;

[0013] Angle sensor, used to obtain real-time steering angle;

[0014] Servo motor;

[0015] An angle PID controller is connected to the angle sensor and the servo motor, and is used to control the servo motor according to a control instruction and a real-time steering angle; wherein the control instruction includes a given angle.

[0016] Furthermore, the system also includes an extended state observer and a speed controller; wherein the extended state observer is connected to the speed controller, the speed controller is connected to the servo motor, the extended state observer is used to estimate the disturbance and feed the disturbance back to the speed controller, and the speed controller is used to control the speed of the servo motor according to the disturbance fed back by the extended state observer.

[0017] Furthermore, the position PID controller takes the error and error change rate of the input desired position information and real-time position information as input, performs fuzzy reasoning to obtain a first fuzzy output, and defuzzifies the first fuzzy output to obtain parameters for real-time adjustment of the position PID controller.

[0018] Furthermore, the angle PID controller takes the error and error change rate between the given angle and the real-time steering angle as input, performs fuzzy reasoning to obtain a second fuzzy output, and defuzzifies the second fuzzy output to obtain parameters for real-time adjustment of the angle PID controller.

[0019] Furthermore, the first data processor is used to determine the expected vehicle distance corresponding to the real-time temperature of the road surface collected by the current temperature sensor according to a preset real-time temperature-expected vehicle distance curve.

[0020] Furthermore, the first data processor is used for:

[0021] Obtaining an expected temperature; wherein the expected temperature is the real-time temperature of the road surface collected by the temperature sensor at the last moment;

[0022] Determine the temperature change rate based on the expected temperature and the current road surface real-time temperature;

[0023] A desired vehicle distance is determined based on the rate of temperature change.

[0024] According to a second aspect of the present invention, a servo power control method for autonomous driving is provided, the method comprising:

[0025] Determine the expected vehicle distance based on the real-time road surface temperature;

[0026] Determine expected position information according to the expected vehicle distance and the real-time position information;

[0027] adjusting the position of the vehicle according to the desired position information;

[0028] The servo motor is controlled according to the control instruction and the real-time steering angle; wherein the control instruction includes a given angle.

[0029] Furthermore, the method further comprises:

[0030] An extended state observer and a speed controller are provided; wherein the extended state observer is connected to the speed controller, the speed controller is connected to the servo motor, the extended state observer is used to estimate the disturbance amount and feed the disturbance amount back to the speed controller, and the speed controller is used to control the speed of the servo motor according to the disturbance amount fed back by the extended state observer.

[0031] Furthermore, the expected vehicle distance corresponding to the current real-time road surface temperature is determined according to a preset real-time temperature-expected vehicle distance curve.

[0032] Furthermore, the expected vehicle distance is determined according to the real-time road surface temperature, including:

[0033] Obtaining an expected temperature; wherein the expected temperature is the real-time temperature of the road surface collected by the temperature sensor at the last moment;

[0034] Determine the temperature change rate based on the expected temperature and the current road surface real-time temperature;

[0035] A desired vehicle distance is determined based on the rate of temperature change.

[0036] The beneficial effects of the present invention are:

[0037] 1. The present invention focuses on solving the problem of the real-time temperature change of the road surface affecting the vehicle distance, as well as the back-EMF disturbance and load torque disturbance generated by the motor at different speeds, and proposes an innovative automatic control system and servo control strategy. The system effectively responds to the back-EMF disturbance of the motor electrical part and the torque disturbance caused by load changes in the mechanical part through a cleverly designed inner and outer loop control mechanism.

[0038] 2. In terms of inner-loop control, the present invention adopts an efficient control strategy to quickly eliminate the back-EMF disturbance of the motor's electrical part. The key lies in the introduction of a servo control scheme based on an extended state observer, which has excellent tracking and response characteristics. By continuously monitoring the state of the motor's electrical part, the extended state observer can accurately estimate the electrical disturbance, allowing the system to make adjustments quickly and accurately to maintain the stability and performance of the system. This high-speed response control scheme provides a powerful tool for inner-loop control, enabling the system to perform well in the face of disturbances in the electrical part and ensure that the system operates in the best state.

[0039] 3. The system focuses on solving the torque disturbance of the motor mechanical part caused by load and load changes in the outer loop control. The realization of this goal mainly depends on the system's carefully designed control algorithm and sensitive sensor feedback. Through the clever use of intelligent control algorithms, the system can quickly and accurately sense load changes and make timely adjustments under the guidance of the feedback system to maintain effective control of mechanical disturbances. This outer loop control strategy not only ensures flexible response to load changes, but also effectively reduces the adverse effects of mechanical disturbances on system performance. Therefore, the system's clever design in outer loop control provides an efficient and reliable solution for dealing with load-related disturbances, further improving the stability and performance of the entire servo system. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The structure of the servo power control system for automatic driving according to an embodiment of the present invention is shown. Figure 1 .

[0041] Figure 2 A working principle diagram of a servo power control system for autonomous driving according to an embodiment of the present invention is shown.

[0042] Figure 3 The structure of the servo power control system for automatic driving according to an embodiment of the present invention is shown. Figure 2 .

[0043] Figure 4 A flow chart of a servo power control method for autonomous driving according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0044] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0045] The specific implementation of the present invention is further described in detail below in conjunction with the drawings and examples.

[0046] The embodiment of the present invention provides a servo power control system for autonomous driving, such as Figure 1 As shown, the system includes a temperature sensor 10 , a first data processor 20 , a positioning module 30 , a second data processor 40 , a position PID controller 50 , an angle sensor 60 , a servo motor 70 and an angle PID controller 80 .

[0047] The temperature sensor 10 is used to collect the real-time temperature of the road surface. Taking the automatic driving of the vehicle as an example, the temperature sensor 10 can use a non-contact temperature sensor, which can be installed at the corresponding position of the vehicle. The real-time temperature of the road surface collected by the temperature sensor is the temperature of the road surface that the vehicle is about to pass in the direction of travel.

[0048] The first data processor 20 is connected to the temperature sensor 10 and is used to determine the expected vehicle distance according to the real-time temperature of the road surface.

[0049] At present, the expected distance is mainly determined by the braking distance. The expected distance refers to the distance between the vehicle and the target. In most autonomous driving scenarios, the target is a vehicle, pedestrian or other obstacle in the direction of the vehicle. Other obstacles usually include buildings, cones, etc. The setting of the expected distance is to ensure that the distance between the vehicle and the target is maintained within a safe distance range. The expected distance is usually determined by the braking distance of the vehicle.

[0050] The formula for vehicle braking distance is as follows:

[0051]

[0052] Where s is the braking distance; τ' 2 is the braking reaction time, τ' 2 ' is the time required for the brake force to increase, μ a0 is the initial braking velocity, a bmax is the maximum deceleration.

[0053] According to the vehicle braking distance formula, the main factors affecting the braking distance of the car are: the action time of the brake, the maximum braking deceleration, i.e. adhesion, and the initial braking speed. For the same model, the same test operation method, and a given initial braking speed, the greater the adhesion, the shorter the braking distance. Adhesion refers to the limit value of the tangential reaction force of the ground on the tire, and on hard roads it is proportional to the normal reaction force of the drive wheel.

[0054] In autonomous driving control, when calculating the expected vehicle distance and considering the adhesion, only the tire type, pattern, tire pressure, tire wear, wheel load and road friction are often considered, and the impact of the real-time road temperature on the braking distance is not considered. However, the road surface temperature is directly related to the performance of some materials and the change of the road surface water phase, so its impact on the adhesion coefficient is also more obvious. For example, when an asphalt road surface is exposed to sunlight during the hot summer, the structural layer will soften, causing the adhesion between the wheel and the road surface to be greatly reduced.

[0055] Therefore, this embodiment takes the real-time road surface temperature into consideration when calculating the expected vehicle distance, thereby improving the calculation accuracy of the expected vehicle distance.

[0056] In some embodiments, the first data processor 20 is used to determine the expected vehicle distance corresponding to the real-time road surface temperature collected by the current temperature sensor according to a preset real-time temperature-expected vehicle distance curve.

[0057] In this embodiment, the preset real-time temperature-expected vehicle distance curve is a curve determined by experiments in advance. The curve takes the real-time temperature as the x-axis and the expected vehicle distance as the y-axis, and each real-time temperature corresponds to an expected vehicle distance. Specifically, different working conditions are set. For example, for a certain type of vehicle, the vehicle is uniformly equipped with a fixed type of tire. The main factors affecting the expected vehicle distance are also load and real-time temperature. The different working conditions set are different loads, such as load 1, load 2, load 3...load n. Each load corresponds to a weight threshold. For example, the range of load 1 is a to b. According to the current vehicle load, if the current vehicle load is at load n, the preset real-time temperature-expected vehicle distance curve corresponding to load n is extracted. According to the real-time temperature of the road surface, the corresponding expected vehicle distance can be found from the preset real-time temperature-expected vehicle distance curve. Of course, when setting different working conditions, in addition to considering the influence of load factors, road surface influencing factors can also be added. Road surface influencing factors include road surface type, such as asphalt road surface / cement road corresponding to different loads as different working conditions. During the vehicle's automatic driving process, the type of the current road surface can be determined based on the road image collected by the camera. When the road surface type has been determined, the real-time temperature-expected vehicle distance curve corresponding to the current driving condition is extracted according to the load situation, and the expected vehicle distance is determined in combination with the real-time road surface temperature. In this way, the influence of temperature can be incorporated into the calculation of the expected vehicle distance.

[0058] In some embodiments, the first data processor is used to: obtain an expected temperature; wherein the expected temperature is the real-time temperature of the road surface collected by the temperature sensor at the previous moment; determine the temperature change rate based on the expected temperature and the real-time temperature of the road surface at the current moment; and determine the expected vehicle distance based on the temperature change rate.

[0059] In this embodiment, the inventors have found through experiments that under different load conditions, the real-time temperature-expected vehicle distance curve has no linear relationship as a whole, but if it is refined to certain temperature ranges, the temperature change rate remains basically unchanged. Therefore, the expected vehicle distance can be determined by the temperature change rate. As an example only, take 5°C as a temperature range, from 5°C to 70°C, specifically: [5,10)...[20,25), [25,30)...[65,70) a total of 13 temperature ranges are set, and the temperature change rate in each temperature range is pre-calculated by linear fitting to obtain a specific slope value. In the case that the expected vehicle distance has been calculated at the previous moment, the temperature range is determined according to the real-time road surface temperature and the expected temperature at the current moment, and the slope value corresponding to the temperature range is used as the temperature change rate. The expected vehicle distance at the previous moment is multiplied by the product of the temperature change rate and the expected vehicle distance at the previous moment, and then added to the expected vehicle distance at the previous moment to obtain the expected vehicle distance at the current moment. Among them, if the real-time road surface temperature and the expected temperature at the previous moment are in two temperature intervals, for example, the real-time road surface temperature at the current moment is 26°C, and the real-time road surface temperature at the previous moment is 24.5°C, which is just in the two temperature intervals of [20,25) and [25,30), then two slope values ​​are extracted at this time, such as slope value k1 and slope value k2, and the current expected vehicle distance s t =(s t-1 +s t-1 *k1)*(1+k2). Among them, s t-1 is the expected vehicle distance at the previous moment.

[0060] The positioning module 30 is used to obtain real-time position information.

[0061] In this embodiment, the positioning module 30 is an existing module unit, for example, it can be a GPS positioning module, a Beidou positioning module, etc., which is not specifically limited in this embodiment.

[0062] The second data processor 40 is connected to the positioning module 30 and the first data processor 20, and is used to determine the expected position information according to the expected vehicle distance and the real-time position information.

[0063] For example, taking the following vehicle scenario as an example, the current vehicle and the preceding vehicle are traveling in the same lane. Assuming that the coordinates of the preceding vehicle are (x, y), when determining the expected vehicle distance s t After that, the expected position information is the target position that the current vehicle wants to reach, which is (x+s t , y). The current position of the vehicle is assumed to be (x0, y). If x0>x+s t , it means that the following car is far away, you can speed up appropriately to follow the front car. t <x+s t , it means that the car you are following is too close and you should slow down appropriately to keep a safe distance.

[0064] The position PID controller 50 is connected to the second data processor 40 and is used to adjust the position of the vehicle according to the desired position information.

[0065] In some embodiments, the position PID controller 50 takes the error and error change rate of the input desired position information and real-time position information as input, performs fuzzy inference to obtain a first fuzzy output, and defuzzifies the first fuzzy output to obtain parameters for real-time adjustment of the position PID controller.

[0066] The angle sensor 60 is used to obtain the real-time steering angle.

[0067] In this embodiment, the angle sensor 60 is a sensor installed on the steering wheel, which is used to detect the real-time steering angle. In the automatic driving scenario, the real-time steering angle of the vehicle needs to be controlled in some vehicle turning scenarios, such as driving on a curve, changing lanes to overtake, etc.

[0068] The servo motor 70 refers to a motor used to provide steering assistance. In the turning control of the vehicle, how to achieve precise control of the servo motor 70 is the key to achieving precise control of the turning angle of the vehicle.

[0069] In this embodiment, an angle PID controller 80 is provided to be connected to the angle sensor 60 and the servo motor 70, and is used to control the servo motor 70 according to a control instruction and a real-time steering angle; wherein the control instruction includes a given angle.

[0070] It should be noted that the issuance of control instructions can be implemented based on currently existing or future autonomous driving algorithms. Existing autonomous driving algorithms include autonomous driving control solutions based on vision, radar, and a combination of the two. The purpose of the present invention is to ensure that the servo motor 70 can accurately execute the control instructions containing the given angle when the issued control instructions include a given angle. The given angle is the steering angle that the vehicle is about to make, and the steering angle is controlled by the servo motor 70.

[0071] In some embodiments, the angle PID controller 80 takes the error and error change rate between the given angle and the real-time steering angle as input, performs fuzzy inference to obtain a second fuzzy output, and defuzzifies the second fuzzy output to obtain parameters for real-time adjustment of the angle PID controller.

[0072] PID control is one of the earliest developed control strategies. It controls the system error by setting three parameters: proportional (P), integral (I) and differential (D). PID control is suitable for systems that are basically linear and whose dynamic characteristics do not change with time. It has the advantages of simple principle, strong robustness and wide applicability, and is widely used in industrial process control. The PID controller compares the error between the actual output of the system and the expected output, and then calculates the control quantity based on the size, duration and rate of change of the error. The proportional part (P) adjusts the output proportionally according to the size of the error to reduce the error; the integral part (I) eliminates the steady-state error of the system; the differential part (D) reflects the changing trend of the error and adjusts the error in advance.

[0073] It should be noted that both the position PID controller and the angle PID controller are based on the PID control principle to achieve control of position and angle.

[0074] In some embodiments, Figure 2 and Figure 3 As shown, the system also includes an extended state observer 90 and a speed controller 100; wherein the extended state observer 90 is connected to the speed controller 100, the speed controller 100 is connected to the servo motor 70, the extended state observer 90 is used to estimate the disturbance amount and feed the disturbance amount back to the speed controller 100, and the speed controller 100 is used to control the speed of the servo motor 70 according to the disturbance amount fed back by the extended state observer 90.

[0075] Exemplarily, the angular velocity estimation of the servo motor 70 by the extended state observer 90 is expressed by the following formula:

[0076]

[0077] In the formula, G, M and O represent the internal parameters of the servo motor, ΔG, ΔM and ΔO represent the changes of the internal parameters of the servo motor, ω m is the mechanical angular velocity of the servo motor, i q is the q-axis current component of the servo motor, is the q-axis reference current of the servo motor; p n is the number of magnetic pole pairs of the servo motor, is the permanent magnet flux of the servo motor, J is the moment of inertia, and B is the viscous friction coefficient.

[0078] The disturbance can be divided into internal disturbance caused by changes in parameters such as pole pairs, flux size and moment of inertia, and external disturbance caused by changes in factors such as current components, load size and motor speed. The total internal and external disturbance d can be expressed as:

[0079]

[0080] Then, the angular velocity estimation of the servo motor by the extended state observer can be simplified as:

[0081]

[0082] Taking the total internal and external disturbance d as the state of the extended state observer and setting a constant H, we can get in Represents the total disturbance estimate for the system.

[0083] The angular velocity estimation of the servo motor by the extended state observer is further expressed as:

[0084]

[0085] In the formula, z 1 =ω m , z 2 =d.

[0086] According to the above, we can estimate the total disturbance of the system Transformed into the extended state observer state z 2 .

[0087] Then the extended state observer is designed as:

[0088]

[0089] In the formula, e 1 is the speed estimation error, Used to estimate the speed feedback value of the servo motor, is the load torque disturbance estimate, p 1 、p 2 is a positive integer and is the design parameter of the extended state observer.

[0090] The control mechanism of the speed loop is to find the best output signal from the controller to ensure that the motor speed ω(t) quickly tracks the given speed ω * (t). Given speed ω * (t) can be determined by a given angle. ω(t) and ω * The error between (t) 1 (t) is given by:

[0091] e 1 (t) = ω * (t)-ω(t)

[0092] The speed controller uses a fractional-order controller. After compensating the disturbance observed by the extended state observer into the speed controller, the control law of the speed controller can be obtained as follows:

[0093]

[0094] In the formula, k 1 , k 2 , k 3 , k 4 , k 5 , k 6 are the setting parameters of the speed controller, D is the fractional calculus operator; λ, β, χ are the parameters of the fractional calculus operator. On the basis of adjusting the sliding surface and reaching law parameters, the controller is fine-tuned within the decimal range from 1 to 0; s(t) is the fractional sliding surface function; μ is a parameter greater than 0, which is used to coordinate k 1 , k 2 , k 3 Control input singularity; k 4 , k 5 , k 6 , κ is the gain parameter of the superhelical reaching law.

[0095] The fractional-order sliding surface function s(t) is expressed as:

[0096] s(t)=k 1 e 1 (t)+k 2 D λ-1 e 1 (t)+k 3 D β-1 |e 1 (t)| μ sgn(e 1 (t))

[0097] The core concept of this invention is to take into full account the different time scales of disturbances in the electrical and mechanical parts, and design corresponding disturbance estimation and control schemes for them. This innovative cascade system structure enables the system to more efficiently cope with disturbances at different time scales, thereby improving the stability and performance of the entire system.

[0098] In summary, the present invention not only solves the problems faced in practical applications such as vehicle distance being affected by temperature changes, motor back electromotive force and load torque disturbances, but also constructs a new servo system by introducing advanced control strategies, bringing significant technological innovation to the field of automatic control.

[0099] The embodiment of the present invention also provides a servo power control method for automatic driving, such as Figure 4 As shown, the method comprises the following steps:

[0100] S1. Determine the expected vehicle distance according to the real-time road surface temperature;

[0101] S2. Determine expected position information according to the expected vehicle distance and the real-time position information;

[0102] S3, adjusting the position of the vehicle according to the expected position information;

[0103] S4. Control the servo motor according to the control instruction and the real-time steering angle; wherein the control instruction includes a given angle.

[0104] In some embodiments, the method also includes: setting an extended state observer and a speed controller; wherein the extended state observer is connected to the speed controller, the speed controller is connected to the servo motor, the extended state observer is used to estimate the disturbance and feed the disturbance back to the speed controller, and the speed controller is used to control the speed of the servo motor according to the disturbance fed back by the expanded state observer.

[0105] In some embodiments, the expected vehicle distance corresponding to the current real-time road surface temperature is determined according to a preset real-time temperature-expected vehicle distance curve.

[0106] Furthermore, the expected vehicle distance corresponding to the current real-time road surface temperature is determined according to a preset real-time temperature-expected vehicle distance curve, including: obtaining the expected temperature; wherein the expected temperature is the real-time road surface temperature collected by the temperature sensor at the previous moment; determining the temperature change rate according to the expected temperature and the real-time road surface temperature at the current moment; and determining the expected vehicle distance based on the temperature change rate.

[0107] It should be noted that the method proposed in this embodiment and the system described previously belong to the same technical concept, have the same technical principles and can achieve the same beneficial effects, which will not be repeated here.

[0108] The above implementation modes are only used to illustrate the present invention, but not to limit the present invention. Ordinary technicians in the relevant technical field can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all equivalent technical solutions also belong to the scope of the present invention. The patent protection scope of the present invention should be defined by the claims.

Claims

1. A servo power control system for autonomous driving, characterized in that: The system comprises: Temperature sensor, used to collect real-time road surface temperature; A first data processor, connected to the temperature sensor, for determining the expected vehicle distance according to the real-time temperature of the road surface; Positioning module, used to obtain real-time location information; a second data processor, connected to the positioning module and the first data processor, and configured to determine expected position information according to the expected vehicle distance and the real-time position information; a position PID controller, connected to the second data processor, and configured to adjust the position of the vehicle according to the desired position information; Angle sensor, used to obtain real-time steering angle; Servo motor; An angle PID controller is connected to the angle sensor and the servo motor, and is used to control the servo motor according to a control instruction and a real-time steering angle; wherein the control instruction includes a given angle.

2. The servo power control system for automatic driving according to claim 1, characterized in that: The system also includes an extended state observer and a speed controller; wherein the extended state observer is connected to the speed controller, and the speed controller is connected to the servo motor, the extended state observer is used to estimate the disturbance amount and feed the disturbance amount back to the speed controller, and the speed controller is used to control the speed of the servo motor according to the disturbance amount fed back by the extended state observer.

3. The servo power control system for automatic driving according to claim 1, characterized in that: The position PID controller takes the error and error change rate of the input expected position information and real-time position information as input, performs fuzzy reasoning to obtain a first fuzzy output, and defuzzifies the first fuzzy output to obtain parameters for real-time adjustment of the position PID controller.

4. The servo power control system for automatic driving according to claim 1, characterized in that: The angle PID controller takes the error and error change rate between the given angle and the real-time steering angle as input, performs fuzzy reasoning to obtain a second fuzzy output, and defuzzifies the second fuzzy output to obtain parameters for real-time adjustment of the angle PID controller.

5. The servo power control system for automatic driving according to claim 1, characterized in that: The first data processor is used to determine the expected vehicle distance corresponding to the real-time road surface temperature collected by the current temperature sensor according to a preset real-time temperature-expected vehicle distance curve.

6. The servo power control system for automatic driving according to claim 1, characterized in that: The first data processor is used for: Obtaining an expected temperature; wherein the expected temperature is the real-time temperature of the road surface collected by the temperature sensor at the last moment; Determine the temperature change rate based on the expected temperature and the current road surface real-time temperature; A desired vehicle distance is determined based on the rate of temperature change.

7. A servo power control method for autonomous driving, characterized in that: The method comprises: Determine the expected vehicle distance based on the real-time road surface temperature; Determine expected position information according to the expected vehicle distance and the real-time position information; adjusting the position of the vehicle according to the desired position information; The servo motor is controlled according to the control instruction and the real-time steering angle; wherein the control instruction includes a given angle.

8. The servo power control method for automatic driving according to claim 7, characterized in that: The method further comprises: An extended state observer and a speed controller are provided; wherein the extended state observer is connected to the speed controller, the speed controller is connected to the servo motor, the extended state observer is used to estimate the disturbance amount and feed the disturbance amount back to the speed controller, and the speed controller is used to control the speed of the servo motor according to the disturbance amount fed back by the extended state observer.

9. The servo power control method for automatic driving according to claim 7, characterized in that: The expected vehicle distance corresponding to the current real-time road temperature is determined according to a preset real-time temperature-expected vehicle distance curve.

10. The servo power control method for automatic driving according to claim 7, characterized in that: Determine the expected vehicle distance based on the real-time road surface temperature, including: Obtaining an expected temperature; wherein the expected temperature is the real-time temperature of the road surface collected by the temperature sensor at the last moment; Determine the temperature change rate based on the expected temperature and the current road surface real-time temperature; A desired vehicle distance is determined based on the rate of temperature change.

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