Longitudinal PID control method and device of mine car, electronic equipment and storage medium

Abstract

The invention provides a longitudinal PID control method and device for a mine car, electronic equipment and a storage medium. The method comprises the steps that a target sliding time window corresponding to the current moment of the mine car is determined; wherein the ending moment of the target sliding time window is the current moment, and the length of the target sliding time window is a fixed value. Acquiring a driving state error of at least one acquisition moment in the target sliding time window; wherein the running state error at each acquisition moment is the error between the running state of the mine car at the acquisition moment and the corresponding target running state. And performing accumulation processing on the running state error at the at least one acquisition moment to obtain an accumulation error, wherein the accumulation error is used for generating a control signal for adjusting the running state of the mine car at the current moment.

Description

Technical Field

[0001] This specification relates to the field of intelligent driving technology, and particularly to a longitudinal PID control method, device, electronic device, and storage medium for mining trucks. Background Art

[0002] In the mining operations of open-pit mines, the longitudinal motion control of mining trucks is a crucial link, which is directly related to the operating efficiency, safety, and ore mining cost of mining trucks. The environment of open-pit mines is relatively complex, such as having steep slopes, unstructured road surfaces, and many obstacles, etc., all of which will affect the longitudinal motion control of mining trucks. When mining trucks drive on such roads, they require higher stability and maneuverability compared to ordinary household vehicles.

[0003] The PID control method is widely used in the motion control system of vehicles. However, in the integral link of the traditional PID control method, when running for a long time or facing complex working conditions, all historical errors will be accumulated, resulting in the phenomenon of integral saturation. Integral saturation will reduce the dynamic response ability of the control system, and even cause system instability problems, making it difficult for the system to adjust in time to adapt to the changes in the operating state of mining trucks in complex environments. This not only affects the operating efficiency of mining trucks, but also may pose a threat to the safety and stability of mining trucks. Therefore, the traditional PID control method is not directly applicable to mining trucks in complex environments. Summary of the Invention

[0004] To overcome the problems existing in the related art, this specification provides a longitudinal PID control method, device, electronic device, and storage medium for mining trucks.

[0005] According to the first aspect of the embodiments of this specification, a longitudinal PID control method for a mining truck is provided, and the method includes:

[0006] Determine the target sliding time window corresponding to the mining truck at the current moment; wherein, the end moment of the target sliding time window is the current moment and the length of the target sliding time window is a fixed value;

[0007] Obtain the driving state error at at least one acquisition moment within the target sliding time window; wherein, the driving state error at each acquisition moment is the error between the driving state of the mining truck at this acquisition moment and the corresponding target driving state;

[0008] Perform cumulative processing on the driving state errors at the at least one acquisition moment to obtain a cumulative error, and the cumulative error is used to generate a control signal for adjusting the driving state of the mining truck at the current moment.

[0009] According to the second aspect of the embodiments of this specification, a longitudinal PID control device for a mining truck is provided, including:

[0010] A target sliding time window determination module, configured to determine a target sliding time window corresponding to the mine car at the current moment; wherein, the end moment of the target sliding time window is the current moment and the length of the target sliding time window is a fixed value;

[0011] A driving state error acquisition module, configured to acquire driving state errors at at least one acquisition moment within the target sliding time window; wherein, the driving state error at each acquisition moment is the error between the driving state of the mine car at this acquisition moment and the corresponding target driving state;

[0012] An accumulated error calculation module, configured to perform an accumulation process on the driving state errors at the at least one acquisition moment to obtain an accumulated error, and the accumulated error is used to generate a control signal for adjusting the driving state of the mine car at the current moment.

[0013] According to a third aspect of the embodiments of the present specification, an electronic device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the method described in the first aspect are implemented.

[0014] According to a fourth aspect of the embodiments of the present specification, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0015] The technical solutions provided by the embodiments of the present specification may include the following beneficial effects:

[0016] In the embodiments of the present specification, when generating a control signal for adjusting the driving state of the mine car at the current moment, the driving state errors at at least one acquisition moment within the target sliding time window corresponding to the mine car at the current moment can be used for generation. Since the length of the sliding time window is a fixed value and the end moment of the target sliding time window corresponding to the current moment is the current moment, each time when generating the control signal, the driving state errors within a fixed time window near the current moment are intercepted, and only the driving state errors at at least one moment within this time window are accumulated, while the driving state errors outside this fixed time window do not participate in the accumulation calculation, which can avoid the integral saturation caused by accumulating all historical errors in the traditional PID control algorithm during long-term operation, thereby improving the response ability of the control system to stably and accurately adapt to the complex environment where the mine car is located.

[0017] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present specification. Description of the Drawings

[0018] The accompanying drawings herein are incorporated into and constitute a part of this specification, showing embodiments consistent with this specification, and are used together with the specification to explain the principles of this specification.

[0019] Figure 1 It is a flowchart of a longitudinal PID control method for a mine car shown according to an exemplary embodiment of this specification.

[0020] Figure 2 It is a schematic diagram of a sliding time window shown according to an exemplary embodiment of this specification.

[0021] Figure 3 It is a flowchart of another longitudinal PID control method for a mine car shown according to an exemplary embodiment of this specification.

[0022] Figure 4 It is a schematic structural diagram of an electronic device shown according to an exemplary embodiment of this specification.

[0023] Figure 5 It is a block diagram of a longitudinal PID control device for a mine car shown according to an exemplary embodiment of this specification. Detailed implementation manners

[0024] In the mining operations of open-pit mines, the longitudinal motion control of mine cars is a crucial link, which is directly related to the operating efficiency, safety of mine cars, and the mining cost of ores. The environment of open-pit mines is relatively complex, such as having steep slopes, unstructured road surfaces, and many obstacles, etc., all of which will affect the longitudinal motion control of mine cars. When a mine car travels on such roads, compared with ordinary household vehicles, it requires higher stability and maneuverability.

[0025] The PID control method is widely used in the motion control systems of vehicles. PID refers to proportion, integral, and derivative. The vehicle uses the control quantity generated by performing proportional, integral, and differential operations on the error between the driving state fed back inside the system or from the outside and the planned target driving state to adjust the longitudinal motion control of the vehicle.

[0026] However, in the integral link of the traditional PID control method, when running for a long time or facing complex working conditions, all historical errors will be accumulated, resulting in the phenomenon of integral saturation. Integral saturation will reduce the dynamic response ability of the control system and even cause system instability problems, making it difficult for the system to adjust in time to adapt to the changes in the operating state of the mine car in a complex environment. This not only affects the operating efficiency of the mine car but also may pose a threat to the safety and stability of the mine car. Therefore, the traditional PID control method is not directly applicable to mine cars in complex environments.

[0027] To address the above technical problems, this specification provides a longitudinal PID control method for a mine car. By improving the integral link in the longitudinal PID control method, it avoids the integral saturation caused by the accumulation of all historical errors in the traditional PID control algorithm during long-term operation, thereby improving the response ability of the control system to stably and accurately adapt to the complex environment where the mine car is located.

[0028] Next, the embodiments of this specification will be described in detail.

[0029] Figure 1 It is a flowchart of a longitudinal PID control method for a mine car shown according to an exemplary embodiment of this specification. As Figure 1 shown, it includes steps 101 - 103:

[0030] Step 101: Determine the target sliding time window corresponding to the mine car at the current moment; wherein, the end moment of the target sliding time window is the current moment and the length of the target sliding time window is a fixed value.

[0031] As Figure 2 shown, ti is the i-th acquisition moment of the mine car, i = 1, 2,..., 10, and the driving state data of the vehicle is acquired at each acquisition moment. Assume the current moment is t4, then the target sliding time window corresponding to the mine car at the current moment is t0 - t4. Assume the current moment is t6, then the target sliding time window corresponding to the mine car at the current moment is t2 - t6. Similarly, assume the current moment is t9, then the target sliding time window corresponding to the mine car at the current moment is t5 - t9.

[0032] Among them, the length of the sliding time window is a fixed value. For example, in Figure 2 it is 4, indicating the four acquisition moments closest to the current moment (including the current moment).

[0033] Assume the current moment is t9. The integral link of the traditional PID control method may utilize the historical driving state errors of all moments from t0 to t9, which will lead to the phenomenon of integral saturation. Integral saturation will reduce the dynamic response ability of the control system, making it difficult for the system to adjust in time to adapt to the changes in the operating state of the mine car.

[0034] In this solution, by setting the sliding time window, when the mine car needs to adjust the driving state of the vehicle at the current moment, it can use the driving state errors within the sliding time window for the cumulative processing of the integral link, and the driving state errors beyond the sliding time window do not participate in the cumulative processing of the integral link, thereby effectively avoiding the problem of easy integral saturation and improving the control accuracy and stability of the system.

[0035] It should be noted that the present solution does not limit the sliding step of the sliding time window. The sliding step can be a variable value or a fixed value, and this specification does not impose any restrictions on the sliding step. For example Figure 2 the first sliding time window in Figure 2 is t0 - t4, the next sliding time window is t2 - t6, and the sliding step between them is 2. And the last sliding time window is t5 - t9, and the sliding step with the previous sliding time window is 3. It can be seen that in

[0036] In one embodiment, in the operation scenario of a mine car, the load of the mine car fluctuates greatly, and the difference between no-load and full-load can reach several times. It is difficult for the traditional PID control method to quickly compensate for the inertial delay caused by load changes. For example, the braking distance when fully loaded is several times longer than that when unloaded. However, ignoring these factors will lead to overshoot or response lag of the control system. Another example is that the road conditions in the operation environment where the mine car is located are complex and there are many external interference factors. The above factors will all cause significant changes in the motion state of the mine car, resulting in large error fluctuations. Due to its fixed control parameters and integral strategy, the traditional PID control method is difficult to quickly adapt to these changes, resulting in poor control effects. This not only reduces the operation efficiency of the mine car, but also may increase the wear and failure rate of the equipment.

[0037] In response to this, the present solution further sets sliding time windows of different lengths for different motion states of the mine car, and the length of each sliding time window is negatively correlated with the change rate of the corresponding motion state. Among them, the higher the change rate of the motion state of the mine car, the more complex the operation environment conditions where the vehicle is located or the more frequent the changes in the vehicle's own working conditions, and it is necessary to improve the quick response ability of the control system of the mine car. In this embodiment, by setting a shorter sliding time window for the motion state with a higher change rate, the control system focuses more on the recent driving state error, thereby improving the quick response ability of the control system. And in a stable motion state, the length of the time window is extended to improve the smoothness and stability of the control. Ultimately, the adaptability and robustness of the control system in complex working conditions can be improved.

[0038] Exemplarily, the target motion state of the mine car at the current moment can be determined. When determining the target sliding time window corresponding to the mine car at the current moment, the target sliding time window corresponding to the mine car at the current moment and the target motion state can be determined. Among them, different motion states correspond to different sliding time windows, and the length of each sliding time window is negatively correlated with the change rate of the corresponding motion state.

[0039] The motion state of the mine car can be represented by the operating conditions of the mine car, such as the driving speed of the mine car, the load of the mine car, the steering frequency, and the braking frequency. For example, different load ranges of the mine car correspond to different motion states; the steering frequency or braking frequency of the mine car within a certain time interval can also correspond to different motion states; different motion states can also be corresponded by dividing the start-stop stage and the uniform driving stage of the mine car. Of course, the motion state of the mine car can also be determined according to the external operating environment where the mine car is located. For example, the complexity of the external environment can be determined by the external environment data detected by the on-vehicle sensors of the mine car, and different environments correspond to different motion states.

[0040] In one embodiment, the driving speed of the mine car can more significantly reflect the motion state of the mine car. For example, the faster the driving speed of the mine car, the flatter the road surface where the mine car is located, and the lower the braking frequency or steering frequency of the mine car. At this time, the motion state of the mine car is more stable, and the length of the sliding time window can be extended to improve the smoothness and stability of control. On the contrary, the slower the driving speed of the mine car, the more it indicates that the vehicle is in the start-stop stage, the road conditions are complex, the load changes greatly, and the steering frequency or braking frequency is higher. At this time, the change rate of the motion state of the vehicle is higher, and the length of the sliding time window can be shortened to quickly respond to the error change and enhance the dynamic response performance of the vehicle.

[0041] Specifically, the motion state may include the driving speed. When determining the target sliding time window corresponding to the target motion state of the mine car at the current moment, when the target driving speed of the mine car at the current moment is not less than the first speed threshold, determine the first sliding time window corresponding to the target driving speed of the mine car at the current moment; when the target driving speed of the mine car at the current moment is less than the first speed threshold, determine the second sliding time window corresponding to the target driving speed of the mine car at the current moment. Among them, the length of the first sliding time window is greater than the length of the second sliding time window.

[0042] In this embodiment, since the speed value is relatively easy to obtain and can simultaneously reflect the motion state of the vehicle to the greatest extent, determining the motion state of the vehicle based on the magnitude of the driving speed of the vehicle and dividing sliding time windows of different lengths can further reduce the complexity of technical implementation on the basis of improving the adaptability and robustness of the control system in complex working conditions.

[0043] Step 102: Obtain the driving state error at at least one acquisition moment within the target sliding time window; wherein, the driving state error at each acquisition moment is the error between the driving state of the mine car at that acquisition moment and the corresponding target driving state.

[0044] Exemplarily, the driving state of the mine car at each acquisition moment can be represented by the position information of the mine car at that acquisition moment and the speed information of the mine car. The error between the driving state of the mine car at the acquisition moment and the corresponding target driving state may include the error between the actual position information and the target position information of the mine car at that acquisition moment and the error between the actual speed information and the target speed information. Of course, in addition to representing the longitudinal driving state of the mine car with position information and speed information, other data such as acceleration and load can also be used for representation, and this specification does not impose any restrictions on the driving state.

[0045] Step 103: Cumulatively process the driving state errors at the at least one acquisition moment to obtain a cumulative error, and the cumulative error is used to generate a control signal for adjusting the driving state of the mine car at the current moment.

[0046] In an embodiment, the current driving state of the mine car at the current moment can be determined, and the current driving state error between the current driving state and the corresponding target driving state can be determined. Obtain a target double-ended queue corresponding to the target sliding time window; when the capacity of the target double-ended queue is full, delete the first driving state error at the head of the target double-ended queue, and insert the current driving state error at the tail of the target double-ended queue; when the capacity of the target double-ended queue is not full, insert the current driving state error at the tail of the target double-ended queue.

[0047] When cumulatively processing the driving state errors at at least one acquisition moment within the target sliding time window to obtain a cumulative error, the driving state errors at at least one acquisition moment stored in the target double-ended queue can be cumulatively processed to obtain a cumulative error.

[0048] In this embodiment, a double-ended queue data structure is used to store the driving state errors. By popping the first driving state error at the head of the queue and inserting the current driving state error when the double-ended queue is full, it can be ensured that the double-ended queue corresponding to the current moment always stores the driving state errors within the latest sliding time window. Moreover, the double-ended queue is a data structure with efficient insertion and deletion operations. By storing the driving state errors within the sliding time window using the double-ended queue, the processing efficiency can also be improved.

[0049] In an embodiment, the cumulative processing may be to cumulatively sum the driving state errors at at least one acquisition moment, or change the summation method, such as performing cumulative summation after transforming the data of the driving state errors at at least one acquisition moment, or performing weighted summation on the data of the driving state errors at at least one acquisition moment. This specification does not limit the specific manner of cumulative processing.

[0050] In one embodiment, the sliding time window mechanism can avoid accumulating the historical driving state errors over a long time to solve the problems brought by the integral saturation phenomenon. On this basis, the present solution further assigns weights to the driving state errors to be cumulatively processed, and the weights of the driving state errors at the acquisition moments closer to the current moment (including the current moment) are greater. Since the driving state errors at the most recent acquisition moment are more relevant to the driving condition of the mine car at the current moment and can better reflect the current operating state of the mine car, the control system can respond more quickly to the changes in the mine car state, especially showing stronger dynamic response capabilities under complex working conditions.

[0051] Exemplarily, when cumulatively processing the driving state errors at at least one acquisition moment within the target sliding time window to obtain a cumulative error, the weights respectively corresponding to the driving state errors at the at least one acquisition moment can be determined, and the driving state errors at the at least one acquisition moment are weighted and cumulatively processed based on the weights to obtain a cumulative error. Among them, the weights of the driving state errors at the acquisition moments closer to the current moment are greater.

[0052] In this embodiment, by assigning different weights to each driving state error used for calculating the cumulative error within the target sliding time window and performing weighted cumulative processing, the calculated cumulative error can better reflect the latest operating state of the mine car, thereby improving the control accuracy and dynamic response capabilities of the control system.

[0053] In one embodiment, the formula used to determine the weights respectively corresponding to the driving state errors at at least one acquisition moment within the target sliding time window is formula (1):

[0054]

[0055] Among them, i is any acquisition moment within the target sliding time window, W(i) is the weight of the driving state error at the i-th moment within the target sliding window, T is the length of the target sliding time window, t is the current moment, and α is an error weight constant and 0 < α < 1. In formula (1), the at least one acquisition moment can be all the moments within the target sliding time window.

[0056] In one embodiment, the formula used to perform weighted cumulative processing on the driving state errors at at least one acquisition moment based on the weights to obtain a cumulative error is formula (2):

[0057]

[0058] Among them, I(t) is the cumulative error, and e(i) is the driving state error at the i-th moment within the target sliding time window.

[0059] In one embodiment, to avoid excessive cumulative error, which may cause the integral term in the PID control method to increase sharply in a short period of time, leading to overshoot or oscillation in the control system. In this solution, further amplitude limiting processing is performed on the obtained cumulative error.

[0060] Exemplarily, when accumulating the driving state errors at at least one acquisition moment within the target sliding time window to obtain a cumulative error, the driving state errors at at least one acquisition moment are accumulated to obtain an initial cumulative error; if the initial cumulative error is less than the first error threshold, the first error threshold can be used as the cumulative error; if the initial cumulative error is greater than the second error threshold, the second error threshold can be used as the cumulative error; if the initial cumulative error is not less than the first error threshold and not greater than the second error threshold, the initial cumulative error can be used as the cumulative error.

[0061] The formula corresponding to the above embodiment is shown in (3):

[0062]

[0063] where I(t) is the cumulative error, -I max is the first error threshold, I max is the second error threshold, I initial (t) is the initial cumulative error.

[0064] In one embodiment, a control signal for adjusting the driving state of the mine car at the current moment can be generated according to the calculation results of the proportional, integral, and differential links. Specifically, refer to formula (4):

[0065]

[0066] where u(t) is the control signal output at the current moment t, K p is the proportional time coefficient, K i is the integral time coefficient, K d is the differential time coefficient, e(t) is the proportion at the current moment t, I(t) is the integral (i.e., cumulative error) of the driving state error within the target sliding time window corresponding to the current moment t, is the differential of the driving state error at the current moment t.

[0067] The output control signal drives the longitudinal motion actuator of the mine car to achieve precise control of the longitudinal motion of the mine car. The actuator adjusts the speed and position of the mine car according to the control signal to make it as close as possible to the target trajectory.

[0068] It should be noted that formula (4) is only an example. This specification does not limit the calculation methods of the proportional and differential terms in the PID control method, nor does it limit the generation method of the control signal.

[0069] In one embodiment, as Figure 3 shown, this specification provides an optimal longitudinal PID control method for a mine car, specifically including steps 301-307:

[0070] Step 301: Collect the driving state data of the mine car at the current moment. For example, the driving state data may be the position data and speed data of the mine car at the current moment.

[0071] Step 302: Compare the driving state data collected at the current moment with the target driving state data issued by the planning system, and calculate the driving state error at the current moment.

[0072] Step 303: Determine the target motion state of the mine car at the current moment. Among them, the target motion state may include the driving speed. If the driving speed of the mine car at the current moment is not less than the first speed threshold, then turn to step 304A; otherwise, turn to step 304B.

[0073] Step 304A: The driving state error at the current moment can be updated to a double-ended queue corresponding to the first sliding time window.

[0074] Step 304B: The driving state error at the current moment can be updated to a double-ended queue corresponding to the second sliding time window.

[0075] Among them, the length of the first sliding time window is greater than the length of the second sliding time window.

[0076] Step 305: Calculate the integral term in the PID control method through a dynamic weight allocation mechanism, and this integral term is the cumulative error. The dynamic weight allocation mechanism can be specifically referred to formula (1), and this specification will not elaborate here.

[0077] Step 306: Perform integral limiting on the integral term generated in step 305. Specifically, the driving state errors in the double-ended queue can be weighted and accumulated to obtain the initial cumulative error; if the initial cumulative error is less than the first error threshold, the first error threshold can be used as the cumulative error; if the initial cumulative error is greater than the second error threshold, the second error threshold can be used as the cumulative error; if the initial cumulative error is not less than the first error threshold and not greater than the second error threshold, the initial cumulative error can be used as the cumulative error.

[0078] Step 307: Generate a control signal for adjusting the driving state of the mine car at the current moment according to the calculation results of the proportional, differential, and integral terms calculated in step 306.

[0079] Corresponding to the embodiments of the foregoing method, this specification also provides embodiments of the device and the terminal to which it is applied.

[0080] Figure 4 This is a schematic structural diagram of an electronic device shown in accordance with an exemplary embodiment of this specification. As Figure 4 shown, at the hardware level, the electronic device 400 includes a processor 402, an internal bus 404, a network interface 406, a memory 408, and a non-volatile memory 410. Of course, it may also include other hardware required for other services. One or more embodiments of this specification can be implemented in a software manner. For example, the processor 402 reads the corresponding computer program from the non-volatile memory 410 into the memory 408 and then runs it. Of course, in addition to the software implementation manner, one or more embodiments of this specification do not exclude other implementation manners, such as a logic device or a combination of software and hardware, etc. That is to say, the execution subject of the following processing flow is not limited to each logic module, and can also be hardware or a logic device.

[0081] Figure 5 This is a block diagram of a longitudinal PID control device for a mine car shown in accordance with an exemplary embodiment of this specification. As Figure 5 shown, this device can be applied to the electronic device 400 as shown in Figure 4 to implement the technical solution of this specification. The device includes:

[0082] A target sliding time window determination module 502, configured to determine a target sliding time window corresponding to the mine car at the current moment; wherein, the end moment of the target sliding time window is the current moment and the length of the target sliding time window is a fixed value.

[0083] A driving state error acquisition module 504, configured to acquire the driving state error at at least one acquisition moment within the target sliding time window; wherein, the driving state error at each acquisition moment is the error between the driving state of the mine car at this acquisition moment and the corresponding target driving state.

[0084] An accumulated error calculation module 506, configured to perform an accumulation process on the driving state errors at the at least one acquisition moment to obtain an accumulated error, and the accumulated error is used to generate a control signal for adjusting the driving state of the mine car at the current moment.

[0085] Optionally, the device further includes a target motion state determination module, configured to determine the target motion state of the mine car at the current moment. The target sliding time window determination module 502 is specifically configured to determine the target sliding time window corresponding to the mine car at the current moment and the target motion state; wherein, different motion states correspond to different sliding time windows and the lengths of each sliding time window are negatively correlated with the change rate of the corresponding motion state.

[0086] Optionally, the motion state includes the traveling speed. The target sliding time window determination module 502 is specifically configured to determine a first sliding time window corresponding to the target traveling speed of the ore car at the current moment when the target traveling speed of the ore car at the current moment is not less than the first speed threshold. When the target traveling speed of the ore car at the current moment is less than the first speed threshold, a second sliding time window corresponding to the target traveling speed of the ore car at the current moment is determined. Wherein, the length of the first sliding time window is greater than the length of the second sliding time window.

[0087] Optionally, the device further includes determining the current traveling state of the ore car at the current moment, and determining the current traveling state error between the current traveling state and the corresponding target traveling state. Obtain a target double-ended queue corresponding to the target sliding time window. When the capacity of the target double-ended queue is full, delete the first traveling state error at the head of the target double-ended queue, and insert the current traveling state error into the tail of the target double-ended queue. The cumulative error calculation module 506 is specifically configured to perform a cumulative process on the traveling state errors at at least one acquisition moment stored in the target double-ended queue to obtain a cumulative error.

[0088] Optionally, the cumulative error calculation module 506 is specifically configured to determine weights corresponding to the traveling state errors at the at least one acquisition moment respectively, and perform a weighted cumulative process on the traveling state errors at the at least one acquisition moment based on the weights to obtain a cumulative error; wherein, the weight of the traveling state error at an acquisition moment closer to the current moment is greater.

[0089] Optionally, the formula used to determine the weights corresponding to the traveling state errors at the at least one acquisition moment respectively includes:

[0090]

[0091] Wherein, i is any acquisition moment within the target sliding time window, W(i) is the weight of the traveling state error at the i-th moment within the target sliding window, T is the length of the target sliding time window, t is the current moment, and α is an error weight constant and 0 < α < 1.

[0092] The formula used to perform a weighted cumulative process on the traveling state errors at the at least one acquisition moment based on the weights to obtain a cumulative error includes:

[0093]

[0094] Wherein, I(t) is the cumulative error, and e(i) is the traveling state error at the i-th moment within the target sliding time window.

[0095] Optionally, the cumulative error calculation module 506 is specifically configured to cumulatively process the driving state errors at the at least one acquisition moment to obtain an initial cumulative error. If the initial cumulative error is less than the first error threshold, the first error threshold is used as the cumulative error; if the initial cumulative error is greater than the second error threshold, the second error threshold is used as the cumulative error; if the initial cumulative error is not less than the first error threshold and not greater than the second error threshold, the initial cumulative error is used as the cumulative error.

[0096] For the implementation processes of the functions and actions of each module in the above device, refer to the implementation processes of the corresponding steps in the above method for details, which will not be elaborated here.

[0097] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can be referred to the partial description of the method embodiment. The device embodiments described above are only illustrative. The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place, or may be distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution in this specification. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0098] This specification also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of any of the foregoing longitudinal PID control methods for a mine car provided by this application are implemented.

[0099] Specifically, computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, such as semiconductor memory devices (such as EPROM, EEPROM, and flash memory devices), magnetic disks (such as internal hard disks or removable disks), magneto-optical disks, and CD-ROM and DVD-ROM disks.

[0100] This specification also provides a computer program product, including a computer program / instructions. When the computer program / instructions are executed by a processor, the steps of any of the foregoing longitudinal PID control methods for a mine car are implemented.

Claims

1. A longitudinal PID control method for a mine car, characterized in that, The method includes: Determining a target sliding time window corresponding to the mine car at the current moment; wherein, the end moment of the target sliding time window is the current moment and the length of the target sliding time window is a fixed value; Obtaining the driving state errors at at least one acquisition moment within the target sliding time window; wherein, the driving state error at each acquisition moment is the error between the driving state of the mine car at this acquisition moment and the corresponding target driving state; Performing cumulative processing on the driving state errors at the at least one acquisition moment to obtain a cumulative error, and the cumulative error is used to generate a control signal for adjusting the driving state of the mine car at the current moment.

2. The method according to claim 1, wherein The method further includes: Determining the target motion state of the mine car at the current moment; The determining of the target sliding time window corresponding to the mine car at the current moment includes: Determining the target sliding time window corresponding to the mine car at the current moment and the target motion state; wherein, different motion states correspond to different sliding time windows and the lengths of the respective sliding time windows are negatively correlated with the change rate of the corresponding motion state.

3. The method according to claim 2, wherein The motion state includes the driving speed, and the determining of the target sliding time window corresponding to the mine car at the current moment and the target motion state includes: When the target driving speed of the mine car at the current moment is not less than the first speed threshold, determining the first sliding time window corresponding to the target driving speed of the mine car at the current moment; When the target driving speed of the mine car at the current moment is less than the first speed threshold, determining the second sliding time window corresponding to the target driving speed of the mine car at the current moment; Wherein, the length of the first sliding time window is greater than the length of the second sliding time window.

4. The method according to claim 1, characterized in that The method further includes: Determining the current driving state of the mine car at the current moment, and determining the current driving state error between the current driving state and the corresponding target driving state; Obtaining a target double-ended queue corresponding to the target sliding time window, and when the capacity of the target double-ended queue is full, deleting the first driving state error at the head of the target double-ended queue and inserting the current driving state error at the tail of the target double-ended queue; The performing of cumulative processing on the driving state errors at the at least one acquisition moment to obtain a cumulative error includes: Performing cumulative processing on the driving state errors at at least one acquisition moment stored in the target double-ended queue to obtain a cumulative error.

5. The method according to claim 1, wherein The performing of cumulative processing on the driving state errors at the at least one acquisition moment to obtain a cumulative error includes: Determining the weights corresponding to the driving state errors at the at least one acquisition moment respectively, and performing weighted cumulative processing on the driving state errors at the at least one acquisition moment based on the weights to obtain a cumulative error; wherein, the weight of the driving state error at the acquisition moment closer to the current moment is greater.

6. The method according to claim 5, wherein The formula used for determining the weights corresponding to the driving state errors at the at least one acquisition moment respectively includes: Wherein, i is any acquisition moment within the target sliding time window, W(i) is the weight of the driving state error at the i-th moment within the target sliding window, T is the length of the target sliding time window, t is the current moment, α is an error weight constant and 0 < α < 1; The formula for performing weighted cumulative processing on the driving state errors at the at least one acquisition moment based on the weights to obtain a cumulative error includes: Wherein, I(t) is the cumulative error, and e(i) is the driving state error at the i-th moment within the target sliding time window.

7. The method according to claim 1, characterized in that, Performing cumulative processing on the driving state errors at the at least one acquisition moment to obtain a cumulative error includes: Performing cumulative processing on the driving state errors at the at least one acquisition moment to obtain an initial cumulative error; If the initial cumulative error is less than the first error threshold, then use the first error threshold as the cumulative error; If the initial cumulative error is greater than the second error threshold, then use the second error threshold as the cumulative error; If the initial cumulative error is not less than the first error threshold and not greater than the second error threshold, then use the initial cumulative error as the cumulative error.

8. A longitudinal PID control device for a mine car, characterized in that, The device includes: A target sliding time window determination module, configured to determine a target sliding time window corresponding to the mine car at the current moment; wherein, the end moment of the target sliding time window is the current moment and the length of the target sliding time window is a fixed value; A driving state error acquisition module, configured to acquire the driving state errors at at least one acquisition moment within the target sliding time window; wherein, the driving state error at each acquisition moment is the error between the driving state of the mine car at this acquisition moment and the corresponding target driving state; A cumulative error calculation module, configured to perform cumulative processing on the driving state errors at the at least one acquisition moment to obtain a cumulative error, and the cumulative error is used to generate a control signal for adjusting the driving state of the mine car at the current moment.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method according to any one of claims 1-7.

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