CN111781818B - AGV control method and system based on improved fuzzy PID control algorithm - Google Patents
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Abstract
The application discloses an AGV control method and system based on an improved fuzzy PID control algorithm, comprising the following steps: acquiring the current position of an Automatic Guided Vehicle (AGV) through a magnetic sensor, and calculating the deviation of the AGV from a set track to obtain the current position deviation of the AGV; inputting the current position deviation and the differential value of the AGV into a fuzzy controller, and outputting a proportional change value, an integral change value and a differential coefficient change value; respectively adjusting corresponding proportional parameters, integral parameters and differential parameters in the improved PID control algorithm in real time by adopting the proportional change value, the integral change value and the differential change value to obtain an improved fuzzy PID control algorithm; and an improved fuzzy PID control algorithm is adopted to control the differential speed of two wheels of the AGV of the automatic guided transport vehicle, so that the AGV returns to the set track.
Description
Technical Field
The application relates to the technical field of industrial automatic control, in particular to an AGV (automatic Guided Vehicle) control method and system based on an improved fuzzy PID (proportion integration differentiation) control algorithm.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The AGV, as an unmanned vehicle, is often subjected to variable and uncertain environments, which causes deviation of the posture of the AGV and the rotation speed of the driving wheels from an expected value, and this presents a great challenge to a control strategy for autonomously executing tasks. The inventor finds that the existing AGV has poor anti-interference performance and poor stability in the operation process.
Disclosure of Invention
In order to overcome the defects of the prior art, the application provides an AGV control method and system based on an improved fuzzy PID control algorithm;
in a first aspect, the present application provides an AGV control method based on an improved fuzzy PID control algorithm;
an AGV control method based on an improved fuzzy PID control algorithm comprises the following steps:
acquiring the current position of an Automatic Guided Vehicle (AGV), and calculating the deviation of the AGV from a set track to obtain the current position deviation of the AGV;
inputting the current position deviation and the differential value of the AGV into a fuzzy controller, and outputting a proportional change value, an integral change value and a differential coefficient change value;
respectively adjusting corresponding proportional parameters, integral parameters and differential parameters in the improved PID control algorithm in real time by adopting the proportional change value, the integral change value and the differential change value to obtain an improved fuzzy PID control algorithm;
and controlling two-wheel differential speed of the AGV of the automatic guided transport vehicle according to the new PID parameters, so that the AGV returns to the set track.
In a second aspect, the present application provides an AGV control system based on an improved fuzzy PID control algorithm;
an AGV control system based on an improved fuzzy PID control algorithm comprises:
an acquisition module configured to: acquiring the current position of an Automatic Guided Vehicle (AGV), and calculating the deviation of the AGV from a set track to obtain the current position deviation of the AGV;
an input module configured to: inputting the current position deviation and the differential value of the AGV into a fuzzy controller, and outputting a proportional change value, an integral change value and a differential coefficient change value;
an adjustment module configured to: respectively adjusting corresponding proportional parameters, integral parameters and differential parameters in the improved PID control algorithm in real time by adopting the proportional change value, the integral change value and the differential change value to obtain an improved fuzzy PID control algorithm;
a control module configured to: and controlling two-wheel differential speed of the AGV of the automatic guided transport vehicle according to the new PID parameters, so that the AGV returns to the set track.
In a third aspect, the present application further provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein a processor is connected to the memory, the one or more computer programs are stored in the memory, and when the electronic device is running, the processor executes the one or more computer programs stored in the memory, so as to make the electronic device execute the method according to the first aspect.
In a fourth aspect, the present application also provides a computer-readable storage medium for storing computer instructions which, when executed by a processor, perform the method of the first aspect.
In a fifth aspect, the present application also provides a computer program (product) comprising a computer program for implementing the method of any of the preceding first aspects when run on one or more processors.
Compared with the prior art, the beneficial effects of this application are:
according to the method and the device, the integral term and the proportional term are improved, so that the anti-interference performance of an AGV control system is improved, and the stability of the AGV control system in the operation process is improved.
By utilizing the method and the device, the response performance of the algorithm under the interference of pulse and white noise can be improved by improving the fuzzy PID algorithm, the high-frequency interference is effectively inhibited, and the stability and the response speed of the system are improved. The provided system performance evaluation index can effectively measure the fluctuation condition of the system response under the input signal, and provides a more definite quantitative reference for the system performance analysis.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a graph of the triangular degree of membership of the first embodiment;
FIG. 2(a) -FIG. 2(c) are views of the fuzzy inference curved surface of the first embodiment;
FIG. 3 is a diagram of a PID algorithm simulation model under impulse interference according to the first embodiment;
FIG. 4 is a response curve diagram of the PID algorithm under impulse interference of the first embodiment;
FIG. 5 is a diagram of a PID algorithm simulation model under white noise interference according to the first embodiment;
FIG. 6 is a response curve diagram of the PID algorithm under white noise interference according to the first embodiment.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Example one
The embodiment provides an AGV control method based on an improved fuzzy PID control algorithm;
an AGV control method based on an improved fuzzy PID control algorithm comprises the following steps:
s101: acquiring the current position of an Automatic Guided Vehicle (AGV) through a magnetic sensor, and calculating the deviation of the AGV from a set track to obtain the current position deviation of the AGV;
s102: inputting the current position deviation and the differential value of the AGV into a fuzzy controller, and outputting a proportional change value, an integral change value and a differential coefficient change value;
s103: respectively adjusting corresponding proportional parameters, integral parameters and differential parameters in the improved PID control algorithm in real time by adopting the proportional change value, the integral change value and the differential change value to obtain an improved fuzzy PID control algorithm;
s104: and an improved fuzzy PID control algorithm is adopted to control the differential speed of two wheels of the AGV of the automatic guided transport vehicle, so that the AGV returns to the set track.
As one or more embodiments, the improved fuzzy PID control algorithm includes: an improvement to the PID algorithm.
Further, S102 is to construct a 2-input 3-output fuzzy controller, input the current position deviation E and the differential value EC of the AGV, and output the coefficient change value delta k of the control algorithm after the inference of the fuzzy logic tablep、Δki、ΔkdTaking the updated data as the coefficient of the fuzzy PID control algorithm; Δ kpIndicating the proportional change value, Δ kiRepresenting the sum of integrated change values and Δ kdIndicating a differential change value.
Constructing a fuzzy controller with 2 inputs and 3 outputs, using the deviation E and the differential value EC thereof as the input of the fuzzy controller, and controlling the coefficient change value delta k of the algorithm after reasoning by a fuzzy logic table (such as the table 1)p、Δki、ΔkdAs the output, the input and the output adopt the triangular membership function as shown in FIG. 1, and FIG. 2(a) -FIG. 2(c) are fuzzy inference curved surface views of the output along with the input.
TABLE 1
Further, the above-mentioned adopting the proportional change value, the integral change value and the differential change value respectively adjusts the corresponding proportional parameter, integral parameter and differential parameter in the improved PID control algorithm in real time to obtain the improved fuzzy PID control algorithm, specifically:
kp＝kp0+k1Δkp
ki＝ki0+k2Δki
kd＝kd0+k3Δkd
in the formula kp0、ki0、kd0The initial values of the proportional, integral and differential coefficients are obtained; Δ kp、Δki、ΔkdThe change values of the proportional, integral and differential coefficients output by the fuzzy controller; k is a radical of1、k2、k3The proportional factor, the integral factor and the differential factor change value are respectively mapped to the actual discourse domain from the fuzzy domain and are taken as 0.6, 2.5 and 3.
Further, the improved PID control algorithm in S103 includes: an integral term improvement, a differential term improvement and a comparative example improvement are carried out.
Further, the improvement on the integral term refers to: an integral separation coefficient is introduced based on integral separation, and an integral term is improved to realize integral saturation resistance and integral amplitude limiting resistance.
It should be understood that the technical effect of avoiding the over-integration phenomenon can be achieved by introducing the integral separation coefficient to prevent the control quantity from being too large when the deviation is large.
Illustratively, when the current value of the control system is far away from the target value, the integral term effect of the PID is cancelled, and the current value of the control system approaches the target value by means of PD calculation; integration accumulation is performed if and only if the current value of the control system is within some acceptable range around the target value.
Specifically, the improvement of the integral term is based on the idea of integral separation to realize the resistance to integral saturation and integral amplitude limiting, and an integral separation coefficient is introduced:
where e (k) represents the position deviation of the AGV at the k-th sampling time, SV represents the position value of the trajectory set by the user, and epsilon represents the integral separation threshold value.
The calculation formula of PID will be adjusted to:
where OUT represents the output value of the controller after adjustment, kp、ki、kdRespectively representing proportional, integral and differential coefficients, EkAnd Ek-1Respectively representing the error values of the current sampling moment and the previous sampling moment. Beta is the integral separation coefficient.
Further, the improvement on the differential term refers to: a first-order inertia link is added in a differentiation link to be used as a filter to inhibit high-frequency interference, and a filtering algorithm is introduced to process data for the problem of large fluctuation of output signals of a control system.
Illustratively, the improving the derivative term specifically includes:
a first-order inertia link is added in a differentiation link to be used as a filter to inhibit high-frequency interference, and a filtering algorithm is introduced to process data for the problem of large fluctuation of an output signal of a control system:
Y(n)＝αX(n)+(1-α)Y(n-1)
wherein, Y (n) represents the output value of the current filtering, alpha represents the filtering coefficient, X (n) represents the system input value, and Y (n-1) represents the output value of the last filtering.
The stability and the sensitivity of the filtering processing are determined by a filter coefficient, the filter coefficient is small, the filtering is stable, and the sensitivity is low, so that the filter coefficient alpha is 0.66 for the system under the condition of considering the stability and the sensitivity. After the filtering processing, the interference and the jitter can be effectively inhibited, and the output curve of the system becomes smooth.
Further, the comparative example item is improved, specifically that: on the basis of calculating PID proportion coefficients by fuzzy logic, properly adjusting the proportion coefficients, and reducing the size of the proportion factors and inhibiting interference when deviation deviates from a target value to be overlarge; when the deviation is close to the target value, the scale factor is amplified to accelerate the system response.
Illustratively, the comparative example is improved, and specifically comprises:
k’p＝k·kp+kp0'
wherein, k'pDenotes the adjusted coefficient of the proportional term, kpDenotes the coefficient of the proportional term before adjustment, k denotes the adjustment coefficient, kp0' means initial adjustment value, and is taken to be 0.35. (obtained by debugging under a conventional PID algorithm)
The adjustment factor is determined as follows:
the action strength and the working condition of different proportion items are matched through scaling so as to achieve the aim of accelerating the response speed while inhibiting high-frequency interference.
Further, the input and the output of the improved fuzzy PID controller both adopt a triangular membership function form.
Further, after S103 and before S104, the method further includes:
s103-41: simulating an improved fuzzy PID control algorithm;
s104-42: the time-multiplied-absolute-error integral Indicator (ITAE) was introduced to quantify the interference rejection performance of the various algorithm outputs throughout the response process.
It should be understood that the simulation of the improved fuzzy PID control algorithm was performed in the Simulink environment of MATLAB.
The simulation of the algorithm specifically comprises the steps of respectively introducing pulse interference and white noise interference in the running process, and comparing response curves of the traditional PID, the fuzzy PID and the improved fuzzy PID under the interference.
A model is established in a Simulink environment of MATLAB to simulate the improved fuzzy PID algorithm, impulse interference is introduced in 10 s-13 s, and the PID algorithm simulation model under the impulse interference is shown in figure 3. Observing the response characteristic of the improved algorithm, the PID algorithm response curve chart 4 under the impulse interference is shown.
Gaussian white noise interference is introduced into the 10 th s to 15 th s, and a PID algorithm simulation model under the white noise interference is shown in FIG. 5. Observing the response characteristic of the improved algorithm, the response curve of the PID algorithm under the interference of white noise is shown in a graph 6.
The time-absolute error integral index mainly considers the relation between the deviation of response on a time domain and a set value, and the formula is as follows:
where e (t) is the deviation of the system at time t, and sv (t) is the target value of the system at time t. J is equal to 0,1, and the smaller J represents the smaller the deviation of the control system response from the target value, the smaller the fluctuation degree.
Compared with the performance indexes of the traditional PID algorithm, the fuzzy PID algorithm and the improved fuzzy PID algorithm under the pulse interference, the performance indexes are shown in the table 2. From the results, the traditional PID can not work normally during the pulse interference, the output of the fuzzy PID fluctuates when the fuzzy PID is interfered, and the improved fuzzy PID has no fluctuation and has faster response speed during the interference.
TABLE 2
The performance indexes of the traditional PID algorithm, the fuzzy PID algorithm and the improved fuzzy PID algorithm under the white noise interference are compared, and are shown in Table 3. The result shows that, during the period of gaussian white noise interference, the influence of the interference on the traditional PID is the largest, the oscillation degree of the output of the fuzzy PID is weakened compared with that of the traditional PID, but the fluctuation is still larger, the improved fuzzy PID has smaller fluctuation during the interference period, and the response speed is faster.
TABLE 3
As one or more embodiments, in S104, an improved fuzzy PID control algorithm is used to control two differential speeds of an AGV of an automatic guided vehicle, so that the AGV returns to a set track; the method comprises the following specific steps:
the real-time position deviation of the AGV of the automatic guided transport vehicle is used as input, and the real-time rotating speed difference of the left and right two-wheel drive of the AGV is output through the real-time control of an improved fuzzy PID control algorithm, so that the AGV is controlled to return to the set track.
The motion control of the AGV generally adopts a classical PID (proportional-integral-derivative) control strategy with a simple principle, along with the development of a modern control theory, a fuzzy control algorithm is applied to the AGV control field more and more with the advantage that the fuzzy control algorithm does not require an accurate controlled model, the advantages of two methods can be fully exerted by combining the fuzzy logic algorithm and a conventional PID control algorithm, and the overall performance of an AGV control system is improved. In order to meet the current situation and the requirement, the application provides an improved fuzzy PID algorithm for an AGV control system to improve the anti-interference performance of the control system, and provides an algorithm performance quantitative evaluation method to quantitatively analyze and compare the system performance before and after the algorithm is improved.
Example two
The embodiment provides an AGV control system based on an improved fuzzy PID control algorithm;
an AGV control system based on an improved fuzzy PID control algorithm comprises:
an acquisition module configured to: acquiring the current position of an Automatic Guided Vehicle (AGV), and calculating the deviation of the AGV from a set track to obtain the current position deviation of the AGV;
an input module configured to: inputting the current position deviation and the differential value of the AGV into a fuzzy controller, and outputting a proportional change value, an integral change value and a differential coefficient change value;
an adjustment module configured to: respectively adjusting corresponding proportional parameters, integral parameters and differential parameters in the improved PID control algorithm in real time by adopting the proportional change value, the integral change value and the differential change value to obtain an improved fuzzy PID control algorithm;
a control module configured to: and controlling two-wheel differential speed of the AGV of the automatic guided transport vehicle according to the new PID parameters, so that the AGV returns to the set track.
It should be noted here that the above-mentioned acquisition module, input module, adjustment module and control module correspond to steps S101 to S104 in the first embodiment, and the above-mentioned modules are the same as the examples and application scenarios realized by the corresponding steps, but are not limited to the disclosure of the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
In the foregoing embodiments, the descriptions of the embodiments have different emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The proposed system can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the above-described modules is merely a logical functional division, and in actual implementation, there may be other divisions, for example, multiple modules may be combined or integrated into another system, or some features may be omitted, or not executed.
EXAMPLE III
The present embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, a processor is connected with the memory, the one or more computer programs are stored in the memory, and when the electronic device runs, the processor executes the one or more computer programs stored in the memory, so as to make the electronic device execute the method according to the first embodiment.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software.
The method in the first embodiment may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
Example four
The present embodiments also provide a computer-readable storage medium for storing computer instructions, which when executed by a processor, perform the method of the first embodiment.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (4)
1. An AGV control method based on an improved fuzzy PID control algorithm is characterized by comprising the following steps:
acquiring the current position of an Automatic Guided Vehicle (AGV), and calculating the deviation of the AGV from a set track to obtain the current position deviation of the AGV;
inputting the current position deviation and the differential value of the AGV into a fuzzy controller, and outputting a proportional change value, an integral change value and a differential coefficient change value;
respectively adjusting corresponding proportional parameters, integral parameters and differential parameters in the improved PID control algorithm in real time by adopting the proportional change value, the integral change value and the differential change value to obtain an improved fuzzy PID control algorithm; the improved fuzzy PID control algorithm comprises the improvement on the PID algorithm; the improvement on the PID algorithm comprises the improvement on an integral term, the improvement on a differential term and the improvement on a proportional term;
the improvement of the integral term is based on the idea of integral separation to realize the resistance to integral saturation and integral amplitude limiting, and an integral separation coefficient is introduced:
wherein E (k) represents the position deviation of the AGV at the k-th sampling moment, SV represents the position value of the track set by the user, and epsilon represents an integral separation threshold value;
the calculation formula of PID will be adjusted to:
where OUT represents the output value of the controller after adjustment, kp、ki、kdRespectively representing proportional, integral and differential coefficients，EkAnd Ek-1Respectively representing the error values of the current sampling moment and the previous sampling moment; beta is an integral separation coefficient;
the improvement of the differential term means that a first-order inertia link is added in a differential link to be used as a filter to inhibit high-frequency interference, and a filtering algorithm is introduced to process data for the problem of large fluctuation of an output signal of a control system:
Y(n)＝αX(n)+(1-α)Y(n-1)
wherein, Y (n) represents the output value of the current filtering, alpha represents the filtering coefficient, X (n) represents the system input value, and Y (n-1) represents the output value of the last filtering;
the improvement of the proportional term means that the proportional term coefficient is properly adjusted on the basis of calculating the PID proportional coefficient by fuzzy logic, and when the deviation deviates from a target value to be too large, the size of the proportional factor is reduced, and the interference is suppressed; when the deviation is close to the target value, the scale factor is amplified, and the system response is accelerated:
k’p＝k·kp+kp0'
wherein, k'pDenotes the adjusted coefficient of the proportional term, kpDenotes the coefficient of the proportional term before adjustment, k denotes the adjustment coefficient, kp0' represents an initial adjustment value, which is taken as 0.35; the adjustment coefficient k is determined as follows:
the action strength and the working condition of different proportion items are matched through scaling so as to achieve the aim of accelerating the response speed while inhibiting high-frequency interference;
simulating an improved fuzzy PID control algorithm; introducing a time-multiplied-absolute-error integral index ITAE to quantify the anti-interference performance of various algorithm outputs in the whole response process;
the time-absolute error integral index mainly considers the relation between the deviation of response on a time domain and a set value, and the formula is as follows:
wherein e (t) is the deviation of the system at the time t, and sv (t) is the target value of the system at the time t; j belongs to [0,1], and the smaller J represents the smaller the deviation of the control system response from the target value, the smaller the fluctuation degree;
and controlling two-wheel differential speed of the AGV of the automatic guided transport vehicle according to the new PID parameters, so that the AGV returns to the set track.
2. AGV control system based on improve fuzzy PID control algorithm, characterized by includes:
an acquisition module configured to: acquiring the current position of an Automatic Guided Vehicle (AGV), and calculating the deviation of the AGV from a set track to obtain the current position deviation of the AGV;
an input module configured to: inputting the current position deviation and the differential value of the AGV into a fuzzy controller, and outputting a proportional change value, an integral change value and a differential coefficient change value;
an adjustment module configured to: respectively adjusting corresponding proportional parameters, integral parameters and differential parameters in the improved PID control algorithm in real time by adopting the proportional change value, the integral change value and the differential change value to obtain an improved fuzzy PID control algorithm; the improved fuzzy PID control algorithm comprises the improvement on the PID algorithm; the improvement on the PID algorithm comprises the improvement on an integral term, the improvement on a differential term and the improvement on a proportional term;
the improvement of the integral term is based on the idea of integral separation to realize the resistance to integral saturation and integral amplitude limiting, and an integral separation coefficient is introduced:
wherein E (k) represents the position deviation of the AGV at the k-th sampling moment, SV represents the position value of the track set by the user, and epsilon represents an integral separation threshold value;
the calculation formula of PID will be adjusted to:
where OUT represents the output value of the controller after adjustment, kp、ki、kdRespectively representing proportional, integral and differential coefficients, EkAnd Ek-1Respectively representing the error values of the current sampling moment and the previous sampling moment; beta is an integral separation coefficient;
the improvement of the differential term means that a first-order inertia link is added in a differential link to be used as a filter to inhibit high-frequency interference, and a filtering algorithm is introduced to process data for the problem of large fluctuation of an output signal of a control system:
Y(n)＝αX(n)+(1-α)Y(n-1)
wherein, Y (n) represents the output value of the current filtering, alpha represents the filtering coefficient, X (n) represents the system input value, and Y (n-1) represents the output value of the last filtering;
the improvement of the proportional term means that the proportional term coefficient is properly adjusted on the basis of calculating the PID proportional coefficient by fuzzy logic, and when the deviation deviates from a target value to be too large, the size of the proportional factor is reduced, and the interference is suppressed; when the deviation is close to the target value, the scale factor is amplified, and the system response is accelerated:
k’p＝k·kp+kp0'
wherein, k'pDenotes the adjusted coefficient of the proportional term, kpDenotes the coefficient of the proportional term before adjustment, k denotes the adjustment coefficient, kp0' represents an initial adjustment value, which is taken as 0.35; the adjustment coefficient k is determined as follows:
the action strength and the working condition of different proportion items are matched through scaling so as to achieve the aim of accelerating the response speed while inhibiting high-frequency interference; simulating an improved fuzzy PID control algorithm; introducing a time-multiplied-absolute-error integral index ITAE to quantify the anti-interference performance of various algorithm outputs in the whole response process;
the time-absolute error integral index mainly considers the relation between the deviation of response on a time domain and a set value, and the formula is as follows:
wherein e (t) is the deviation of the system at the time t, and sv (t) is the target value of the system at the time t; j belongs to [0,1], and the smaller J represents the smaller the deviation of the control system response from the target value, the smaller the fluctuation degree;
a control module configured to: and controlling two-wheel differential speed of the AGV of the automatic guided transport vehicle according to the new PID parameters, so that the AGV returns to the set track.
3. An electronic device, comprising: one or more processors, one or more memories, and one or more computer programs; wherein a processor is connected to the memory, the one or more computer programs being stored in the memory, the processor executing the one or more computer programs stored in the memory when the electronic device is running, to cause the electronic device to perform the method of claim 1.
4. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of claim 1.
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