CN111781818A - AGV control method and system based on improved fuzzy PID control algorithm - Google Patents

Abstract

The present application discloses an AGV control method and system based on an improved fuzzy PID control algorithm, including: collecting the current position of the automatic guided transport vehicle AGV through a magnetic sensor, calculating the deviation from the set trajectory, and obtaining the current position of the automatic guided transport vehicle AGV Deviation; input the current position deviation and differential value of the AGV of the automatic guided vehicle into the fuzzy controller, and output the proportional change value, integral change value and differential coefficient change value; using proportional change value, integral change value and differential change value, The corresponding proportional parameters, integral parameters and differential parameters in the improved PID control algorithm are adjusted in real time to obtain the improved fuzzy PID control algorithm; the improved fuzzy PID control algorithm is used to control the differential speed of the two wheels of the automatic guided vehicle AGV, so that The AGV returns to the set trajectory.

Description

AGV control method and system based on improved fuzzy PID control algorithm

technical field

The present application relates to the technical field of industrial automatic control, and in particular to an AGV (Automated Guided Vehicle, automatic guided vehicle) control method and system based on an improved fuzzy PID control algorithm.

Background technique

The statements in this section merely mention the background art related to the present application and do not necessarily constitute prior art.

As an unmanned vehicle, AGV often faces a changeable and uncertain environment, which causes the attitude of the AGV and the rotational speed of the driving wheel to deviate from the expected value, which poses a huge challenge to the control strategy of autonomous task execution. The inventor found that the existing AGV control method has poor anti-interference performance and poor stability during operation.

SUMMARY OF THE INVENTION

In order to solve the deficiencies of the prior art, the present application provides an AGV control method and system based on an improved fuzzy PID control algorithm;

In the first aspect, the present application provides an AGV control method based on an improved fuzzy PID control algorithm;

AGV control method based on improved fuzzy PID control algorithm, including:

Collect the current position of the automatic guided transport vehicle AGV, calculate its deviation from the set trajectory, and obtain the current position deviation of the automatic guided transport vehicle AGV;

Input the current position deviation and differential value of the automatic guided transport vehicle AGV to the fuzzy controller, and output the proportional change value, integral change value and differential coefficient change value;

Using proportional change value, integral change value and differential change value, the corresponding proportional parameters, integral parameters and differential parameters in the improved PID control algorithm are adjusted in real time respectively, and the improved fuzzy PID control algorithm is obtained;

According to the new PID parameters, the two-wheel differential speed of the AGV of the automatic guided transport vehicle is controlled to make the AGV return to the set trajectory.

In the second aspect, the present application provides an AGV control system based on an improved fuzzy PID control algorithm;

AGV control system based on improved fuzzy PID control algorithm, including:

a collection module, which is configured to: collect the current position of the automatic guided transport vehicle AGV, calculate its deviation from the set trajectory, and obtain the current position deviation of the automatic guided transport vehicle AGV;

an input module, which is configured to: input the current position deviation of the automatic guided transport vehicle AGV and its differential value to the fuzzy controller, and output the proportional change value, the integral change value and the differential coefficient change value;

The adjustment module is configured to: adopt the proportional change value, the integral change value and the differential change value to adjust the corresponding proportional parameter, integral parameter and derivative parameter in the improved PID control algorithm in real time respectively, so as to obtain the improved fuzzy PID control algorithm ;

The control module is configured to: control the 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 trajectory.

In a third aspect, the present application also provides an electronic device, comprising: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and one or more of the above The computer program is stored in the memory, and when the electronic device runs, the processor executes one or more computer programs stored in the memory, so that the electronic device performs the method described in the first aspect above.

In a fourth aspect, the present application further provides a computer-readable storage medium for storing computer instructions, and when the computer instructions are executed by a processor, the method described in the first aspect is completed.

In a fifth aspect, the present application also provides a computer program (product), including a computer program, which when run on one or more processors, is used to implement the method of any one of the foregoing first aspects.

Compared with the prior art, the beneficial effects of the present application are:

This application improves the anti-interference performance of the AGV control system and improves the stability of the AVG during operation by improving the integral term, the integral term and the proportional term.

Using the improved fuzzy PID algorithm of the present application can improve the response performance of the algorithm under the interference of impulse and white noise, effectively suppress high-frequency interference, and improve the stability and response speed of the system. The proposed system performance evaluation index can effectively measure the fluctuation of the system response under the input signal, and provide a clearer quantitative reference for system performance analysis.

Description of drawings

The accompanying drawings that form a part of the present application are used to provide further understanding of the present application, and the schematic embodiments and descriptions of the present application are used to explain the present application and do not constitute improper limitations on the present application.

Fig. 1 is the triangular membership degree curve diagram of the first embodiment;

Fig. 2(a)-Fig. 2(c) are surface views of fuzzy inference of the first embodiment;

Fig. 3 is the PID algorithm simulation model diagram under the pulse disturbance of the first embodiment;

Fig. 4 is the PID algorithm response curve diagram under the impulse disturbance of the first embodiment;

Fig. 5 is the PID algorithm simulation model diagram under the white noise interference of the first embodiment;

FIG. 6 is a response curve diagram of the PID algorithm under the interference of white noise of the first embodiment.

Detailed ways

It should be noted that the following detailed description is exemplary and intended to provide further explanation of the application. Unless otherwise defined, 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 should be noted that the terminology used herein is for the purpose of describing specific embodiments only, and is not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural as well, furthermore, it is to be understood that the terms "including" and "having" and any conjugations thereof are intended to cover the non-exclusive A process, method, system, product or device comprising, for example, a series of steps or units is not necessarily limited to those steps or units expressly listed, but may include those steps or units not expressly listed or for such processes, methods, Other steps or units inherent in the product or equipment.

The embodiments in this application and the features in the embodiments may be combined with each other without conflict.

Example 1

This embodiment provides an AGV control method based on an improved fuzzy PID control algorithm;

AGV control method based on improved fuzzy PID control algorithm, including:

S101: Collect the current position of the automatic guided transport vehicle AGV through the magnetic sensor, calculate its deviation from the set trajectory, and obtain the current position deviation of the automatic guided transport vehicle AGV;

S102: Input the current position deviation and differential value of the AGV of the automatic guided transport vehicle into the fuzzy controller, and output the proportional change value, the integral change value and the differential coefficient change value;

S103: Using the proportional change value, the integral change value and the differential change value, respectively adjust 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;

S104: The improved fuzzy PID control algorithm is used to control the differential speed between the two wheels of the AGV of the automatic guided transport vehicle, so that the AGV returns to the set trajectory.

As one or more embodiments, the improved fuzzy PID control algorithm includes: improvement of the PID algorithm.

Further, the S102 refers to constructing a 2-input 3-output fuzzy controller, inputting the current position deviation E of the automatic guided vehicle AGV and its differential value EC, and outputting the control algorithm coefficient change values Δk _p and Δk after inference by the fuzzy logic table. _i and Δk _d , take the updated data as the fuzzy PID control algorithm coefficient; Δk _p represents the proportional change value, Δk _i represents the integral change value and Δk _d represents the differential change value.

Construct a fuzzy controller with 2 inputs and 3 outputs, take the deviation E and its differential value EC as the input of the fuzzy controller, and the change values Δk _p , Δk _i , Δk _d of the control algorithm coefficients after inference by the fuzzy logic table (see Table 1) are used as The output, input and output all adopt the triangular membership function as shown in Fig. 1, and Fig. 2(a)-Fig. 2(c) are the surface views of the output and input fuzzy inference.

Table 1

Further, the proportional change value, the integral change value and the differential change value are used to adjust the corresponding proportional parameter, integral parameter and differential parameter in the improved PID control algorithm in real time respectively, so as to obtain the improved fuzzy PID control algorithm, specifically: :

k _p =k _p0 +k ₁ Δk _p

k _i =k _i0 +k ₂ Δk _i

k _d =k _d0 +k ₃ Δk _d

where k _p0 , k _i0 , k _d0 are the initial values of proportional, integral and differential coefficients; Δk _p , _Δki , Δk _d are the proportional, integral and differential coefficient changes of the fuzzy controller output; k ₁ , k ₂ , k ₃ are the scaling factors for the change values of proportional, integral and differential coefficients to be mapped from the fuzzy universe to the actual universe, which are taken as 0.6, 2.5, and 3.

Further, the improved PID control algorithm described in S103 includes: improving the integral term, improving the differential term, and improving the proportional term.

Further, the improvement of the integral term refers to: an integral separation coefficient is introduced based on the integral separation, and the integral term is improved to realize anti-integral saturation and integral limiting.

It should be understood that the introduction of the integral separation coefficient can achieve the technical effect of preventing an excessively large control amount when the deviation is large, and avoiding the phenomenon of over-integration.

Exemplarily, when the current value of the control system is far from the target value, the integral term effect of the PID is cancelled, and the PD calculation is used to make the current value of the control system approach the target value; if and only if the current value of the control system is at a certain value near the target value. Points are accumulated only when an acceptable range is reached.

Specifically, the improvement of the integral term is based on the idea of integral separation to achieve anti-integral saturation and integral limiting, and the integral separation coefficient is introduced:

Among them, E(k) represents the position deviation of the AGV at the kth sampling time, SV represents the position value of the trajectory set by the user, and ε represents the integral separation threshold.

The calculation formula of PID will be adjusted to:

Among them, OUT represents the output value of the controller after adjustment, k _p , k _i , and k _d represent the proportional, integral, and differential coefficients, respectively, and E _k and E _k-1 represent the error values at the current sampling moment and the previous sampling moment, respectively. . β is the integral separation coefficient.

Further, the improvement of the differential term refers to adding a first-order inertial link to the differential link as a filter to suppress high-frequency interference, and for the problem that the output signal of the control system fluctuates greatly, a filtering algorithm is introduced to process the data.

Exemplarily, the improving the differential term specifically includes:

A first-order inertial link is added to the differential link as a filter to suppress high-frequency interference. For the problem that the output signal of the control system fluctuates greatly, a filtering algorithm is introduced to process the data:

Y(n)=αX(n)+(1-α)Y(n-1)

Among them, Y(n) represents the current filtering output value, α represents the filtering coefficient, X(n) represents the system input value, and Y(n-1) represents the last filtering output value.

The smoothness and sensitivity of the filtering process are determined by the filtering coefficient. The filtering coefficient is small, the filtering is relatively smooth, and the sensitivity is low. Therefore, the system selects the filtering coefficient α=0.66 under the consideration of smoothness and sensitivity. After filtering, it can effectively suppress interference and jitter, and make the system output curve smooth.

Further, the improvement of the proportional item specifically refers to: on the basis of the PID proportional coefficient calculated by the fuzzy logic, the proportional item coefficient is properly adjusted, and when the deviation deviates from the target value too large, the size of the proportional factor is reduced, and the Interference; when the deviation is close to the target value, the scale factor is amplified to speed up the system response.

Exemplarily, the comparative example items are improved, specifically including:

k′ _p = k·k _p +k _p0 ′

Among them, k′ _p represents the adjusted proportional item coefficient, k _p represents the proportional item coefficient before adjustment, k represents the adjustment coefficient, and k _p0 ′ represents the adjusted initial value, which is taken as 0.35. (from debugging under the conventional PID algorithm)

The adjustment factor is determined according to the following formula:

By scaling to match the action intensities and working conditions of different proportional terms, it can speed up the response while suppressing high-frequency interference.

Further, the input and output of the improved fuzzy PID controller are in the form of triangular membership functions.

Further, after the S103 and before the S104, it also includes:

S103-41: simulate the improved fuzzy PID control algorithm;

S104-42: Introduce time multiplied absolute error integral index (ITAE) to quantify the anti-jamming performance of various algorithm outputs in the whole response process.

It should be understood that the simulation of the improved fuzzy PID control algorithm is performed under the Simulink environment of MATLAB.

The simulation of the algorithm is to introduce impulse interference and white noise interference respectively in the running process, and compare the response curves of traditional PID, fuzzy PID and improved fuzzy PID under interference.

In the Simulink environment of MATLAB, a model is established to simulate the improved fuzzy PID algorithm. A pulse disturbance is introduced in the 10s to 13s. The simulation model of the PID algorithm under the pulse disturbance is shown in Figure 3. Observing the response characteristics of the improved algorithm, the response curve of the PID algorithm under pulse disturbance is shown in Figure 4.

In the 10s to 15s, a Gaussian white noise interference is introduced, and the simulation model of the PID algorithm under the white noise interference is shown in Figure 5. Observing the response characteristics of the improved algorithm, the response curve of the PID algorithm under white noise interference is shown in Figure 6.

The time multiplied absolute error integral index mainly considers the relationship between the deviation of the response in the time domain and the set value, and its formula is:

Among them, 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∈[0,1], the smaller the J is, the smaller the response of the control system deviates from the target value and the smaller the degree of fluctuation.

The performance indexes of traditional PID algorithm, fuzzy PID algorithm and improved fuzzy PID algorithm under pulse disturbance are compared, as shown in Table 2. It can be seen from the results that during the pulse disturbance, the traditional PID can no longer work normally, and the output of the fuzzy PID will fluctuate when it is disturbed. The improved fuzzy PID not only has no fluctuation during the disturbance, but also has a faster response speed.

Table 2

The performance indicators of the traditional PID algorithm, the fuzzy PID algorithm and the improved fuzzy PID algorithm under the interference of white noise are compared, as shown in Table 3. It can be seen from the results that during the interference period of Gaussian white noise, the traditional PID is most affected by the interference. The output oscillation of the fuzzy PID is weaker than that of the traditional PID, but the fluctuation is still large. The improved fuzzy PID has less fluctuation during the interference period, and the response faster.

table 3

As one or more embodiments, in the S104, an improved fuzzy PID control algorithm is used to control the differential speed between the two wheels of the AGV of the automatic guided transport vehicle, so that the AGV returns to the set trajectory; the specific steps include:

Taking the real-time position deviation of the automatic guided transport vehicle AGV as the input, through the real-time control of the improved fuzzy PID control algorithm, the real-time speed difference of the left and right two-wheel drives of the AGV is output, and the AGV is controlled to return to the set trajectory.

The motion control of AGV generally adopts the classical PID (proportional-integral-derivative) control strategy with a simple principle. With the development of modern control theory, fuzzy control algorithms are more and more applied because of their advantages of not requiring an accurate controlled model. In the field of AGV control, the combination of fuzzy logic algorithm and conventional PID control algorithm can give full play to the advantages of the two methods and improve the overall performance of the AGV control system. In view of the above situation and needs, this application proposes an improved fuzzy PID algorithm for AGV control system to improve the anti-interference performance of the control system, and proposes a quantitative evaluation method of algorithm performance to quantify the system performance before and after the algorithm improvement. Analyze and compare.

Embodiment 2

This embodiment provides an AGV control system based on an improved fuzzy PID control algorithm;

AGV control system based on improved fuzzy PID control algorithm, including:

a collection module, which is configured to: collect the current position of the automatic guided transport vehicle AGV, calculate its deviation from the set trajectory, and obtain the current position deviation of the automatic guided transport vehicle AGV;

an input module, which is configured to: input the current position deviation of the automatic guided transport vehicle AGV and its differential value to the fuzzy controller, and output the proportional change value, the integral change value and the differential coefficient change value;

The adjustment module is configured to: adopt the proportional change value, the integral change value and the differential change value to adjust the corresponding proportional parameter, integral parameter and derivative parameter in the improved PID control algorithm in real time respectively, so as to obtain the improved fuzzy PID control algorithm ;

The control module is configured to: control the 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 trajectory.

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 examples and application scenarios implemented by the above-mentioned modules and the corresponding steps are the same, but are not limited to the above-mentioned steps. The content disclosed in the first embodiment. It should be noted that the above modules may be executed in a computer system such as a set of computer-executable instructions as part of the system.

The description of each embodiment in the foregoing embodiments has its own emphasis. For the part that is not described in detail in a certain embodiment, reference may be made to the relevant description of other embodiments.

The proposed system can be implemented in other ways. For example, the system embodiments described above are only illustrative. For example, the division of the above modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into Another system, or some features can be ignored, or not implemented.

Embodiment 3

This embodiment also provides an electronic device, comprising: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are Stored in the memory, when the electronic device runs, the processor executes one or more computer programs stored in the memory, so that the electronic device executes the method described in 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 array FPGA or other programmable logic devices , discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory may include read-only memory and random access memory and 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 the implementation process, each step of the above-mentioned method can be completed by a hardware integrated logic circuit in a processor or an instruction in the form of software.

The method in the first embodiment can be directly embodied as being executed by a hardware processor, or executed by a combination of hardware and software modules in the processor. The software modules may be located in random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers and other storage media mature in the art. The storage medium is located in the memory, and the processor reads the information in the memory, and completes the steps of the above method in combination with its hardware. To avoid repetition, detailed description is omitted here.

Those of ordinary skill in the art can realize that the unit, that is, the algorithm step of each example described in conjunction with this embodiment, can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application.

Embodiment 4

This embodiment also provides a computer-readable storage medium for storing computer instructions, and when the computer instructions are executed by a processor, the method described in the first embodiment is completed.

The above descriptions are only preferred embodiments of the present application, and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included within the protection scope of this application.

Claims (10)

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;

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. The method of claim 1, wherein the modified fuzzy PID control algorithm comprises: an improvement to the PID algorithm.

3. The method of claim 2, wherein the improvement to the PID algorithm comprises: an integral term improvement, a differential term improvement and a comparative example improvement are carried out.

4. The method of claim 3, wherein the modifying the integral term is: 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.

5. The method of claim 3, wherein the modifying the derivative term is: 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.

6. The method of claim 3, wherein the comparative example is modified by: 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.

7. The method as claimed in claim 1, wherein the step of using the proportional variation value, the integral variation value and the differential variation value to respectively adjust the proportional parameter, the integral parameter and the differential parameter corresponding to the improved PID control algorithm in real time, and after obtaining the improved fuzzy PID control algorithm, using the improved fuzzy PID control algorithm to control the differential speed of two AGVs of the automatic guided vehicle, so that the AGVs return to the set trajectory, further comprises:

simulating an improved fuzzy PID control algorithm;

and introducing a time-multiplied absolute error integral index ITAE to quantify the anti-interference performance of various algorithm outputs in the whole response process.

8. 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;

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.

9. 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 any of the preceding claims 1-7.

10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 7.

Images