CN108132603A - A kind of Self-tuning Fuzzy PID Control and system - Google Patents

Abstract

The application discloses a fuzzy self-tuning PID control method, which includes using a tracker and a differentiator to obtain an error tracking signal and an error differential signal respectively; The error differential signal is used for self-tuning of PID parameters, and the total control quantity is calculated according to the PID parameters after self-tuning, and output to the controlled object, so as to regulate and control the controlled object. This application uses a tracker and a differentiator that are independent of each other. By setting the relevant parameters of the tracker and the differentiator reasonably, the error tracking signal and the error differential signal with better performance can be obtained respectively, and the phase delay during signal tracking can be avoided. The contradiction between the problem and the noise amplification and waveform distortion problems during signal differentiation can effectively improve the overall control effect. The present application also discloses a fuzzy self-tuning PID control system, which also has the above beneficial effects.

Description

A fuzzy self-tuning PID control method and system

technical field

The present application relates to the technical field of automatic control, in particular to a fuzzy self-tuning PID control method and system.

Background technique

PID control technology is still the basic process control technology commonly used in industrial sites. Fuzzy self-tuning PID control technology combines fuzzy intelligent control technology and PID parameter self-tuning technology on the basis of classic PID control technology, and outputs a reasonable total control amount according to the error signal and the differential signal of the error signal, so that the controlled object has relative Better dynamic performance and static performance.

In the prior art, in order to solve the contradiction between rapidity and overshoot in the PID control process, the tracking differentiator in the active disturbance rejection control is used to track and differentiate the error signal of the system, so that according to the obtained tracking signal and The differential signal is used for fuzzy self-tuning PID control.

However, since the same tracking differentiator is used in the prior art to output the tracking signal and the differential signal, that is, the relevant parameters used to generate the tracking signal are exactly the same as those used to generate the differential signal, and the same set of parameters cannot be simultaneously The perfect tracking signal and differential signal are obtained, so the phase delay problem of the tracking signal and the noise amplification and waveform distortion problems of the differential signal cannot always be solved at the same time, so that there is a contradiction between the two signals.

It can be seen that what kind of fuzzy self-tuning PID control method to use to simultaneously solve the phase delay problem during signal tracking and the noise amplification and waveform distortion problems during signal differentiation, thereby improving the control effect, is what those skilled in the art need to solve important technical issues.

Contents of the invention

The purpose of this application is to provide a fuzzy self-tuning PID control method and system to effectively solve the phase delay problem during signal tracking and the noise amplification and waveform distortion problems during signal differentiation, thereby improving the control effect.

In order to solve the above technical problems, the application provides a fuzzy self-tuning PID control method, including:

Using a tracker and a differentiator to obtain an error tracking signal and an error differential signal respectively; the tracker and the differentiator are independent of each other;

Adopt fuzzy self-tuning PID algorithm, perform PID parameter self-tuning according to the error tracking signal and the error differential signal, calculate the total control amount according to the PID parameter after self-tuning, and output it to the controlled object, so as to control the controlled object Objects are regulated and controlled.

Optionally, said obtaining the error tracking signal and the error differential signal by using the tracker and the differentiator respectively includes:

Using the first tracker and the second tracker to obtain the first tracking signal of the system input signal and the second tracking signal of the system output signal respectively; using the first differentiator and the second differentiator to obtain the first tracking signal of the system input signal a differential signal and a second differential signal of the system output signal;

The difference between the first tracking signal and the second tracking signal is used as the error tracking signal; the difference between the first differential signal and the second differential signal is used as the error differential signal.

Optionally, the expression of the tracker is:

Among them, v is the input signal of the tracker; v1 is the tracking signal of v; v2 is the differential signal of v1; T1 is the integration step of the tracker; r1 is the speed factor of the tracker; The filter factor of the above-mentioned tracker; n is the forecast compensation step size, n>1.

Optionally, n ∈ [2,2h1T1].

Optionally, the expression of the differentiator is:

Among them, u is the input signal of the differentiator; u1 is the tracking signal of u; u2 is the differential signal of u1; T2 is the integration step of the differentiator; r2 is the speed factor of the differentiator; filter factor of the differentiator.

Optionally, T1=T2, h1=h2 and r1=r2.

Optionally, the fuzzy self-tuning PID control algorithm is a fuzzy self-tuning PID control algorithm of discrete universe.

Optionally, the fuzzy self-tuning PID control algorithm is a two-dimensional fuzzy self-tuning PID control algorithm.

Optionally, said calculating the total control quantity according to the PID parameters after self-tuning includes:

Calculate the total control quantity according to the following PID control calculation formula:

Among them, U _(k+1) is the total control quantity at k+1 moment; K _p ^(k+1) , K _i ^(k+1) and K _d ^(k+1) are all the above-mentioned PID parameters; e _(i) is the error tracking signal at the moment of i; is the error differential signal at time k+1.

The application also provides a fuzzy self-tuning PID control system, including:

Tracker: used to obtain error tracking signal;

A differentiator: used to obtain an error differential signal; the tracker and the differentiator are independent of each other;

Fuzzy self-tuning PID controller: used for adopting fuzzy self-tuning PID algorithm, performing PID parameter self-tuning according to the error tracking signal and the error differential signal; calculating the total control amount according to the PID parameters after self-tuning, and outputting it to the controlled object, so as to regulate and control the controlled object.

The fuzzy self-tuning PID control method provided by the present application includes: using a tracker and a differentiator to obtain an error tracking signal and an error differential signal respectively; the tracker and the differentiator are independent of each other; using a fuzzy self-tuning PID algorithm, The PID parameter self-tuning is performed on the error tracking signal and the error differential signal, the total control quantity is calculated according to the self-tuning PID parameter, and output to the controlled object, so as to regulate and control the controlled object.

It can be seen that compared with the prior art, in the fuzzy self-tuning PID control method provided by the present application, independent trackers and differentiators are used to obtain the error tracking signal and the error differential signal respectively, so that the tracker and the error differential signal can be reasonably set The relevant parameters of the differentiators are used to obtain the error tracking signal and the error differential signal with better performance, which avoids the contradiction between the phase delay problem during signal tracking and the waveform distortion and noise amplification problems during signal differentiation. Effectively improve the overall control effect of the system.

Description of drawings

In order to illustrate the prior art and the technical solutions in the embodiments of the present application more clearly, the following briefly introduces the drawings that need to be used in the description of the prior art and the embodiments of the present application. Of course, the following drawings related to the embodiments of the application describe only a part of the embodiments of the application, and those of ordinary skill in the art can obtain other The accompanying drawings, and other obtained drawings also belong to the protection scope of the present application.

Fig. 1 is the flow chart of a kind of fuzzy self-tuning PID control method provided by the embodiment of the application;

Fig. 2 is the control block diagram of a kind of fuzzy self-tuning PID control method provided by the embodiment of the application;

FIG. 3 is a waveform diagram of a system input signal provided by an embodiment of the present application;

Fig. 4 is the oscillogram of the error tracking signal obtained according to the system input signal shown in Fig. 3;

Fig. 5 is the oscillogram of the error differential signal obtained according to the system input signal shown in Fig. 3;

Fig. 6 is the oscillogram of the system output signal obtained according to the system input signal shown in Fig. 3;

FIG. 7 is a waveform diagram of another system input signal provided by the embodiment of the present application;

Fig. 8 is the waveform diagram of the error tracking signal obtained according to the system input signal shown in Fig. 7;

Fig. 9 is the waveform diagram of the error differential signal obtained according to the system input signal shown in Fig. 7;

Fig. 10 is a waveform diagram of the system output signal obtained according to the system input signal shown in Fig. 7;

Fig. 11 is a structural block diagram of a fuzzy self-tuning PID control system provided by the embodiment of the present application.

Detailed ways

The core of this application is to provide a fuzzy self-tuning PID control method and system to effectively solve the phase delay problem during signal tracking and the waveform distortion and noise amplification problems during signal differentiation, thereby improving the control effect.

In order to describe the technical solutions in the embodiments of the present application more clearly and completely, the technical solutions in the embodiments of the present application will be introduced below in conjunction with the drawings in the embodiments of the present application. Apparently, the described embodiments are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

Please refer to Fig. 1, Fig. 1 is a flow chart of a kind of fuzzy self-tuning PID control method provided by the embodiment of the present application, which mainly includes the following steps:

Step 1: Use tracker and differentiator to obtain error tracking signal and error differential signal respectively.

Among them, the tracker and differentiator are independent of each other.

Specifically, the fuzzy self-tuning PID control method provided in this application also uses the error tracking signal and the error differential signal for fuzzy self-tuning PID control, so as to eliminate the contradiction between rapidity and overshoot in the PID control process. However, unlike the prior art, the present application specifically adopts a tracker and a differentiator that are independent of each other, and uses the two to respectively obtain the error tracking signal and the error differential signal of the system. Therefore, the tracker and the differentiator in the present application can respectively adopt the most appropriate parameters, so as to obtain the most accurate error tracking signal and error differential signal respectively.

What needs to be explained here is that fuzzy self-tuning PID control is a closed-loop control with output feedback, and its control goal is to stabilize the output signal of the system at the input signal of the system. The errors mentioned here and below refer to the errors between the input signal of the system and the output signal of the system.

Of course, after adopting the structure of the tracker and the differentiator independently of each other, the specific types of the tracker and the differentiator can also be selected and set by those skilled in the art according to the actual situation. For example, since the tracking differentiator in the ADRR algorithm has both the tracking function and the differentiating function, the tracking differentiator can still be specifically selected as the tracker or differentiator; for another example, other algorithms such as quadrature mixing can also be selected differentiator to obtain the differential signal.

Step 2: Use the fuzzy self-tuning PID algorithm, perform PID parameter self-tuning according to the error tracking signal and error differential signal, calculate the total control quantity according to the PID parameters after self-tuning, and output it to the controlled object, so as to adjust the controlled object control.

Specifically, the fuzzy self-tuning PID algorithm can self-tune the PID parameters in the PID control process according to the system error tracking signal and error differential signal, as well as the preset fuzzy rules, so as to obtain PID parameters with better real-time control effect, And use the adjusted PID parameters to output the control quantity, and act on the controlled object, so that the output signal is stable at the input signal, and the control target state is achieved.

It can be seen that in the fuzzy self-tuning PID control method provided by the embodiment of the present application, the independent tracker and differentiator are used to obtain the error tracking signal and the error differential signal respectively, so that the respective correlations of the tracker and differentiator can be reasonably set. Parameters, to obtain the error tracking signal and error differential signal with better performance, avoiding the contradiction between the phase delay problem during signal tracking and the waveform distortion and noise amplification problems during signal differentiation, thereby effectively improving the overall performance of the system Control effect.

The fuzzy self-tuning PID control method provided by this application, on the basis of the above-mentioned embodiments:

Please refer to FIG. 2 , which is a control block diagram of a fuzzy self-tuning PID control method provided by an embodiment of the present application.

As shown in Figure 2, as a preferred embodiment, the tracker and differentiator are used to obtain the error tracking signal e and the error differential signal include:

Use the first tracker and the second tracker to obtain the first tracking signal x1 of the system input signal x and the second tracking signal y1 of the system output signal y respectively; use the first differentiator and the second differentiator to obtain the system input signal x The first differential signal x2 of the system output signal y and the second differential signal y2 of the system;

The difference between the first tracking signal x1 and the second tracking signal y1 is used as the error tracking signal e; the difference between the first differential signal x2 and the second differential signal y2 is used as the error differential signal

Specifically, since the error is the error between the system input signal x and the system output signal y, two trackers can be used to respectively obtain the first tracking signal x1 of the system input signal x and the second tracking signal y1 of the system output signal y, and then The error tracking signal e is obtained by subtraction.

Similarly, two differentiators can be used to obtain the first differential signal x2 of the system input signal x and the second differential signal y2 of the system output signal y, and then obtain the error tracking signal by subtraction

In addition, those skilled in the art can also only use the control method of single tracker plus single differentiator, that is, firstly, after the error signal is obtained by making a difference between the system input signal x and the system output signal y, the error signal is calculated by using the tracker and differentiator respectively. The signal is tracked and differentiated to obtain the error tracking signal e and the error differential signal Of course, the tracking objects of these two methods are different, and the obtained error tracking signal e and error differential signal The specific values are also different. In fact, the control method of double tracker plus double differentiator is to track first, and then make a difference after obtaining a smooth tracking result, so it has the effect of advanced control compared with the control method of single tracker plus single differentiator , can suppress overshoot more effectively.

As a preferred embodiment, the expression of the tracker is:

Among them, v is the input signal of the tracker; v1 is the tracking signal of v; v2 is the differential signal of v1; T1 is the integral step of the tracker; r1 is the speed factor of the tracker; h1 is the filter factor of the tracker; n is the forecast compensation step size, n>1.

Specifically, as mentioned above, the tracker provided by the embodiment of the present application may adopt the same structure as the tracking differentiator in ADRC, and the output v1 is the tracking signal to be obtained. Compared with the conventional tracking differentiator without forecast compensation (that is, the forecast compensation step size n is 1), the tracking differentiator adopted here can further accelerate the tracking process by setting the forecast compensation step size n>1. The input signal of the device is tracked, and the tracking signal is output, which effectively solves the phase delay problem of the error tracking signal.

In the above expression, the fhan function is the fastest control comprehensive function in the field of automatic control, which can reasonably arrange the transition process of the control terminal, and achieve the effect of solving the contradiction between the response speed and the overshoot. The specific expression of the fhan function is:

As a preferred embodiment, n∈[2,2h1T1].

Specifically, when performing forecast compensation, the value of the forecast compensation step size n cannot be too large blindly, otherwise serious overshoot or even oscillation will be caused. According to experience, the value range of the forecast compensation step size n provided by the embodiment of the present application is preferably n∈[2,2h1T1]. Of course, those skilled in the art can choose the optimal forecast compensation step size n according to actual application conditions, so as to obtain an ideal control effect, which is not limited in this embodiment of the present application.

As a preferred embodiment, the expression of the differentiator is:

Among them, u is the input signal of the differentiator; u1 is the tracking signal of u; u2 is the differential signal of u1; T2 is the integral step of the differentiator; r2 is the speed factor of the differentiator; h2 is the filter factor of the differentiator.

Similarly, according to the foregoing, the differentiator provided in the embodiment of the present application can also be selected to have the same structure as the tracking differentiator in the ADRR algorithm, and the output u2 is the differential signal to be obtained.

It should be noted that, as for the differentiator, the key point is to use its differential function to output the differential signal, so there is no need to pursue the rapidity too much, so as not to affect the quality of the differential signal, causing noise amplification and waveform distortion. Therefore, the differentiator here is the same as the previous introduction. The tracker is different, specifically adopts the structure of the tracking differentiator without forecast compensation, that is, the forecast compensation step size n=1.

As a preferred embodiment, T1=T2, h1=h2 and r1=r2.

Specifically, in the embodiment of the present application, the tracker and the differentiator actually need to track and differentiate the same signal respectively. Therefore, except for the parameter of the forecast compensation step size n, the other corresponding parameters of the tracker and the differentiator It is preferably consistent, that is, the integral step size, speed factor and filter factor of the two are respectively equal.

Of course, those skilled in the art can also choose other forms of differentiators according to actual application conditions, such as differentiators using the quadrature mixing method, etc., and can choose and set the specific values of relevant parameters by themselves. Not limited.

As a preferred embodiment, the fuzzy self-tuning PID control algorithm is a fuzzy self-tuning PID control algorithm of discrete domain.

Specifically, using the fuzzy self-tuning PID control algorithm of the discrete universe can effectively reduce the amount of calculation, simplify the control process, and improve the control effect.

As a preferred embodiment, the fuzzy self-tuning PID control algorithm is a two-dimensional fuzzy self-tuning PID control algorithm.

Specifically, those skilled in the art can further obtain the error quadratic differential signal based on the technology of the embodiment of the present application to perform three-dimensional fuzzy self-tuning PID control, but the calculation process is relatively complicated. In contrast, the fuzzy self-tuning PID control with a two-dimensional structure can obtain control accuracy sufficient to meet general control requirements without affecting the control effect and increasing the computational burden, so it is more widely used.

As a preferred embodiment, calculating the total control quantity according to the PID parameters after self-tuning includes:

Calculate the total control quantity according to the following PID control calculation formula:

Among them, U _(k+1) is the total control quantity at k+1 time; K _p ^(k+1) , K _i ^(k+1) and K _d ^(k+1) are all PID parameters at k+1 time ; e _(i) is the error tracking signal of the i moment; is the error differential signal at time k+1.

Specifically, in the fuzzy self-tuning PID control method provided by the embodiment of the present application, in order to further eliminate the adverse effects of the integral when calculating the total control amount, the integral control in the classic PID control is replaced by the summation control, which can effectively Eliminate integral control lag problem,.

An application embodiment of the fuzzy self-tuning PID control method provided by the embodiment of the present application will be introduced below in combination with a specific controlled object.

Assuming that the controlled object is a second-order system, its transfer function is:

Then, according to the control method shown in the control block diagram in Figure 2, the system input signal x and the system output signal y are respectively tracked and differentiated to obtain the first tracking signal x1 and the first differential signal x2 of the system input signal x, and the system The second tracking signal y1 and the second differential signal y2 of the output signal y, and then make a difference to obtain the error tracking signal e and the error differential signal It is input to the two-dimensional discrete domain fuzzy self-tuning PID controller, and the total control quantity U is output after the PID parameter self-tuning is performed.

When performing PID parameter tuning, the discrete universe specifically set for the error tracking signal e is [-6,-5,-4,-3,-2,-1,0,1,2,3,4, 5,6], the specifically set fuzzy subsets are {NB (negative large), NM (negative medium), NS (negative small), NZ (negative zero), PZ (positive zero), PS (positive small) , PM(center), PB(zhengda)}, where the membership function of each fuzzy subset is shown in Table 1; for the error differential signal As well as the fine-tuning variables ΔK _p , ΔK _i and ΔK _d of the PID parameters, the specifically set discrete domains are [-3, -2, -1, 0, 1, 2, 3], and the specifically set fuzzy sub The sets are {NB (negative big), NM (negative middle), NS (negative small), ZO (zero), PS (positive small), PM (positive middle), PB (positive big)}, error differential signal The membership functions of each fuzzy subset of fine-tuning variables ΔK _p , ΔK _i or ΔK _d are shown in Table 2.

Table 1

Table 2

When the three PID parameters are self-tuned through fuzzy control, the PID parameters obtained after specific tuning are:

Among them, K _p ^(k) , K _i ^(k) and K _d ^(k) are all PID parameters at time k; the fine-tuning variables ΔK _p , ΔK _i and ΔK _d are specifically determined by the error tracking signal e, the error differential signal And the corresponding fuzzy tuning rule table is determined. Table 3, Table 4 and Table 5 are the fuzzy tuning rule tables of ΔK _p , ΔK _i and ΔK _d respectively.

table 3

Table 4

table 5

According to the error tracking signal e and the error differential signal The membership function table of the fine-tuning variable ΔK _p and the fuzzy tuning rule table of the fine-tuning variable ΔK p can get the specific control table of the fine-tuning variable ΔK _p , as shown in Table 6.

Table 6

According to the error tracking signal e and the error differential signal The membership function table of the fine-tuning variable ΔK _i and the fuzzy tuning rule table of the fine-tuning variable ΔK i can get the specific control table of the fine-tuning variable ΔK _i , as shown in Table 7.

Table 7

According to the error tracking signal e and the error differential signal The membership function table of the fine-tuning variable ΔK _d and the fuzzy tuning rule table of the fine-tuning variable ΔK d can get the specific control table of the fine-tuning variable ΔK _d , as shown in Table 8.

Table 8

After obtaining the adjusted PID parameters through the above setting process, the total control quantity U input to the controlled object can be calculated according to the PID control calculation formula mentioned above, so as to adjust the system output signal y of the controlled object to achieve Control objectives.

If the system input signal x is a unit step signal, as shown in Figure 3, the error tracking signal e and error differential signal obtained in this application example The waveform diagrams of are shown in Figure 4 and Figure 5 respectively; and the waveform diagram of the final system output signal y is shown in Figure 6.

It can be seen from Fig. 4 to Fig. 6 that the error tracking signal e and error differential signal obtained in the embodiment of the present application The curve of is smooth, and the signal quality is better; while the system output signal y is also smooth and has no overshoot, and the control effect is better.

If the system input signal x is a unit step signal with white noise, as shown in Figure 7, the error tracking signal e and error differential signal obtained in this application example The waveform diagrams of are shown in Figure 8 and Figure 9 respectively; and the waveform diagram of the final system output signal y is shown in Figure 10.

It can be seen from Fig. 7 to Fig. 10 that the error tracking signal e and the error differential signal obtained in the embodiment of the present application When there is serious noise interference in the system input signal x, a relatively smooth curve can still be obtained, which has better filtering effect and noise suppression ability; thus, the system output signal y is also smooth and has no overshoot at the same time, and has a relatively smooth curve. Good control effect.

The fuzzy self-tuning PID control system provided by the embodiment of the present application is introduced below.

Please refer to Figure 11, Figure 11 is a structural block diagram of a fuzzy self-tuning PID control system provided by the present application; including a tracker 1, a differentiator 2, a fuzzy self-tuning PID controller 3 and a controlled object 4;

Tracker 1 is used to obtain error tracking signal;

Differentiator 2 is used to obtain the error differential signal; tracker 1 and differentiator 2 are independent of each other;

The fuzzy self-tuning PID controller 3 is used to adopt the fuzzy self-tuning PID algorithm, and perform PID parameter self-tuning according to the error tracking signal and the error differential signal; calculate the total control quantity according to the PID parameters after self-tuning, and output it to the controlled object 4, In order to regulate and control the controlled object.

It can be seen that the fuzzy self-tuning PID control system provided by the present application uses independent tracker 1 and differentiator 2 to obtain the error tracking signal and error differential signal respectively, so that the respective Relevant parameters are used to obtain error tracking signals and error differential signals with better performance, which avoids the contradiction between the phase delay problem during signal tracking and the noise amplification and waveform distortion problems during signal differentiation, thereby effectively improving the system performance. overall control effect.

The specific implementation manners of the fuzzy self-tuning PID control system provided in this application and the fuzzy self-tuning PID control method described above can be referred to in correspondence with each other, and will not be repeated here.

Each embodiment in the present application is described in a progressive manner, each embodiment focuses on the differences from other embodiments, and the same and similar parts of the various embodiments can be referred to each other. As for the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and for the related information, please refer to the description of the method part.

It should also be noted that in this application, relative terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between such entities or operations. Furthermore, the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, but also includes elements not expressly listed. other elements of or also include elements inherent in such a process, method, article, or device. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising said element.

The technical solution provided by the present application has been introduced in detail above. In this paper, specific examples are used to illustrate the principles and implementation methods of the present application, and the descriptions of the above embodiments are only used to help understand the methods and core ideas of the present application. It should be pointed out that those skilled in the art can make some improvements and modifications to the application without departing from the principles of the application, and these improvements and modifications also fall within the protection scope of the claims of the application.

Claims (10)

1. A fuzzy self-tuning PID control method is characterized in that, comprising: Using a tracker and a differentiator to obtain an error tracking signal and an error differential signal respectively; the tracker and the differentiator are independent of each other; Adopt fuzzy self-tuning PID algorithm, perform PID parameter self-tuning according to the error tracking signal and the error differential signal, calculate the total control amount according to the PID parameter after self-tuning, and output it to the controlled object, so as to control the controlled object Objects are regulated and controlled. 2. fuzzy self-tuning PID control method according to claim 1, is characterized in that, described respectively utilizing tracker and differentiator to obtain error tracking signal and error differential signal comprises: Using the first tracker and the second tracker to obtain the first tracking signal of the system input signal and the second tracking signal of the system output signal respectively; using the first differentiator and the second differentiator to obtain the first tracking signal of the system input signal a differential signal and a second differential signal of the system output signal; The difference between the first tracking signal and the second tracking signal is used as the error tracking signal; the difference between the first differential signal and the second differential signal is used as the error differential signal. 3. fuzzy self-tuning PID control method according to claim 1, is characterized in that, the expression of described tracker is: Among them, v is the input signal of the tracker; v1 is the tracking signal of v; v2 is the differential signal of v1; T1 is the integration step of the tracker; r1 is the speed factor of the tracker; The filter factor of the above-mentioned tracker; n is the forecast compensation step size, n>1. 4. The fuzzy self-tuning PID control method according to claim 3, characterized in that n∈[2,2h 1/T 1]. 5. fuzzy self-tuning PID control method according to claim 3, is characterized in that, the expression of described differentiator is: Among them, u is the input signal of the differentiator; u1 is the tracking signal of u; u2 is the differential signal of u1; T2 is the integration step of the differentiator; r2 is the speed factor of the differentiator; filter factor of the differentiator. 6. The fuzzy self-tuning PID control method according to claim 5, characterized in that T1=T2, h1=h2 and r1=r2. 7. The fuzzy self-tuning PID control method according to claim 5, characterized in that, the fuzzy self-tuning PID control algorithm is a fuzzy self-tuning PID control algorithm of discrete universe. 8. The fuzzy self-tuning PID control method according to claim 7, wherein the fuzzy self-tuning PID control algorithm is a two-dimensional fuzzy self-tuning PID control algorithm. 9. according to the fuzzy self-tuning PID control method described in any one of claims 1 to 8, it is characterized in that, the PID parameter calculation total control quantity according to the described self-tuning comprises: Calculate the total control quantity according to the following PID control calculation formula: Among them, U _(k+1) is the total control quantity at k+1 moment; K _p ^(k+1) , K _i ^(k+1) and K _d ^(k+1) are all the above-mentioned PID parameters; e _(i) is the error tracking signal at the moment of i; is the error differential signal at time k+1. 10. A fuzzy self-tuning PID control system, characterized in that, comprising: Tracker: used to obtain error tracking signal; A differentiator: used to obtain an error differential signal; the tracker and the differentiator are independent of each other; Fuzzy self-tuning PID controller: used for adopting fuzzy self-tuning PID algorithm, performing PID parameter self-tuning according to the error tracking signal and the error differential signal; calculating the total control amount according to the PID parameter after self-tuning, and outputting it to the controlled object, so as to regulate and control the controlled object.