CN110649648B - Power spring control method based on variable-discourse-domain fuzzy PI self-adaptive control - Google Patents
Power spring control method based on variable-discourse-domain fuzzy PI self-adaptive control Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/30—Reactive power compensation
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Abstract
The invention discloses a power spring control method based on variable universe fuzzy PI self-adaptive control, which comprises the following steps: determining fuzzy input variables and output variables, designing a scale factor capable of adjusting the input variable domain and the output variable domain, combining the quantization factor, the scale factor and the corresponding scale factor to realize domain variability, designing a membership function and a fuzzy rule of a fuzzy controller, and finishing fuzzy reasoning and clarification. The invention considers the influence of the change of the system working condition or parameter in the system operation process on the power spring adjusting performance, designs the variable universe fuzzy PI self-adaptive control strategy aiming at the characteristics of the power spring, can solve the problems of weak self-adaptive ability and low control precision when the existing fuzzy control is applied to the power spring control system, further enhances the system stability and improves the power spring adjusting capability.
Description
Technical Field
The invention relates to the technical field of power spring control, in particular to a power spring control method based on variable universe fuzzy PI self-adaptive control.
Background
The proportion of renewable energy power generation grid connection is gradually increased, and the intermittence and instability of generated power can bring great disturbance to a power grid. To solve this problem, the tree resource topic group of hong kong university first proposed the concept of power spring and divided the load in the circuit into critical load and non-critical load. By analogy with the principle of a mechanical spring, the power spring can stabilize the voltage of the critical load when the voltage of the power grid fluctuates and transfer the voltage fluctuation to the non-critical load, so that the aim of protecting the critical load is fulfilled.
In a traditional electric spring control system, a PI parameter cannot be changed after being selected, and the self-adaptive capacity is not strong. There are also learners who use the fuzzy PI control to improve the shortcomings of the traditional PI control method, however, the general fuzzy control itself has a great limitation. In general fuzzy control, the values of the quantization factor and the scale factor directly affect the control effect of the system, but usually they are set and then will not be changed during the operation of the system. When the system working condition or parameter changes, the adjustment can not be made in time, and the self-adaptive capacity of the system is low. Especially, when the non-critical load resistance value having a large influence on the voltage regulation performance of the power spring changes, the power spring cannot obtain a good control effect. And the fuzzy rule is also designed on a fixed theory domain, when the error is reduced and approaches to zero, the available fuzzy rule is greatly reduced, and the original domain division is difficult to obtain a good control effect.
Disclosure of Invention
The invention aims to provide a power spring control method based on variable universe fuzzy PI self-adaptive control, which solves the problem that the self-adaptive capacity of a general fuzzy control method applied to a power spring control system is not strong, and improves the adjusting performance of a power spring when the system working condition or parameter changes.
The technical scheme for realizing the purpose of the invention is as follows: a power spring control method based on variable discourse domain fuzzy PI self-adaptive control comprises the following steps:
step 2, designing a scale factor alpha for adjusting the input variable discourse domain size e And alpha ec And scaling factor beta to adjust the output variable domain size p And beta i (ii) a Wherein alpha is e Scaling factor, alpha, representing the size of the fuzzy domain of the adjustment input variable e ec Scaling factor, beta, representing the size of the fuzzy universe of the adjusted input variable ec p Indicating the regulated output variable Δ K p Scale factor, β, of the size of the ambiguity domain i Indicating the regulated output variable Δ K i Scaling factors of the size of the fuzzy universe;
step 3, theory of proceedingDomain adjustment, namely a variable domain; the quantization factor K of the input variable e e Divided by the scaling factor alpha e The quantization factor K of the input variable ec ec Divided by the scaling factor alpha ec Will output a variable Δ K p Scale factor L of p Multiplying by a scaling factor beta p Will output a variable Δ K i Scale factor L of i Multiplying by a scaling factor beta i ；
Step 4, designing a membership function and a fuzzy rule in the fuzzy controller, and completing fuzzy reasoning and clarification by utilizing a Mandani fuzzy reasoning and gravity center method to obtain a PI parameter correction quantity delta K of the PI controller in the power spring control system p And Δ K i ；
Step 5, correcting quantity delta K of the PI parameter obtained in the step 4 p And Δ K i Respectively with the original PI parameter K p0 And K i0 Adding to obtain final control parameter K of PI controller in power spring control system p And K i 。
Compared with the prior art, the invention has the following remarkable advantages: when the working condition or parameter of the system changes, the expansion factor of the invention can adjust the domain in time according to the adjusting characteristic of the power spring, so as to obtain better adjusting effect than the common fuzzy control, improve the self-adapting capability of the system, enable the power spring to adjust the key load voltage more quickly and improve the stability of the system.
Drawings
FIG. 1 is a circuit diagram of a system using an electric spring device in an embodiment of the present invention.
FIG. 2 is a schematic diagram of a power spring control using variable universe fuzzy PI adaptive control according to an embodiment of the present invention.
Fig. 3 is a comparison graph of waveforms of critical load voltages using the general fuzzy PI control and the variable domain fuzzy PI control in the case where the non-critical load resistance abruptly increases from 50.5 Ω to 80 Ω at 0.4 s.
Fig. 4 is a graph comparing the waveforms of the critical load voltage using the general fuzzy PI control and the variable domain fuzzy PI control in the case where the non-critical load resistance value is abruptly decreased from 50.5 Ω to 25 Ω at 0.4 s.
FIG. 5 is a graph comparing the critical load voltage waveforms using the general fuzzy PI control and the variable domain fuzzy PI control when the system is subjected to continuous reactive power disturbance.
Detailed Description
The topological structure of the power spring device consists of a single-phase voltage source type full-bridge inverter and an LC low-pass filter, the power spring device is connected with a non-critical load in series to form an intelligent load, and the intelligent load is connected with the critical load in parallel to play the roles of protecting the voltage of the critical load and transferring the voltage fluctuation to the non-critical load. The circuit schematic diagram is shown in fig. 1.
The invention provides a power spring control method based on variable universe fuzzy PI self-adaptive control, a control schematic diagram of which is shown in figure 2, and the method comprises the following steps:
Step 2, designing a scale factor alpha for adjusting the input variable discourse domain size e And alpha ec And scaling factor beta to adjust the output variable domain size p And beta i Wherein α is e Scaling factor, alpha, representing the size of the fuzzy domain of the adjustment input variable e ec Scaling factor, beta, representing the size of the fuzzy universe of the adjusted input variable ec p Indicating the regulated output variable Δ K p Scale factor, β, of the size of the ambiguity domain i Indicating the regulated output variable Δ K i A scale factor of the size of the ambiguity domain.
Step 3, adjusting the discourse domain, namely changing the discourse domain according to the step2 derived scaling factor alpha e 、α ec 、β p 、β i A quantization factor K of the input variable e e Divided by the scaling factor alpha e The quantization factor K of the input variable ec ec Divided by the scaling factor alpha ec Will output a variable Δ K p Scale factor L of p Multiplying by a scaling factor beta p Will output a variable Δ K i Scale factor L of i Multiplying by a scaling factor beta i Thus, the domain of discourse is variable.
Step 4, designing a membership function and a fuzzy rule in the fuzzy controller, and completing fuzzy reasoning and clarification by utilizing Mandani fuzzy reasoning and a gravity center method to obtain a PI parameter correction quantity delta K of the PI controller in the power spring control system p And Δ K i 。
Step 5, correcting quantity delta K of the PI parameter obtained in the step 4 p And Δ K i Respectively with the original PI parameter K p0 And K i0 Adding to obtain final control parameter K of PI controller in power spring control system p And K i 。
Further, fuzzy input variables e, ec and output variables Δ K are determined in step 1 p 、ΔK i The specific process comprises the following steps:
the input variable e of the fuzzy controller represents the effective value V of the key load voltage s And a critical load voltage reference value V s-ref An error of (2); the input variable ec represents the effective value V of the key load voltage s And a critical load voltage reference value V s-ref The rate of change of error of; the key load represents a load with a small allowable input voltage range in normal operation; output variable Δ K p A correction amount representing a parameter P of a PI controller in the power spring control system; output variable Δ K i Represents the correction of the parameter I of the PI controller in the power spring control system. Setting a key load voltage effective value V s And a critical load voltage reference value V s-ref The error e of (a) is within a range of [ -6,6]The error change rate ec is in the range of-600,600]. Quantizing the fundamental domains of input variable error e and error rate of change ec to fuzzy domain [ -6,6]Can obtain error e and error variationThe quantization factors of the rate ec are respectively K e 6/6 ═ 1 and K ec 6/600-0.01. Setting output variable Δ K p And Δ K i Has a domain range of [ -6,6]Clear value Δ K p Has a variation range of [ -0.1,0.1]，ΔK i Has a variation range of [ -22 ]]Then the scale factor L p ＝0.1/6＝1/60，L i ＝2/6＝1/3。
Further, step 2 designs a scale factor alpha for adjusting the domain size of the input variable e And alpha ec The specific process comprises the following steps:
key load voltage effective value V s And a critical load voltage reference value V s-ref Error e of (2) and scaling factor alpha of error change rate ec e And alpha ec Calculated by the equations (1) and (2), respectively:
wherein E represents the boundary value of the variation range of the input variable E, EC represents the boundary value of the variation range of the input variable EC, and according to the design of step 1, E is 6, and EC is 600.
Further, step 2 designs a scaling factor beta for adjusting the domain size of the output variable p And beta i The specific process comprises the following steps:
output variable Δ K p And Δ K i Scaling factor beta of p And beta i Comprehensively considering the critical load voltage V s Change of (1) and Δ K p And Δ K i The variation trend of the data is obtained by fuzzy reasoning. According to the key load voltage V in the operation process of the power spring s To the PI parameter correction quantity delta K p And Δ K i Respectively make beta p And beta i Corresponding fuzzy rules. Critical load voltage variation and Δ K p And Δ K i The change conditions of (A) have the following corresponding relations: when the temperature is higher than the set temperatureKey load voltage effective value V s And a critical load voltage reference value V s-ref When the error e is larger than 0 and the absolute value is gradually reduced, and the error change rate ec is smaller than 0, Δ K p Should be gradually decreased, Δ K i Should be gradually increased; when key load voltage effective value V s And a critical load voltage reference value V s-ref The error e of (1) is less than 0 and the absolute value is gradually increased, and meanwhile, the error change rate ec is less than 0 and tends to be flat, and delta K p Should be gradually increased, Δ K i Should be gradually decreased; when key load voltage effective value V s And a critical load voltage reference value V s-ref Is less than 0 and the absolute value of the error e is gradually reduced, and the error change rate ec is more than 0 and delta K p Should be gradually decreased, Δ K i Should be gradually increased; when key load voltage effective value V s And a critical load voltage reference value V s-ref The error e is larger than 0 and the absolute value is gradually increased, and meanwhile, the error change rate ec is larger than 0 and tends to be flat, and delta K p Should be gradually increased by Δ K i It should be gradually decreased.
For beta is p And beta i The input variable is still the key load voltage effective value V when the fuzzy reasoning is adopted s And a critical load voltage reference value V s-ref Error e and error change rate ec of (1), output variable being conversion delta K p And Δ K i Scale factor beta for ambiguity domain p And beta i . The input and output state variables are symmetrically divided, and the membership functions are triangular functions. Input variable key load voltage effective value V s And a critical load voltage reference value V s-ref The ambiguity domain of the error e and the error change rate ec thereof are [ -6,6 [)]The language values are expressed by { NB, NM, NS, ZE, PS, PM, PB }. The output state variable linguistic value is expressed by { small, medium, large }, i.e., { VS, S, M, B, VB }, and the output variable β p And beta i In the range of [0,1]. Summarizing to obtain the scaling factor beta according to the change rule and the discourse domain division p The fuzzy rule of (1) is:
| e\ec | NB | NM | NS | ZE | PS | PM | PB |
| NB | VB | VB | VB | B | B | M | M |
| NM | VB | VB | B | B | B | M | S |
| NS | VB | B | B | B | M | S | S |
| ZE | B | B | B | M | S | S | S |
| PS | B | B | M | S | S | S | VS |
| PM | M | M | S | S | S | VS | VS |
| PB | M | S | S | S | VS | VS | VS |
obtaining the scaling factor beta i The fuzzy rule of (1) is:
further, the specific process of designing the membership function and the fuzzy rule of the fuzzy controller in the step 4 is as follows:
the input state variable and the output state variable are symmetrically divided, and the membership function adopts a triangular membership function. Input variables e, ec and output variables Δ K p 、ΔK i Are all defined as 7 fuzzy subsets, and the fuzzy domain ranges are [ -6,6 [ -6 [ ]]The language variable value is expressed by { negative large, negative middle, negative small, zero, positive small, positive middle, positive large }, and the corresponding fuzzy set is { NB, NM, NS, ZE, PS, PM, PB }. The delta K is given according to the PI parameter adjustment rule and by combining simulation experience p The fuzzy rule of (1) is as follows:
| e\ec | NB | NM | NS | ZE | PS | PM | PB |
| NB | PB | PB | PM | PM | PS | ZE | ZE |
| NM | PB | PM | PM | PS | PS | ZE | ZE |
| NS | PM | PM | PM | PS | ZE | NS | NS |
| ZO | PM | PS | PS | ZE | NS | NM | NM |
| PS | PS | PS | ZE | NS | NS | NM | NM |
| PM | ZE | ZE | NS | NM | NM | NM | NB |
| PB | ZE | NS | NS | NM | NM | NB | NB |
ΔK i the fuzzy rule of (1) is as follows:
| e\ec | NB | NM | NS | ZE | PS | PM | PB |
| NB | NB | NB | NM | NM | NS | NS | ZE |
| NM | NB | NM | NM | NS | NS | ZE | ZE |
| NS | NB | NM | NS | NS | ZE | PS | PS |
| ZO | NM | NS | NS | ZE | PS | PS | PM |
| PS | NM | NS | ZE | PS | PS | PM | PM |
| PM | NS | ZE | PS | PS | PM | PB | PB |
| PB | ZE | ZE | PS | PM | PM | PB | PB |
using Mandani fuzzy reasoning and using a gravity method to clarify to obtain a clear value delta K of the correction quantity of the PI parameter p And Δ K i 。
Further, step 5 is to correct the PI parameter correction quantity delta K p And Δ K i And the original PI parameter K p0 And K i0 Adding to obtain final control parameter K of PI controller in power spring control system p And K i The specific method comprises the following steps:
obtaining the final control parameter of the PI controller in the power spring control system according to the formula (3):
wherein K p And K i Representing the current final parameter, K, of the PI controller in the power spring control system p0 And K i0 Representing the value of the initial parameter of the controller, Δ K p And Δ K i And 4, correcting the PI parameter obtained in the step 4.
The present invention will be further described with reference to the following specific examples.
Examples
The power spring system is designed as follows: the power spring device consists of a single-phase voltage source type full-bridge inverter and an LC low-pass filter, wherein the inductance value of the low-pass filter is 0.5mH, the capacitance value is 13.2 muF, and the voltage of the direct current side of the inverter is 400V. The critical load resistance value is 53 omega, the non-critical load resistance value is 50.5 omega, and the critical load voltage reference value is 220V.
In order to verify that the adjusting performance of the power spring when the variable domain fuzzy PI self-adaptive control method is used is better than that under the control of a general fuzzy PI, under the condition of ensuring that all parameters of a system are consistent, the resistance value of a non-critical load which has a large influence on the adjusting performance of the power spring is changed in the running process of the system, and MATLAB simulation is utilized to compare the adjusting performance of the power spring under two control modes:
fig. 3 is a comparison graph of waveforms of critical load voltages using the general fuzzy PI control and the variable domain fuzzy PI control in the case where the non-critical load resistance abruptly increases from 50.5 Ω to 80 Ω at 0.4 s. Fig. 4 is a graph comparing the waveforms of the critical load voltage using the general fuzzy PI control and the variable domain fuzzy PI control in the case where the non-critical load resistance value is abruptly decreased from 50.5 Ω to 25 Ω at 0.4 s. FIG. 5 is a comparison graph of critical load voltage waveforms using general fuzzy PI control and variable universe fuzzy PI control when the system suffers continuous reactive power disturbance after 0.4 s.
Compared with the general fuzzy PI control, the variable universe fuzzy PI self-adaptive control can improve the power spring adjusting capacity, improve the system response speed and stability and reduce the fluctuation range of the key load voltage.
The above discussion is merely an example of the present invention, and any equivalent variations on the present invention are intended to be included within the scope of the present invention.
Claims (6)
1. A power spring control method based on variable discourse domain fuzzy PI self-adaptive control is characterized by comprising the following steps:
step 1, determining fuzzy input variables e and ec and output variable delta K p 、ΔK i (ii) a Wherein the input variable e represents the effective value V of the key load voltage s And a critical load voltage reference value V s-ref An error of (2); the input variable ec represents the key load voltage effective value V s And a critical load voltage reference value V s-ref The rate of change of error of; output variable Δ K p A correction amount representing a parameter P of a PI controller in the power spring control system; output variable Δ K i A correction quantity of a parameter I representing a PI controller in the power spring control system;
step 2, designing a scale factor alpha for adjusting the input variable discourse domain size e And alpha ec And scaling factor beta for adjusting output variable domain size p And beta i (ii) a Wherein alpha is e Scaling factor, alpha, representing the size of the fuzzy domain of the adjustment input variable e ec Presentation adjustmentScale factor, beta, of input variable ec fuzzy universe size p Indicating the regulated output variable Δ K p Scale factor, β, of the size of the ambiguity domain i Indicating the regulated output variable Δ K i Scaling factors for the size of the fuzzy universe;
step 3, adjusting the domain, namely changing the domain; the quantization factor K of the input variable e e Divided by the scaling factor alpha e The quantization factor K of the input variable ec ec Divided by the scaling factor alpha ec Will output a variable Δ K p Scale factor L of p Multiplying by a scaling factor beta p Will output a variable Δ K i Scale factor L of i Multiplying by a scaling factor beta i ；
Step 4, designing a membership function and a fuzzy rule in the fuzzy controller, and completing fuzzy reasoning and clarification by utilizing Mandani fuzzy reasoning and a gravity center method to obtain a PI parameter correction quantity delta K of the PI controller in the power spring control system p And Δ K i ；
Step 5, correcting quantity delta K of the PI parameter obtained in the step 4 p And Δ K i Respectively with the original PI parameter K p0 And K i0 Adding to obtain final control parameter K of PI controller in power spring control system p And K i 。
2. The power spring control method based on variable discourse domain fuzzy PI adaptive control as claimed in claim 1, wherein fuzzy input variables e, ec and output variable delta K are determined in step 1 p 、ΔK i The specific process comprises the following steps:
setting a key load voltage virtual value V s And a critical load voltage reference value V s-ref Has an error e of [ -6,6 ] in a range]The error change rate ec is in the range of-600,600](ii) a Quantizing the fundamental domains of input variable error e and error rate of change ec to the fuzzy domain [ -6,6]The quantization factors for obtaining the error e and the error change rate ec are respectively K e 6/6 ═ 1 and K ec 6/600 ═ 0.01; setting output variable Δ K p And Δ K i Has a domain range of [ -6,6]Clear value Δ K p Has a variation range of [ -0.1,0 [).1]，ΔK i Has a variation range of [ -22 ]]Then the scale factor L p ＝0.1/6＝1/60，L i ＝2/6＝1/3。
3. The power spring control method based on variable-domain fuzzy PI adaptive control of claim 2, wherein in step 2, a scaling factor α for adjusting the size of an input variable domain is designed e And alpha ec The specific process comprises the following steps:
key load voltage effective value V s And a critical load voltage reference value V s-ref Error e of (2) and scaling factor alpha of error change rate ec e And alpha ec Calculated by the equations (1) and (2), respectively:
wherein E represents the boundary value of the variation range of the input variable E, EC represents the boundary value of the variation range of the input variable EC, and according to the design of step 1, E is 6, and EC is 600.
4. The power spring control method based on variable universe fuzzy PI adaptive control of claim 3, wherein in step 2, a scaling factor beta for adjusting output variable universe size is designed p And beta i The specific process comprises the following steps:
output variable Δ K p And Δ K i Stretch factor beta of p And beta i Comprehensively considering the critical load voltage V s Change of (1) and Δ K p And Δ K i The variation trend of the system is obtained by adopting fuzzy reasoning; according to the key load voltage V in the operation process of the power spring s To the PI parameter correction quantity delta K p And Δ K i Respectively make beta p And beta i Corresponding diePasting rules; critical load voltage variation and delta K p And Δ K i The following correspondence exists between the change conditions of (1): when key load voltage effective value V s And a critical load voltage reference value V s-ref When the error e is larger than 0 and the absolute value is gradually reduced, and the error change rate ec is smaller than 0, Δ K p Should decrease gradually,. DELTA.K i Should be gradually increased; when key load voltage effective value V s And a critical load voltage reference value V s-ref The error e of (1) is less than 0 and the absolute value is gradually increased, and meanwhile, the error change rate ec is less than 0 and tends to be flat, and delta K p Should be gradually increased, Δ K i Should be gradually reduced; when key load voltage effective value V s And a critical load voltage reference value V s-ref Is less than 0 and the absolute value of the error e is gradually reduced, and the error change rate ec is more than 0 and delta K p Should be gradually decreased, Δ K i Should be gradually increased; when key load voltage effective value V s And a critical load voltage reference value V s-ref The error e is larger than 0 and the absolute value thereof is gradually increased, and the error change rate ec is larger than 0 and tends to be flat, delta K p Should be gradually increased by Δ K i Should be gradually decreased;
for beta is p And beta i The input variable is still the key load voltage effective value V when fuzzy reasoning is adopted s And a critical load voltage reference value V s-ref Error e and error change rate ec, output variable is conversion delta K p And Δ K i Scale factor beta of ambiguity domain p And beta i (ii) a The input state variable and the output state variable are symmetrically divided, and the membership functions are triangular functions; input variable key load voltage effective value V s And a critical load voltage reference value V s-ref The ambiguity domain of error e and its error change rate ec is [ -6,6 [ ]]The language value is represented by { NB, NM, NS, ZE, PS, PM, PB }; the output state variable linguistic value is expressed as { small, medium, large }, i.e., { VS, S, M, B, VB }, the output variable β p And beta i In the range of [0,1](ii) a Summarizing to obtain the scaling factor beta according to the change rule and the discourse domain division p The fuzzy rule of (1) is:
obtaining the scaling factor beta i The fuzzy rule of (1) is:
。
5. the power spring control method based on variable-discourse-domain fuzzy PI adaptive control of claim 1, wherein the specific process of designing the fuzzy controller membership function and the fuzzy rule in the step 4 is as follows:
the input state variable and the output state variable are symmetrically divided, and the membership function adopts a triangular membership function; input variables e, ec and output variables Δ K p 、ΔK i All are defined as 7 fuzzy subsets, and the fuzzy domain ranges are [ -6,6 [)]The language variable values are expressed by { negative big, negative middle, negative small, zero, positive small, positive middle, positive big }, and the corresponding fuzzy sets are { NB, NM, NS, ZE, PS, PM, PB }; obtaining delta K according to PI parameter adjustment rule and simulation experience p The fuzzy rule of (1) is as follows:
ΔK i the fuzzy rule of (1) is as follows:
obtaining the clear value delta K of the correction quantity of the PI parameter by utilizing Mandani fuzzy reasoning and using a gravity center method for clearing p And Δ K i 。
6. The power spring control method based on variable universe fuzzy PI adaptive control of claim 1, wherein in step 5, PI parameter correction quantity delta K is calculated p And Δ K i And the original PI parameter K p0 And K i0 Adding to obtain the final control parameter K in the power spring control system p And K i The specific method comprises the following steps:
obtaining the final control parameter of the PI controller in the power spring control system according to the formula (3):
wherein K p And K i Representing the current final parameter, K, of a PI controller in an electric spring control system p0 And K i0 Representing the value of the initial parameter of the controller, Δ K p And Δ K i And (4) correcting the PI parameter obtained in the step (4).
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