CN111948946B - Robust evaluation inversion control system based on HJI theory and design method thereof - Google Patents

Robust evaluation inversion control system based on HJI theory and design method thereof Download PDF

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CN111948946B
CN111948946B CN202010859805.7A CN202010859805A CN111948946B CN 111948946 B CN111948946 B CN 111948946B CN 202010859805 A CN202010859805 A CN 202010859805A CN 111948946 B CN111948946 B CN 111948946B
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CN111948946A (en
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杨艳
王业琴
洪程
李文涛
吴婷婷
陈煜洋
邵友成
李子昕
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Huaiyin Institute of Technology
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    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
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Abstract

The invention relates to the technical field of power electronics, and discloses a robust evaluation inversion control system based on an HJI theory and a design method thereof. Firstly, carrying out mathematical modeling on a single-phase inverter; constructing a fuzzy approximation function to directly and dynamically adjust uncertain parameters; determining uncertain parameters existing in the model and the environment and influences of external interference signals on the tracking performance; and (3) introducing an evaluation signal to evaluate the robust performance of the system, and designing a robust control law containing evaluation gain to suppress interference. Compared with the prior art, under the condition that an accurate mathematical model cannot be obtained, the influence of uncertainty is weakened by a fuzzy approximation function, the disturbance rejection capability of the system is calculated, the robust evaluation is carried out on the system, a design basis is provided for a robust control law for counteracting the uncertainty and disturbance, the oscillation of output voltage is avoided, and the stability of the system is maintained.

Description

Robust evaluation inversion control system based on HJI theory and design method thereof
Technical Field
The invention relates to the technical field of power electronics, in particular to a robust evaluation inversion control system based on HJI theory and a design method thereof.
Background
The inverter is a power electronic converter for converting direct current at an input side into alternating current and outputting the alternating current, and is called a DC/AC converter for short. With the improvement of the world electrification level, the inverter is widely applied to various industrial equipment as a core component, such as an uninterruptible power supply, a photovoltaic power generation system, a wind power generation system, a variable frequency speed control system and the like. And the performance of the inverter directly determines the overall performance of these devices. Therefore, the high-performance inverter is a research hotspot in the field of power electronics, and high steady-state precision, fast transient response and strong anti-interference capability become important indexes for measuring the performance of the inverter. In the existing inversion control system, due to the existence of nonlinear factors, the quality of an output waveform of an inverter is deteriorated, even the system is unstable, and the quality is mainly reflected in a high Total Harmonic Distortion (THD) and a large steady-state error. The main causes of such non-linear factors are modeling uncertainty errors, system oscillation caused by disturbance, and the like.
On one hand, the theoretical field usually pursues model accuracy, and most control theories are explained based on an accurate model. In practice, however, most accurate models of the system cannot be obtained, or even if the system model can be established, the models are highly approximate, and under the objective uncertainty, if perturbation is generated by the resistance inductance to change the system structure, the uncertainty error is continuously enlarged, and the network-side voltage fluctuation is caused. On the other hand, the inverter often operates in a relatively severe environment due to interference from external factors such as dc side voltage fluctuation, network side load jump, and transmission noise. This also places high robustness requirements on the control system.
There has been much research into conventional inverter control strategies: for example, although the PI control has a simple structure and good adaptability, the parameter setting is difficult, and the disturbance resistance performance is poor; the dead beat control and the PR control have the advantages of quick instant response, high precision and the like, but are still weak in anti-interference performance; the robust H-infinity control method is developed aiming at improving the anti-interference capability and has excellent performance in reducing uncertain parameters, but the controller is difficult to construct and complex in parameterization, and needs to be studied deeply.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems in the prior art, the invention provides a robust evaluation inversion control system based on HJI theory and a design method thereof, so that under the condition that a precise mathematical model cannot be obtained, the influence of uncertainty is weakened by a fuzzy approximation function, the disturbance rejection of the system is calculated, the robust evaluation is carried out on the system, a design basis is provided for a robust control law for counteracting uncertainty and disturbance, the oscillation of output voltage is avoided, and the stability of the system is maintained.
The technical scheme is as follows: the invention provides a robust evaluation inversion control system based on HJI theory, which comprises a direct current source, an inversion bridge, a filter, a fuzzy approximation module, a robust evaluation module, a robust control module and a drive module, wherein the inversion bridge is connected with the direct current source, the filter, the fuzzy approximation module, the robust evaluation module, the robust control module and the drive module are connected, the drive module is connected with the input end of a switching device of the inversion bridge, the output end of the inversion bridge is connected with the filter, one side of the filter is provided with a load, and the inductance perturbation change rate delta L of the filter are respectively connected with the input end of the filter and the output end of the filter*The robust evaluation module takes the system uncertainty delta L and the external interference signal W (t) obtained by the fuzzy approximation module as input signals and outputs the overall anti-interference capability c of the systemrdThe output end of the robust evaluation module and the output end of the fuzzy approximation module are respectively used as the input of a robust control module, and the output end of the robust control module is connected with the input end of the driving module; the inversion control system controls the capacitor voltage u by trackingoAnd the driving module drives the switching device of the inverter bridge to act so that the direct-current voltage charges and discharges the inductor, and the alternating-current voltage is obtained at the load side after passing through the filter.
Further, the robust evaluation module calculates to obtain a capacitance voltage u in the inverter system according to the system uncertainty Δ L and the external interference signal w (t) obtained by the fuzzy approximation moduleoFurther defining an evaluation signal z for the state equation of the state variable and a gain analysis L in the nonlinear system2Logical relation of indexes used for expressing the overall anti-interference capability c of the systemrdAnd providing a basis for the robust control law design of the robust control module.
Further, the robust control module is directed to interference rejection crdAdding an evaluation gain gamma calculated by a Hamilton-Jacobian equation to design uncertain factors and interferences still existing in a control law T offset systemAnd the signal is moved, so that the purpose of improving the system robustness is achieved by avoiding interference oscillation.
Further, the switching device selects an IGBT and is provided with a commutation freewheeling diode, and the filter is a commonly used LC filter.
Further, the driving module is a PWM driving module.
The invention also discloses a design method of the robust evaluation inversion control system based on the HJI theory, which comprises the following steps:
step 1: constructing a mathematical model of the single-phase inversion system, selecting a state variable and outputting the variable;
step 2: designing a fuzzy approximation module:
step 2.1: quantized physical discourse domain [ alpha ]i，βi]The uncertainties, i.e. the inductance perturbation DeltaL and the inductance perturbation change rate DeltaL, are selected*Defining fuzzy sets as
Figure BDA0002647698610000021
And
Figure BDA0002647698610000022
as binary inputs to the approximation function:
Figure BDA0002647698610000023
step 2.2: using Cartesian product to calculate sequence even matrix of all input combination conditions
Figure BDA0002647698610000031
Characterizing the function g (x) of unknown analytic expressions by membership of the order matrix to obtain an output
Figure BDA0002647698610000032
Step 2.3: establishing a rule base and an inference machine to calculate and weaken the influence of perturbation on the system stability;
and step 3: designing a robust evaluation module:
step 3.1: by using Hamilton-Jacobi equation, an evaluation signal is introducedNumber z, selecting a suitable sliding mode function to define L of interference W (t)2Has the index of
Figure BDA0002647698610000033
Step 3.2: calculating to obtain the anti-interference performance index expression of the whole system
Figure BDA0002647698610000034
crdIs L of the system2The gain is used to represent the robust performance of the system;
and 4, step 4: and designing a robust control module.
Further, the specific steps of designing the robust control module are as follows:
step 4.1: assuming that the upper level still has uncertain delta q for elimination, the mathematical model of the single-phase inverter is simplified into
Figure BDA0002647698610000035
Where, M ═ LC, V ═ RC + ω · Δ L, w (T) is an interference signal, and T is a control law;
step 4.2: introducing a reference voltage udAnd calculating the error e ═ uo-ud；
Step 4.3: designing the control law T to obtain equations of state about the errors
Figure BDA0002647698610000036
Step 4.4: by using Hamilton-Jacobian inequality theory, a control law capable of gradually stabilizing the state equation of the error is designed
Figure BDA0002647698610000037
Where u is a feedback control law, and γ is an evaluation gain, and is an arbitrary given positive real number.
Has the advantages that:
the invention introduces a fuzzy approximation module, can collect proper data according to the approximation precision requirement, reasonably and fuzzily divide and select as few rules as possible, and approximate a response function with uncertainty by a universal approximation theorem; in addition, the evaluation signal can evaluate the overall anti-interference capability of the system, the approximate relation between the nonlinear system and the linear system established by the system robustness can be intuitively known, the oscillation influence of various complex disturbances on the system can be dealt with, the control law of the design is stronger in pertinence, and the system stability is maintained.
Drawings
FIG. 1 is a structural diagram of a robust evaluation inversion control system based on HJI theory, which is designed by the invention;
FIG. 2 is a main circuit structure diagram of the single-phase inverter of the present invention;
FIG. 3 is a block diagram of a fuzzy approximation module according to the present invention;
FIG. 4 is a block diagram of a robust evaluation module of the present invention;
FIG. 5 is a closed loop configuration of the robust controller of the present invention;
FIG. 6 is a simulated waveform of the output voltage tracking reference voltage according to the present invention;
FIG. 7 illustrates the interference caused to the system when the load is suddenly applied according to the present invention;
FIG. 8 is a comparison of the current distortion of the present invention compared to H ∞ control at different degrees of inductive perturbation;
FIG. 9 shows the THD of the present invention in steady operation.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Referring to fig. 1, the invention discloses a robust evaluation inversion control system based on an HJI theory and a design method thereof. The driving module is connected with the input end of a switching device of the inverter bridge, the output end of the inverter bridge is connected with the filter, one side of the filter is provided with a load, and the inductance perturbation change rate delta L of the filter are respectively controlled by the load and the load*As input of the fuzzy approximation module, the robust evaluation module and the fuzzy approximation moduleThe obtained system uncertainty Delta L and external interference signals W (t) are input signals, and the overall anti-interference capability c of the system is outputrdThe output end of the robust evaluation module and the output end of the fuzzy approximation module are respectively used as the input of the robust control module, and the output end of the robust control module is connected with the input end of the driving module; the inversion control system controls the capacitor voltage u by trackingoAnd the driving module drives the switching device of the inverter bridge to act so that the direct-current voltage charges and discharges the inductor, and the alternating-current voltage is obtained at the load side after passing through the filter.
The fuzzy approximation module does not depend on an accurate mathematical model of a controlled object, but integrates human experience summarized by an operator into the controller, identifies system parameters on line by a self-adaptive learning algorithm, constructs an approximation function f (x) by means of obtained data information, and reduces system uncertainty delta L.
The robust evaluation module calculates and obtains a capacitance voltage u in the inverter system according to a system uncertainty delta L and an external interference signal W (t) obtained by preceding stage fuzzy approximationoFurther defining an evaluation signal z for the state equation of the state variable and a gain analysis L in the nonlinear system2Logical relation of indexes used for expressing the overall anti-interference capability c of the systemrdAnd a basis is provided for the design of the later-stage robust control law.
Robust control module for interference rejection crdThe evaluation gain gamma calculated by the Hamilton-Jacobian equation is added, so that uncertain factors and disturbance signals still existing in the control law T offset system are designed, and the purpose of improving the robustness of the system by avoiding interference oscillation is achieved.
The method mainly comprises the following steps:
step 1: constructing a mathematical model of the single-phase inverter;
referring to fig. 2, the network side output is accessible to the load, with the RLC filter circuit consisting of resistor R, inductor L and capacitor C. According to kirchhoff's law, the loop current is set to i and uiAs an input amount, uoThe mathematical model of the differential equation for the output quantity from which the circuit system can be derived is:
Figure BDA0002647698610000051
Figure BDA0002647698610000052
substituting (1) into (2) and eliminating the intermediate variable i to obtain a second-order linear ordinary differential equation describing the input-output relationship of the circuit system, wherein the second-order linear ordinary differential equation comprises the following steps:
Figure BDA0002647698610000053
for the second-order dynamic circuit, the capacitance voltage and the inductance current can be accurately measured, other parameters are determined accordingly, and the capacitance voltage and the inductance current are taken as state variables, so that the state equation is obtained:
Figure BDA0002647698610000054
the influence of system uncertainty on a control system generally comprises a network side resistance parameter perturbation and an inductance parameter perturbation. The former only affects the open loop gain of low frequency, but has no effect on the cut-off frequency and bandwidth of the system; the latter will have a large effect on the high frequency gain and affect the system stability. The actual inputs to the controlled object for which the inductance perturbation Δ L is added are:
us＝ω·i·ΔL+uo-ud (5)
wherein, when the output fundamental frequency is 50Hz, ω is 100 π, udIs a reference voltage. In addition, an external disturbance W (t) is arranged at the output end of the controller, and a second-order linear ordinary differential equation containing uncertainty and disturbance can be obtained by combining the equation (3):
Figure BDA0002647698610000055
step 2: designing a fuzzy approximation module;
step 2.1: defining two-dimensional matrix input and output;
referring to FIG. 3, the structure of the fuzzy approximation module is shown, which utilizes the universal approximation feature of the fuzzy algorithm to reduce the uncertainty Δ L and the uncertainty change rate obtained from the above analysis
Figure BDA0002647698610000061
Impact on system stability.
In the domain of physics [ alpha ]i，βi]Above definition of Ni(i-1, 2) formally standardized, well-defined fuzzy sets of elements
Figure BDA0002647698610000062
The two-dimensional fuzzy set represented by the method quantifies uncertainty parameter delta L and change rate parameter delta L*The mapping relation between the physical discourse domain and the discrete discourse domain is obtained. If the fuzzy set of Δ L is
Figure BDA0002647698610000063
And Δ L*Is a fuzzy set of
Figure BDA0002647698610000064
The two-dimensional matrix of the fuzzy input can be defined by the operation of cartesian products as:
Figure BDA0002647698610000065
wherein N is1、N2For the number of two-dimensional fuzzy input elements, R2Representing a two-dimensional matrix. Further, calculating the two-dimensional matrix order couple can obtain a fuzzy approximation module input set:
Figure BDA0002647698610000066
if the function g (x) of the unknown analytic expression is an element on the matrix. For any x ∈ U, the fuzzy relation can be corresponded through the experience summarized by experts,solving the output of two-dimensional input to obtain a membership function which can represent g (x), namely a fuzzy set
Figure BDA0002647698610000068
Step 2.2: designing a fuzzy rule;
in order to make machine language "identify and understand" symbolic expression like human, the invention introduces M ═ N1×N2Strip IF-THEN rule
Figure BDA0002647698610000067
It reflects the operating experience of people as follows.
Figure BDA0002647698610000071
Wherein NB, NM, NS, ZO, PS, PM, PB represent respectively negative big, negative middle, negative small, zero, positive small, middle, positive big. Set deviation
Figure BDA0002647698610000072
Are respectively as
Figure BDA0002647698610000073
Can be set into fuzzy
Figure BDA0002647698610000074
The center of (d) is represented as:
Figure BDA0002647698610000075
step 2.3: designing an approximation function to construct a fuzzy inference engine;
the inference engine uses fuzzy judgment as a premise and applies fuzzy language rules to provide a new approximate method for reducing the uncertainty of the system by using a fuzzy judgment conclusion. According to the fuzzy rule of the step 2.2, when the value of the input deviation is large, the control force of the system is increased, so that the time constant and the damping coefficient of the system can be reduced, and in addition, in order to avoid that the out-of-range control action possibly caused when the system starts to be large, the control precision of NM and NS can be selected; when the deviation is moderate, the control precision is reduced as much as possible, and the response speed is prevented from being influenced; when the deviation is small, the system has good stability, and the control precision is high, so that small-amplitude oscillation near a balance point is avoided, and the stability of the system is ensured.
Adopting single-point fuzzification, taking a minimum product inference engine, and constructing an approximation function f (x) as follows:
Figure BDA0002647698610000076
wherein N is1、N2For the number of two-dimensional fuzzy input elements,
Figure BDA0002647698610000077
representing a two-dimensional input element i1、i2Corresponding value x1、x2With respect to the degree of membership of the domain of interest,
Figure BDA0002647698610000078
as a fuzzy set
Figure BDA0002647698610000079
Of the center of (c). If approximating the function f (x), the uncertainty expression g (x) is set in U ═ alpha1，β1]×[α2，β2]Approximation precision h of upper continuous micro fuzzy approximation moduleiThe following relationships exist:
Figure BDA0002647698610000081
Figure BDA0002647698610000082
wherein | | g (x) -f (x) of formula (9 | | non-volatile phosphor∞Represents the infinite norm of the internal function g (x) -f (x). Simplification ofEquation (10) can then be derived, with arbitrary x defined as the internal function belonging to the two-dimensional fuzzy set U ═ α1，β1]×[α2，β2]Corresponding to any j epsilon [1, N ∈)i-1]The maximum value of two adjacent errors.
And step 3: designing a robust evaluation module;
referring to FIG. 4, a block diagram of the robust evaluation module of the present invention considers the state equation of the Hamilton-Jacobian system of the following forms:
Figure BDA0002647698610000083
wherein d is an interference signal, and z is an evaluation signal of the system. Defining evaluation signal z-shape as sliding mode function on phase plane
Figure BDA0002647698610000084
(c is a constant greater than 0), if z → 0, error e → 0 and derivative of error with respect to time
Figure BDA0002647698610000085
L defining interference W (t)2Has the index of
Figure BDA0002647698610000086
To express the interference rejection of the system, the signal and interference L are evaluated2The performance index can be obtained by performing the following calculation and analysis on the index:
Figure BDA0002647698610000087
wherein, crdIs L of the system2The gain is used to represent the robust performance of the system.
From formula (12):
Figure BDA0002647698610000088
the initial state of zero during the operation of the invention can be rewritten as follows:
Figure BDA0002647698610000089
it is obvious that the larger the interference W (t), the higher the L of the signal and interference is evaluated2The smaller the index ratio, the better the robustness of the system.
And 4, step 4: designing a robust control module;
referring to fig. 5, which is a closed-loop structure diagram of the robust controller of the present invention, the uncertainty Δ L of the module should already go to zero by fuzzy approximation, and in order to further eliminate the uncertainty, assuming that there is still a small amount of uncertainty Δ q in the superior control, the mathematical model of the single-phase inverter obtained by equation (6) is:
Figure BDA0002647698610000091
where, M ═ LC, V ═ RC + ω · Δ L, w (T) is the interference signal, and T is the control law.
If the inverter has a desired output voltage udThe error e ═ u can be obtainedo-udThen, the control law can be designed as follows:
Figure BDA0002647698610000092
wherein u is a feedback control law.
Substituting formula (16) into (15) yields:
Figure BDA0002647698610000093
taking d ═ Δ q + w (t), we get:
Figure BDA0002647698610000094
the sliding mode function s obtained according to the step 3 is an evaluation signal z, namely
Figure BDA0002647698610000095
It is possible to obtain:
Figure BDA0002647698610000096
wherein the content of the first and second substances,
Figure BDA0002647698610000097
in order to use the Hamilton-Jacobian equation form of equation (11), one can rewrite (19) to
Figure BDA0002647698610000098
The combination formula (11) utilizes the Hamilton-Jacobian inequality theory to obtain the product which can satisfy crdThe stable condition of gamma is less than or equal to gamma, and the control law of a robust controller for preventing the closed-loop system from generating oscillation is as follows:
Figure BDA0002647698610000099
where γ is an evaluation gain and is any given positive real number.
According to the method, the influence of the perturbation of the inductance on the stability of the system is reduced by using the fuzzy approximation characteristic, and then an evaluation signal is introduced, so that on one hand, the standard of the family uncertainty approximate conversion between a nonlinear system and a linear system can be determined; on the other hand, corresponding control law parameters can be designed according to the robustness of the actual system; and finally, designing a robust control law according to the evaluation signal, and taking stability and reliability as primary control targets to counteract interference and uncertainty. The control algorithm is particularly suitable for a system with large uncertain factor variation range and small stability margin. However, since the robust control system generally does not work in an optimal state, the steady-state accuracy of the system needs to be further improved. Aiming at the defect, the system can also adopt the idea of an observer to predict error change more accurately so as to improve the steady-state precision of robust control. Besides, an adaptive algorithm can be used for dynamically adjusting the intelligent robust controller for evaluating the gain.
The following calculations were performed in order to verify the feasibility of the invention.
The fuzzy approximation module passes each xiMore fuzzy sets are defined, more accurate approximators can be obtained, and theoretically, the more rules are, the better approximation effect is. Let x beiThe number of fuzzy sets is NiThe length of which varies within a range of LiThe approximation accuracy obtained by combining equation (10) is:
Figure BDA0002647698610000101
an approximation function in the form of f (x) can be chosen to be small enough h for any given precision ε > 01，h2Satisfy the requirement of
Figure BDA0002647698610000102
Thereby ensuring that the supremum of the internal function g (x) -f (x) is less than epsilon.
Aiming at a two-dimensional uncertainty function g (x), an approximation function f (x) is designed, and the error consistent approximation is defined in the range of U [ -0.6, 0.6]×[-0.6.0.6]The continuous function of (c) has: g (x) 0.52+0.1x1+0.28x2-0.06x1x2。
Obtaining x from equation (9)1，x2Derivative boundary:
Figure BDA0002647698610000103
Figure BDA0002647698610000104
when h is equal to 0.1, the precision is obtained1＝0.2，h2(x) y | | | g (x) -f (x) y when 0.2∞The precision requirement is met by not more than 0.16 multiplied by 0.2 and 0.34 multiplied by 0.2 to 0.1.
Since L is 0.6- (-0.6) ═ 1.2, as can be seen from equation (11), the number of fuzzy sets in this case is equal to
Figure BDA0002647698610000105
Then the approximation function can be found as:
Figure BDA0002647698610000111
and after the uncertainty is reduced by using fuzzy approximation, the stability of the inversion system is further analyzed and evaluated by using the robustness.
For any given positive real number γ, according to the description of Hamilton-Jacobian inequality, if there is a positive definite and differentiable function L ≧ 0 and for any W (t)
Figure BDA0002647698610000112
The performance index is then considered to be asymptotically stable for a given evaluation gain, i.e., crd≤γ。
Defining a definite differentiable Lyapunov function as
Figure BDA0002647698610000113
And then, the derivation is carried out to obtain:
Figure BDA0002647698610000114
substituting formula (19) for formula (23) to obtain:
Figure BDA0002647698610000115
definition of
Figure BDA0002647698610000116
Substituting formula (24) to obtain:
Figure BDA0002647698610000117
wherein the content of the first and second substances,
Figure BDA0002647698610000118
and is
Figure BDA0002647698610000119
H is less than or equal to 0, namely
Figure BDA00026476986100001110
Thus, from the Hamilton-Jacobian inequality, the system is crdGamma is less than or equal to gamma and is gradually stable.
Referring to fig. 6, it can be found that the output voltage is tracked after the inversion control system is more than 0.1s through robust evaluation based on the HJI theory, which shows that the control method of the invention has good rapidity.
Referring to fig. 7, the load is suddenly applied at 1.15s, and by comparing simulation results, it is easy to see that the controlled voltage fluctuates only slightly and returns to the stable operation at 1.36s, and the robust control method provided by the invention has good robustness.
Referring to FIG. 8, for the performance of the H ∞ control and the present invention for different perturbation variations of inductance within + -18% of the nominal value of 5.5mH, the results of comparison can be: in the process of changing perturbation from 4.5mH to 6.5mH, the total current distortion rate of the control method is obviously controlled by about 30 percent compared with H infinity, the maximum change of the THD value of H infinity is 11 percent, and the maximum change of the THD value of the control method of the invention is 8 percent, so that the control method has stronger anti-interference capability.
Referring to the harmonic analysis of the net side current of fig. 9, from the simulation results, it can be obtained: according to the requirement of 311V and 50Hz of output voltage, the total harmonic distortion rate of the network side current of the control method is 1.21% in an allowable fluctuation range.
The above embodiments are merely illustrative of the technical concepts and features of the present invention, and the purpose of the embodiments is to enable those skilled in the art to understand the contents of the present invention and implement the present invention, and not to limit the protection scope of the present invention. All equivalent changes and modifications made according to the spirit of the present invention should be covered within the protection scope of the present invention.

Claims (6)

1. A robust evaluation inversion control system based on HJI theory is characterized by comprising a direct current source, an inversion bridge, a filter, a fuzzy approximation module, a robust evaluation module, a robust control module and a driving module, wherein the inversion bridge is connected with the direct current source, the filter, the fuzzy approximation module, the robust evaluation module, the robust control module and the driving module are connected, the driving module is connected with the input end of a switching device of the inversion bridge, the output end of the inversion bridge is connected with the filter, a load is arranged on one side of the filter, and the inductance perturbation change rate delta L of the filter are respectively connected with the input end of the switching device of the inversion bridge and the output end of the filter*The robust evaluation module takes the inductance perturbation delta L and the external interference signal W (t) obtained by the fuzzy approximation module as input signals and outputs the overall anti-interference capability c of the systemrdThe output end of the robust evaluation module and the output end of the fuzzy approximation module are respectively used as the input of a robust control module, and the output end of the robust control module is connected with the input end of the driving module; the inversion control system controls the capacitor voltage u by trackingoThe driving module drives the switching device of the inverter bridge to act so that the direct-current voltage charges and discharges the inductor, and the alternating-current voltage is obtained at the load side after passing through the filter;
the robust control module aims at the overall anti-interference capability c of the systemrdThe evaluation gain gamma calculated by the Hamilton-Jacobian equation is added, so that uncertain factors and disturbance signals still existing in the control law T offset system are designed, and the purpose of improving the robustness of the system by avoiding interference oscillation is achieved.
2. The robust evaluation inversion control system based on HJI theory as claimed in claim 1, wherein the robust evaluation module calculates the capacitance voltage u in the inversion system according to the inductance perturbation Δ L and the external interference signal W (t) obtained by the fuzzy approximation moduleoFurther defining the evaluation signal z and the gain analysis index L in the nonlinear system for the state equation of the state variable2Is used to represent the overall interference immunity c of the systemrdProviding basis for robust control law design of the robust control module。
3. The HJI theory based robust evaluation inverter control system according to claim 1, wherein the switching device selects an IGBT with a free wheeling diode for commutation, and the filter is a commonly used LC filter.
4. The HJI theory-based robust evaluation inverter control system according to claim 1, wherein the driving module is a PWM driving module.
5. A design method of a robust evaluation inversion control system based on HJI theory according to any claim 1 to 4, characterized by comprising the following steps:
step 1: constructing a mathematical model of the single-phase inversion system, selecting a state variable and outputting the variable;
and 2, step: designing a fuzzy approximation module:
step 2.1: quantized physical discourse domain [ alpha ]i，βi]The uncertainties, i.e. the inductance perturbation DeltaL and the inductance perturbation change rate DeltaL, are selected*Defining fuzzy sets as
Figure FDA0003522760870000021
And
Figure FDA0003522760870000022
as binary inputs to the approximation function:
Figure FDA0003522760870000023
step 2.2: using Cartesian product to calculate sequence even matrix of all input combination conditions
Figure FDA0003522760870000024
Characterizing the function g (x) of unknown analytic expressions by membership of the order matrix to obtain an output
Figure FDA0003522760870000025
Step 2.3: establishing a rule base and an inference machine to calculate and weaken the influence of perturbation on the system stability;
and step 3: designing a robust evaluation module:
step 3.1: using Hamilton-Jacobian equation, introducing evaluation signal z, selecting proper sliding mode function, defining gain analysis index L of external interference signal W (t)2Is composed of
Figure FDA0003522760870000026
Step 3.2: calculating to obtain the anti-interference capability of the whole system and the performance index expression thereof
Figure FDA0003522760870000027
And 4, step 4: and designing a robust control module.
6. The method for designing an HJI theory-based robust evaluation inverter control system according to claim 5, wherein the specific steps for designing the robust control module are as follows:
step 4.1: supposing that the upper level still has an unremoved uncertainty delta q, the mathematical model of the single-phase inverter is simplified into Muo+Vuo+uo+Δq+W(t)-uiT, where M ═ LC, V ═ RC + ω · Δ L, w (T) is an external interference signal, and T is a control law; r is a resistor, L is an inductor and C is a capacitor; when the output fundamental frequency is 50Hz, omega is 100 pi; u. ofiIs the input quantity of the filter circuit;
step 4.2: introducing a reference voltage udAnd calculating the error e-uo-ud；
Step 4.3: designing a control law T to obtain an error-related state equation Me + Ve + e + delta q + W (T) ═ u;
step 4.4: by using Hamilton-Jacobian inequality theory, a feedback control law capable of gradually stabilizing the state equation of the error is designed
Figure FDA0003522760870000031
Wherein u is a feedback control law, and gamma is an evaluation gain and is any given positive real number; s is a sliding mode function, s is e + ce, and c is more than 0; ρ ═ Mce + (Vc-1) e.
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