CN111682792A - Multi-step prediction converter model prediction control method - Google Patents
Multi-step prediction converter model prediction control method Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
- H02M7/00—Conversion of ac power input into dc power output; Conversion of dc power input into ac power output
- H02M7/42—Conversion of dc power input into ac power output without possibility of reversal
- H02M7/44—Conversion of dc power input into ac power output without possibility of reversal by static converters
- H02M7/48—Conversion of dc power input into ac power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode
- H02M7/483—Converters with outputs that each can have more than two voltages levels
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
- H02M7/00—Conversion of ac power input into dc power output; Conversion of dc power input into ac power output
- H02M7/42—Conversion of dc power input into ac power output without possibility of reversal
- H02M7/44—Conversion of dc power input into ac power output without possibility of reversal by static converters
- H02M7/48—Conversion of dc power input into ac power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode
- H02M7/53—Conversion of dc power input into ac power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal
- H02M7/537—Conversion of dc power input into ac power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only, e.g. single switched pulse inverters
- H02M7/5387—Conversion of dc power input into ac power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only, e.g. single switched pulse inverters in a bridge configuration
- H02M7/53871—Conversion of dc power input into ac power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only, e.g. single switched pulse inverters in a bridge configuration with automatic control of output voltage or current
- H02M7/53875—Conversion of dc power input into ac power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only, e.g. single switched pulse inverters in a bridge configuration with automatic control of output voltage or current with analogue control of three-phase output
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
- H02M7/00—Conversion of ac power input into dc power output; Conversion of dc power input into ac power output
- H02M7/42—Conversion of dc power input into ac power output without possibility of reversal
- H02M7/44—Conversion of dc power input into ac power output without possibility of reversal by static converters
- H02M7/48—Conversion of dc power input into ac power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode
- H02M7/53—Conversion of dc power input into ac power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal
- H02M7/537—Conversion of dc power input into ac power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only, e.g. single switched pulse inverters
- H02M7/539—Conversion of dc power input into ac power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only, e.g. single switched pulse inverters with automatic control of output wave form or frequency
- H02M7/5395—Conversion of dc power input into ac power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only, e.g. single switched pulse inverters with automatic control of output wave form or frequency by pulse-width modulation
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Abstract
The invention discloses a converter model prediction control method based on multi-step prediction, which comprises the following specific steps: setting the lengths of a control time domain and a prediction time domain, wherein the control time domain comprises a plurality of control cycles, the prediction time domain has one more control cycle than the control time domain, and the control quantity is updated according to the control time domain; in each control period of the current control time domain, sampling the system state, implementing corresponding control, gradually calculating the system state in the prediction time domain, and optimally calculating the corresponding optimal control quantity in the next control time domain by adopting a model prediction control algorithm according to the finally predicted system state; performing pulse optimization on all control quantities in the next control time domain to reduce the switching frequency; and implementing the optimized control quantity in the control period of the next control time domain. The design method reduces the calculation amount of multi-step prediction by adopting a strategy of increasing a prediction time domain but not increasing an optimization time domain, reduces the switching frequency by constructing a control time domain and adopting a pulse optimization strategy, and improves the control performance.
Description
Technical Field
The invention relates to a converter model prediction control method, in particular to a multi-step prediction converter model prediction control method.
Background
The model predictive control algorithm of the converter is a research hotspot of a converter control method, is based on a model predictive control theory, combines the control characteristics of the converter, adopts a predictive model for calculation, and carries out optimization based on a cost function, thereby realizing the comprehensive optimization control of the converter.
In the existing converter model predictive control algorithm, a converter limited control set model predictive control algorithm (FCS-MPC) is used for calculating a system state predicted value under the respective action of all switch function combinations by designing switch function combinations and utilizing the characteristic that the number of the switch function combinations is limited according to the discrete characteristic of a converter switch control signal; the control performance of the converter system is synthesized by constructing a cost function, and the switching function group with the minimum cost function is selected to be cooperatively used for the converter. The FCS-MPC algorithm has the advantages of direct modeling, direct control, fast dynamic response, no need of a PWM (pulse-width modulation) module in a control structure of a classical converter and the like, but also has the problems of complex online calculation, high switching frequency and uncontrollable property, difficulty in realizing multi-step prediction calculation in a prediction control theory of the classical model and the like, so that the algorithm is conservative and the anti-interference performance of the algorithm is influenced, and the high switching frequency increases the heat loss of the converter and reduces the efficiency of the converter.
Aiming at the problem of multi-step prediction of an FCS-MPC algorithm, the disclosed method mainly comprises the following steps: 1. in the literature (a multi-step prediction converter limited control set model prediction control algorithm, Chinese Motor engineering report 2012,32(33):37-44.), the conservative property of the algorithm is reduced by adopting a strategy of three-step prediction calculation and two-step optimization calculation, and the method is difficult to directly increase the prediction steps. 2. The literature (Model predictive direct current control: Formulation of the stator current bases and the control of the switching horizontal. IEEE Industrial applications Magazine, 2012, 18(2):47-59.) is a classic multi-step Model predictive control algorithm. The algorithm carries out prediction calculation of the system state based on an extrapolation method, and organizes a prediction time domain into two states of S and E, wherein S is a switch state and E is an extrapolation state. In the multi-step prediction calculation process, optimization calculation is only carried out in the 'S' state, the system response is calculated only on the basis of the optimal control quantity of the 'S' state in the 'E' state, the switching of the two states is determined by the width of state quantity tracking deviation, and therefore the length of the prediction step number and the algorithm tracking control performance need to be considered in a compromise mode. 3. In the literature (Long-horizontal fine-control-set model predictive control with non-reciprocal sphere decoding an FPGA. IEEE Transactions on power electronics, 2020,35(7):7520 and 7531), multi-step prediction is realized by combining a sphere decoding algorithm with FPGA parallel computing hardware.
Disclosure of Invention
In order to solve the technical problems of the existing converter model prediction control algorithm, the invention provides an easily-realized, efficient and reliable multi-step prediction converter model prediction control method.
The technical scheme for solving the technical problems comprises the following steps:
a1, setting prediction time domain TpAnd control time domain TcLength of (1), Tc=nTs，Tp=(n+1)Ts；
A2, the second in the current control time domainkSampling the state in one control periodx(k) To implement control SLK (k) Sop is obtained by multi-step prediction optimization calculation (k)；
A3, the control amount Sop (1:n) Pulse optimization was performed to obtain SLK (1:n)；
a4, and implementing the optimized control quantity SLK (k)。
The invention has the technical effects that: the method is based on a model predictive control theory, sets a control time domain and a prediction time domain of a converter model predictive control algorithm, samples and implements control in each control period in the control time domain, completes predictive calculation of system states in the prediction time domain, further obtains the optimal control quantity of the next control time domain, finally performs pulse optimization on all control quantities in the next control time domain, and achieves the purpose of reducing the switching frequency. The design method reduces the calculated amount of multi-step prediction by adopting a strategy of increasing a prediction time domain without increasing an optimization time domain, calculates a state predicted value in the prediction time domain by using a state sampling value of each control period, reduces the switching frequency by constructing a control time domain and adopting a pulse optimization strategy, and improves the control performance of the system.
Drawings
FIG. 1 is a schematic diagram of a converter finite control set model predictive control algorithm (FCS-MPC) in the present invention.
FIG. 2 is a flow chart of the present invention.
FIG. 3 is a flow chart of timing calculation according to the present invention.
FIG. 4 is a control time domain of the present inventionnCalculation timing chart of = 6.
Fig. 5 is a schematic diagram of pulse optimization based on the area equivalence principle in the present invention.
Fig. 6 is a simulation waveform diagram of the output voltage of the three-phase inverter in the invention.
FIG. 7 is a diagram illustrating the output control pulse in comparison with the detail of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and the specific embodiments.
Aiming at the defects of short prediction time domain and difficult reduction of switching frequency of an FCS-MPC (finite control set predictive control, FCS-MPC) existing in the conventional converter finite control set model prediction control algorithm, the invention designs a multi-step prediction mechanism and a pulse optimization strategy.
The structure block diagram of the classic FCS-MPC algorithm is shown in FIG. 1. Defining the switching function of a converterS，S=1 indicates that the switch is closed,Sand =0 denotes that the switch is off.
For a three-phase converter, the combination of switching functions acting on the converter at a certain moment is represented as a vectorS=[S a ,S b ,S c ] T . For three-phase two-level converters, switching function
WhereinS jp The switching function of the switching tube of the upper bridge arm is shown,S jn represents the switching function of the switching tube of the lower bridge arm, andS j ∈{0,1}，j=a,b,c。
at the current momentkFirst, the optimum switching function combination calculated from the previous control cycle is implementedS(k) Then sampling values according to the statex(k) Calculated from a prediction modelkPredicted value of state at time +1x p (k+1). Predicting value in statex p (k+1), the prediction model calculates the state prediction values under the respective action of all the switch function combinationsx pi (k+2) of whichi=1,..,g，gThe number of all the switching function combinations of the converter. Calculating the cost function corresponding to each switching function combination, and taking the switching function combination with the minimum cost function as the control quantity of the next control periodS(k+1). From the FCS-MPC algorithm principle, the traversal calculation of the prediction model and the cost function is the main reason for the large online calculation amount of the algorithm, and will rise exponentially with the increase of the prediction time domain, so that it is not feasible to simply increase the prediction time domain. The prediction control theory shows that the proper increase of the prediction time domain is beneficial to improving the control performance of the algorithm, so that the local optimal control quantity has certain global optimality, and therefore, the increase of the prediction time domain is an effective way for further improving the control performance of the algorithm. In addition, compared with the traditional converter control algorithm, the FCS-MPC algorithm has the most significant structural feature of no PWM waveform modulator, so that the switching frequency of the converter can only be determined by the control period and the cost function, and simply increasing the length of the control period reduces the width of the minimum pulse, and the design of the cost function can only reduce the switching times by constructing a constraint.
Aiming at the problems of the classic FCS-MPC algorithm, the invention constructs a multi-step prediction converter model prediction control algorithm, realizes multi-step prediction calculation by using the concepts of prediction time domain and control time domain in a prediction control theory as reference, adopts an area equivalent principle to construct a simple and effective pulse optimization strategy to realize the purpose of reducing the switching frequency, and the calculated amount of the algorithm can meet the requirement of real-time control.
The flow of the multi-step prediction converter model prediction control algorithm is shown in fig. 2, and the method comprises the following steps:
a1, setting prediction time domain TpAnd control time domain TcLength of (d);
Tc=nTs(1)
Tp=(n+1)Ts(2)
wherein the content of the first and second substances,nis an integer greater than 1; t issIs the control period in seconds.
A2, the second in the current control time domainkSampling the state in one control periodx(k) To implement control SLK (k) Sop is obtained by multi-step prediction optimization calculation (k)；
The calculation flow and multi-step prediction mechanism of the algorithm are shown in FIG. 3, and the control quantity SLK in the control time domain is every timenAnd updating once, wherein the specific control process comprises the following steps:
a21, constructing control quantity array SLKn]And the prediction optimizing result array Sopn]；
Construction of the array SLK [ alpha ]n]And the array Sop [ alpha ], [n]The control amount and the calculation result of the prediction optimization in the control time domain are stored. The control period and the sampling period of the algorithm are both Ts，TsIs also the minimum pulse width of the algorithm output;
a22, performing multi-step prediction optimization calculation in the current control time domain;
controlling timingk=1, control SLK (1) is performed, and system status is sampledx(ii) a Based on statexBinding to SLK (1:n) Using a current transformer state prediction functionf p To carry outnStep prediction calculation to obtain state prediction valuex p (n+ 1); in thatx p (n+1), calculating the predicted values of all converter switching function combinations under respective actionx pi (n+2) of whichi=1,..,g，gThe number of all the switching function combinations of the converter. By cost functionf c Optimizing and calculating to obtain an optimal control quantity, and assigning the optimal control quantity to the Sop (1);
controlling timingk=2, control SLK (2) implementation, sampling System Statex(ii) a Based on statexBinding to SLK (2:n) And Sop (1), using a current transformer state prediction functionf p To carry outnStep prediction calculation to obtain state prediction valuex p (n+ 2); in thatx p (n+2), traversing and calculating the predicted value corresponding to each switching function combination of the converterx pi (n+3) from the cost functionf c Optimizing calculation is carried out to obtain the optimal control quantity, and the optimal control quantity is assigned to the Sop (2);
by analogy with the above calculation method, the time sequence is controlledk=mThen, control SLK (m) Sampling the system statex(ii) a Based on statexIn combination with SLK (m:n) And Sop (1:m-1) using a prediction functionf p To carry outnStep prediction calculation to obtain state prediction valuex p (n+m) (ii) a In thatx p (n+m) Based on the above, the corresponding predicted value of each switching function combination is calculated in a traversal wayx pi (n+m+1) from a cost functionf c Optimizing calculation to obtain optimal control quantity, and assigning to Sop (m)；
Controlling timingk=nTo implement control SLK (n) Sampling the system statex(ii) a Based on statexIn combination with SLK (n) And Sop (1:n-1) using a prediction functionf p To carry outnStep prediction calculation to obtain state prediction valuex p (2n) (ii) a In thatx p (2n) Based on the above, the corresponding predicted value of each switching function combination is calculated in a traversal wayx pi (2n+1) from a cost functionf c Optimizing calculation to obtain optimal control quantity, and assigning to Sop (n)；
As can be seen from the above algorithm calculation process, the control period is TsControlling the time width of the time domain to benTsThe time width of the prediction time domain is (n+1)Ts. In one control cycle, the function is predictedf p Is counted asn+gSub, cost functionf c Is counted asgNext, the process is carried out. Compared with the classic FCSMPC algorithm, the control period is increased within a single control periodnFunction of sub-calculationf p But the prediction time domain is increasednMultiple, and at the same time, the control period T can be reduced appropriatelysTo obtain the desired minimum pulse width. In addition, in each control period, the future is measured based on the state sampling value at the current momentnPredicting the system state of the step, and calculating the corresponding control quantity in the next control time domain based on the system state, thereby realizing the combination of multi-step prediction calculation and single-step optimization;
to further describe the multi-step prediction mechanism described above, FIG. 4 showsnPredictive computation process of = 6. At the current momentkTo carry out controlS 0Is sampled to obtainx 0In combination with the control amount [ 2 ]S 0,S 1,S 2,S 3,S 4,S 5]From a prediction functionf p Obtaining a predicted value by iterative computationx p6. To be provided withx p6On the basis of a prediction functionf p Traversing and calculating state quantity predicted values corresponding to all switch function combinationsx p i7，i=1,..,g. On the basis of the above, the cost function is selectedf c Assigning a minimum combination of switching functions toS 01. Thereby, at the timekWithin the corresponding control period, the control quantity is implementedS 0And sampling values from the statesx 0Calculate outkOptimal control amount at time +6S 01. In thatkThe control is carried out by performing similar operations at the +1 timeS 1Is sampled to obtainx 1In combination with the control amount [ 2 ]S 1,S 2,S 3,S 4,S 5,S 01]From a prediction functionf p Obtaining a predicted value by iterative computationx p7. To be provided withx p7On the basis of a prediction functionf p Traversing and calculating the state quantity predicted value corresponding to each switch function combinationx p i8From a cost functionf c The optimal switch function combination obtained by optimization calculation is assigned toS 11. And so on untilkAt time +5, control is performedS 5Is sampled to obtainx 5In combination with the control amount [ 2 ]S 5,S 01,S 11,S 21,S 31,S 41]From a prediction functionf p Obtaining a predicted value by iterative computationx p11. Using predictive functionsf p Traversing and calculating state quantity predicted valuex p i12From a cost functionf c The optimal switch function combination obtained by optimization calculation is assigned toS 51。
A3, the control amount Sop (1:n) Pulse optimization was performed to obtain SLK (1:n)；
and optimizing the single-phase pulse of the converter by adopting an area equivalent principle so as to reduce the switching times in a control time domain. For a three-phase two-level converter, Sop (1:n) Is developed into the form of three-phase pulse for each phasenThe pulses are summed to obtain the number of cycles of 1 pulseh. Number of cycleshIs equal tonIf so, the phase pulse is 1 in the next control time domain, and optimization is not needed; number of cycleshIf the phase pulse is equal to 0, the phase pulse is 0 in the next control time domain, and optimization is not needed; number of cycleshIn 1 ton-1, the phase pulse in the next control time domain ishThe period is 1, firstly, according to the pulse of last control period in current control time domain the pulse of first control period in next control time domain is defined so as to ensure that said phase pulse is continuously twoNo jump occurs between the control time domains while ensuring that the jump is only once in the next control time domain. The new control quantity obtained by the pulse optimization method is assigned to an SLK (1:n)；
in FIG. 4, the calculated control amount [ 2 ]S 01,S 11,S 21,S 31,S 41,S 51]Optimizing the pulse, and using the optimized pulse as the control quantity of the next control time domainS 0,S 1,S 2,S 3,S 4,S 5]；
FIG. 5 showsnPulse optimization process of = 6. The number of cycles of each phase pulse being 1 is calculated first,h a =3，h b =6，h c and = 1. For theaPhase, since the last pulse of the current control period is 0, the phaseaFirst three 0 s and then three 1 s. For thebNumber of phase and periodh b Equal to 6, without optimization. For thecPhase, the last pulse of the current control period is 0, and thuscThe first five 0's are arranged, and the second 1's are arranged. The results before and after optimization are shown in fig. 5, and it can be seen that the switching times are reduced by pulse optimization.
A4, and implementing the optimized control quantity SLK (k)；
Assigning the optimized pulse sequence to SLK (k) Repeating the above process in the next control time domain to realize the continuous control of the algorithm;
in order to verify the feasibility of the designed method, the output voltage simulation oscillogram of the three-phase inverter controlled by the method is shown in FIG. 6, and the control period Ts=10μs，n=6, harmonic content THD value of output voltage 3.42%, switching frequency 6.127 kHZ. For comparison, the same three-phase inverter simulation model is controlled by adopting a classic FCSMPC algorithm, and the control period Ts=10μ sThe switching frequency is 19.27 kHZ; when the control period Ts=33μsWhen the switching frequency is 7.592 kHZ;
FIG. 7 shows the output of the present method and the classical FCSMPC algorithmaPhase pulse contrast diagram, control period Ts=10μs，nAnd (6). As can be seen from the figure, the method adopts a mechanism of multi-step prediction and pulse optimization, so that the pulses in one control time domain jump at most once, the pulses in the adjacent control time domains do not jump as much as possible, and the minimum width of the output pulses is also ensured. In contrast, in a control time domain, the pulse output by the classic FCSMPC algorithm has many transitions, and thus the corresponding switching frequency is also high.
Claims (5)
1. A multi-step prediction converter model prediction control method comprises the following steps:
a1, setting prediction time domain TpAnd control time domain TcLength of (1), Tc=nTs，Tp=(n+1)Ts；
A2, the second in the current control time domainkSampling the state in one control periodx(k) To implement control SLK (k) Sop is obtained by multi-step prediction optimization calculation (k)；
A3, the control amount Sop (1:n) Pulse optimization was performed to obtain SLK (1:n)；
a4, and implementing the optimized control quantity SLK (k)。
2. The multi-step prediction current transformer model prediction control method as claimed in claim 1, wherein the step A1 predicts the time domain TpAnd control time domain TcCalculating according to the following formula;
Tc=nTs(1)
Tp=(n+1)Ts(2)
wherein the content of the first and second substances,nis an integer greater than 1; t issIs the control period in seconds.
3. The converter model predictive control method based on the multi-step prediction as claimed in claim 1, wherein the specific steps of step a2 are;
a21, constructing control quantity array SLKn]And the prediction optimizing result array Sopn]；
The control amount in the control time domain is stored in the array SLK [ alpha ], [ beta ], [n]In the method, the calculation result of prediction optimization is stored in the array Sop [ 2 ]n]In (1), the control period and the sampling period are both Ts，TsIs also the minimum pulse width of the algorithm output;
a22, performing multi-step prediction optimization calculation in the current control time domain;
controlling timingk=1, control SLK (1) is performed, and system status is sampledx(ii) a Based on statexBinding to SLK (1:n) Using a current transformer state prediction functionf p To carry outnStep prediction calculation to obtain state prediction valuex p (n+ 1); in thatx p On the basis of (n +1), the predicted values of all converter switching function combinations under respective action are calculated in a traversing mannerx pi (n+2) of whichi=1,..,g，gThe number of all switch function combinations of the converter is determined by a cost functionf c Optimizing and calculating to obtain an optimal control quantity, and assigning the optimal control quantity to the Sop (1);
controlling timingk=2, control SLK (2) implementation, sampling System Statex(ii) a Based on statexBinding to SLK (2:n) And Sop (1), using a current transformer state prediction functionf p To carry outnStep prediction calculation to obtain state prediction valuex p (n+ 2); in thatx p (n+2), traversing and calculating the predicted value corresponding to each switching function combination of the converterx pi (n+3) from the cost functionf c Optimizing calculation is carried out to obtain the optimal control quantity, and the optimal control quantity is assigned to the Sop (2);
by analogy with the above calculation method, the time sequence is controlledk=mThen, control SLK (m) Sampling the system statex(ii) a Based on statexIn combination with SLK (m:n) And Sop (1:m-1)，using predictive functionsf p To carry outnStep prediction calculation to obtain state prediction valuex p (n+m) (ii) a In thatx p (n+m) Based on the above, the corresponding predicted value of each switching function combination is calculated in a traversal wayx pi (n+m+1) from a cost functionf c Optimizing calculation to obtain optimal control quantity, and assigning to Sop (m)；
Controlling timingk=nTo implement control SLK (n) Sampling the system statex(ii) a Based on statexIn combination with SLK (n) And Sop (1:n-1) using a prediction functionf p To carry outnStep prediction calculation to obtain state prediction valuex p (2n) (ii) a In thatx p (2n) Based on the above, the corresponding predicted value of each switching function combination is calculated in a traversal wayx pi (2n+1) from a cost functionf c Optimizing calculation to obtain optimal control quantity, and assigning to Sop (n)。
4. The method for predictive control of a converter model with multi-step prediction according to claim 1, wherein in step a3, an area equivalence principle is applied to each phase pulse, and the total control quantity Sop (1:n) And (5) performing pulse optimization, and assigning the optimized control quantity to the SLK.
5. The current transformer model predictive control method based on the multi-step prediction as claimed in claim 1, wherein the step a4 has entered the next control time domain for the continuation of the control process.
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| CN113328622A (en) * | 2021-06-04 | 2021-08-31 | 江南大学 | Control method of flying capacitor type three-level direct current buck converter |
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| CN113328622A (en) * | 2021-06-04 | 2021-08-31 | 江南大学 | Control method of flying capacitor type three-level direct current buck converter |
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