A model predictive control method and system for multi-active bridge converter

Abstract

The present invention proposes a model predictive control method and system for a multi-active bridge converter. Based on the relationship between the output transmission power of each port and the transmission power between other ports, the multi-active bridge converter is modeled and analyzed to obtain a deviation model of each port; a recursive least squares algorithm is used to estimate the linearized control gain in the deviation model, and the estimation result is substituted into the deviation model to solve the optimal phase shift control amount of each port at the current moment, and then phase shift modulation is performed on each port; the method of the present invention can greatly improve the dynamic response speed of the system, realize power decoupling between ports, and the control target of each port under the control strategy is only related to the control amount of the port, which is easy to realize modular expansion.

Description

Technical Field

[0001] The present invention belongs to the technical field of converter model predictive control, and in particular relates to a model predictive control method and system for a multi-active bridge converter. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] The energy exchange between the energy storage device and the DC bus needs to be realized through a multi-port bidirectional converter. The multi-active bridge series resonant converter has a high sinusoidal current of the energy storage inductor and no DC bias in the transformer. Compared with the traditional multi-active bridge (MAB) converter, it has a broader application prospect. Under the single-port proportional integral control, the voltage or power fluctuation of any port will be coupled to other ports through the transformer, which will have an adverse effect on the power quality of other ports and the stable operation of the converter.

[0004] The traditional decoupling control strategy based on inverse matrix transformation has high decoupling cost, complex calculation process, and the operation time increases exponentially with the increase of the number of ports.

[0005] Existing literature divides load types into resistive loads and battery loads, and uses an extended state observer to observe the interference caused by coupling between ports, and uses linear active disturbance rejection control to suppress the interference, which effectively improves the dynamic performance of the system. However, this method relies on the small signal model of the converter. When the actual operating conditions deviate significantly from the preset operating conditions, the dynamic performance of the system is limited.

[0006] The existing decoupling control strategies for multi-port resonant converters have either heavy computational burden or poor dynamic performance, and most optimization algorithms are based on the MAB converter topology. Few literatures provide detailed analysis of multi-active bridge converters (such as SR-MAB converters). Model predictive control, as a nonlinear control algorithm, has the advantages of fast dynamic response and easy multi-objective optimization. It can achieve fast dynamic response of port voltage or power and reduce interference between ports. However, model predictive control is highly dependent on the accuracy of prediction model parameters. The state space matrix of the resonant converter has a high dimension, making it difficult to solve the accurate time domain model. The modeling accuracy based on the fundamental wave analysis method is low, which causes steady-state errors in the control target under model predictive control.

[0007] In summary, in the current model prediction of multi-active bridge converters, the coupling between the ports causes the subsequent decoupling calculation to be complicated or the dynamic performance to be poor. Summary of the invention

[0008] In order to overcome the shortcomings of the above-mentioned prior art, the present invention provides a model predictive control method and system for a multi-active bridge converter. The method of the present invention can greatly improve the dynamic response speed of the system and effectively reduce the power coupling between ports. Under the control strategy of the present invention, the control target of each port is only related to the control quantity of the port, which is easy to realize modular expansion.

[0009] To achieve the above object, a first aspect of the present invention provides a model predictive control method for a multi-active bridge converter, comprising:

[0010] Modeling and analysis of multi-active bridge converters are carried out. Based on the relationship between the output transmission power of each port and the transmission power between other ports, the deviation model of each port of the multi-active bridge converter is constructed.

[0011] Obtain the output voltage and output current of each port of the multi-active bridge converter at the current moment;

[0012] Using a recursive least squares algorithm, based on the output voltage and output current of each port at the current moment, the linearization control gain in the deviation model is estimated;

[0013] According to the estimation result, the optimal phase shift control amount of each port at the current moment is calculated through the deviation model;

[0014] Based on the optimal phase shift control amount of each port at the current moment, phase shift modulation is performed on each port of the multi-active bridge converter.

[0015] A second aspect of the present invention provides a model predictive control system for a multi-active bridge converter, comprising:

[0016] A model building module is used to model and analyze the multi-active bridge converter, and to build a deviation model of each port of the multi-active bridge converter based on the relationship between the output transmission power of each port and the transmission power between other ports;

[0017] An acquisition module, used to acquire the output voltage and output current of each port of the multi-active bridge converter at the current moment;

[0018] An estimation module, used for estimating the linearization control gain in the deviation model based on the output voltage and output current of each port at the current moment by using a recursive least squares algorithm;

[0019] A calculation module, used to calculate the optimal phase shift control amount of each port at the current moment through the deviation model according to the estimation result;

[0020] The control module is used to perform phase shift modulation on each port of the multi-active bridge converter based on the optimal phase shift control amount of each port at the current moment.

[0021] The third aspect of the present invention provides a computer device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, a model predictive control method for a multi-active bridge converter is performed.

[0022] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, executes a model predictive control method for a multi-active bridge converter.

[0023] One or more of the above technical solutions have the following beneficial effects:

[0024] In the present invention, based on the relationship between the output transmission power of each port and the transmission power between other ports, the multi-active bridge converter is modeled and analyzed to obtain the deviation model of each port; the linearization control gain in the deviation model is estimated by using a recursive least squares algorithm, and the estimation result is substituted into the deviation model to solve the optimal phase shift control amount of each port at the current moment, and then phase shift modulation is performed on each port; the method of the present invention can greatly improve the dynamic response speed of the system, realize power decoupling between ports, and the control target of each port under the control strategy is only related to the control amount of the port, which is easy to realize modular expansion.

[0025] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0027] Figure 1 1 is a topological structure diagram of the SR-MAB converter in the first embodiment of the present invention;

[0028] Figure 2 1 is a working waveform diagram of the SR-MAB converter under SPS in the first embodiment of the present invention;

[0029] FIG3( a ) is a Y-type equivalent circuit of the SR-MAB converter in the first embodiment of the present invention;

[0030] FIG3( b ) is a Δ-type equivalent circuit of the SR-MAB converter in the first embodiment of the present invention;

[0031] FIG4( a ) is a schematic diagram of a resistive load in Embodiment 1 of the present invention;

[0032] FIG4( b ) is a schematic diagram of a battery load in Embodiment 1 of the present invention;

[0033] Figure 5 This is a block diagram of DBSRC zero-return power control based on predictive control in Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0034] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0035] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0036] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0037] Embodiment 1

[0038] This embodiment discloses a model predictive control method for a multi-active bridge converter, comprising:

[0039] Modeling and analysis of multi-active bridge converters are carried out. Based on the relationship between the output transmission power of each port and the transmission power between other ports, the deviation model of each port of the multi-active bridge converter is constructed.

[0040] Obtain the output voltage and output current of each port of the multi-active bridge converter at the current moment;

[0041] Using a recursive least squares algorithm, based on the output voltage and output current of each port at the current moment, the linearization control gain in the deviation model is estimated;

[0042] According to the estimation result, the optimal phase shift control amount of each port at the current moment is calculated through the deviation model;

[0043] Based on the optimal phase shift control amount of each port at the current moment, phase shift modulation is performed on each port of the multi-active bridge converter.

[0044] This embodiment is described by taking a series resonant multi-active-bridge (SR-MAB) converter as an example, and specifically includes:

[0045] 1. Modeling of single phase-shift control of SR-MAB converter.

[0046] like Figure 1As shown in the figure, the SR-MAB converter includes multiple full-bridge sub-modules, an N-port high-frequency transformer, and a resonant energy storage element. Specifically, each full-bridge sub-module is composed of four switch tubes and four freewheeling diodes, which can realize the bidirectional flow of energy on the AC and DC sides; the N-port high-frequency transformer turns ratio is 1 / n1:1 / n2:…:1 / n N .

[0047] V i is the DC side voltage of the multi-port converter, I i is the DC side input current, v ac.i is the AC side voltage of the full-bridge submodule, where i=1,2,…,N represents the port number.

[0048] Assume that the N-port transformer is an ideal transformer, ignore the leakage inductance of its winding, and the resonant inductance and resonant capacitance are L r.i and C r.i , the series impedance of the two is X r.i It can be expressed as:

[0049] X r.i =2πf s L r.i -1 / 2πf s C r.i (1)

[0050] Among them, f s is the switching frequency, which remains constant during the control process.

[0051] Now define a virtual reference port. Assume that the number of turns of the reference port winding is 1. To facilitate the subsequent power analysis of the SR-MAB converter, all electrical quantities of each port are converted to the reference port. After conversion, the AC side voltage and resonant cavity impedance are:

[0052]

[0053] Figure 2 The working waveform of the three-port SR-MAB under single phase shift modulation. ij represents the driving signal of the switch tube j at the i-th port, i r.i is the resonant current of the i-th port. SR-MAB adopts a fixed-frequency phase-shift modulation strategy, and the duty cycle of the switch tube drive signal is 50%. The drive signal S under single phase-shift modulation i1 and S i4 In phase, the driving signal S under multiple phase shift modulation i1 and S i4 There is a certain phase difference to optimize the steady-state performance.

[0054] Figure 3(a)-Figure 3(b) Middle D ijrepresents the phase shift between the driving signals of port i and port j, where πD represents the phase that port j lags behind port i.

[0055] By adjusting the phase of the driving signal of each port, the bidirectional flow of energy between ports can be achieved. The phase shift between different ports satisfies:

[0056] D ij =D ik +D kj (3)

[0057] Among them, D ij It represents the shift ratio between the driving signals of port i and port j, D ik It represents the shift ratio between the driving signals of port i and port k, D kj Represents the phase shift between the driving signals at port k and port j.

[0058] Figure 3(a) is a Y-type equivalent circuit of a four-port high-frequency transformer winding. To facilitate the calculation of the power transmitted between ports, it is now converted into a Δ-connected equivalent circuit as shown in Figure 3(b). m It is much larger than the port inductance, so the excitation inductance can be ignored when performing Y-Δ conversion.

[0059] The voltage at each port remains unchanged, and the equivalent impedance becomes:

[0060]

[0061] Where X' r.ij is the equivalent impedance between ports i and j after conversion to the reference port. As the number of ports increases, the calculation of equivalent impedance and power flow between ports becomes more complicated.

[0062] Since the resonant current has very little high-order harmonic content, the active power is mainly fundamental active power, so the SR-MAB converter transmission power can be approximately calculated using the fundamental wave analysis method. The phase of the i-port drive signal lagging the reference port drive signal is The fundamental component of the midpoint voltage of the bridge arm at port i can be written as:

[0063]

[0064] The current flowing between any two voltage sources in the Δ-connected equivalent circuit is:

[0065]

[0066] Among them, i r,ij is the current flowing from the i-th port to the j-th port, M ij is the voltage gain of the jth port to the ith port, ω s is the switching angular frequency.

[0067] Therefore, the average value of the transmission power between ports can be obtained by equations (5) and (6):

[0068]

[0069] The transmission power of each port is equal to the sum of the transmission power between the port and other ports. The calculation formula is as follows:

[0070]

[0071] 2. Load port classification.

[0072] The multi-port converter load in the energy storage system can be divided into resistive load and battery load. The simplified circuit diagram of the resistive load port is shown in Figure 4(a), and the simplified circuit diagram of the battery load port is shown in Figure 4(b). The resistive load generally takes the output voltage as the control target, and the battery load generally takes the charge and discharge current as the control target.

[0073] Take port 2 as an example to model the output voltage deviation of the resistive load and write the KCL equation for the positive node of the filter capacitor:

[0074]

[0075] In order to make the output voltage quickly track the reference value at the next moment, V C.2 (k+1) should be replaced by the reference voltage, so the output voltage deviation at the current moment can be written as:

[0076]

[0077] Where I2 is the output current measurement value, which can be directly measured by the current sensor, I o.2 is the actual output current average value of the port, and its magnitude is related to the phase shift angle of the drive signal of each port at the current moment. C2 is the capacitance of the filter capacitor, V c.2 is the voltage across the filter capacitor, T c To control the frequency.

[0078] From the above SR-MAB converter power analysis, it can be seen that the output power of a certain port is affected by the phase shift angle of other ports, and it is difficult to obtain an explicit solution for the output power. Therefore, this embodiment linearizes the output power and the phase shift angle in a small range, so that the output current I o.2 It is only related to the current port phase shift angle, that is:

[0079] ΔV2=G2·D 12 -I2 / C2f c (11)

[0080] As the load size of each port changes, the linearization gain G also changes accordingly, so the estimation of the coefficient G is the key to achieving accurate control of the control target. The analysis of the battery port is the same as above, and the KVL equation is written for the voltage across the filter inductor:

[0081]

[0082] By linearizing the current deviation formula, we can get:

[0083] ΔI3=G3·D 13 -V3 / C3f c (13)

[0084] 3. Prediction model calibration

[0085] Equations (11) and (13) are the voltage / current deviation models of each port of the SR-MAB converter. When external disturbances act on the converter system, the preset linearization gain cannot make the control target track the reference value. In actual work, it is necessary to identify it online.

[0086] In order to achieve accurate control of the output voltage / current, a recursive least squares algorithm is used to optimally estimate G. Taking the resistive load port as an example, the port voltage should track the reference value, and equations (11) and (13) are converted into matrix form:

[0087]

[0088] The proposed data-driven model can be written as:

[0089]

[0090] Where P is the covariance matrix of the estimation error, K is the estimation gain matrix, I is the identity matrix, and f c To control the frequency, λ is the forgetting factor, which is usually in the range of 0.9 to 1. When λ deviates greatly from 1, the optimization solution time of the parameters to be identified is shortened, but the system stability is poor; when λ is close to 1, the system robustness is enhanced but the parameter tracking performance is limited. Considering that the deviation of the parameters to be identified has little effect on the control target, the value of λ is usually 0.99, which is also taken in this embodiment.

[0091] 4. The specific implementation of DBSRC zero return power control is as follows:

[0092] Step 1: The sensor measures the output voltages V2(k), V3(k)…V of each port of the converter at the kth moment n (k) and output current I2(k), I3(k)…I n (k) taking samples;

[0093] Step 2: Substitute the control gain G(k-1) obtained in step 2 at the previous moment into equation (16) to estimate the optimal control gain G(k) at the current moment;

[0094] Step 3: Substitute G(k) into the unified simplified model formula (11) or formula (13) to obtain the optimal phase shift angle of each port;

[0095] Step 4: According to the phase shift angle obtained in step 3, the phase shift adjuster is controlled to output a corresponding pulse signal to achieve fast and non-biased control of the control target of each port;

[0096] By looping the above steps, the model predictive control of the multi-port series resonant converter can be realized. This control strategy effectively improves the dynamic performance of the system, realizes the decoupling of power / voltage / current of each port, and has a small amount of calculation.

[0097] This embodiment studies a multi-active bridge series resonant converter topology to solve the problems of DC bias and switch tube shutdown at peak current in traditional multi-active bridge DC / DC converters, and proposes a SR-MAB converter model predictive control strategy based on a data-driven model. This control strategy implements model predictive control of multi-port resonant converters with low computational complexity, effectively reduces the power coupling between ports, greatly improves the dynamic response speed of the system, and the control target of each port under this control strategy is only related to the control quantity of the port, which is easy to implement modular expansion.

[0098] Embodiment 2

[0099] The purpose of this embodiment is to provide a model predictive control system for a multi-active bridge converter, comprising:

[0100] A model building module, for building a deviation model of each port of the multi-active bridge converter based on the relationship between the output transmission power of each port and the transmission power between other ports;

[0101] An acquisition module, used to acquire the output voltage and output current of each port of the multi-active bridge converter at the current moment;

[0102] An estimation module, used for estimating the linearization control gain in the deviation model based on the output voltage and output current of each port at the current moment by using a recursive least squares algorithm;

[0103] A calculation module, used to calculate the optimal phase shift control amount of each port at the current moment through the deviation model according to the estimation result;

[0104] The control module is used to perform phase shift modulation on each port of the multi-active bridge converter based on the optimal phase shift control amount of each port at the current moment.

[0105] Embodiment 3

[0106] The purpose of this embodiment is to provide a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the program.

[0107] Embodiment 4

[0108] The purpose of this embodiment is to provide a computer-readable storage medium.

[0109] A computer-readable storage medium stores a computer program, which executes the steps of the above method when executed by a processor.

[0110] The steps involved in the apparatuses of the above embodiments 2, 3 and 4 correspond to the method embodiment 1, and the specific implementation methods can refer to the relevant description part of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0111] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0112] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A model predictive control method for a multi-active bridge converter, characterized in that: include: Based on the relationship between the output transmission power of each port and the transmission power between other ports, a deviation model of each port of the multi-active bridge converter is constructed, including: A virtual reference port is defined, and the electrical quantities of each port of the multi-active bridge converter are converted to the reference port to obtain the converted AC side voltage and the equivalent impedance between the ports; The equivalent impedance between the AC side voltage and the port after conversion is: Among them, X' r.ij is the equivalent impedance between ports i and j after conversion to the reference port, and N represents the port number; Calculate the average value of the power transmitted between ports based on the converted AC side voltage and the equivalent impedance between ports; The average transmission power between the ports is: Among them, P ij is the average value of the transmission power between ports, v a ' c.i (t) is the fundamental component of the midpoint voltage of the bridge arm at port i, i r.ij (t) is the current flowing from the i-th port to the j-th port, X' r.ij is the equivalent impedance between ports i and j after conversion to the reference port, The phase of the i-port driving signal lagging behind the reference port driving signal, The j-port driving signal lags behind the reference port driving signal phase; Based on the relationship that the output transmission power of each port is equal to the sum of the average transmission power between each port and other ports, the output transmission power of each port and the phase shift angle are linearized so that the actual output current average value of the port is only related to the current port phase shift angle. Based on the output voltage deviation of different loads, the deviation model of each port of the multi-active bridge converter is constructed. The deviation model is: ΔV2=G2·D 12 -I2 / C2f c , Alternatively, ΔI3 = G3·D 13 -V3 / C3f c , Among them, △V2 is the voltage deviation, G2 and G3 are the control gains of the corresponding ports, and D 12 , D 13 are the shift ratios between the corresponding ports, I2 is the output current measurement value, C2 and C3 are the capacitance values ​​of the corresponding port filter capacitors, f c To control the frequency, V3 is the DC side voltage of the port converter, and △I3 is the current deviation; Obtain the output voltage and output current of each port of the multi-active bridge converter at the current moment; The recursive least squares algorithm is used to estimate the linear control gain in the deviation model based on the output voltage and output current of each port at the current moment, specifically: The gain matrix at the current moment is estimated using the covariance matrix of the estimation error at the previous moment, the average value of the actual output current of the port at the current moment, and the phase shift control amount; The control gain at the current moment is estimated by using the gain matrix at the current moment, the average value of the actual output current of the port at the current moment, the phase shift control amount, and the output voltage deviation at the current moment; According to the estimation result, the optimal phase shift control amount of each port at the current moment is calculated through the deviation model; Based on the optimal phase shift control amount of each port at the current moment, phase shift modulation is performed on each port of the multi-active bridge converter.

2. The model predictive control method for a multi-active bridge converter according to claim 1, characterized in that: The construction of output voltage deviation for different loads is as follows: Modeling the output voltage deviation of the resistive load port or the battery load port, and establishing the Kirchhoff current law equation for the voltage across the positive node of the filter capacitor or the filter inductor; The reference voltage is used to replace the port voltage at the next moment in the Kirchhoff's current law equation, so that the output voltage at the next moment can quickly track the reference value to obtain the output voltage deviation at the current moment.

3. The model predictive control method for a multi-active bridge converter according to claim 1, characterized in that: The output transmission power of each port of the multi-active bridge converter is approximately calculated using the fundamental wave analysis method.

4. The model predictive control method for a multi-active bridge converter according to claim 1, characterized in that: The load of the multi-active bridge converter is divided into a resistive load and a battery load. The resistive load takes the output voltage as the control target, and the battery load takes the charge and discharge current as the control target.

5. A model predictive control system for a multi-active bridge converter, characterized in that: include: The model building module is used to build a deviation model of each port of the multi-active bridge converter based on the relationship between the output transmission power of each port and the transmission power between other ports, specifically including: A virtual reference port is defined, and the electrical quantities of each port of the multi-active bridge converter are converted to the reference port to obtain the converted AC side voltage and the equivalent impedance between the ports; The equivalent impedance between the AC side voltage and the port after conversion is: Among them, X' r.ij is the equivalent impedance between ports i and j after conversion to the reference port, N represents the port number; Calculate the average value of the power transmitted between ports based on the converted AC side voltage and the equivalent impedance between ports; The average transmission power between the ports is: Among them, P ij is the average value of the transmission power between ports, v a ' c.i (t) is the fundamental component of the midpoint voltage of the bridge arm at port i, i r.ij (t) is the current flowing from the i-th port to the j-th port, X' r.ij is the equivalent impedance between ports i and j after conversion to the reference port, The i port driving signal lags behind the reference port driving signal phase, The j-port driving signal lags behind the reference port driving signal phase; Based on the relationship that the output transmission power of each port is equal to the sum of the average transmission power between each port and other ports, the output transmission power of each port and the phase shift angle are linearized so that the actual output current average value of the port is only related to the current port phase shift angle. Based on the output voltage deviation of different loads, the deviation model of each port of the multi-active bridge converter is constructed. The deviation model is: ΔV2=G2·D 12 -I2 / C2f c , Or, ΔI3 = G3·D 13 -V3 / C3f c , Among them, △V2 is the voltage deviation, G2 and G3 are the control gains of the corresponding ports, and D 12 , D 13 are the shift ratios between the corresponding ports, I2 is the output current measurement value, C2 and C3 are the capacitance values ​​of the corresponding port filter capacitors, f c To control the frequency, V3 is the DC side voltage of the port converter, and △I3 is the current deviation; An acquisition module, used to acquire the output voltage and output current of each port of the multi-active bridge converter at the current moment; The estimation module is used to estimate the linearization control gain in the deviation model based on the output voltage and output current of each port at the current moment by using a recursive least squares algorithm, specifically: The gain matrix at the current moment is estimated using the covariance matrix of the estimation error at the previous moment, the average value of the actual output current of the port at the current moment, and the phase shift control amount; The control gain at the current moment is estimated by using the gain matrix at the current moment, the average value of the actual output current of the port at the current moment, the phase shift control amount, and the output voltage deviation at the current moment; A calculation module, used to calculate the optimal phase shift control amount of each port at the current moment through the deviation model according to the estimation result; The control module is used to perform phase shift modulation on each port of the multi-active bridge converter based on the optimal phase shift control amount of each port at the current moment.

6. A computer device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, a model predictive control method for a multi-active bridge converter as described in any one of claims 1 to 4 is executed.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the model predictive control method for a multi-active bridge converter according to any one of claims 1 to 4 is executed.

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