Improved model-free current prediction control method, device and system based on forgetting factor

Abstract

The invention discloses an improved model-free current prediction control method, device and system based on a forgetting factor. According to the method, system parameters do not need to be acquired in advance, the current increment of the current is approximately calculated by acquiring the load current and through historical current data, then the optimal inverter switching state is output through a minimum current value function, and meanwhile, a forgetting factor is introduced to weaken the adverse effect of a sampling error on the system; and model-free current prediction control can be realized. According to the method, on the basis of ensuring that parameter-free control is realized, a forgetting factor is introduced to weaken the adverse effect of a sampling error on the system, the current quality is improved while the response speed is relatively high, and the control robustness is improved. The method is suitable for different types of power electronic topological structures, does not need to independently carry out mathematical modeling on different types of power electronic topological structures, has reference significance for model-free predictive current control of the power electronic converter, and has wide application prospects.

Description

Technical Field

[0001] The invention belongs to the field of power electronic control, and in particular relates to an improved model-free current prediction control method, device and system based on a forgetting factor. Background Art

[0002] With the rapid development of power electronics technology, power electronic converters are increasingly used in various power electronic systems. The control strategy of power electronic converters directly affects the performance and efficiency of the system. Traditional control methods usually rely on accurate system parameter models for control design. However, in practical applications, system parameters are often difficult to obtain accurately, and the dynamic changes in system operating conditions make it difficult to ensure the accuracy of model parameters. This leads to the lack of accuracy and robustness of traditional control methods in dealing with system parameter changes and sampling errors.

[0003] Although the existing model-free predictive current control (MFPCC) method is only based on detecting load current and current difference without requiring system parameters, which improves the control accuracy and robustness to a certain extent, its sensitivity to sampling errors still limits its application in high-frequency power electronic systems.

[0004] Therefore, further research on current predictive control methods that do not rely on system parameter models, can effectively weaken the impact of sampling errors and adapt to changes in operating conditions is of great significance for improving the control performance and system stability of power electronic converters. Summary of the invention

[0005] The object of the present invention is to provide an improved model-free current predictive control method, device and system based on forgetting factor, so as to solve the above-mentioned problems.

[0006] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0007] In a first aspect, the present invention provides an improved model-free current predictive control method based on a forgetting factor, the method comprising:

[0008] The inverter state at the kth moment is collected in real time and saved as historical data record; the inverter state includes the three-phase current at the kth moment and the inverter switch state at the k-1th moment; the three-phase current at the kth moment is expressed as

[0009] Perform Clark transformation on the three-phase current collected at the kth moment to obtain the current of the inverter in the α-β stationary coordinate system;

[0010] The current increment at the kth moment is defined as the current difference between the kth moment and the k-1th moment, and two adjacent current increments under the same switching state are approximately equal;

[0011] Calculate the current increment using historical current data;

[0012] A forgetting factor is introduced to update the current increment, and the predicted current is calculated based on the updated current increment;

[0013] A current value function is obtained according to the relationship between the current prediction value and the current reference value;

[0014] Calculate the current value function under all switching states of the inverter, and select the switching state corresponding to the minimized current value function as the output.

[0015] In one implementation, based on the collected current at the kth moment and the defined current increments at the kth moment and the k+1th moment, a general mathematical expression for current prediction is obtained as follows:

[0016]

[0017] Among them, i=[i α i β ] T Indicates α and β axis current; V k is the inverter switching state at the kth moment, the switching state V∈{V 0 ,V 1 ,…,V m}; is the sampling value of the current at the kth moment; They are the current increments at the kth moment and the k+1th moment calculated based on the historical current data.

[0018] In one embodiment, the current increment is calculated using historical current data, and the current increment at the kth moment and the k+1th moment is Its general form is:

[0019]

[0020] in, They are the current sampling values ​​at the kth moment, the k+1th moment, and the k+2th moment respectively.

[0021] In one embodiment, two adjacent current increments in the same switching state are approximately equal, and the current increment at the kth moment is The general form is:

[0022]

[0023] in, The last switch state is V k The current increment under The last switch state is Vk The current collection value at the next moment and the current collection value at the previous moment.

[0024] In one embodiment, a forgetting factor is introduced to update the current increment, and the updated current increment The general form of is:

[0025]

[0026] Among them, η is the forgetting factor, The last switch state is V j The current increment under The current increment after the forgetting factor is introduced.

[0027] In one implementation, the predicted current is calculated based on the updated current increment, and its general form is expressed as:

[0028]

[0029] Among them, V j is the selected switch state.

[0030] In one embodiment, the general form of the current value function is expressed as:

[0031]

[0032] in, are the reference currents of the α and β axes respectively, Select the switch state as V j The predicted current of α and β axes at the k+2th moment, To select the switch state as V j The current value function, min is the minimum element function, is the minimum value of the current value function, V n Output the optimal inverter switching state at the k+1th moment.

[0033] In a second aspect, the present invention provides an improved model-free current predictive control device based on a forgetting factor, the device comprising:

[0034] The acquisition storage module is used to acquire the inverter state at the kth moment in real time and save it as historical data record; the inverter state includes the three-phase current at the kth moment and the inverter switch state at the k-1th moment; the three-phase current at the kth moment is expressed as

[0035] The mathematical model module in the α-β stationary coordinate system is used to obtain the three-phase current at the kth moment. Perform Clark transformation to obtain the inverter current in the α-β stationary coordinate system;

[0036] The increment calculation module is used to define the current increment at the kth moment and the k+1th moment as the current difference between the two moments before and after, and to make two adjacent current increments under the same switching state approximately equal; and to calculate the current increment using historical current data;

[0037] A current increment update module is used to introduce a forgetting factor to update the current increment, and calculate the predicted current from the updated current increment;

[0038] A current value function building module is used to obtain a current value function according to a current prediction value and a current reference value;

[0039] The output module is used to calculate the current value function under all switching states of the inverter, and select the switching state corresponding to the minimized current value function as the output.

[0040] In a third aspect, the present invention provides an improved model-free current prediction control system based on forgetting factor, the system comprising the above-mentioned improved model-free current prediction control device based on forgetting factor.

[0041] In a fourth aspect, the present invention provides a computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the above-mentioned improved model-free current predictive control method based on forgetting factor.

[0042] Compared with the prior art, the present invention has the following advantages:

[0043] (1) The model-free predictive control method described in the present invention does not require prior knowledge of system parameters and only requires historical current data to predict current. Therefore, it has a wider applicability than traditional model-based predictive control methods and does not require a cumbersome parameter setting process.

[0044] (2) The model-free predictive control method described in the present invention is applicable to different types of power electronic topologies because it makes predictions based on historical current data and unifies the current control law of the system. It does not require separate mathematical modeling of different types of power electronic topologies.

[0045] (3) The model-free predictive control method described in the present invention uses a forgetting factor to suppress the sampling error caused by the sampling circuit. At the same time, the forgetting factor is adjusted to adapt to different operating conditions. While requiring lower precision of the sampling circuit, it can achieve more accurate tracking of the current reference value under changing operating conditions, and thus has better steady-state and dynamic performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings are part of the present invention and 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, but do not constitute an improper limitation of the present invention. Obviously, the accompanying drawings described below are only some embodiments. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0047] Figure 1 A flow chart of an improved model-free current predictive control method based on forgetting factor provided by an embodiment of the present invention;

[0048] Figure 2 The schematic diagram of the three-phase two-level inverter used in the improved model-free current prediction control based on the forgetting factor of the present invention;

[0049] Figure 3 The figure is a diagram illustrating the implementation process of the improved model-free current predictive control method based on forgetting factor;

[0050] Figure 4 A current waveform comparison diagram of the improved model-free current prediction control method based on the forgetting factor in the embodiment of the present invention and the current waveform comparison diagram of the traditional FCS model-free method;

[0051] Figure 5 The figure is a current dynamic waveform diagram of an improved model-free current prediction control method based on a forgetting factor when a peak value of a current reference value suddenly changes in an embodiment of the present invention.

[0052] It should be noted that these drawings and textual descriptions are not intended to limit the conceptual scope of the present invention in any way, but are intended to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] like Figure 1 As shown, the improved model-free current prediction control method based on forgetting factor provided by the embodiment of the present disclosure comprises the following specific steps:

[0055] Step S100: collecting the inverter state at the kth moment in real time and saving it as a historical data record.

[0056] Specifically, the state of the inverter includes the three-phase current at the kth moment and the inverter switch state at the k-1th moment. The three-phase current at the kth moment is expressed as At the k-1th moment, the inverter switch state is V k-1 , current sampling value It is acquired by the sampling circuit and saved as historical current data.

[0057] Step S200: performing Clark transformation on the three-phase current collected at the kth moment to obtain the current of the inverter in the α-β stationary coordinate system.

[0058] Specifically, the general expression of the two-phase current in the α-β stationary coordinate system at the kth moment is:

[0059]

[0060] in, is the two-phase current in the α-β stationary coordinate system at the kth moment, is the three-phase current at the kth moment, T αβ is the Clark transformation matrix. When Clark transformation is performed according to the amplitude invariance principle, T αβ The general expression is:

[0061]

[0062] Step S300: define the current increment at the kth moment as the current difference between the kth moment and the k-1th moment, and make two adjacent current increments in the same switching state approximately equal.

[0063] According to the inverter current in the α-β stationary coordinate system and the current increments at the kth moment and the k+1th moment defined as the two moments before and after, the model-free current prediction result can be expressed in the following general form:

[0064]

[0065] Among them, i=[i α i β ] T Indicates α and β axis current; V k is the inverter switching state at the kth moment, the switching state V∈{V 0 ,V 1 ,…,V m}; is the sampling value of the current at the kth moment; They are the current increments at the kth moment and the k+1th moment calculated based on the historical current data.

[0066] Step S400: Calculate the current increment using historical current data.

[0067] Specifically, the current increment at the kth moment and the k+1th moment is Approximately calculated from historical current data.

[0068] Use historical current data to calculate the current increment, the current increment at the kth moment and the k+1th moment Its general form can be expressed as:

[0069]

[0070] in, They are the current sampling values ​​at the kth moment, the k+1th moment, and the k+2th moment respectively.

[0071] Considering It cannot be sampled at the kth moment, and in the same switching state, when the sampling interval is fixed and short enough, two adjacent current increments in the same switching state can be approximately equal, so the current increment in the previous same switching state is used to approximate the current increment.

[0072] Furthermore, the kth moment and the current increment in the above formula (4) are The general form is further expressed as:

[0073]

[0074] in, The last switch state is V k The current increment under The last switch state is V k The current collection value at the next moment and the current collection value at the previous moment.

[0075] Step S500: introducing a forgetting factor to update the current increment, and calculating the predicted current from the updated current increment.

[0076] The forgetting factor η∈(0,1) is used to forget the earlier historical current data, which is given based on the sampling error and operating conditions.

[0077] Furthermore, the updated current increment The general form can be expressed as:

[0078]

[0079] in, The last switch state is V j The current increment under The current increment after the forgetting factor is introduced.

[0080] The algorithm's anti-interference ability is enhanced by using historical data, which makes the earlier historical current data decay exponentially, thereby improving dynamic performance. This is achieved by introducing the forgetting factor η to update the current increment, predicting the future current from the updated current increment, and calculating the current value function. The forgetting factor η is set based on:

[0081] For systems with large sampling errors, the forgetting factor should be appropriately increased to improve the current prediction system's ability to resist sampling error interference; for systems with large changes in operating conditions, the forgetting factor should be appropriately reduced to improve the dynamic performance of the current prediction system.

[0082] Furthermore, the predicted current is calculated based on the updated current increment, and its general form can be expressed as:

[0083]

[0084] Among them, V j is the selected switch state.

[0085] Step S600: deriving a current value function according to the relationship between the current prediction value and the current reference value.

[0086] Furthermore, the current value function is the relationship between the current prediction value and the current reference value at the k+2th moment, and its general form can be expressed as:

[0087]

[0088] in, are the reference currents of the α and β axes respectively, Select the switch state as V j The predicted current of α and β axes at the k+1th moment, To select the switch state as V j The current value function, min is the minimum element function, is the minimum value of the current value function, V n Output the optimal inverter switching state at the k+1th moment.

[0089] Step S700: Calculate the current cost function of all switching states of the inverter, and select the switching state corresponding to the minimized current cost function as the output.

[0090] In a specific embodiment, a three-phase two-level inverter (hereinafter referred to as inverter) system is taken as an example. Figure 2 As shown in the figure, the basic structure of the inverter is given, where each phase consists of two power devices, the output filter consists of a single L-type filter, and the resistor R is the equivalent resistance of the internal resistance of the power switch device and the internal resistance of the inductor. Each phase of the inverter has two switches, and the combination of different switch states can obtain 2 3= 8 basic switch states, using V k ∈{V 0 ,V 1 ,…,V 7} represents the inverter switching state at the kth moment, as shown in Table 1.

[0091] Table 1 Switching status of three-phase two-level inverter

[0092]

[0093] Now, combining the 8 switching states of the inverter and the current collected by the sampling circuit, the method of predicting current is used to select the switching state of the inverter at the k+1th moment. Figure 2 As shown in the figure, when a three-phase two-level inverter is used, the specific process of the improved model-free current predictive control method based on the forgetting factor is as follows:

[0094] Collect the three-phase current at the kth moment And the inverter switching status at the k-1th moment, refer to Table 1 to determine the inverter switching status V at the k-1th moment k-1 The three-phase current sampled at the kth moment is transformed by Clark to obtain the two-phase current in the α-β stationary coordinate system at the kth moment.

[0095] The two-phase current in the α-β stationary coordinate system at the kth moment can be obtained from the three-phase current at the kth moment through the Clark transformation, and its general expression is:

[0096]

[0097] in, is the two-phase current in the α-β stationary coordinate system at the kth moment, is the three-phase current at the kth moment, T αβ is the Clark transformation matrix. When Clark transformation is performed according to the amplitude invariance principle, T αβ The general expression is:

[0098]

[0099] Define the current increment at the kth moment and the k+1th moment is the current difference between the previous and the next moment, and its general form can be expressed as:

[0100]

[0101] in, They are the current sampling values ​​at the kth moment, the k+1th moment, and the k+2th moment respectively.

[0102] because It is impossible to obtain the current increment by sampling at the kth moment, and in the same switching state, when the sampling interval is fixed and short enough, two adjacent current increments in the same switching state can be approximately equal. Therefore, the current increment in the previous same switching state is used to approximate the current increment. The kth moment and current increment in the above formula (modified) are The general form is further expressed as:

[0103]

[0104] in, The last switch state is V k The current increment under The last switch state is V k The current collection value at the next moment and the current collection value at the previous moment.

[0105] In order to reduce the adverse effects of sampling errors and ensure that the method can work properly when different operating conditions change, the forgetting factor η∈(0,1) is introduced to make the earlier historical current data decay exponentially with time and update the current increment. Its general form can be expressed as:

[0106]

[0107] At this time, the general expression of the predicted current at the k+1th moment can be expressed as:

[0108]

[0109] Among them, V j ∈{V 0 ,V 1 ,…,V 7} is the selected inverter switching state.

[0110] The current value function is expressed as:

[0111]

[0112] in, are the reference currents of the α and β axes respectively, Select the switch state as V j The predicted current of α and β axes at the k+1th moment, To select the switch state as V j The current value function, min is the minimum element function, is the minimum value of the current value function, V n Output the optimal inverter switching state at the k+1th moment.

[0113] The implementation process of the improved model-free current predictive control method based on forgetting factor for three-phase two-level inverter is described in Figure 3 As shown, from Figure 3 The specific execution process of data collection, calculation, storage update and switch state selection in the implementation example of the present invention can be seen in FIG.

[0114] Figure 4 This is a comparison diagram of the current waveform in the circuit when using the improved model-free current predictive control method based on forgetting factor (the method part on the left side of the figure, the forgetting factor is set to 0.95) under high-frequency sampling (sampling period is 20us) provided in an embodiment of the present invention and using the traditional FCS model-free method (the traditional method part on the right side of the figure); compared with the traditional method, the current waveform in the circuit using the method proposed by the present invention is more sinusoidal, indicating that the method proposed by the present invention can reduce the impact of sampling errors on system performance and improve the accuracy and robustness of control to a certain extent.

[0115] Figure 5 The dynamic performance of the current in the circuit when the improved model-free current predictive control method based on the forgetting factor is used under the sudden change of the peak value of the current reference value provided in the embodiment of the present invention; it can be seen that when the method proposed in the present invention is used, when the peak value of the current reference value suddenly decreases or increases, the current in the circuit tracks the reference value within 0.5ms and 1.5ms respectively, indicating that the method proposed in the present invention has good dynamic performance.

[0116] The following is an embodiment of the improved model-free current prediction control device based on the forgetting factor of the present invention, which can be used to execute the improved model-free current prediction control method embodiment based on the forgetting factor of the present invention. For details not disclosed in the embodiment of the improved model-free current prediction control device based on the forgetting factor of the present invention, please refer to the embodiment of the improved model-free current prediction control method based on the forgetting factor of the present invention.

[0117] In one embodiment, an improved model-free current predictive control device based on a forgetting factor is proposed, the device comprising:

[0118] The acquisition storage module is used to acquire the inverter state at the kth moment in real time and save it as historical data record; the inverter state includes the three-phase current at the kth moment and the inverter switch state at the k-1th moment; the three-phase current at the kth moment is expressed as

[0119] The mathematical model module in the α-β stationary coordinate system is used to obtain the three-phase current at the kth moment. Perform Clark transformation to obtain the inverter current in the α-β stationary coordinate system;

[0120] The increment calculation module is used to define the current increment at the kth moment and the k+1th moment as the current difference between the two moments before and after, and to make two adjacent current increments under the same switching state approximately equal; and to calculate the current increment using historical current data;

[0121] A current increment update module is used to introduce a forgetting factor to update the current increment, and calculate the predicted current from the updated current increment;

[0122] A current value function building module is used to obtain a current value function according to the relationship between the current prediction value and the current reference value;

[0123] The output module is used to calculate the current value function under all switching states of the inverter, and select the switching state corresponding to the minimized current value function as the output.

[0124] It should be noted that the improved model-free current prediction control device based on forgetting factor provided in the above embodiment only uses the division of the above functional modules as an example when executing the improved model-free current prediction control method based on forgetting factor. In practical applications, the above functional distribution can be completed by different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the improved model-free current prediction control device based on forgetting factor provided in the above embodiment and the improved model-free current prediction control method based on forgetting factor embodiment belong to the same concept. The implementation process thereof is detailed in the improved model-free current prediction control method embodiment based on forgetting factor, which will not be repeated here.

[0125] In one embodiment, an improved model-free current prediction control system based on forgetting factor is proposed. The system includes the improved model-free current prediction control device based on forgetting factor.

[0126] For the description of the improved model-free current predictive control based on the forgetting factor, please refer to the description of the same or similar parts mentioned above, which will not be repeated here.

[0127] In one embodiment, a computer program product is provided. When the computer program / instruction is executed by a processor, one or more processors execute the following steps: real-time acquisition of the inverter state at the kth moment and storage as historical data record; the inverter state includes the three-phase current at the kth moment and the inverter switch state at the k-1th moment; the three-phase current at the kth moment is expressed as The three-phase current collected at the kth moment is subjected to Clark transformation to obtain the current of the inverter in the α-β stationary coordinate system; the current increment at the kth moment is defined as the current difference between the kth moment and the k-1th moment, and two adjacent current increments under the same switching state are approximately equal; the current increment is calculated using historical current data; the forgetting factor is introduced to update the current increment, and the predicted current is calculated based on the updated current increment; the current value function is obtained based on the relationship between the current prediction value and the current reference value; the current value function under all switching states of the inverter is calculated, and the switching state corresponding to the minimized current value function is selected as the output.

[0128] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0129] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0130] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. An improved model-free current predictive control method based on forgetting factor, characterized in that: The method comprises: The inverter state at the kth moment is collected in real time and saved as historical data record; the inverter state includes the three-phase current at the kth moment and the inverter switch state at the k-1th moment; the three-phase current at the kth moment is expressed as Perform Clark transformation on the three-phase current collected at the kth moment to obtain the current of the inverter in the α-β stationary coordinate system; The current increment at the kth moment is defined as the current difference between the kth moment and the k-1th moment, and two adjacent current increments under the same switching state are approximately equal; Calculate the current increment using historical current data; A forgetting factor is introduced to update the current increment, and the predicted current is calculated based on the updated current increment; A current value function is obtained according to the relationship between the current prediction value and the current reference value; Calculate the current value function under all switching states of the inverter, and select the switching state corresponding to the minimized current value function as the output.

2. The improved model-free current predictive control method based on forgetting factor according to claim 1, characterized in that: According to the current at the kth moment and the defined current increments at the kth moment and the k+1th moment, the general mathematical expression for current prediction is obtained as follows: Among them, i=[i α i β ] T Indicates α and β axis current; V k is the inverter switching state at the kth moment, the switching state V∈{V0,V1,…,V m }; is the sampling value of the current at the kth moment; They are the current increments at the kth moment and the k+1th moment calculated based on the historical current data.

3. The improved model-free current predictive control method based on forgetting factor according to claim 2, characterized in that: Use historical current data to calculate the current increment, the current increment at the kth moment and the k+1th moment Its general form is: in, They are the current sampling values ​​at the kth moment, the k+1th moment, and the k+2th moment respectively.

4. The improved model-free current predictive control method based on forgetting factor according to claim 3 is characterized in that: The two adjacent current increments in the same switching state are approximately equal, and the current increment at the kth moment is The general form of is: in, The last switch state is V k The current increment under The last switch state is V k The current collection value at the next moment and the current collection value at the previous moment.

5. The improved model-free current predictive control method based on forgetting factor according to claim 4, characterized in that: Introduce the forgetting factor to update the current increment, the updated current increment The general form of is: Among them, η∈(0,1) is the forgetting factor, The last switch state is V j The current increment under .

6. The improved model-free current predictive control method based on forgetting factor according to claim 5, characterized in that: The predicted current is calculated based on the updated current increment, and its general form is expressed as: Among them, V j is the selected switch state.

7. The improved model-free current predictive control method based on forgetting factor according to claim 3, characterized in that: The general form of the current value function is expressed as: in, are the reference currents of the α and β axes respectively, Select the switch state as V j The predicted current of α and β axes at the k+2th moment, To select the switch state as V j The current value function, min is the function for selecting the minimum element, is the minimum value of the current value function, V n Output the optimal inverter switching state at the k+1th moment.

8. An improved model-free current predictive control device based on forgetting factor, characterized in that: The device comprises: The acquisition storage module is used to acquire the inverter state at the kth moment in real time and save it as historical data record; the inverter state includes the three-phase current at the kth moment and the inverter switch state at the k-1th moment; the three-phase current at the kth moment is expressed as The mathematical model module in the α-β stationary coordinate system is used to collect the three-phase current at the kth moment. Perform Clark transformation to obtain the inverter current in the α-β stationary coordinate system; The increment calculation module is used to define the current increment at the kth moment and the k+1th moment as the current difference between the two moments before and after, and to make two adjacent current increments under the same switching state approximately equal; and to calculate the current increment using historical current data; A current increment update module is used to introduce a forgetting factor to update the current increment, and calculate the predicted current from the updated current increment; A current value function building module is used to obtain a current value function according to the relationship between the current prediction value and the current reference value; The output module is used to calculate the current value function under all switching states of the inverter, and select the switching state corresponding to the minimized current value function as the output.

9. An improved model-free current predictive control system based on forgetting factor, characterized in that: The system includes the improved model-free current predictive control device based on forgetting factor as claimed in claim 8.

10. A computer program product comprising a computer program / instructions, characterized in that: When the computer program / instructions are executed by a processor, the improved model-free current predictive control method based on forgetting factor as claimed in any one of claims 1 to 7 is implemented.

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