A finite control set model predictive control method and device with high control accuracy

Abstract

This invention discloses a finite control set model predictive control method and apparatus with high control accuracy. The method obtains the state-space equation of the controlled object based on the system optimization objective, acquires the increment of the optimization objective from time k to k+1, and obtains the increment of the optimization objective from time k to k+1 based on x... j The peak value and the error at time k+1 are used to construct a multi-objective optimization value function for the system at time k. By minimizing the constructed multi-objective optimization value function, the optimal switching action position and optimal switching state at the current time k are obtained. A value function considering compensation for the delay time is constructed. By minimizing the value function considering compensation, the optimal switching action position and optimal switching state at time k+1 are obtained. This invention allows for flexible changes to the switching state position, and the switching time is flexible, variable, and adjustable online. This not only improves the switching accuracy of the inverter but also retains the characteristic that the switching does not exceed once per control cycle.

Description

Technical Field

[0001] This invention belongs to the technical field of power electronics and electric drive, and particularly relates to a finite control set model predictive control method and device with high control accuracy. Background Technology

[0002] Finite control set model predictive control (FCS-MPC) has wide applications in power electronics and electric drive fields. In industrial AC motor speed control systems, such as asynchronous motors and permanent magnet synchronous motors, its precise control characteristics can be used to achieve high-precision regulation of motor speed, torque, etc., thereby improving the dynamic performance of the system.

[0003] During the grid connection of renewable energy power generation such as solar photovoltaic power generation and wind power generation, this control method accurately controls the switching action of the inverter, efficiently and precisely converting DC power into AC power that meets the grid requirements, ensuring power quality and stable grid connection of the power generation system, and improving the utilization efficiency of renewable energy and grid compatibility.

[0004] The main advantage of traditional FCS-MPC lies in its ability to easily achieve multi-objective optimization through a unified value function, while its controller design is simple and intuitive, offering better system dynamic performance compared to traditional proportional-integral controllers. However, it has significant drawbacks: the switch can only be moved to a fixed position, and the possible switching positions are only related to the initial value, which reduces control accuracy. In fields such as grid connection and motor control, this can degrade the output waveform quality of the power electronic converter. Summary of the Invention

[0005] To address the shortcomings of existing Finite Control Set Model Predictive Control (FCS-MPC) technology, the present invention aims to provide a high-precision FCS-MPC method and apparatus that can improve the problem of power electronic converter output waveform quality degradation caused by the fixed switching action position of traditional FCS-MPC, while maintaining the high dynamic performance advantage of traditional FCS-MPC. The method has a wide range of applications.

[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 a finite control set model predictive control method with high control accuracy, the method comprising:

[0008] The state space equation of the controlled object is obtained according to the system optimization objective, and the state space equation is written in discrete form to obtain the increment of the optimization objective from k to k+1.

[0009] Based on the optimization objective at time k to k+1, x jThe peak value and the error at time k+1 are used to construct the value function for multi-objective optimization of the system at time k.

[0010] By minimizing the value function of the constructed multi-objective optimization, the optimal switching action position and optimal switching state at the current time k are solved.

[0011] A value function considering compensation is constructed to compensate for the delay time. By minimizing the value function considering compensation, the optimal switching action position and optimal switching state at time k+1 are obtained.

[0012] In one implementation, the state-space equation of the controlled object obtained according to the system optimization objective is:

[0013]

[0014] In the formula, x represents the state space, u represents the system input; A represents the state transition matrix; and B is the input matrix.

[0015]

[0016] Discrete time is t s Then the j-th optimization objective x j The discrete form is:

[0017] x j (k+1)=(t s a j +1)x j (k)+t s b j u(k), j = 1, 2, 3, ..., n

[0018] The change Δx of the j-th optimization objective at time k to k+1 j (k) can be represented as:

[0019] Δx j (k)=x j (k+1)-x j (k), j = 1, 2, 3...n

[0020] In the formula, x j (k+1) represents the value of the j-th optimization objective at time k+1; x j (k) represents the value of the j-th optimization objective at time k.

[0021] In one implementation, the value function of the multi-objective optimization is j optimization objective functions. The weighted value, the value function J of multi-objective optimization i The expression for (k) is:

[0022]

[0023] In the formula, λ j The weight factor for the j-th optimization objective;

[0024] The objective function to be optimized is:

[0025]

[0026] In the formula, d i (k) represents the switch operation position. Let x represent the system reference value for the j-th optimization objective. j (k+d i (k) represents the optimization objective at k+d. i The value of x at time (k), j (k+d i (k)) and x j (k+1) can be determined by the duty cycle d. i (k) represents the expression:

[0027] x j (k+d i (k))=x j (k)+Δx j (k-1)d i (k), j = 1, 2, 3, ..., n

[0028] x j (k+1)=x j (k+d i (k))+Δx j (k)(1-d i (k)), j = 1, 2, 3, ..., n

[0029] In the formula, Δx j (k-1) represents the change of the j-th optimization objective from time k-1 to time k; Δx j (k) represents the change of the j-th optimization objective from time k to time k+1.

[0030] In one implementation, the step of solving for the optimal switching action position and optimal switching state at the current time k by constructing a multi-objective optimization value function includes:

[0031] The method of solving for the optimal switching action position and optimal switching state at time k by constructing a multi-objective optimization value function includes:

[0032] Let the value function J of multi-objective optimization i The derivative of (k) with respect to the switching action position is 0, and the switching state V corresponding to maintaining the switching state of the previous control cycle is solved.i The optimal duty cycle d of (k) opt (k);

[0033] The expression for duty cycle is:

[0034] in,

[0035]

[0036] In the formula, y i (k) and x i (k) does not represent a specific meaning; it is merely a symbol. x represents the reference value for the j-th optimization objective; j (k-1)x j (k) represents the value of the j-th optimization objective at time k-1 and time k; Δx j (k-1) represents the change of the j-th optimization objective from time k-1 to time k; Δx ji (k) represents the change of the j-th optimization objective from time k to time k+1 when the i-th candidate vector is applied;

[0037] Substitute all candidate vectors into x i (k) and y i (k), to obtain the voltage vector V corresponding to the i-th switching state. i (k) and duty cycle d i (k);

[0038] By minimizing the value function J of multi-objective optimization i (k), to obtain the optimal duty cycle d opt (k) and optimal switching state V opt (k).

[0039] In one implementation, a two-step prediction method is used to compensate for time delays.

[0040] In one implementation, at system time k+1, the optimal start point d(k) considering delay compensation is introduced into the value function through the expression of the optimization objective at time k+1. The expression of the j-th optimization objective at time k+1 is:

[0041] x j (k+1)=x j (k)+Δx j (k)d opt (k)+Δx j (k+1)(1-d opt (k)), j = 1, 2, ..., n

[0042] In the formula, Δxj (k) represents the change of the j-th optimization objective from time k to time k+1;

[0043] d opt (k) represents the optimal duty cycle at time k;

[0044] Δx j (k+1) represents the change of the j-th optimization objective from time k+1 to time k+2.

[0045] In one implementation, the value function considering compensation is:

[0046]

[0047] In the formula, λ j The weight factor for the j-th optimization objective; When the i-th candidate vector is applied, the optimization objective x is... j The value function of J; i (k+1) represents the value function of multi-objective optimization, where k+1 is the value function of all optimization objectives when the i-th candidate vector is applied. The sum of.

[0048] Secondly, the present invention provides a finite control set model predictive control device with high control accuracy, the device comprising:

[0049] The acquisition module is used to obtain the state space equation of the controlled object according to the system optimization objective, write the state space equation in discrete form, and obtain the increment of the optimization objective from k to k+1.

[0050] The value function module for multi-objective optimization is used to evaluate x based on the optimization objective at time k to k+1. j The peak value and the error at time k+1 are used to construct the value function for multi-objective optimization of the system at time k.

[0051] Module 1 is used to solve for the optimal switching action position and optimal switching state at time k by minimizing the value function of the constructed multi-objective optimization.

[0052] Module 2 calculates the delay time and constructs a value function that takes the compensation into account. By minimizing the value function that takes the compensation into account, the optimal switching action position and the optimal switching state at time k+1 are obtained.

[0053] Thirdly, the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the above-described high-precision finite control set model predictive control method.

[0054] Fourthly, the present invention provides an electronic device, including a processor and a memory; wherein, when the processor executes a computer program stored in the memory, it implements the above-described high-precision finite control set model predictive control method.

[0055] Compared with the prior art, the present invention has the following advantages:

[0056] In the field of power electronics and electric drives, existing FCS-MPC technology uses a fixed switch-on point, which leads to a decrease in the quality of the converter's output waveform. The high-precision FCS-MPC disclosed in this invention uses a variable switch-on point, allowing for online optimization to obtain the optimal converter switch switching position—a feature lacking in existing FCS-MPC technology. This invention effectively reduces the total harmonic distortion (THD) of the output sine wave, significantly improving the inverter's output quality. Attached Figure Description

[0057] The accompanying drawings, as part of this invention, are provided to further illustrate the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention, but do not constitute an undue limitation thereof. Clearly, the drawings described below are merely some embodiments, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0058] Figure 1 A flowchart of a finite control set model predictive control method with high control accuracy is provided in one embodiment of the present invention;

[0059] Figure 2 This is a space voltage vector diagram of a midpoint clamped three-level inverter.

[0060] Figure 3 This paper compares the implementation principles of the high-precision finite control set model predictive control method of the present invention with those of the traditional FCS-MPC method.

[0061] Figure 4 This is a schematic diagram illustrating the implementation principle of delay compensation in the high-precision finite control set model predictive control method of the present invention.

[0062] Figure 5 The experimental waveforms of the phase current of a three-level grid-connected inverter and its FFT analysis results are shown below, based on the high-precision finite control set model predictive control method of this invention.

[0063] Figure 6 The experimental waveforms of the phase current and their FFT analysis results for a three-level grid-connected inverter using a traditional FCS-MPC are shown.

[0064] Figure 7Experimental waveforms of active power, reactive power, and neutral point potential of a three-level grid-connected inverter using the high-precision finite control set model predictive control method of this invention;

[0065] Figure 8 The experimental waveforms of active power, reactive power, and neutral point potential of a three-level grid-connected inverter using the traditional FCS-MPC are shown.

[0066] It should be noted that these accompanying drawings and textual descriptions are not intended to limit the scope of the invention in any way, but rather to illustrate the concept of the invention to those skilled in the art by referring to specific embodiments. Detailed Implementation

[0067] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] like Figure 1 As shown, this disclosure provides a high-precision finite control set model predictive control method. This method is applied to the high-precision finite control set model predictive control of a three-phase neutral-point clamped three-level grid-connected inverter. The method specifically includes the following steps:

[0069] Step S100: Obtain the state space equation of the controlled object according to the system optimization objective, and write the state space equation in discrete form to obtain the change of the optimization objective from k to k+1.

[0070] Furthermore, based on the system optimization objective, the state-space equation of the controlled object is obtained as follows:

[0071]

[0072] In the formula, x represents the state space, u represents the system input, A represents the state transition matrix, and B is the input matrix.

[0073]

[0074] Discrete time is t s Then the j-th optimization objective x j The discrete form is:

[0075] x j (k+1)=(t s a j +1)x j (k)+t s bj u(k), j=1, 2, 3..., n (3)

[0076] The change Δx of the j-th optimization objective at time k to k+1 j (k) can be represented as:

[0077] Δx j (k)=x j (k+1)-x j (k), j = 1, 2, 3...n (4)

[0078] In the formula, x j (k+1) represents the value of the j-th optimization objective at time k+1; x j (k) represents the value of the j-th optimization objective at time k.

[0079] Step S200: Based on the optimization objective at time k to k+1, x j The peak value and the error at time k+1 are used to construct the value function for multi-objective optimization of the system at time k.

[0080] The value function of multi-objective optimization is j optimization objective functions The weighted value, where λ j Let J be the weight factor for the j-th optimization objective, and J be the value function. i The general form of the expression for (k) can be expressed as:

[0081]

[0082] In the formula, λ j The weight factor for the j-th optimization objective;

[0083] Furthermore, optimize the objective function. Expressed as:

[0084]

[0085] In the formula, d i (k) represents the switch operation position. Let x represent the system reference value for the j-th optimization objective. j (k+d i (k) represents the optimization objective at k+d. i The value of x at time (k), j (k+d i (k)) and x j (k+1) can be determined by the duty cycle d. i (k) represents the expression:

[0086] x j (k+d i(k))=x j (k)+Δx j (k-1)d i (k), j=1, 2, 3..., n (7)

[0087] x j (k+1)=x j (x+d i (k))+Δx j (k)(1-d i (k)), j=1, 2, 3..., n (8)

[0088] In the formula, Δx j (k-1) represents the change of the j-th optimization objective from time k-1 to time k;

[0089] Δx j (k) represents the change of the j-th optimization objective from time k to time k+1.

[0090] Step S300: By minimizing the constructed multi-objective optimization value function, the optimal switching action position and optimal switching state at the current time k are solved.

[0091] In this embodiment of the application, minimizing the value function of the constructed multi-objective optimization to solve for the optimal switching action position and optimal switching state at time k includes:

[0092] Minimize value function J i (k) By letting J i (k) Regarding the switch operating position d i The derivative of (k) is 0, and the switching state V corresponding to maintaining the switching state of the previous control cycle is solved. i (k)(as shown) Figure 2 As shown, a three-phase two-level inverter contains 8 switching states, while a three-phase three-level inverter contains 27 optimal duty cycles (d). opt (k) can be represented as:

[0093]

[0094] Specifically, let the optimization objective function J i The general solution in (k) with a derivative of 0 with respect to the switch position requires a constraint: the duty cycle is [0,1]. Therefore, when the duty cycle is outside this range, it equals 0 or 1; when the general solution is within the range [0,1], the expression is... Let represent the switch position, i.e., the duty cycle, calculated from the i-th candidate vector. The solution process is as follows: Let That is, the partial derivative of the value function J with respect to the optimal duty cycle (switch position) is 0, and d can be obtained through the first derivative expression.i The general solution expression for (k).

[0095] Furthermore, the duty cycle d i (k) expression, y calculated for the i-th candidate switch state i The general expression for (k) is:

[0096]

[0097] x calculated for the i-th candidate switch state i The general expression for (k) is:

[0098]

[0099] Among them, y i (k) and x i (k) does not represent a specific meaning; it is merely a symbol. x represents the reference value for the j-th optimization objective; j (k-1)x j (k) represents the value of the j-th optimization objective at time k-1 and time k; Δx j (k-1) represents the change of the j-th optimization objective from time k-1 to time k; Δx ji (k) represents the change of the j-th optimization objective from time k to time k+1 when the i-th candidate vector is applied;

[0100] Substitute all possible candidate vectors into x i (k) and y i (k) can be used to obtain the voltage vector V corresponding to the i-th switching state. i (k) and duty cycle (switch operation position) d i (k), by minimizing the value function J i (k) can obtain the optimal duty cycle (switch action position) d opt (k) and optimal switching state V opt (k).

[0101] Step S400: Compensate for the delay time and construct a value function that takes compensation into account. By minimizing the value function that takes compensation into account, obtain the optimal switching action position and the optimal switching state at time k+1.

[0102] Since the real-time controller introduces a delay, a two-step prediction strategy is used to compensate for the time delay.

[0103] Furthermore, at system time k+1, the optimal start point d(k) considering delay compensation is introduced into the value function through the expression of the optimization objective at time k+1. The expression of the j-th optimization objective at time k+1 is: xj (k+1)=x j (k)+Δx j (k)d opt (k)+Δx j (k+1)(1-d opt (k)),j=1,2,..,n(12)

[0104] Optimal switching state V considering delay compensation opt (k+1) and the optimal switch position d opt (k+1) will minimize the value function J that considers compensation. i (k+1) implementation.

[0105] Furthermore, consider the value function J of the compensation. i The general form of the expression for (k+1) is:

[0106]

[0107] In the formula, λ j The weight factor for the j-th optimization objective; When the i-th candidate vector is applied, the optimization objective x is... j The value function of J; i (k+1) represents the value function of multi-objective optimization, where k+1 is the value function of all optimization objectives when the i-th candidate vector is applied. The sum of.

[0108] In one specific embodiment, the optimization objectives of the system include active power p, reactive power q, and midpoint potential u. o .

[0109] The expressions for the target active power p and reactive power q are:

[0110]

[0111] Midpoint potential u o The expression is:

[0112]

[0113] The value function of multi-objective optimization consists of three optimization objective functions. The weighted value, where λ j Let J be the weight factor for the j-th optimization objective, and J be the value function. i The expression for (k) can be represented as:

[0114] J i (k)=λ1j 1i (k)+λ2j 2i (k)+λ3j3i (k) (16)

[0115] like Figure 3 As shown, optimize the objective function. Let K be the expression for the active power, reactive power, and peak value of the midpoint potential from time k to k+1, and the error at time k+1. The expression is:

[0116]

[0117] Where p* represents the reference value of active power and q* represents the reference value of reactive power;

[0118]

[0119] d i (k) represents the duty cycle, i.e., the position point of the switch operation.

[0120] The optimal switching action position d at time k. opt (k) and optimal switching state V opt The solution to (k) is obtained by minimizing the value function J. i (k) is implemented.

[0121] Minimize value function J i (k) By letting J i (k) Regarding the switch operating position d i The derivative of (k) is 0, and the switching state V corresponding to maintaining the switching state of the previous control cycle is solved. i (k)(as shown) Figure 2 As shown, the three-phase three-level inverter has 27 switching states (i = 1, 2, 3, ..., 27) and the optimal duty cycle d is... opt (k). Duty cycle d i y is calculated from the i-th candidate switch state in expression (k). i (k) and x i The general expression for (k) is:

[0122]

[0123] Substitute the 27 candidate vectors into x. i (k) and y i (k) can be used to obtain the voltage vector V corresponding to the i-th switching state. i (k) and switch action position d i (k), by minimizing the value function J i (k) can obtain the optimal switching action position d opt (k) and optimal switching state V opt (k).

[0124] like Figure 4 As shown, due to the delay introduced by the real-time controller, a two-step prediction strategy is used to compensate for the time delay. At system time k+1, the expression for the optimization objective is rewritten to include the optimal starting point d. opt The expression for (k):

[0125]

[0126] Optimal switching state V considering delay compensation opt (k+1) and the optimal switch position d opt (k+1) will minimize the value function J i (k+1) realization, value function J i (k+1) is written as:

[0127] J i (k+1)=λ1j 1i (k+1)+λ2j 2i (k+1)+λ3j 3i (k+1) (22)

[0128] Figure 5-8 Experimental waveforms and analysis results are presented for a three-phase, three-level grid-connected inverter under the same operating conditions, using the high-precision finite control set model predictive control method of this invention and the traditional FCS-MPC method. (Refer to...) Figure 5 As shown, the phase current using the method disclosed in this invention has a lower THD value of 3.63%; (Refer to...) Figure 6 It can be seen that when the traditional FCS-MPC method is used, the output phase current THD is 3.81%, indicating that the method of the present invention has a superior control effect compared with the traditional FCS-MPC. (Refer to...) Figure 7 As shown, the inverter using the method disclosed in this invention has an active power fluctuation range of 175W, a reactive power fluctuation range of 140W, and a midpoint potential fluctuation of 6V; (Refer to...) Figure 8 As shown, when using the traditional FCS-MPC method, the inverter's active power fluctuation range is 190W, the reactive power fluctuation range is 150W, and the midpoint potential fluctuation is 6V, indicating that the method of the present invention has higher control accuracy compared to the traditional FCS-MPC.

[0129] Traditional FCS-MPC methods can only switch at fixed positions, and the switching state is only related to the predicted initial value, which reduces control accuracy. This invention discloses a high-accuracy Finite Control Set Model Predictive Control (FCS-MPC) method that can flexibly change the switching position, with flexible and online adjustable switching times. This not only improves the inverter's switching accuracy but also retains the characteristic of switching no more than once per control cycle. It retains the advantages of FCS-MPC, such as simple and intuitive design and ease of achieving multi-objective optimization through a unified value function. The real-time optimization and adjustment of the switching position design of this invention can improve the output waveform quality of power electronic converters, demonstrating high practical value and broad application prospects in typical power electronics and electric drive applications such as grid connection and motor drive.

[0130] The following is an embodiment of a finite control set model predictive control device with high control accuracy according to the present invention, which can be used to execute an embodiment of a finite control set model predictive control method with high control accuracy according to the present invention. For details not disclosed in the embodiment of the finite control set model predictive control device with high control accuracy according to the present invention, please refer to the embodiment of the finite control set model predictive control method with high control accuracy according to the present invention.

[0131] In one embodiment, a finite control set model predictive control device with high control accuracy is proposed, the device comprising:

[0132] The acquisition module is used to obtain the state space equation of the controlled object according to the system optimization objective, write the state space equation in discrete form, and obtain the increment of the optimization objective from k to k+1.

[0133] The value function module for multi-objective optimization is used to evaluate x based on the optimization objective at time k to k+1. j The peak value and the error at time k+1 are used to construct the value function for multi-objective optimization of the system at time k.

[0134] Module 1 is used to solve for the optimal switching action position and optimal switching state at time k by minimizing the value function of the constructed multi-objective optimization.

[0135] Module 2 calculates the delay time and constructs a value function that takes the compensation into account. By minimizing the value function that takes the compensation into account, the optimal switching action position and the optimal switching state at time k+1 are obtained.

[0136] It should be noted that the high-precision finite control set model predictive control device provided in the above embodiments, when executing a high-precision finite control set model predictive control method, is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. Furthermore, the high-precision finite control set model predictive control device and the high-precision finite control set model predictive control method embodiment provided in the above embodiments belong to the same concept, and their implementation process is detailed in the high-precision finite control set model predictive control method embodiment, which will not be repeated here.

[0137] In one embodiment, a computer program product is proposed that, when executed by a processor, causes one or more processors to perform the following steps: obtaining the state-space equation of the controlled object according to the system optimization objective, and writing the state-space equation in discrete form to obtain the increment of the optimization objective from time k to k+1; based on x of the optimization objective from time k to k+1... j The peak value and the error at time k+1 are used to construct a multi-objective optimization value function for the system at time k. By minimizing the constructed multi-objective optimization value function, the optimal switching action position and optimal switching state at time k are obtained. A value function considering compensation is constructed to compensate for the delay time. By minimizing the value function considering compensation, the optimal switching action position and optimal switching state at time k+1 are obtained.

[0138] In one embodiment, an electronic device is provided, the computer device including a processor and a memory; wherein, when the processor executes a computer program stored in the memory, it performs the following steps: obtaining the state-space equation of the controlled object according to the system optimization objective, and writing the state-space equation in discrete form, obtaining the increment of the optimization objective from time k to k+1; based on x of the optimization objective from time k to k+1... j The peak value and the error at time k+1 are used to construct a multi-objective optimization value function for the system at time k. By minimizing the constructed multi-objective optimization value function, the optimal switching action position and optimal switching state at time k are obtained. A value function considering compensation is constructed to compensate for the delay time. By minimizing the value function considering compensation, the optimal switching action position and optimal switching state at time k+1 are obtained.

[0139] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0140] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.

[0141] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A finite control set model predictive control method with high control accuracy, characterized in that: The method includes: The state space equation of the controlled object is obtained according to the system optimization objective, and the state space equation is written in discrete form to obtain the change of the optimization objective from k to k+1. Based on the optimization objective at time k to k+1, x j The peak value and the error at time k+1 are used to construct the value function for multi-objective optimization of the system at time k. The value function of the multi-objective optimization is j optimization objective functions. The weighted value, the value function of multi-objective optimization. The expression is: ， In the formula, The weight factor for the j-th optimization objective; Optimize objective function for: ， In the formula, This is the position point where the switch is activated. This represents the system reference value for the j-th optimization objective. Indicates that the j-th optimization objective is in The value at time, and It can be controlled by duty cycle The expression is: ， ， In the formula, This represents the change of the j-th optimization objective from time k-1 to time k; This represents the change of the j-th optimization objective from time k to time k+1; By minimizing the value function of the constructed multi-objective optimization, the optimal switching action position and optimal switching state at the current time k are solved. The process of solving for the optimal switching action position and optimal switching state at time k using the constructed multi-objective optimization value function includes: Let the value function of multi-objective optimization The derivative with respect to the switch action position is 0, and the switch state V corresponding to maintaining the switching state of the previous control cycle is solved. i Optimal duty cycle of (k) ; The expression for duty cycle is: , in, ， ， In the formula, and It does not represent any specific meaning; it is merely a symbol. This represents the reference value for the j-th optimization objective; This represents the value of the j-th optimization objective at time k-1 and time k; This represents the change of the j-th optimization objective from time k-1 to time k; This represents the change of the j-th optimization objective from time k to the future time k+1 when the i-th candidate vector is applied; Substitute all candidate vectors into and Obtain the voltage vector V corresponding to the i-th switching state. i (k) and duty cycle ; By minimizing the value function of multi-objective optimization To obtain the optimal duty cycle and optimal switching state ; A value function considering compensation is constructed to compensate for the delay time. By minimizing the value function considering compensation, the optimal switching action position and optimal switching state at time k+1 are obtained.

2. The high-precision finite control set model predictive control method according to claim 1, characterized in that: The general form of the state-space equation of the controlled object obtained according to the system optimization objective is as follows: ， In the formula, x represents the state space, u represents the system input, A represents the state transition matrix, and B is the input matrix; ， Discrete time is t s Then the j-th optimization objective The discrete form is: ， The change of the j-th optimization objective at time k to k+1 It can be represented as: ， In the formula, This represents the value of the j-th optimization objective at time k+1; Let represent the value of the j-th optimization objective at time k.

3. The high-precision finite control set model predictive control method according to claim 1, characterized in that: A two-step prediction method is used to compensate for time delay.

4. The high-precision finite control set model predictive control method according to claim 3, characterized in that: At system time k+1, what is the optimal start point d considering delay compensation? opt (k) Introduce the value function by optimizing the objective at time k+1. The expression of the j-th objective at time k+1 is: ， In the formula, d represents the change of the j-th optimization objective from time k to time k+1. opt (k) represents the optimal duty cycle at time k; This represents the change in the j-th optimization objective from time k+1 to time k+2.

5. The high-precision finite control set model predictive control method according to claim 1, characterized in that: The value function considering compensation is: ， In the formula, The weight factor for the j-th optimization objective; Indicates the optimization objective when the i-th candidate vector is applied. The value function; Let represent the value function of multi-objective optimization, where is the value function of all optimization objectives when the i-th candidate vector is applied. The sum of.

6. A finite control set model predictive control device with high control accuracy, characterized in that: The device includes: The acquisition module is used to obtain the state space equation of the controlled object according to the system optimization objective, write the state space equation in discrete form, and obtain the increment of the optimization objective from k to k+1. The value function module for multi-objective optimization is used to evaluate x based on the optimization objective at time k to k+1. j The peak value and the error at time k+1 are used to construct the value function for multi-objective optimization of the system at time k. The value function of the multi-objective optimization is j optimization objective functions. The weighted value, the value function of multi-objective optimization. The expression is: ， In the formula, The weight factor for the j-th optimization objective; Optimize objective function for: ， In the formula, This is the position point where the switch is activated. This represents the system reference value for the j-th optimization objective. Indicates that the j-th optimization objective is in The value at time, and It can be controlled by duty cycle The expression is: ， ， In the formula, This represents the change of the j-th optimization objective from time k-1 to time k; This represents the change of the j-th optimization objective from time k to time k+1; Module 1 is used to solve for the optimal switching action position and optimal switching state at time k by minimizing the value function of the constructed multi-objective optimization. The process of solving for the optimal switching action position and optimal switching state at time k using the constructed multi-objective optimization value function includes: Let the value function of multi-objective optimization The derivative with respect to the switch action position is 0, and the switch state V corresponding to maintaining the switching state of the previous control cycle is solved. i Optimal duty cycle of (k) ; The expression for duty cycle is: , in, ， ， In the formula, and It does not represent any specific meaning; it is merely a symbol. This represents the reference value for the j-th optimization objective; This represents the value of the j-th optimization objective at time k-1 and time k; This represents the change of the j-th optimization objective from time k-1 to time k; This represents the change of the j-th optimization objective from time k to the future time k+1 when the i-th candidate vector is applied; Substitute all candidate vectors into and Obtain the voltage vector V corresponding to the i-th switching state. i (k) and duty cycle ; By minimizing the value function of multi-objective optimization To obtain the optimal duty cycle and optimal switching state ; Module 2 calculates the delay time and constructs a value function that takes the compensation into account. By minimizing the value function that takes the compensation into account, the optimal switching action position and the optimal switching state at time k+1 are obtained.

7. A computer program product comprising a computer program / instructions, characterized in that: When the computer program / instructions are executed by the processor, they implement the high-precision finite control set model predictive control method as described in any one of claims 1 to 5.

8. An electronic device, characterized in that: It includes a processor and a memory; wherein, when the processor executes a computer program stored in the memory, it implements the high-precision finite control set model predictive control method as described in any one of claims 1 to 5.

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