DC converter control method and system based on event-triggered sliding mode predictive control

Abstract

The invention provides a direct-current converter control method and system based on event-triggered sliding mode predictive control, relates to the technical field of direct-current converter control methods, and aims at reducing output overshoot and adjusting time of a phase-shifted full-bridge converter. The method specifically comprises the following steps: firstly, establishing a discrete state space equation of the phase-shifted full-bridge converter, then embedding model predictive control in an equivalent sliding mode control framework, and accelerating the convergence process of the disturbed backward sliding mode surface of the system through predictive control. Meanwhile, an event triggering mechanism is designed, and the calculation burden is reduced by adjusting the activation frequency of model prediction control. Through the design, sliding mode control and model prediction control influence each other, so that the response speed and the stability of the converter are improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of direct current converter control methods, and particularly relates to a direct current converter control method and system based on event-triggered sliding mode predictive control. BACKGROUND

[0002] With the rapid development of emerging technologies such as electric vehicles, artificial intelligence and 5G communication, global power demand is growing exponentially. Under this background, the application scale of direct current converters has expanded dramatically. Among them, the phase-shifted full-bridge topology has become the mainstream solution in medium and high power scenarios due to its high efficiency and electrical isolation characteristics. However, the traditional PID control has obvious limitations in the application of phase-shifted full-bridge direct current converters: slow dynamic response, severe output voltage overshoot and insufficient disturbance rejection capability. These defects make it difficult for the phase-shifted full-bridge converter to meet the stringent requirements of modern power electronic systems for high dynamic performance of DC / DC converters.

[0003] To improve transient response performance, various advanced control methods are widely used in phase-shifted full-bridge converters, such as adaptive gain nonlinear PID control based on fuzzy algorithm or neural network, sliding mode control, model predictive control, etc. Among them, the variable structure mode of sliding mode control naturally fits the switching characteristics of power electronic converters. However, the chattering problem of sliding mode control not only worsens the electromagnetic environment of the phase-shifted full-bridge, but also complicates the design of output filter parameters and the calculation of filter effect. Equivalent sliding mode control can effectively alleviate chattering, but at the cost of sacrificing fast regulation capability. Model predictive control accelerates response by adjusting DC / DC control input in advance, but it is extremely sensitive to model parameter changes and has poor robustness when parameters are mismatched. As can be seen, equivalent sliding mode control and model predictive control have complementary advantages: the strong robustness of sliding mode control can make up for the defects of model predictive control in parameter mismatch, while model predictive control can not only improve the dynamic performance of equivalent sliding mode control, but also avoid the chattering problem. Based on the combination of the two methods, the application proposes an event-driven sliding mode predictive control method for improving the transient response of phase-shifted full-bridge DC / DC converters.

[0004] Sliding mode control (SMC) is a robust control method that can achieve fast convergence of state variables by designing a sliding surface. It is especially suitable for nonlinear DC / DC converter systems. A large number of studies have shown that improved sliding surface design can significantly improve dynamic performance. For example, integral sliding surface that combines current or voltage information can improve transient response and steady-state accuracy, and can be compatible with other control methods such as backstepping and PID to form a hybrid controller with higher performance. However, this method usually requires the addition of a current sensor, resulting in an increase in hardware costs. Another improvement strategy is to introduce a disturbance observer to assist in sliding surface design, which can further improve dynamic characteristics and system stability, but at the cost of increased computational burden. The inherent robustness of SMC leads to a typical contradiction: chattering caused by the switching control law degrades the electromagnetic compatibility of the converter and increases the output voltage ripple. To alleviate this problem, researchers have proposed equivalent SMC methods that convert discontinuous switching signals into continuous duty cycles by deriving a sliding surface, effectively suppressing the chattering phenomenon, but at the cost of reduced dynamic performance. To reduce implementation costs and avoid the use of capacitor current sensors, a simplified equivalent SMC scheme has been developed that relies only on output voltage feedback. In addition, for complex isolated topologies such as phase-shifted full-bridge and LLC, nonlinear factors such as soft switching and dead-time effects make SMC design and implementation more challenging.

[0005] Model predictive control (MPC) is another mainstream advanced control strategy for DC / DC converters. It achieves optimal control under multiple constraints through rolling optimization, making it particularly suitable for handling multivariable coupling problems in complex topologies such as phase-shifted full-bridge. Research focuses on disturbance handling and computational acceleration, such as embedding a disturbance observer in the prediction model to handle large-scale load fluctuations, or using offline computation to accelerate online optimization. However, the core defect of MPC is its sensitivity to model parameter mismatches, which can lead to a sudden drop in control performance or even instability. To address this, more physical information such as inductor current prediction can be introduced into the cost function to enhance parameter variation robustness, but this increases the complexity of the prediction model and hardware requirements.

[0006] In recent years, the sliding mode predictive control, which combines the strong robustness of the sliding mode and the optimization ability of the model predictive control, has developed rapidly. The current research on the combination of sliding mode and predictive control mainly follows three directions: 1) model predictive control enhancement based on sliding mode observer: the sliding mode observer is used to estimate the key parameters online, and the model accuracy and control performance under the model predictive control framework are improved. This method enhances the model predictive control with the sliding mode tool, but does not realize the deep integration of the strategy; 2) cascaded structure of sliding mode control-model predictive control: the sliding mode is used as the outer loop voltage controller, the model predictive control is used as the inner loop current controller, and the sliding mode output is used as the current reference of the model predictive control. Although this structure improves the dynamic response compared with the PI-model predictive control cascade, the high-precision current detection requirement increases the hardware cost; 3) prediction control guided by sliding mode function: the sliding mode surface dynamics is embedded into the model predictive control cost function, and the anti-disturbance and transient performance are optimized simultaneously through the rolling time domain optimization. Although the deep integration of robustness and predictive ability is realized, the calculation burden is heavy because the real-time tasks of sliding mode condition evaluation and model predictive control optimization need to be executed simultaneously.

[0007] In summary, the sliding mode predictive control has important theoretical value and practical potential in improving the response speed, robustness and anti-disturbance ability of the DC / DC converter. However, its wide application is still restricted by factors such as calculation load, system complexity and hardware requirement, and therefore, it is urgent to propose a sliding mode predictive control method with simple algorithm and simple structure for the phase-shifted full-bridge converter. SUMMARY

[0008] The purpose of the present application is to provide an event-triggered sliding mode predictive control method and system for a DC converter, which can start the model predictive control in dynamic state to improve the dynamic state and shut down the model predictive control in steady state to reduce the calculation amount.

[0009] To achieve the above-mentioned purpose, the present application realizes the following technical solutions.

[0010] An event-triggered sliding mode predictive control method for a DC converter, comprising the following steps: S1, the phase-shifted full-bridge is reduced to a single-switch buck converter, and the voltage and current equations are established based on the switching state; S2, the output voltage prediction model is established by the Euler forward method, and the error, integral and differential of the error of the output voltage are selected as the state variables to establish the discrete state space equation; S3, a third-order sliding mode surface is constructed, an equivalent sliding mode controller is designed, and the selection range of the sliding mode coefficient is determined according to the local reaching condition, and the equivalent control law is obtained by deriving the sliding mode surface; S4, a model predictive controller is designed, the maximum convergence rate of the sliding mode surface is taken as the optimization objective, the prediction convergence rate is taken as the cost function, the output signal obtained by solving the cost function is superimposed on the sliding mode equivalent control law through explicit solving, and the output signal obtained by solving the cost function is superimposed on the sliding mode equivalent control law. S5. Construct an event triggering mechanism. When the triggering condition is met, model predictive control is started, and the converter operates under the dual action of model predictive control and sliding mode control. When the triggering condition is not met, model predictive control is shut down, and the converter operates only under the action of sliding mode control.

[0011] Furthermore, the order reduction equivalent of step S1 includes the following steps: The phase-shifted full-bridge topology is equivalent to a Buck converter, and the duty cycle control signal of the Buck converter is defined as follows: D u The high-frequency transformer with a center tap has a turns ratio of 1:n:n. The output filter inductor is L, and the filter capacitor is C. Choosing the inductor current and capacitor voltage as state variables, establish the circuit equations: ; in, i L ( t ) is a current filtering inductor L Instantaneous current, t For the current moment, i o ( t () represents the instantaneous value of the load current. v o ( t () represents the instantaneous value of the output voltage. This is the input voltage.

[0012] Furthermore, the voltage prediction model establishment step in step S2 includes: ; Where k is the sampling index, The switching frequency; Select output voltage error The integral and derivative of the error are treated as state variables: ; Among them, state variables They are 、 Points, The differential; Establish the discrete state-space model of the phase-shifting full-bridge converter: ; in, Represents the state matrix of key model parameters. Represents the input matrix, This represents the perturbation matrix.

[0013] Furthermore, the third-order sliding surface in step S3 is: ; in, Represents the sliding mode coefficient vector; The solution to the equivalent control law is based on the condition for the existence of the predicted sliding surface: in, Represents the output voltage under discrete conditions. That is, the first Output voltage value at the next sampling step; Calculate the continuous duty cycle signal: ; in, , , .

[0014] Furthermore, the design process of the model predictive controller in step S4 includes: The incremental form of the sliding mode control signal is chosen as the optimization variable for the model predictive controller, i.e. ， This indicates that the model predicts the controller's response to the control variables. The optimization effect; The convergence rate of the sliding mode is used as the optimization objective. ; in, H The optimization range and convergence rate of the model predictive control. ; Setting the derivative of the objective function to zero, we obtain the explicit solution formula for the model predictive controller. The control increment is then calculated using this formula. ; By combining this control increment with the continuous control signal Superposition is used to optimize the dynamic response of sliding mode control.

[0015] Furthermore, , .

[0016] Furthermore, the triggering condition for the event triggering mechanism in step S5 is as follows: Set the trigger sampling period , , For switching frequency, It is a positive integer; Based on the input-state stability theory of linear systems, the triggering conditions for an event-driven mechanism are designed as follows: in, e ( k () represents the degree of deviation between the system state and the sliding surface. k i The sampling time step when the preset trigger condition is activated. X ( k i ) represents the initial state of the system on the sliding surface. I It is the identity matrix. V om For the allowable transient peak value of the output voltage, As a triggering factor, This is used to adjust the frequency of event triggering.

[0017] This invention also discloses a DC-DC converter control system based on event-triggered sliding mode predictive control, comprising: The phase-shifted full-bridge power main circuit includes full-bridge MOSFET switches, high-frequency transformers, filter inductors, and filter capacitors; A digital signal processor configured to perform the control method according to any one of claims 1-7; The event triggering module monitors state deviations in real time and activates or deactivates model predictive control operations.

[0018] Furthermore, the power main circuit parameters satisfy: Input voltage The voltage is 24V, the transformer turns ratio is 1:n:n=6:9:9, and the filter inductor... Filter capacitor Switching frequency for .

[0019] The advantages of this invention are as follows: This invention provides robustness through equivalent sliding mode control, optimizes dynamic performance through model predictive control, and reduces computational burden through event triggering mechanism, thereby achieving high-performance control of the phase-shifted full-bridge converter. This enables the phase-shifted full-bridge converter to achieve low overshoot and fast dynamic response under typical operating conditions such as constant current load step, constant power load step, load ripple, and output voltage step, and can still maintain the output stability of the phase-shifted full-bridge under parameter adaptation.

[0020] Experimental results show that this method outperforms traditional PID and sliding mode control in terms of dynamic response, disturbance rejection capability, and parameter robustness, and is suitable for application scenarios with high requirements for power supply dynamic performance, such as communication power supply and renewable energy. Attached Figure Description

[0021] Figure 1This is a schematic diagram of the DC-DC converter control method based on event-triggered sliding mode predictive control according to the present invention; Figure 2 This is a flowchart of the DC-DC converter control method based on event-triggered sliding mode predictive control according to the present invention; Figure 3 This is the experimental comparison result of Example 2 of the present invention; Figure 4 The experimental comparison results are for Example 3 of the present invention; Figure 5 The experimental comparison results are for Example 4 of the present invention; Figure 6 This is the experimental comparison result of Example 5 of the present invention; Figure 7 The experimental comparison results are for Example 6 of the present invention. Detailed Implementation

[0022] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0023] Example 1 This embodiment proposes a DC-DC converter control system and method based on event-triggered sliding mode predictive control. Please refer to the system documentation for details. Figure 1 It includes a phase-shifted full-bridge power main circuit, a control unit, and a drive circuit (event triggering module).

[0024] Phase-shifted full-bridge power main circuit (PSFB converter) consists of an input voltage port ( V i ） Output voltage port ( V o ), full-bridge MOSFET switches (S1-S4), and a high-frequency transformer with a center tap (turns ratio 1: n: n ), rectifier diodes (D1-D2), output filter inductor ( L ) and capacitor ( C Composition: (See Table 1 for parameters) Table 1 Design parameters of PSFB converter The control unit uses a digital signal processor (DSP TMS320F28034) to implement the control method described below, and samples the output voltage and load current signals.

[0025] The drive circuit uses an isolated driver chip (IR2110) to drive the full-bridge MOSFET switches, ensuring the accurate and safe execution of the phase-shift control signal.

[0026] Correspondingly, this invention proposes a control method for the aforementioned system, comprising three parts: equivalent sliding mode control (SMC), model predictive control (MPC), and event-triggered mechanism (ETM). Please refer to [the relevant documentation]. Figure 1 This study utilizes sliding mode control to enhance the stability and robustness of the converter system, and model predictive control to accelerate the convergence process to the sliding surface, thereby improving dynamic characteristics. It also employs an event-triggered mechanism to automatically start and stop model predictive control, enabling it to improve dynamics under dynamic conditions and deactivate it in steady-state conditions to reduce computational load.

[0027] Specifically, the steps include: S1. Based on the switching logic of the phase-shifted full-bridge converter, reduce the phase-shifted full-bridge to an equivalent single-switch buck converter. Establish the voltage and current equations of the circuit based on the on / off states of the buck converter. S2. Based on the circuit model, the Euler forward method is used to establish a voltage prediction model, and the output voltage error, the integral of the error, and the derivative of the error are selected as state variables to establish a discretized state-space equation. S3. Design a sliding mode controller with a third-order sliding surface, determine the selection range of the sliding mode coefficient based on the local arrival conditions, and then obtain the equivalent control law by differentiating the sliding surface. S4. Design a model predictive controller to optimize the control signal of the sliding mode controller. First, design a model predictive controller with the predicted approach rate as the cost function, and explicitly solve the cost function. Then, superimpose the solved output signal onto the sliding mode equivalent control law. S5. Based on the input-state stability theory of linear systems, event triggering conditions are established to reduce the computational load during converter operation. When the triggering conditions are met, model predictive control is activated, and the converter operates under the dual action of model predictive control and sliding mode control. When the triggering conditions are not met, model predictive control is shut down, and the converter operates only under the action of sliding mode control.

[0028] Step S1, the equivalent of order reduction, includes the following steps: The phase-shifted full-bridge topology is equivalent to a Buck converter, and the duty cycle control signal of the Buck converter is defined as follows: D u The high-frequency transformer with a center tap has a turns ratio of 1:n:n. The output filter inductor is L, and the filter capacitor is C. Choosing the inductor current and capacitor voltage as state variables, establish the circuit equations: ; in, i L ( t ) is a current filtering inductor L Instantaneous current, tFor the current moment, i o ( t () represents the instantaneous value of the load current. v o ( t () represents the instantaneous value of the output voltage. This is the input voltage.

[0029] Step S2, the voltage prediction model establishment steps, include: ; Where k is the sampling index, The switching frequency; Select output voltage error The integral and derivative of the error are treated as state variables: ; Among them, state variables They are 、 Points, The differential; Establish the discrete state-space model of the phase-shifting full-bridge converter: ; in, Represents the state matrix of key model parameters. Represents the input matrix, This represents the perturbation matrix.

[0030] The third-order sliding surface in step S3 is: ; in, Represents the sliding mode coefficient vector; The solution to the equivalent control law is based on the condition for the existence of the predicted sliding surface: in, Represents the output voltage under discrete conditions. That is, the first Output voltage value at the next sampling step; Calculate the continuous duty cycle signal: ; in, , , .

[0031] The design process of the model predictive controller in step S4 includes: The incremental form of the sliding mode control signal is chosen as the optimization variable for the model predictive controller, i.e. ， This indicates that the model predicts the controller's response to the control variables. The optimization effect; The convergence rate of the sliding mode is used as the optimization objective. ; in, H The optimization range and convergence rate of the model predictive control. In this implementation case, , .

[0032] Setting the derivative of the objective function to zero, we obtain the explicit solution formula for the model predictive controller. The control increment is then calculated using this formula. ; By combining this control increment with the continuous control signal Superposition is used to optimize the dynamic response of sliding mode control.

[0033] The triggering condition for the event triggering mechanism in step S5 is: Trigger sampling period , , For switching frequency, It is a positive integer; Based on the input-state stability theory of linear systems, the triggering conditions for an event-driven mechanism are designed as follows: in, e ( k () represents the degree of deviation between the system state and the sliding surface. k i The sampling time step when the preset trigger condition is activated. X ( k i ) represents the initial state of the system on the sliding surface. I It is the identity matrix. V om For the allowable transient peak value of the output voltage, As a triggering factor, This is used to adjust the frequency of event triggering.

[0034] Please refer to Figure 2 The control flowchart of this invention includes four core steps: (1) Initialization: Set the initial state X ( k i )=0, disable the MPC module.u e =0; (2) Steady-state operation: Only SMC is enabled, via u e Adjust the output voltage; (3) Dynamic response: When the ETM detects that the state deviation meets the triggering condition, it activates the MPC and outputs an optimized control signal. This accelerates the system's convergence to the sliding surface.

[0035] (4) Restore steady state: After the system state stabilizes, ETM shuts down MPC and switches back to pure equivalent SMC mode to reduce the amount of computation.

[0036] Example 2 The controller parameters used in this embodiment are shown in Table 2: Table 2 Controller Parameters Constant current load step test: Figure 3 The dynamic response of a constant current load switching from 4A to 1A is demonstrated. (a) PID control is used, (b) equivalent sliding mode control is used, and (c) the technology of this invention is used. Experimental results show that under PID control, the voltage overshoot is 1.32V, the settling time is 2.12ms, and there is significant oscillation. Sliding mode control significantly improves the transient response, reducing voltage overshoot by 34% and shortening the settling time by 62%. This embodiment further optimizes the dynamic characteristics, reducing voltage overshoot by 55% and shortening the settling time by 82% compared to PID control, while effectively suppressing oscillation. This verifies the advantages of MPC in accelerating the convergence of sliding mode controllers.

[0037] Example 3 This embodiment uses the parameters of Embodiment 2 to perform a constant power load step test. Figure 4 The output voltage waveforms under constant power load conditions are shown in (a) using PID control technology, (b) using equivalent sliding mode control technology, and (c) using the technology of this invention. Figure 2 In the test, when the load power switched from 100W to 10W, all three control methods could achieve stable output. However, when using the technology of this invention, the voltage overshoot was reduced by 44% and the settling time was shortened by 25% compared with PID control; and the voltage overshoot was reduced by 18% and the settling time was shortened by 14% compared with sliding mode control.

[0038] Example 4 This embodiment uses the parameters from Embodiment 2 to perform a pulsating load test. Figure 5The voltage waveforms of the load pulsating between 1A and 4A at a frequency of 100Hz are shown. (a) PID control technology is used, (b) equivalent sliding mode control technology is used, and (c) the technology of this invention is used. Under traditional PID control, the peak-to-peak value of the pulsating voltage reaches 3.98V; after using sliding mode control, this value is reduced to 2.22V; and after applying the technology of this invention, the peak-to-peak value of the pulsating voltage is further reduced to 1.79V.

[0039] Example 5 This embodiment uses the parameters from Embodiment 2 to perform a reference voltage step test. Figure 6 The response waveforms when the output voltage jumps from 20V to 24V are shown: (a) using PID control, (b) using equivalent sliding mode control, and (c) using the technology of this invention. It can be seen that when using the technology of this invention, the system response speed is improved by 49% and 39% respectively compared to the traditional PID method and sliding mode control method, and the convergence time is significantly shortened.

[0040] Example 6 This embodiment uses the parameters from Embodiment 2 to perform parameter mismatch testing. Figure 7 The experimental waveforms are shown when the capacitance and inductance parameters are reduced by 30%, including: (a) waveform of the unloaded experiment and (b) waveform of the loaded experiment. It can be seen that although the transient response of the system decreases after parameter mismatch, the system can still maintain stable operation due to the inherent robustness of sliding mode control.

[0041] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A DC-DC converter control method based on event-triggered sliding mode predictive control, characterized in that, Including the following steps: S1. Reduce the phase-shifted full-bridge to an equivalent single-switch buck converter and establish voltage and current equations based on the switching state. S2. Establish an output voltage prediction model using the Euler forward method, and select the output voltage error, the integral of the error, and the derivative of the error as state variables to establish a discretized state-space equation. S3. Construct a third-order sliding surface, design an equivalent sliding controller, determine the selection range of the sliding coefficient based on the local arrival conditions, and obtain the equivalent control law by differentiating the sliding surface. S4. Design a model predictive controller with the goal of maximizing the sliding surface convergence rate and the cost function of the predicted approach rate. Solve the cost function explicitly and then superimpose the output signal obtained into the sliding equivalent control law. S5. Construct an event triggering mechanism. When the triggering condition is met, model predictive control is started, and the converter operates under the dual action of model predictive control and sliding mode control. When the triggering condition is not met, model predictive control is shut down, and the converter operates only under the action of sliding mode control.

2. The DC-DC converter control method based on event-triggered sliding mode predictive control according to claim 1, characterized in that, The order reduction equivalence described in step S1 includes the following steps: The phase-shifted full-bridge topology is equivalent to a Buck converter, and the duty cycle control signal of the Buck converter is defined as follows: D u The high-frequency transformer with a center tap has a turns ratio of 1:n:n. The output filter inductor is L, and the filter capacitor is C. Choosing the inductor current and capacitor voltage as state variables, establish the circuit equations: ； in, i L ( t ) is a current filtering inductor L Instantaneous current, t For the current moment, i o ( t () represents the instantaneous value of the load current. v o ( t () represents the instantaneous value of the output voltage. This is the input voltage.

3. The DC-DC converter control method based on event-triggered sliding mode predictive control according to claim 2, characterized in that, The voltage prediction model establishment step in step S2 includes: ； Where k is the sampling index, The switching frequency; Select output voltage error The integral and derivative of the error are treated as state variables: ； Among them, state variables They are 、 Points, The differential; Establish the discrete state-space model of the phase-shifting full-bridge converter: ； in, Represents the state matrix of key model parameters. Represents the input matrix, This represents the perturbation matrix.

4. The DC-DC converter control method based on event-triggered sliding mode predictive control according to claim 3, characterized in that, The third-order sliding surface mentioned in step S3 is: ； in, Represents the sliding mode coefficient vector; The solution to the equivalent control law is based on the condition for the existence of the predicted sliding surface: in, Represents the output voltage under discrete conditions. That is, the first Output voltage value at the next sampling step; Calculate the continuous duty cycle signal: ； in, , , .

5. The DC-DC converter control method based on event-triggered sliding mode predictive control according to claim 4, characterized in that, The design process of the model predictive controller in step S4 includes: The incremental form of the sliding mode control signal is chosen as the optimization variable for the model predictive controller, i.e. ， This indicates that the model predicts the controller's response to the control variables. The optimization effect; The convergence rate of the sliding mode is used as the optimization objective. ； in, H The optimization range and convergence rate of the model predictive control. ; Setting the derivative of the objective function to zero, we obtain the explicit solution formula for the model predictive controller. The control increment is then calculated using this formula. ； By combining this control increment with the continuous control signal Superposition is used to optimize the dynamic response of sliding mode control.

6. The DC-DC converter control method based on event-triggered sliding mode predictive control according to claim 5, characterized in that, ， 。 7. The DC-DC converter control method based on event-triggered sliding mode predictive control according to claim 4, characterized in that, The triggering condition for the event triggering mechanism described in step S5 is: Set the trigger sampling period , , For switching frequency, It is a positive integer; Based on the input-state stability theory of linear systems, the triggering conditions for an event-driven mechanism are designed as follows: in, It refers to the degree of deviation between the system state and the sliding surface. The sampling time step when the preset trigger condition is activated. This represents the initial state of the system on the sliding surface. It is the identity matrix. For the allowable transient peak value of the output voltage, As a triggering factor, This is used to adjust the frequency of event triggering.

8. A DC-DC converter control system based on event-triggered sliding mode predictive control, characterized in that, include: The phase-shifted full-bridge power main circuit includes full-bridge MOSFET switches, high-frequency transformers, filter inductors, and filter capacitors; A digital signal processor configured to perform the control method according to any one of claims 1-7; The event triggering module monitors state deviations in real time and activates or deactivates model predictive control operations.

9. The DC-DC converter control system based on event-triggered sliding mode predictive control according to claim 8, characterized in that, The power main circuit parameters meet the following requirements: Input voltage The voltage is 24V, the transformer turns ratio is 1:n:n=6:9:9, and the filter inductor... Filter capacitor Switching frequency for .

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