A control method of interleaved parallel buck converter based on double-loop control of sliding mode and model prediction

Abstract

With the development of smart breeding and pet health management, smart collars are increasingly used in animal behavior monitoring and physiological state assessment. However, due to the strong nonlinearity, time-varying nature and significant environmental interference of animal motion, traditional data processing and state estimation methods cannot balance accuracy and real-time performance. To solve this problem, this paper introduces the improved non-singular integral terminal sliding mode combined with adaptive model predictive control method proposed in the thesis into the smart collar system, and constructs a high-precision state estimation and control framework for complex dynamic scenarios. This method realizes fast convergence of error through the sliding mode outer ring, and uses the model predictive inner ring for rolling optimization. At the same time, a double adaptive mechanism is introduced to improve the robustness of the system to parameter changes and noise disturbances. Experimental results show that this method can effectively improve the data processing accuracy and response speed of the smart collar in complex working conditions, providing a reliable technical solution for animal health monitoring and intelligent management.

Description

Technical Field

[0001] This invention relates to the field of power supply technology, and in particular to a control method for an interleaved parallel Buck converter based on sliding mode and model prediction dual-loop control. Background Technology

[0002] With the rapid development of IoT technology and smart farming and pet health management, animal behavior monitoring and physiological state assessment based on wearable devices have gradually become a research hotspot. Among these, smart collars, as an important wearable terminal for animals, can achieve real-time acquisition of animal movement status, activity trajectory, and health indicators by integrating sensor modules (such as accelerometers, temperature sensors, heart rate sensors, and positioning modules). However, due to the highly random and complex nature of animal activity, its movement process is often accompanied by drastic dynamic changes, environmental interference, and sensor noise, resulting in the acquired signals exhibiting obvious nonlinear, time-varying, and uncertain characteristics, which seriously affects the accuracy of state estimation and data analysis.

[0003] Meanwhile, complex environments often suffer from problems such as strong model dependence, insufficient robustness, and lag in dynamic response, making it difficult to achieve high-precision real-time assessment of critical animal states. Furthermore, collar devices are typically limited by size and power consumption, resulting in limited computational resources for their embedded processing units, which further restricts the direct application of complex algorithms. In addition, in battery energy storage systems, the accuracy of state-of-charge estimation also depends on stable voltage and current sampling conditions, further increasing the requirements for the power system's disturbance rejection capability. Therefore, related applications place higher demands on the output accuracy, dynamic response speed, and disturbance rejection performance of DC-DC converters. Summary of the Invention

[0004] To address the aforementioned issues, this invention proposes a control method for an interleaved parallel Buck converter based on dual-loop control of sliding mode and model prediction. By constructing a dual-closed-loop structure of an outer sliding mode voltage loop and an inner model prediction current loop, and designing a cost function that balances current tracking and current sharing based on a discrete prediction model, and introducing delay compensation, parameter reconstruction, and adaptive adjustment mechanisms, the system achieves fast dynamic response, high-precision control, and good robustness under multiple operating conditions.

[0005] The present invention proposes a control method for an interleaved parallel Buck converter based on sliding mode and model prediction dual-loop control, the technical solution of which includes the following steps:

[0006] Step a: Construct the systematic error and design an improved non-singular integral terminal sliding surface;

[0007] Step b: Design the reaching law and derive the outer loop control law to ensure that the system state can converge to the sliding surface in a finite time, while reducing chattering.

[0008] Step c: Discretize the dynamic characteristics of the system and establish a prediction model.

[0009] Step d: Based on the prediction model obtained above, construct the cost function and solve for the optimal control quantity to describe the error relationship between the system output and the reference value;

[0010] Step e introduces a dual adaptive mechanism and implements closed-loop control to further improve the system's control performance under parameter perturbations and external disturbances.

[0011] As a further improvement of the present invention, the specific steps of constructing the systematic error and designing the improved non-singular integral terminal sliding surface in step a are as follows:

[0012] Step a1: Define the deviation between the system output and the reference value, and establish the error variable:

[0013]

[0014] in, System reference input, This is the actual output of the system.

[0015] Step a2, construct the improved non-singular integral terminal sliding surface:

[0016]

[0017] in, For design parameters, It is a positive odd number and satisfies .

[0018] Step a3, introduce the integral separation function:

[0019]

[0020] This allows the integral action to participate in regulation only when the error is small, thereby improving system stability and dynamic performance.

[0021] As a further improvement of the present invention, in step b, a reaching law is designed and an outer-loop control law is derived to ensure that the system state can converge to the sliding surface within a finite time, while reducing chattering. The specific formula is as follows:

[0022] Design of the variable speed reaching law:

[0023]

[0024] Selecting Lyapunov functions:

[0025]

[0026] Substituting into the approach law, we get:

[0027]

[0028] This ensures the global asymptotic stability of the system.

[0029] By further combining the system dynamic equations, the outer-loop control law can be derived, and the reference control quantity can be obtained:

[0030]

[0031] The final output is the outer-loop control result, which serves as the input to the inner-loop model predictive control, thus achieving hierarchical control of the system.

[0032]

[0033] in, To control the gain, , It is a saturation function.

[0034] As a further improvement of the present invention, in step c, the dynamic characteristics of the system are discretized to establish a prediction model, the specific formula of which is as follows:

[0035] To analyze the dynamic characteristics of the system, it is discretized, and a predictive model is established. Taking typical current dynamics as an example, its discretized form can be expressed as:

[0036]

[0037] in, The current state. To control the input, The sampling period is These are system parameters.

[0038] This model is used to predict the system state at the next moment and is the core foundation of the model predictive control algorithm. Meanwhile, to improve model accuracy, this invention introduces an online parameter update mechanism, expanding the system parameters to:

[0039]

[0040] This enables the model to dynamically reflect changes in the system's operating state.

[0041] As a further improvement of the present invention, in step d, based on the prediction model obtained above, a cost function is constructed and the optimal control quantity is solved to describe the error relationship between the system output and the reference value. The specific steps are as follows;

[0042] Based on the prediction model obtained in step three, an optimization objective function is constructed to describe the error relationship between the system output and the reference value:

[0043]

[0044] Substituting the prediction model, we get:

[0045]

[0046] To obtain the optimal control input, the derivative of the objective function with respect to the control quantity is taken and set to zero:

[0047]

[0048] Thus, the optimal control quantity can be obtained:

[0049]

[0050] This control variable is continuously optimized during each sampling period to achieve a fast dynamic response of the system.

[0051] As a further improvement of the present invention, a dual adaptive mechanism is introduced in step e to implement closed-loop control, thereby further improving the system's control performance under parameter perturbations and external disturbances. The specific steps are as follows;

[0052] Step e1: Model parameters are adapted.

[0053] Based on system operating errors, key parameters are corrected online:

[0054]

[0055] in This is the adjustment coefficient.

[0056] Step e2, adaptive adjustment of the control law;

[0057] Dynamically adjust the control gain based on error changes:

[0058]

[0059] in These are control parameters.

[0060] Step e3, dual closed-loop control is achieved;

[0061] Overall system control structure, outer loop (sliding mode control):

[0062]

[0063] Inner loop (model predictive control):

[0064]

[0065] Execution control:

[0066]

[0067] Through the aforementioned dual-closed-loop collaborative control mechanism, the system achieves rapid response, high-precision tracking, and robust control under complex operating conditions.

[0068] Compared with the prior art, the technical solution of the present invention has the following advantages:

[0069] (1) Integrating sliding mode and predictive control improves dynamic response speed and system stability under nonlinear conditions.

[0070] (2) Introduce a dual adaptive mechanism to enhance robustness and accuracy under parameter uncertainty and external disturbances.

[0071] (3) A hierarchical closed-loop structure design is adopted to reduce computational complexity and energy consumption while ensuring real-time performance. Attached Figure Description

[0072] Figure 1 This is a system block diagram of the present invention.

[0073] Figure 2 This is the phase trajectory diagram of the conventional sliding mode variable structure control system of the present invention.

[0074] Figure 3 This is a waveform diagram of the output voltage response during the startup and steady-state phases of the present invention.

[0075] Figure 4 This is the circuit topology diagram of the three-phase interleaved parallel Buck converter of the present invention.

[0076] Figure 5 This is a block diagram of the MPC current inner loop structure of the present invention.

[0077] Figure 6 This is a diagram of the dual closed-loop control framework of the present invention. Detailed Implementation

[0078] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings. Specific Implementation Method 1

[0080] This specific embodiment presents a control method for an interleaved parallel Buck converter based on sliding mode and model prediction dual-loop control, such as... Figure 1 As shown, it includes the following steps:

[0081] Step a: Construct the systematic error and design an improved non-singular integral terminal sliding surface;

[0082] Step b: Design the reaching law and derive the outer loop control law to ensure that the system state can converge to the sliding surface in a finite time, while reducing chattering.

[0083] Step c: Discretize the dynamic characteristics of the system and establish a prediction model.

[0084] Step d: Based on the prediction model obtained above, construct the cost function and solve for the optimal control quantity to describe the error relationship between the system output and the reference value;

[0085] Step e introduces a dual adaptive mechanism and implements closed-loop control to further improve the system's control performance under parameter perturbations and external disturbances. Specific Implementation Method Two

[0087] This specific embodiment of the interleaved parallel Buck converter control method based on sliding mode and model prediction dual-loop control further defines, based on the first specific embodiment:

[0088] like Figure 2 As shown, the specific steps for constructing the systematic error and designing the improved non-singular integral terminal sliding surface in step a are as follows:

[0089] Step a1: Define the deviation between the system output and the reference value, and establish the error variable:

[0090]

[0091] in, System reference input, This is the actual output of the system.

[0092] Step a2, construct the improved non-singular integral terminal sliding surface:

[0093]

[0094] in, For design parameters, It is a positive odd number and satisfies .

[0095] Step a3, introduce the integral separation function:

[0096]

[0097] This allows the integral action to participate in regulation only when the error is small, thereby improving system stability and dynamic performance. Specific Implementation Method 3

[0099] This specific embodiment of the interleaved parallel Buck converter control method based on sliding mode and model prediction dual-loop control further defines, based on specific embodiment two, the following:

[0100] like Figure 3 As shown, in step b, a reaching law is designed and an outer-loop control law is derived to ensure that the system state converges to the sliding surface within a finite time, while reducing chattering. The specific formula is as follows:

[0101] Design of the variable speed reaching law:

[0102]

[0103] Selecting Lyapunov functions:

[0104]

[0105] Substituting into the approach law, we get:

[0106]

[0107] This ensures the global asymptotic stability of the system.

[0108] By further combining the system dynamic equations, the outer-loop control law can be derived, and the reference control quantity can be obtained:

[0109]

[0110] The final output is the outer-loop control result, which serves as the input to the inner-loop model predictive control, thus achieving hierarchical control of the system.

[0111]

[0112] in, To control the gain, , It is a saturation function. Specific implementation method four:

[0114] This specific embodiment of the interleaved parallel Buck converter control method based on sliding mode and model prediction dual-loop control further defines, based on specific embodiment three, the following:

[0115] like Figure 4 As shown, in step c, the dynamic characteristics of the system are discretized to establish a prediction model, and the specific formula is as follows:

[0116] To analyze the dynamic characteristics of the system, it is discretized, and a predictive model is established. Taking typical current dynamics as an example, its discretized form can be expressed as:

[0117]

[0118] in, The current state. To control the input, The sampling period is These are system parameters.

[0119] This model is used to predict the system state at the next moment and is the core foundation of the model predictive control algorithm. Meanwhile, to improve model accuracy, this invention introduces an online parameter update mechanism, expanding the system parameters to:

[0120]

[0121] This enables the model to dynamically reflect changes in the system's operating state. Specific implementation method five:

[0123] This specific embodiment of the interleaved parallel Buck converter control method based on sliding mode and model prediction dual-loop control further defines, based on the fourth embodiment:

[0124] like Figure 5 As shown, in step d, based on the prediction model obtained above, a cost function is constructed and the optimal control quantity is solved to describe the error relationship between the system output and the reference value. The specific steps are as follows;

[0125] Based on the prediction model obtained in step three, an optimization objective function is constructed to describe the error relationship between the system output and the reference value:

[0126]

[0127] Substituting the prediction model, we get:

[0128]

[0129] To obtain the optimal control input, the derivative of the objective function with respect to the control quantity is taken and set to zero:

[0130]

[0131] Thus, the optimal control quantity can be obtained:

[0132]

[0133] This control variable is continuously optimized during each sampling period to achieve a fast dynamic response of the system. Specific implementation method six:

[0135] This specific embodiment of the interleaved parallel Buck converter control method based on sliding mode and model prediction dual-loop control further defines, based on specific embodiment five, the following:

[0136] like Figure 6 As shown, step e introduces a dual adaptive mechanism and implements closed-loop control to further improve the system's control performance under parameter perturbations and external disturbances. The specific steps are as follows;

[0137] Step e1: Model parameters are adapted.

[0138] Based on system operating errors, key parameters are corrected online:

[0139]

[0140] in This is the adjustment coefficient.

[0141] Step e2, adaptive adjustment of the control law;

[0142] Dynamically adjust the control gain based on error changes:

[0143]

[0144] in These are control parameters.

[0145] Step e3, dual closed-loop control is achieved;

[0146] Overall system control structure, outer loop (sliding mode control):

[0147]

[0148] Inner loop (model predictive control):

[0149]

[0150] Execution control:

[0151]

[0152] Through the aforementioned dual-closed-loop collaborative control mechanism, the system achieves rapid response, high-precision tracking, and robust control under complex operating conditions.

[0153] It should be noted that the above specific embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Any person skilled in the art can modify or alter the above specific embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A control method for an interleaved parallel Buck converter based on sliding mode and model prediction dual-loop control, characterized in that, The method includes the following steps: a) Construct systematic errors and design an improved non-singular integral terminal sliding surface; b) Design a reaching law and derive an outer-loop control law to ensure that the system state can converge to the sliding surface in a finite time while reducing chattering. c) Discretize the dynamic characteristics of the system and establish a prediction model; d) Based on the prediction model obtained above, construct the cost function and solve for the optimal control quantity to describe the error relationship between the system output and the reference value; e) Introduce a dual adaptive mechanism and implement closed-loop control to further improve the system's control performance under parameter perturbations and external disturbances.

2. The control method for an interleaved parallel Buck converter based on sliding mode and model prediction dual-loop control according to claim 1, characterized in that, The specific steps for constructing the systematic error and designing the improved non-singular integral terminal sliding surface in step a are as follows: Step a1: Define the deviation between the system output and the reference value, and establish the error variable: in, System reference input, This is the actual output of the system. Step a2, construct the improved non-singular integral terminal sliding surface: in, For design parameters, It is a positive odd number and satisfies . Step a3, introduce the integral separation function: This allows the integral action to participate in regulation only when the error is small, thereby improving system stability and dynamic performance.

3. The control method for an interleaved parallel Buck converter based on sliding mode and model prediction dual-loop control according to claim 2, characterized in that, In step b, a reaching law is designed and an outer-loop control law is derived to ensure that the system state converges to the sliding surface within a finite time, while reducing chattering. The specific formula is shown below: Design of the variable speed reaching law: Selecting Lyapunov functions: Substituting into the approach law, we get: This ensures the global asymptotic stability of the system. By further combining the system dynamic equations, the outer-loop control law can be derived, and the reference control quantity can be obtained: The final output is the outer-loop control result, which serves as the input to the inner-loop model predictive control, thus achieving hierarchical control of the system. in, To control the gain, , It is a saturation function.

4. The control method for an interleaved parallel Buck converter based on sliding mode and model prediction dual-loop control according to claim 3, characterized in that, In step c, the dynamic characteristics of the system are discretized to establish a prediction model. The specific steps are as follows: To analyze the dynamic characteristics of the system, it is discretized, and a predictive model is established. Taking typical current dynamics as an example, its discretized form can be expressed as: in, The current state. To control the input, The sampling period is These are system parameters. This model is used to predict the system state at the next moment and is the core foundation of the model predictive control algorithm. Meanwhile, to improve model accuracy, this invention introduces an online parameter update mechanism, expanding the system parameters to: This enables the model to dynamically reflect changes in the system's operating state.

5. The control method for an interleaved parallel Buck converter based on sliding mode and model prediction dual-loop control according to claim 4, characterized in that, In step d, based on the prediction model obtained above, a cost function is constructed and the optimal control quantity is solved to describe the error relationship between the system output and the reference value. The specific steps are as follows: Based on the prediction model obtained in step three, an optimization objective function is constructed to describe the error relationship between the system output and the reference value: Substituting the prediction model, we get: To obtain the optimal control input, the derivative of the objective function with respect to the control quantity is taken and set to zero: Thus, the optimal control quantity can be obtained: This control variable is continuously optimized during each sampling period to achieve a fast dynamic response of the system.

6. The control method for an interleaved parallel Buck converter based on sliding mode and model prediction dual-loop control according to claim 5, characterized in that, Step e introduces a dual adaptive mechanism and implements closed-loop control to further improve the system's control performance under parameter perturbations and external disturbances. The specific steps are as follows: Step e1: Model parameters are adapted. Based on system operating errors, key parameters are corrected online: in This is the adjustment coefficient. Step e2, adaptive adjustment of the control law; Dynamically adjust the control gain based on error changes: in These are control parameters. Step e3, dual closed-loop control is achieved; Overall system control structure, outer loop (sliding mode control): Inner loop (model predictive control): Execution control: Through the aforementioned dual-closed-loop collaborative control mechanism, the system achieves rapid response, high-precision tracking, and robust control under complex operating conditions.

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