Passive control method for DC-DC boost converter based on adaptive unscented Kalman filtering

Abstract

The invention discloses a DC-DC boost converter passive control method based on adaptive unscented Kalman filtering, and belongs to the technical field of power systems. In order to develop a more accurate and more real-time boost converter passive control method, the method comprises the following steps: constructing an average dynamic model of a DC-DC boost converter, designing the average dynamic model as a state space expression, and discretizing the state space expression by using an Euler equation to obtain the discretized state space expression; designing a self-adaptive unscented Kalman filter, performing twice unscented transformation based on the discretized state space expression, predicting the mean value and covariance of the system state quantity, and obtaining the estimated values of the input voltage and the constant power load power; constructing a passive controller; and inputting the input voltage and the estimated value of the constant-power load power into a passive controller, generating a PWM signal for driving a switching tube to work, and controlling the DC-DC boost converter. According to the invention, the robustness of the system is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of power systems, and in particular relates to a DC-DC boost converter passive control method based on adaptive unscented Kalman filtering. Background Art

[0002] In recent years, in the technological wave in the field of power systems, as a new type of power system with a multi-in-one structure of renewable energy, DC loads, energy storage systems, etc., DC microgrids have advantages over existing AC microgrids, such as simple structure, relatively low control difficulty, no reactive power, and phase problems. The distributed control of DC microgrids has a higher response speed. As one of the most important components in the DC microgrid structure, the DC / DC boost converter needs to have a very high bidirectional energy conversion efficiency. Therefore, for the Boost converter, it is crucial to study a new control strategy that reduces the impact of disturbances and quickly adjusts the voltage under the influence of constant power loads.

[0003] Common power electronic loads in DC microgrids can be mainly divided into resistive loads and constant power loads. Resistive loads generate positive damping input in DC microgrid systems due to their passivity. On the contrary, constant power loads exhibit negative impedance characteristics at their system inputs because their power is always constant. This characteristic will interact with other converters in the system to reduce system damping, affect system stability, and have the risk of oscillation. With the continuous increase in the penetration rate of renewable energy, the constant power loads used also bring greater stability risks to DC microgrid systems. According to the differences in different working scenarios and disturbance sizes, the stability analysis of power systems can be divided into large signal stability and small signal stability. Most of the current research is based on small signal stability analysis. There are two main control strategies. The passive damping method is to add passive devices to the load circuit. This strategy can directly improve the system damping, but it increases the system cost and weight, which is inconsistent with the trend of lightweighting. The active damping method is to improve the system impedance by modifying the control system structure, but it can only be corrected based on the linearization model of small signals and can only be performed near the equilibrium point, and the application range is small.

[0004] Based on the nonlinear characteristics of the power electronic converter used and the negative impedance characteristics of the constant power load, it is necessary to select corresponding nonlinear control strategies to improve the global stability of the DC microgrid system, such as passive control, backstepping control, model predictive control, sliding mode control and deep learning. The above control strategies effectively guarantee the large signal stability of the system, but the nonlinear control strategy has high requirements for the construction of the system model. Therefore, the system will inevitably produce steady-state deviations under source-load random disturbances. In order to reduce or eliminate this output deviation, an integral compensation link is added at the corresponding position. However, this strategy will affect the dynamic response speed of the system. In addition, a Kalman filter link can be added to the passive control system to accurately predict and estimate the load power, but the traditional Kalman filter and the extended Kalman filter cause a large amount of calculation for model predictive control, and the iterative calculation of the Jacobian matrix takes up a lot of time. Summary of the invention

[0005] The problem to be solved by the present invention is to develop a more accurate and more real-time boost converter passive control method, and propose a DC-DC boost converter passive control method based on adaptive unscented Kalman filtering.

[0006] To achieve the above object, the present invention is implemented through the following technical solutions:

[0007] A DC-DC boost converter passive control method based on adaptive unscented Kalman filtering comprises the following steps:

[0008] S1. construct an average dynamic model of the DC-DC boost converter, then design it into a state space expression, and then discretize the state space expression using the Euler equation to obtain a discretized state space expression;

[0009] S2. Design an adaptive unscented Kalman filter, perform two unscented transformations based on the discretized state space expression, predict the mean and covariance of the system state quantity, and obtain the estimated values ​​of the input voltage and constant power load power;

[0010] S3. Build a passive controller;

[0011] S4. Input the input voltage obtained in step S2 and the estimated value of the constant power load power into the passive controller constructed in step S3, generate a PWM signal to drive the switch tube to work, and control the DC-DC boost converter.

[0012] Furthermore, the specific implementation method of step S1 includes the following steps:

[0013] S1.1. Construct the average dynamic model of the DC-DC boost converter, expressed as:

[0014]

[0015] Among them, L, C, E, v o 、i L , μ, R and P CPL They are the inductance value, output side capacitance value, input voltage, output voltage, inductor current, switch duty cycle, resistive load and constant power load power of the DC-DC boost converter;

[0016] Set the input voltage E and constant power load power P CPL is the estimated state variable;

[0017] S1.2. Design the average dynamic model of the DC-DC boost converter as follows:

[0018]

[0019] Among them, the state variable matrix x=[i L v0] T , S is the system matrix, B is the input matrix, d is the nonlinear term matrix, h is the output matrix, y is the output quantity, and ρ is the influence factor based on the capacitor voltage;

[0020]

[0021] S1.3. Design the constant power load power as one of the state variables and add it into the state space expression. The state space expression based on the constant power load power is as follows:

[0022]

[0023] in, is the state variable matrix with constant power load added, u(t) is the time-varying system input, f is the Euler equation of the dynamic model after adding the output power to the state variable matrix, ω is the Gaussian white noise with zero mean in the state variable matrix, and ν is the Gaussian white noise with zero mean in the output;

[0024] S1.4. Use the Euler equation to discretize the state space expression obtained in step S1.3 to obtain the discretized state space expressions at time k+1 and time k:

[0025]

[0026] Among them, f(x aukf,k ,u(t) k ) represents the Euler equation of the dynamic model after adding the output power to the state variable matrix at time k, T s is the sampling time.

[0027] Furthermore, the specific implementation method of step S2 includes the following steps:

[0028] S2.1. Design an adaptive unscented Kalman filter. According to the dimension n of the state variable, determine the scale of the sigma point set to be 2n+1, select the sigma point set, and design the unscented transformation process as follows:

[0029]

[0030] in, is the sigma point set at time k, is the mean value of the state variable at time k, P k / k is the covariance matrix at time k, and λ represents the distance from each point in the sigma point set to the mean point;

[0031] S2.2. Based on the sigma point set selected in step S2.1, make predictions, substitute the state space expression to calculate the state variables at time k+1, predict the mean and covariance matrix of the state variables at time k+1, and obtain the expression:

[0032]

[0033] Among them, ω i are the weights corresponding to each mean and variance, Q is the covariance matrix of Gaussian white noise with zero mean of the state variable matrix;

[0034] S2.3. Perform an untraceable transformation again on the mean and variance of the state variables predicted at time k+1 obtained in step S2.2 to obtain the sigma point set at time k+1:

[0035]

[0036] Substitute the sigma point set at time k+1 into the observation equation to obtain the predicted observation value, where Z is the discretized observation value:

[0037]

[0038] S2.4. Calculate the mean and covariance matrix of the predicted observations based on the predicted observations:

[0039]

[0040] Among them, R is the covariance matrix of Gaussian white noise with zero mean of the output, is the mean value of the observation at time k+1, is the covariance matrix of the observation at time k+1, is the covariance matrix of state variables and observations at time k+1;

[0041] S2.5. Calculate the Kalman gain K at time k+1 k+1 By adding feedforward compensation, the mean value of the system state variable at time k+1 and the covariance matrix at time k+1 are output:

[0042]

[0043] The estimated state variables obtained by iteration are the input voltage E and the power P of the constant power load CPL CPL .

[0045] Furthermore, the specific implementation method of step S3 includes the following steps:

[0046] S3.1. Based on the design of passive controller, the average dynamic model of DC-DC boost converter is constructed in matrix form, and the expression is obtained:

[0047]

[0048] Among them, H is the transient quantity in the passive controller, X is the state variable matrix in the passive controller, Γ is the input voltage matrix in the passive controller, G is the voltage transformation coefficient matrix in the passive controller, and R(x) is the system impedance;

[0049]

[0050] S3.2. Design of passive controller including energy reshaping stage and damping injection stage;

[0051] S3.2.1. Energy reshaping stage: Adoption After transformation, the expression is:

[0052]

[0053] Among them, X d is the state quantity of the equilibrium point, represents the interference value of X, where R(x m ) is the actual quantity matrix of system impedance, R(x d ) is the reference value of the system impedance at the equilibrium point;

[0054] S3.2.2. Energy reshaping stage: by injecting a virtual damping matrix R V X modifies the system dissipation function and obtains the expression:

[0055]

[0056] Among them, R p is the system impedance after injecting virtual damping, R v is the virtual damping coefficient;

[0057]

[0058] S3.2.3. To ensure that the system injected with the virtual damping matrix in step S3.2.2 satisfies Lyapunov stability, the global stable equilibrium point of the system is set to Set the energy function of the system for:

[0059]

[0060] The expression of the time derivative of the energy function is obtained as follows:

[0061]

[0062] S3.3. Based on the global stability convergence of the system, set the injected virtual damping coefficient R v Let the matrix R p All elements are positive, and the expression of the control law of the passive controller is:

[0063]

[0064] Furthermore, the specific implementation method of step S4 is to substitute the estimated values ​​of the input voltage and the constant power load power obtained in step S2 into the control law of the passive controller obtained in step S3 to obtain the expression:

[0065]

[0066] Among them, i L,ref 、v ref is the reference value of the state quantity;

[0067] According to the properties of the DC-DC boost converter, a PWM signal is generated to drive the switch tube to work, and the control result of the switch duty cycle μ is obtained, which is expressed as:

[0068]

[0069] Beneficial effects of the present invention:

[0070] The passive control method of a DC-DC boost converter based on an adaptive unscented Kalman filter described in the present invention estimates system parameters online, including output power, input voltage and other state variables, by designing an adaptive unscented Kalman filter, and then proposes an adaptive passive model prediction technology based on the state variables of the filter output, which is used to control a DC / DC boost converter with a constant power load. In addition, before the current loop, a simple current limiting can be achieved through a saturation current limiting link. The present invention is combined with a passive control loop, has the advantages of simple structure, large signal stability, etc., and greatly improves the robustness of the system.

[0071] The passive control method of a DC-DC boost converter based on an adaptive unscented Kalman filter described in the present invention can not only significantly improve the large signal stability and robustness of the system, but also has precise current limiting control and fast voltage regulation capabilities, thereby effectively ensuring the stability and reliability of the DC microgrid under various complex operating conditions. The nonlinear control loop proposed in the present invention avoids the difficulties of obtaining nominal parameters and performing complex multi-degree-of-freedom parameter adjustments in traditional control methods, and has a relatively simple and practical control structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a flow chart of a passive control method of a DC-DC boost converter based on an adaptive unscented Kalman filter according to the present invention;

[0073] Figure 2 This is the block diagram of the unscented Kalman filter passive control of the present invention;

[0074] Figure 3 This is a simulation result diagram of the load mutation of the unscented Kalman filter passive control of the present invention. DETAILED DESCRIPTION

[0075] In order to make the purpose, technical solution and advantages of the present invention more clear, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the specific embodiments described are only part of the embodiments of the present invention, rather than all of the specific embodiments. The components of the specific embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations, and the present invention can also have other embodiments.

[0076] Therefore, the following detailed description of the specific embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents the selected specific embodiments of the present invention. Based on the specific embodiments of the present invention, all other specific embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0077] In order to further understand the content, features and effects of the present invention, the following specific implementation methods are given as examples, and the attached Figure 1 -Attached Figure 3 The detailed instructions are as follows:

[0078] Embodiment 1:

[0079] A DC-DC boost converter passive control method based on adaptive unscented Kalman filtering comprises the following steps:

[0080] S1. construct an average dynamic model of the DC-DC boost converter, then design it into a state space expression, and then discretize the state space expression using the Euler equation to obtain a discretized state space expression;

[0081] Furthermore, the specific implementation method of step S1 includes the following steps:

[0082] S1.1. Construct the average dynamic model of the DC-DC boost converter, expressed as:

[0083]

[0084] Among them, L, C, E, v o 、i L , μ, R and P CPL They are the inductance value, output side capacitance value, input voltage, output voltage, inductor current, switch duty cycle, resistive load and constant power load power of the DC-DC boost converter;

[0085] Set the input voltage E and constant power load power P CPL is the estimated state variable;

[0086] S1.2. Design the average dynamic model of the DC-DC boost converter as follows:

[0087]

[0088] Among them, the state variable matrix x=[i L v0] T , S is the system matrix, B is the input matrix, d is the nonlinear term matrix, h is the output matrix, y is the output quantity, and ρ is the influence factor based on the capacitor voltage;

[0089]

[0090] Based on the unscented Kalman filtering method proposed in the present invention, it is necessary to regard the power of the constant power load as one of the state variables and add it into the state space expression to obtain a new state variable matrix:

[0091]

[0092] Due to the power characteristics of the constant power load, it is easy to conclude that P is equal to 0, and the state variable matrix becomes:

[0093]

[0094] where u(t) is the time-varying system input, and f is the function of u(t) and x. aukfThe function represents the Euler equation of the dynamic model after adding the output power to the state variable matrix. In addition, the output of the UKF algorithm is updated based on the change of the state variables:

[0095]

[0096] S1.3. Design the constant power load power as one of the state variables and add it into the state space expression. The state space expression based on the constant power load power is as follows:

[0097]

[0098] Among them, x aukf is the state variable matrix with constant power load added, u(t) is the time-varying system input, f is the Euler equation of the dynamic model after adding the output power to the state variable matrix, ω is the Gaussian white noise with zero mean in the state variable matrix, and ν is the Gaussian white noise with zero mean in the output;

[0099] S1.4. Use the Euler equation to discretize the state space expression obtained in step S1.3 to obtain the discretized state space expressions at time k+1 and time k:

[0100]

[0101] Among them, f(x aukf,k ,u(t) k ) represents the Euler equation of the dynamic model after adding the output power to the state variable matrix at time k, T s is the sampling time.

[0102] S2. Design an adaptive unscented Kalman filter, perform two unscented transformations based on the discretized state space expression, predict the mean and covariance of the system state quantity, and obtain the estimated values ​​of the input voltage and constant power load power;

[0103] Furthermore, the specific implementation method of step S2 includes the following steps:

[0104] S2.1. Design an adaptive unscented Kalman filter. According to the dimension n of the state variable, determine the scale of the sigma point set to be 2n+1, select the sigma point set, and design the unscented transformation process as follows:

[0105]

[0106] in, is the sigma point set at time k, is the mean value of the state variable at time k, P k / k is the covariance matrix at time k, and λ represents the distance from each point in the sigma point set to the mean point;

[0107] Furthermore, it should be noted that (n+λ)P must be a semi-positive definite matrix when given;

[0108] S2.2. Based on the sigma point set selected in step S2.1, make predictions, substitute the state space expression to calculate the state variables at time k+1, predict the mean and covariance matrix of the state variables at time k+1, and obtain the expression:

[0109]

[0110] Among them, ω i are the weights corresponding to each mean and variance, Q is the covariance matrix of Gaussian white noise with zero mean of the state variable matrix;

[0111] Furthermore, we get the predicted mean and variance. It can be seen that after one iteration, the two weights are equal, where λ = α 2 (N+ε)-N, assuming that the noise is still white Gaussian distribution, take N+ε=3, ε is the parameter to be selected, here it is 0, α affects the distribution state of the parameter, usually a small positive number. β is a hyperparameter that reflects the high-order state history information, for Gaussian distribution, β is 2. The weight calculation is as follows:

[0112]

[0113] S2.3. Perform an untraceable transformation again on the mean and variance of the state variables predicted at time k+1 obtained in step S2.2 to obtain the sigma point set at time k+1:

[0114]

[0115] Substitute the sigma point set at time k+1 into the observation equation to obtain the predicted observation value, where Z is the discretized observation value:

[0116]

[0117] S2.4. Calculate the mean and covariance matrix of the predicted observations based on the predicted observations:

[0118]

[0119] Among them, R is the covariance matrix of Gaussian white noise with zero mean of the output, is the mean value of the observation at time k+1, is the covariance matrix of the observation at time k+1, is the covariance matrix of state variables and observations at time k+1;

[0120] S2.5. Calculate the Kalman gain K at time k+1k+1 By adding feedforward compensation, the mean value of the system state variable at time k+1 and the covariance matrix at time k+1 are output:

[0121]

[0122] The estimated state variables obtained by iteration are the input voltage E and the power P of the constant power load CPL CPL .

[0124] S3. Build a passive controller;

[0125] Furthermore, the specific implementation method of step S3 includes the following steps:

[0126] S3.1. Based on the design of passive controller, the average dynamic model of DC-DC boost converter is constructed in matrix form, and the expression is obtained:

[0127]

[0128] Among them, H is the transient quantity in the passive controller, X is the state variable matrix in the passive controller, Γ is the input voltage matrix in the passive controller, G is the voltage transformation coefficient matrix in the passive controller, and R(x) is the system impedance;

[0129]

[0130] Furthermore, G = -G T , R = R T ,The average dynamic model matrix form of the constructed DC-DC boost converter satisfies the characteristics of the Euler-Lagrangian model;

[0131] S3.2. Design of passive controller including energy reshaping stage and damping injection stage;

[0132] S3.2.1. Energy reshaping stage: Adoption After transformation, the expression is:

[0133]

[0134] Among them, X d is the state quantity of the equilibrium point, represents the interference value of X, where R(x m ) is the actual quantity matrix of system impedance, R(x d ) is the reference value of the system impedance at the equilibrium point;

[0135] S3.2.2. Energy reshaping stage: by injecting a virtual damping matrix R V X modifies the system dissipation function and obtains the expression:

[0136]

[0137] Among them, R p is the system impedance after injecting virtual damping, R v is the virtual damping coefficient;

[0138]

[0139] S3.2.3. To ensure that the system injected with the virtual damping matrix in step S3.2.2 satisfies Lyapunov stability, the global stable equilibrium point of the system is set to Set the energy function of the system for:

[0140]

[0141] The expression of the time derivative of the energy function is obtained as follows:

[0142]

[0143] S3.3. Based on the global stability convergence of the system, set the injected virtual damping coefficient R v Let the matrix R p All elements are positive, and the expression of the control law of the passive controller is:

[0144]

[0145] S4. Input the input voltage obtained in step S2 and the estimated value of the constant power load power into the passive controller constructed in step S3, generate a PWM signal to drive the switch tube to work, and control the DC-DC boost converter.

[0146] Furthermore, the specific implementation method of step S4 is to substitute the estimated values ​​of the input voltage and the constant power load power obtained in step S2 into the control law of the passive controller obtained in step S3 to obtain the expression:

[0147]

[0148] i L,ref 、v ref It is the reference value of the state quantity.

[0149] According to the properties of the DC-DC boost converter, a PWM signal is generated to drive the switch tube to work, and the control result of the switch duty cycle μ is obtained, which is expressed as:

[0150]

[0151] By applying a current limiting link after the current reference, the peak current during startup and interference can be suppressed, thereby reducing the current stress of the device. By combining the passive control voltage loop and the unscented Kalman filter, a nonlinear controller with simple structure, high robustness and low computational burden can be formed. Combining the above controller design, the adaptive unscented Kalman filter passive control method of the present invention is finally as follows Figure 2 shown.

[0152] In order to verify the effectiveness of the method of this embodiment, the method of this embodiment is simulated and verified, and the parameters are set as follows: converter input voltage E = 100V, L = 2mH, C = 940μF, R = 100Ω. First, the performance of the controller proposed in this embodiment is simulated and verified. The initial startup parameters of the system are input voltage 100V and constant power load 100W. The simulation waveform is as follows: Figure 3 As shown, the output voltage achieves precise convergence. At 0.2s, the constant power load power increases to 500W, at 0.6s, the constant power load decreases to 100W, and at 1.0s, the resistance R decreases to 50Ω. From the simulation results, it can be seen that the control method proposed in the present invention can quickly stabilize the bus voltage when the load changes suddenly.

[0153] The control method proposed in this embodiment mainly combines unscented Kalman filter technology, passive control and model predictive control. First, based on the converter information, the unknown input voltage and load power after considering the time-varying characteristics are accurately predicted through the designed unscented Kalman filter controller. The predicted information is sent to the proposed passive controller to generate a PWM signal to drive the switch tube to work, thereby controlling the boost converter with a constant power load. The proposed method can significantly improve the large signal stability and robustness of the system, and also has accurate current limiting control and fast voltage regulation capabilities, thereby effectively ensuring the stability and reliability of the DC microgrid under various complex operating conditions. In addition, the proposed nonlinear control loop avoids the difficulty of obtaining nominal parameters and performing complex multi-degree-of-freedom parameter adjustment in the traditional control method in design, so it has a relatively simple and practical control structure. In order to further evaluate the superiority of the proposed control method, a simulation study was carried out, and the results showed that the control method proposed in this embodiment has excellent control performance.

[0154] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0155] Although the present application has been described above with reference to specific embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present application. In particular, as long as there is no structural conflict, the various features in the specific embodiments disclosed in the present application may be used in combination with each other in any manner, and the fact that these combinations are not exhaustively described in this specification is only for the sake of omitting space and saving resources. Therefore, the present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A DC-DC boost converter passive control method based on adaptive unscented Kalman filtering, characterized in that: The steps include: S1. construct an average dynamic model of the DC-DC boost converter, then design it into a state space expression, and then discretize the state space expression using the Euler equation to obtain a discretized state space expression; S2. Design an adaptive unscented Kalman filter, perform two unscented transformations based on the discretized state space expression, predict the mean and covariance of the system state quantity, and obtain the estimated values ​​of the input voltage and constant power load power; S3. Build a passive controller; S4. Input the input voltage obtained in step S2 and the estimated value of the constant power load power into the passive controller constructed in step S3, generate a PWM signal to drive the switch tube to work, and control the DC-DC boost converter.

2. The passive control method of a DC-DC boost converter based on an adaptive unscented Kalman filter according to claim 1, characterized in that: The specific implementation method of step S1 includes the following steps: S1.

1. Construct the average dynamic model of the DC-DC boost converter, expressed as: Among them, L, C, E, v o 、i L , μ, R and P CPL They are the inductance value, output side capacitance value, input voltage, output voltage, inductor current, switch duty cycle, resistive load and constant power load power of the DC-DC boost converter; Set the input voltage E and constant power load power P CPL is the estimated state variable; S1.

2. Design the average dynamic model of the DC-DC boost converter as follows: Among them, the state variable matrix x=[i L v0] T , S is the system matrix, B is the input matrix, d is the nonlinear term matrix, h is the output matrix, y is the output quantity, and ρ is the influence factor based on the capacitor voltage; h＝[1 1] S1.

3. Design the constant power load power as one of the state variables and add it into the state space expression. The state space expression based on the constant power load power is as follows: in, is the state variable matrix with constant power load added, u(t) is the time-varying system input, f is the Euler equation of the dynamic model after adding the output power to the state variable matrix, ω is the Gaussian white noise with zero mean in the state variable matrix, and ν is the Gaussian white noise with zero mean in the output; S1.

4. Use the Euler equation to discretize the state space expression obtained in step S1.3 to obtain the discretized state space expressions at time k+1 and time k: Among them, f(x aukf,k ,u(t) k ) represents the Euler equation of the dynamic model after adding the output power to the state variable matrix at time k, T s is the sampling time.

3. The passive control method of a DC-DC boost converter based on an adaptive unscented Kalman filter according to claim 2, characterized in that: The specific implementation method of step S2 includes the following steps: S2.

1. Design an adaptive unscented Kalman filter. According to the dimension n of the state variable, determine the scale of the sigma point set to be 2n+1, select the sigma point set, and design the unscented transformation process as follows: in, is the sigma point set at time k, is the mean value of the state variable at time k, P k / k is the covariance matrix at time k, and λ represents the distance from each point in the sigma point set to the mean point; S2.

2. Based on the sigma point set selected in step S2.1, make predictions, substitute the state space expression to calculate the state variables at time k+1, predict the mean and covariance matrix of the state variables at time k+1, and obtain the expression: Among them, ω i are the weights corresponding to each mean and variance, Q is the covariance matrix of Gaussian white noise with zero mean of the state variable matrix; S2.

3. Perform an untraceable transformation again on the mean and variance of the state variables predicted at time k+1 obtained in step S2.2 to obtain the sigma point set at time k+1: Substitute the sigma point set at time k+1 into the observation equation to obtain the predicted observation value, where Z is the discretized observation value: S2.

4. Calculate the mean and covariance matrix of the predicted observations based on the predicted observations: Among them, R is the covariance matrix of Gaussian white noise with zero mean of the output, is the mean value of the observation at time k+1, is the covariance matrix of the observation at time k+1, is the covariance matrix of state variables and observations at time k+1; S2.

5. Calculate the Kalman gain K at time k+1 k+1 By adding feedforward compensation, the mean value of the system state variable at time k+1 and the covariance matrix at time k+1 are output: The estimated state variables obtained by iteration are the input voltage E and the power P of the constant power load CPL CPL .

4. The passive control method of a DC-DC boost converter based on an adaptive unscented Kalman filter according to claim 3, characterized in that: The specific implementation method of step S3 includes the following steps: S3.

1. Based on the design of passive controller, the average dynamic model of DC-DC boost converter is constructed in matrix form, and the expression is obtained: Among them, H is the transient quantity in the passive controller, X is the state variable matrix in the passive controller, Γ is the input voltage matrix in the passive controller, G is the voltage transformation coefficient matrix in the passive controller, and R(x) is the system impedance; S3.

2. Design of passive controller including energy reshaping stage and damping injection stage; S3.2.

1. Energy reshaping stage: Adoption After transformation, the expression is: Among them, X d is the state quantity of the equilibrium point, represents the interference value of X, where R(x m ) is the actual quantity matrix of system impedance, R(x d ) is the reference value of the system impedance at the equilibrium point; S3.2.

2. Energy reshaping stage: by injecting a virtual damping matrix R V X modifies the system dissipation function and obtains the expression: Among them, R p is the system impedance after injecting virtual damping, R v is the virtual damping coefficient; S3.2.

3. To ensure that the system injected with the virtual damping matrix in step S3.2.2 satisfies Lyapunov stability, the global stable equilibrium point of the system is set to Set the energy function of the system for: The expression of the time derivative of the energy function is obtained as follows: S3.

3. Based on the global stability convergence of the system, set the injected virtual damping coefficient R v Let the matrix R p All elements are positive, and the expression of the control law of the passive controller is:

5. The passive control method of a DC-DC boost converter based on an adaptive unscented Kalman filter according to claim 4, characterized in that: The specific implementation method of step S4 is to substitute the input voltage and the estimated value of the constant power load power obtained in step S2 into the control law of the passive controller obtained in step S3 to obtain the expression: Among them, i L,ref 、v ref is the reference value of the state quantity; According to the properties of the DC-DC boost converter, a PWM signal is generated to drive the switch tube to work, and the control result of the switch duty cycle μ is obtained, which is expressed as:

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