Self-evolution fuzzy control CC-Dickson fusion rectifier power dynamic allocation method

Abstract

The invention relates to the technical field of rectifier power dynamic allocation methods, in particular to a CC-Dickson fusion rectifier power dynamic allocation method based on self-evolution fuzzy control, which comprises the following steps of: acquiring actual operation parameters of a rectification system; determining a load power dynamic distribution coefficient; establishing a load characteristic model; constructing a fuzzy control rule; generating a membership function relation matrix; establishing a proportional-integral control algorithm model; a multi-level self-adaptive control algorithm based on a Riemann-Zeta function is executed; adjusting the working mode and parameters of the CC-Dickson fusion rectifier based on the output of the multi-level adaptive control algorithm; the system performance is continuously monitored, the multi-level self-adaptive control algorithm is executed circularly, and the projection of the Riemann-Zeta function is matched with the mapping of transformation, so that the calculation complexity is reduced, and the key nonlinear characteristics of the system are kept.

Description

Technical Field

[0001] This invention relates to the technical field of dynamic power allocation methods for rectifiers, and specifically to a CC-Dickson fusion rectifier dynamic power allocation method based on self-evolving fuzzy control. Background Technology

[0002] With the rapid development of power electronics technology, high-efficiency and high-reliability power management systems are increasingly widely used in various fields. Among them, the CC-Dickson fused rectifier has attracted widespread attention due to its excellent performance over a wide load range. However, traditional CC-Dickson fused rectifier control methods still face many challenges when dealing with complex and variable load environments.

[0003] Currently, the industry commonly uses PI control-based methods to regulate the output of CC-Dickson fused rectifiers. For example, Chinese patent CN116961374A, "A Dual-Closed-Loop Control Strategy for PWM Rectifiers" (publication date 2023.08.01), discloses a typical control scheme using a dual-closed-loop structure. This scheme can achieve high voltage accuracy and stronger anti-interference capability under rated load conditions. However, although this method is simple to implement, when dealing with dynamic load changes (current surges), PI control struggles to adjust parameters in a timely manner, leading to large output voltage fluctuations, long response times, and potentially even system instability. Furthermore, fixed-parameter PI controllers struggle to maintain optimal performance across the entire load range, especially under light load conditions, where system efficiency often drops significantly.

[0004] Another common approach is to employ fuzzy control strategies. For example, Chinese patent CN103840678B, "A Fuzzy Sliding Mode Control Method for a Matrix Rectifier" (authorization announcement date: August 17, 2016), discloses a fuzzy sliding mode control technique that uses fuzzification, fuzzy inference, and defuzzification to obtain a fuzzy output, thereby improving robustness and dynamic performance. This method improves the system's dynamic response capability to some extent. However, traditional fuzzy control rules are static and cannot be automatically adjusted according to the system's operating state. This leads to a gradual deterioration in control effectiveness during long-term operation, especially when system parameters drift.

[0005] Some researchers have attempted to introduce neural networks into control systems to improve their adaptability. For example, CN116436327A, "An Adaptive Predictive Control Method and System for Converters Based on Neural Networks" (publication date: July 14, 2023), discloses an adaptive predictive control method for rectifiers under conditions such as parameter mismatch and external disturbances, achieved by using neural network weights instead of model parameters within the framework of classical model predictive control. However, such methods often require a large amount of training data, which is difficult and expensive to obtain in practical engineering applications. Furthermore, the black-box nature of neural networks raises questions about the interpretability and reliability of the system.

[0006] In summary, existing CC-Dickson fused rectifier control methods generally suffer from the following problems: difficulty in maintaining high efficiency over a wide load range; insufficient dynamic response capability, especially during load changes; lack of adaptive capability, unable to automatically optimize the control strategy based on the long-term operating status of the system; and low efficiency under light load conditions. Summary of the Invention

[0007] To address these issues, this invention proposes a dynamic power allocation method for CC-Dickson fused rectifiers based on self-evolving fuzzy control. This method cleverly combines the flexibility of fuzzy control with the adaptive capability of self-evolving algorithms, while introducing a state mapping technique based on the Riemann-Zeta function to achieve efficient characterization and processing of the system state.

[0008] This invention provides a method for dynamic power allocation in a CC-Dickson fused rectifier using self-evolving fuzzy control, comprising the following steps:

[0009] Obtain the actual operating parameters of the rectifier system; determine the dynamic load power allocation coefficient; establish a load characteristic model; construct fuzzy control rules; generate a membership function relation matrix; establish a proportional-integral control algorithm model; execute a multi-level adaptive control algorithm based on the Riemann-Zeta function; adjust the operating mode and parameters of the CC-Dickson fused rectifier based on the output of the multi-level adaptive control algorithm; continuously monitor system performance and repeatedly execute the multi-level adaptive control algorithm.

[0010] Preferably, the multi-level adaptive control algorithm based on the Riemann-Zeta function includes: performing state-space projection; and performing... Transform the state mapping; design the Bessel function controller; perform Lyapunov function stability analysis; implement the parameter adaptive law.

[0011] Preferably, in the state-space projection step, the system state is nonlinearly projected using the following Riemann-Zeta function:

[0012] Preferably, in the state-space projection step, the system state is nonlinearly projected using the following Riemann Zeta function: Where Z(s) is the projected state value, x n The original state vector X = [U i n,I out ,ω,T em The nth component of ρ, where s is a complex parameter, U i n is the input voltage, I out ω represents the output current, ω represents the load characteristic, and T represents the output current. em Where ρ is temperature, and ρ is the dynamic load power distribution coefficient.

[0013] Preferably, the In the state transformation mapping step, the following is used: The transformation performs a nonlinear mapping on the projected state values:

[0014] The In the state transformation mapping step, the following is used: The transformation performs a nonlinear mapping on the projected state values: Where M(Z) is the mapped state value, Z is the projected state value, and a, b, c, d are real numbers that satisfy ad-bc≠0.

[0015] Preferably, in the Bessel function controller design step, the following first type of Bessel function is used to design the nonlinear controller:

[0016] u(t) = J α (M(Z)), where u(t) is the control input, J α Let M(Z) be a Bessel function of the first kind, order a, where M(Z) is the mapped state value and α is the order of the Bessel function. Further, the first kind of Bessel function of the first kind, order a, is defined as follows: Where Γ is the gamma function and m is the summation index.

[0017] Preferably, in the Lyapunov function stability analysis step, the Lyapunov function is constructed as follows: V(e) = e T Pe, where V(e) is the Lyapunov function, and e is the systematic error, defined as e = M(Z) - M(Z) desired M(Z) represents the current mapped state value. desiredLet P be the desired mapped state value, and P be a positive definite symmetric matrix; further, calculate the time derivative of the Lyapunov function: in, Let t be the time derivative of the Lyapunov function, A and B be matrices in the system state-space representation, and u(t) be the control input.

[0018] Preferably, in the parameter adaptive law step, based on Lyapunov stability theory, the following parameter adaptive law is designed:

[0019] in, Let θ be the rate of change of the controller parameter vector, γ be the controller parameter vector, γ be the positive adaptive gain, e be the system error, P be the positive definite symmetric matrix, B be the matrix in the system state-space representation, and φ(M(Z)) be the basis function vector.

[0020] Preferably, the step of obtaining the actual operating parameters of the rectifier system includes: obtaining the input voltage U in ; Obtain the output current I out ; Obtain load characteristic ω; Obtain temperature T em ; Obtain the dynamic load power allocation coefficient ρ.

[0021] Preferably, the step of determining the load power dynamic allocation coefficient includes: determining the value of the load power dynamic allocation coefficient ρ according to the current operating state of the system; and dividing the load operating state into three intervals: light load, medium load, and heavy load based on the load power dynamic allocation coefficient ρ.

[0022] Preferably, the steps for adjusting the operating mode and parameters of the CC-Dickson fused rectifier include:

[0023] When the load is light, Dickson rectification mode is mainly used;

[0024] When the load is medium, the Dickson rectification and CC rectification modes are dynamically switched.

[0025] When the load is heavy, CC rectification mode is mainly used;

[0026] Based on the output of the multi-level adaptive control algorithm, the duty cycle, switching frequency and other relevant parameters of the rectifier are adjusted in real time.

[0027] The present invention has the following beneficial effects:

[0028] The core of this invention lies in constructing a multi-level adaptive control framework. First, by introducing a dynamic load power allocation coefficient, precise division of the load state is achieved, laying the foundation for subsequent control strategy selection. Second, the Riemann-Zeta function is used to perform nonlinear projection of the system state, significantly reducing the dimensionality of the state space and simplifying subsequent control calculations. Third, through… Transformation is used for state mapping, enhancing the system's ability to express nonlinear characteristics. Finally, a Bessel function is introduced to design a nonlinear controller, effectively handling the periodic fluctuation problem in the system.

[0029] This multi-level design not only solves the specific problems of each control link, but more importantly, it creates a synergistic relationship between these links. For example, the projection of the Riemann-Zeta function and... The transformation mappings work together to reduce computational complexity while preserving the system's key nonlinear characteristics. The Bessel function controller complements the parameter adaptive law, with the former handling short-term fluctuations and the latter ensuring long-term optimization.

[0030] Another major highlight of this invention is its self-evolutionary capability. By introducing a parameter adaptive mechanism based on Lyapunov stability theory, the system can continuously optimize control parameters according to real-time operating data. This self-evolutionary characteristic enables the system to continuously improve performance during long-term operation and can even adapt to changes caused by factors such as equipment aging.

[0031] In practical applications, the method of this invention has demonstrated significant advantages. First, it maintains high efficiency across the entire load range, with a particularly noticeable improvement under light load conditions. Second, the method exhibits excellent dynamic response capabilities, quickly adapting to load changes and keeping output voltage fluctuations within a small range. Furthermore, due to the adoption of a self-evolutionary strategy, the long-term reliability of the system is significantly improved.

[0032] In summary, the self-evolving fuzzy control-based dynamic power allocation method for CC-Dickson fused rectifiers proposed in this invention not only solves the problems of low efficiency, slow response, and poor adaptability in existing technologies, but also achieves an overall performance improvement through the synergistic effect of various control components. This method opens up new avenues for efficient, stable, and intelligent power management systems, and has broad application prospects in fields such as data centers, new energy power generation, and electric vehicle charging. Attached Figure Description

[0033] Figure 1 This is a flowchart of the method of the present invention.

[0034] Figure 2 This is a flowchart of the state space projection of the present invention.

[0035] Figure 3 For the present invention A flowchart of the state transformation mapping.

[0036] Figure 4 A flowchart illustrating the design of the Bessel function controller for this invention.

[0037] Figure 5 This is a flowchart illustrating the adaptive parameter update process of this invention. Detailed Implementation

[0038] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the specific implementation methods, structures, features, and effects are described in detail below with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0040] See Figure 1-5 This invention discloses a self-evolving fuzzy control-based dynamic power allocation method for CC-Dickson fused rectifiers. This method aims to improve the efficiency and stability of CC-Dickson fused rectifiers under different load conditions and includes several key steps. In practical applications, this invention first requires obtaining the actual operating parameters of the rectifier system. These parameters typically include input voltage, output current, load characteristics, temperature, and load power dynamic allocation coefficient. Obtaining these parameters aims to accurately reflect the current state of the system, providing a basis for subsequent control decisions.

[0041] Next, this invention needs to determine the dynamic load power allocation coefficient. This coefficient plays a crucial role in this invention, helping to divide the load operating state into three intervals: light load, medium load, and heavy load. For example, this invention can empirically set the value range of the dynamic load power allocation coefficient ρ to 0 to 1, where below 0.3 is light load, 0.3 to 0.7 is medium load, and above 0.7 is heavy load. This division method allows this invention to adopt corresponding control strategies for different load states.

[0042] After determining the load state, this invention needs to establish a load characteristic model. This model should be able to accurately describe the load behavior under different input powers. Preferably, this invention can use methods such as polynomial fitting or neural networks to construct this model. For example, for a simple load characteristic model, this invention can use a quadratic polynomial: Where P out For output power, P in Let be the input power, and a, b, and c be the fitting coefficients.

[0043] The next crucial step is to construct fuzzy control rules. In this step, the invention needs to establish a series of IF-THEN rules based on the system's input and output variables. For example, a simple rule might be: IF input voltage is high AND output current is low THEN reduce switching frequency. The formulation of these rules requires combining expert experience with system characteristics.

[0044] Subsequently, this invention requires generating a membership function relation matrix. This matrix describes the fuzzy relationship between the input and output variables. Typically, this invention can use Gaussian or trigonometric functions to define the membership function. For example, for the input voltage, this invention can define three fuzzy sets: low, medium, and high, whose membership functions can be expressed as:

[0045]

[0046] Where x is the input voltage, a, b, and c are the center values, and σ is the standard deviation. Establishing the proportional-integral (PI) control algorithm model is the core part of the control system. In the CC-Dickson fused rectifier, this invention typically uses PI control to regulate the output voltage. The output of the PI controller can be expressed as:

[0047]

[0048] Where u(t) is the control output, e(t) is the error signal, and K p For proportional gain, K i This is the integral gain. In practical applications, K... p and K i The choice of K needs to be adjusted according to the system characteristics. For example, for systems with high response speed requirements, this invention may choose a larger K. p For systems with strict steady-state error requirements, this invention may increase the value of K. i value.

[0049] A key innovation of this invention lies in implementing a multi-level adaptive control algorithm based on the Riemann-Zeta function. This algorithm comprises several sub-steps, each of which makes a significant contribution to the adaptive control of the system.

[0050] The first step is to perform state-space projection. In this step, the invention uses the Riemann-Zeta function to perform a nonlinear projection of the system state. The Riemann-Zeta function is defined as follows:

[0051]

[0052] Where Z(s) is the projected state value, x n The original state vector X = [U i n,I out ,ω,T em The nth component of ρ is denoted by s, where s is a complex parameter. The advantage of this projection method is that it can compress a high-dimensional state into a complex value, greatly simplifying the subsequent processing.

[0053] In practical applications, this invention typically selects the real part of s to be between 0.5 and 1, because the Riemann-Zeta function has good convergence in this range.

[0054] Next, the present invention will proceed. Transform state mapping. The transformation is a conformal transformation on the complex plane, defined as:

[0055]

[0056] Where M(Z) is the mapped state value, Z is the projected state value, and a, b, c, and d are real numbers satisfying ad-bc≠0. In the control of the CC-Dickson fused rectifier, Transformation helps this invention to perform nonlinear transformations of the state space, enabling the controller to better adapt to the nonlinear characteristics of the system. For example, this invention can select a=1, b=0, c=1, d=1. Such a transformation can map the upper half-plane on the complex plane to the unit circle, which is beneficial for subsequent controller design.

[0057] The Bessel function controller design is another innovation of this invention. This invention uses a first-class Bessel function to design a nonlinear controller:

[0058] u(t) = J α (M(Z))

[0059] Where u(t) is the control input, J α Let M(Z) be a Bessel function of the first kind, order α, where M(Z) represents the mapped state values ​​and α is the order of the Bessel function. The definition of a Bessel function of the first kind, order α, is:

[0060]

[0061] Where Γ is the gamma function and m is the summation index. The advantage of the Bessel function controller lies in its ability to effectively handle the periodic fluctuations of the system, which is particularly important in CC-Dickson fused rectifiers, as the rectifier output typically exhibits some ripple. In practical applications, this invention usually chooses α = 0 or α = 1 because the Bessel function has good numerical stability in these two cases. To ensure the stability of the control system, this invention performs Lyapunov function stability analysis. This invention constructs the following Lyapunov function:

[0062] V(e)=e T Pe

[0063] Where V(e) is the Lyapunov function, and e is the systematic error, defined as e = M(Z) - M(z). desired M(Z) represents the current mapped state value. desired Let P be the desired mapped state value, and P be a positive definite symmetric matrix. In practical applications, the choice of the P matrix directly affects the stability and convergence speed of the system. A common choice is to use the identity matrix, but in some cases, this invention may require solving the Lyapunov equations to obtain a better P matrix.

[0064] Furthermore, this invention also requires calculating the time derivative of the Lyapunov function:

[0065]

[0066] in, Let be the time derivative of the Lyapunov function, A and B be matrices in the system's state-space representation, and u(t) be the control input. Through analysis... The present invention can determine whether a system is stable by using the symbol. If The system is then asymptotically stable. Finally, this invention implements a parameter adaptive law. Based on Lyapunov stability theory, this invention designs the following parameter adaptive law:

[0067]

[0068] in, Let θ be the rate of change of the controller parameter vector, γ be the controller parameter vector, γ be the positive adaptive gain, e be the system error, P be a positive definite symmetric matrix, B be the matrix in the system state-space representation, and φ(M(Z)) be the basis function vector. This adaptive law can adjust the controller parameters according to the real-time state of the system, improving the system's adaptability and robustness. In practical applications, the selection of γ needs to balance the system's convergence speed and stability. Typically, this invention can start with a small value (e.g., 0.01) and then gradually increase it until satisfactory performance is obtained.

[0069] Through the above steps, the method of this invention can effectively adapt to the operating states of the CC-Dickson fused rectifier under different load conditions, achieving efficient and stable dynamic power allocation. The advantage of this method lies in its combination of traditional control theory and modern mathematical tools, ensuring control stability while improving system adaptability and efficiency. In practical applications, this method can significantly reduce rectifier power losses and improve the overall system performance.

[0070] In a preferred embodiment of the present invention, the present invention further explores the specific implementation method of Lyapunov function stability analysis. As mentioned above, the present invention constructs the Lyapunov function V(e) = e T Pe. The choice of this function is not arbitrary, but rather the result of careful consideration. In the control of the CC-Dickson fused rectifier, the change in system error e directly affects the output stability of the rectifier. By selecting a quadratic Lyapunov function, this invention can intuitively represent the system's "energy" and determine the system's stability by analyzing the changes in this "energy".

[0071] It is worth noting that the choice of matrix P is crucial for stability analysis. In practical applications, this invention typically starts with a diagonal matrix, for example:

[0072]

[0073] Here, p1, p2, and p3 are positive real numbers. This choice ensures that the P matrix is ​​always positive definite, satisfying the requirements of Lyapunov stability theory. In some cases, this invention may require the use of off-diagonal elements to capture the coupling relationships between state variables.

[0074] Next, this invention calculates the time derivative of the Lyapunov function. In this expression, matrices A and B are derived from the system's state-space representation. For the CC-Dickson fused rectifier, these matrices have the following form:

[0075]

[0076]

[0077] Where R is the equivalent load resistance, C and C o These are the input and output capacitors, respectively; L is the inductance; V... in This refers to the input voltage. The specific values ​​for these parameters need to be determined based on the actual CC-Dickson fused rectifier design.

[0078] In actual control, this invention needs to ensure This means that the system's energy decreases over time, thus ensuring the system's asymptotic stability. To achieve this goal, the present invention can influence the system's energy by adjusting the control input u(t). The symbol.

[0079] Further exploring another key aspect of the invention, namely the parameter adaptive law, as described in claim 7, the invention employs the following parameter adaptive law:

[0080]

[0081] The core idea of ​​this adaptive law is to adapt to changes in the system by adjusting the controller parameters θ in real time. In the application of CC-Dickson fused rectifiers, θ may include the proportional gain K of the PI controller. p and integral gain K i Or, more complexly, the parameters of a nonlinear controller. The choice of adaptive gain γ requires a trade-off between the system's convergence speed and stability. A common approach is to use a variable γ, for example:

[0082] γ=γ0exp(-βt)

[0083] Where γ0 is the initial gain, β is the attenuation coefficient, and t is time. This design allows the system to learn quickly in the initial stage, and then gradually decreases the gain to improve stability.

[0084] The selection of the basis function vector φ(M(Z)) is also a key point. In the control of the CC-Dickson fused rectifier, this invention can select basis functions of the following form:

[0085] φ(M(Z))=[1,M(Z),M(Z) 2 sin(M(Z)),cos(M(Z))] T

[0086] This choice combines polynomial basis functions and trigonometric basis functions, which can better fit complex nonlinear relationships.

[0087] Next, the present invention will describe in detail the specific method for obtaining the actual operating parameters of the rectifier system. First, it is necessary to obtain the input voltage U. in Output current I out Load characteristics ω, temperature U em and the load power dynamic allocation coefficient ρ o For the input voltage U in and output current I out Real-time measurements can be performed using high-precision voltage and current sensors. For example, for input voltage, we can use a voltage divider circuit combined with an analog-to-digital converter (ADC) to obtain the digitized voltage value. A typical voltage divider circuit might be designed as follows:

[0088]

[0089] R1 and R2 are voltage divider resistors. By selecting appropriate resistor values, this invention can convert high voltages to a range acceptable to the ADC.

[0090] Obtaining the load characteristic ω is relatively complex. In CC-Dickson fused rectifiers, the load characteristic is typically related to the relationship between output voltage and current. This invention characterizes the load characteristic by calculating output power and efficiency in real time:

[0091]

[0092] Temperature T em Temperature measurement is crucial for ensuring the safe operation of a system. This invention can use thermocouples or thermistors to measure the temperature of critical components (such as switching transistors and transformers). For example, when using an NTC thermistor, the relationship between temperature and resistance can be expressed by the following formula:

[0093]

[0094] Where T is the current temperature, T0 is the reference temperature, R is the current resistance, R0 is the reference resistance, and B is the material constant. The dynamic load power distribution coefficient ρ is an innovation of this invention.

[0095] The value of ρ is determined based on the current operating status of the system, and the load operating status is divided into three intervals: light load, medium load, and heavy load. In practical applications, this invention can define ρ based on the ratio of output power to rated power.

[0096]

[0097] Among them, P out P is the current output power. ratedThe rated power of the system is given. Based on experience, the present invention can set the following classification criteria: when 0≤ρ<0.3, the system is in a light load state; when 0.3≤ρ<0.7, the system is in a medium load state; when 0.7≤ρ≤1, the system is in a heavy load state.

[0098] This classification method can better reflect the operating characteristics of the CC-Dickson fused rectifier under different load conditions.

[0099] Finally, this invention details how to adjust the operating mode and parameters of the CC-Dickson fused rectifier based on the output of a multi-level adaptive control algorithm. This invention employs different rectification modes according to the load condition and adjusts relevant parameters in real time.

[0100] Under light load conditions, this invention primarily employs the Dickson rectification mode. Dickson rectifiers exhibit high efficiency under light load conditions. In this case, the invention may reduce the switching frequency to minimize switching losses. For example, the switching frequency can be adjusted using the following formula:

[0101] f sw =f bade (1-k1(1-ρ))

[0102] Among them, f sw f is the actual switching frequency. b ase is the reference frequency, and k1 is the adjustment coefficient (usually between 0.2 and 0.5).

[0103] Under medium load conditions, this invention dynamically switches between Dickson rectification and CC rectification modes. The switching decision can be based on the output voltage ripple magnitude. For example, when the ripple exceeds a set threshold, it switches to CC rectification mode.

[0104]

[0105] Where, ΔV out The peak-to-peak value of the output voltage ripple, v out δ is the average output voltage, and δ is the ripple threshold (usually between 0.05 and 0.1).

[0106] Under heavy load conditions, this invention primarily employs CC rectification mode. CC rectification offers better performance at high current outputs. In this case, the invention may require increasing the switching frequency to reduce ripple.

[0107] f sw =f base (1+k2(ρ-0.7))

[0108] Where k2 is the adjustment coefficient (usually between 0.5 and 1).

[0109] Furthermore, this invention requires real-time adjustment of the duty cycle to maintain a stable output voltage. This can be achieved using a PI controller:

[0110] D = D prev +K p (V ref -V out )+K i ∫(V ref -V out )dt

[0111] Where D is the current duty cycle, D prev V represents the duty cycle of the previous cycle. ref V is the reference voltage. out K represents the actual output voltage. p and K i These are the proportional and integral gains, respectively.

[0112] Through the above method, this invention achieves adaptive control of the CC-Dickson fused rectifier under different load conditions, significantly improving the system efficiency and stability. This method is not only applicable to traditional power management systems but can also be extended to fields such as new energy power generation and electric vehicle charging, showing broad application prospects. In practical applications, this invention conducted a series of experiments to verify the superiority of the proposed adaptive control method for the CC-Dickson fused rectifier based on the Riemann-Zeta function. To comprehensively evaluate the performance of this method, a set of comparative experiments were designed, testing both the method of this invention and the traditional PI control method.

[0113] Example 1 employs the adaptive control method proposed in this invention. This invention uses a 1kW CC-Dickson fusion rectifier as the test platform. The input voltage range is 180V-264V AC, and the output voltage is 48VDC. To simulate real-world application scenarios, this invention designs a dynamic load that can rapidly change between 50W and 1000W.

[0114] Comparative Example 1 employs a traditional PI control method. This invention uses the same hardware platform as Example 1, with only the control algorithm replaced. The parameters of the PI controller are tuned using the Ziegler-Nichols method to achieve better static and dynamic performance.

[0115] This invention focuses on the following performance indicators: output voltage stability, dynamic response time, system efficiency, and load adaptability. The testing methods and standards for these indicators are as follows:

[0116] 1. Output Voltage Stability: The output voltage ripple is measured using a high-precision digital oscilloscope. The standard is that the peak value does not exceed 1% of the output voltage.

[0117] 2. Dynamic response time: The time required for the output voltage to recover to a steady state after a sudden load change (from 10% of rated load to 90% of rated load). The standard is no more than 5ms.

[0118] 3. System Efficiency: Input and output power are measured using a power analyzer to calculate efficiency. Tests are conducted under different load conditions, and the average value is taken.

[0119] 4. Load adaptability: Test the stability and efficiency of the system under different load conditions (light load, medium load, heavy load).

[0120] The test results are shown in the table below:

[0121] Performance indicators Example 1 Comparative Example 1 Output voltage stability (peak-to-peak ripple) 0.32V(0.67％) 0.58V(1.21％) Dynamic response time 2.8ms 6.5ms System efficiency (average) 94.8％ 92.1％ Light load efficiency (10% load) 88.5％ 82.3％ Medium load efficiency (50% load) 95.2％ 93.6％ Heavy load efficiency (90% load) 96.3％ 95.1％

[0122] The test results show that the method of this invention exhibits significant advantages in all indicators. Let's analyze these results in more detail:

[0123] First, regarding output voltage stability, the peak-to-peak ripple of the method in this invention is only 0.67%, far lower than the 1.21% of traditional PI control, and meets the standard of not exceeding 1%. This indicates that the method of this invention can more effectively suppress output voltage fluctuations and provide a more stable DC output. This is mainly due to the Bessel function controller used in this invention, which can better handle the periodic fluctuations of the system.

[0124] Secondly, regarding dynamic response time, the method of this invention can restore the output voltage to stability in just 2.8ms, while traditional PI control requires 6.5ms. This rapid response capability is crucial for handling sudden load changes, especially in applications such as data centers or industrial automation. This advantage mainly stems from the multi-level adaptive control algorithm of this invention, particularly... The application of state transformation mapping and parameter adaptive law enables the system to quickly adapt to load changes.

[0125] In terms of system efficiency, the method of this invention achieves an average efficiency of 94.8%, which is 2.7 percentage points higher than that of traditional PI control. This efficiency improvement is particularly important in high-power applications, as it can significantly reduce energy consumption and heat dissipation requirements. More notably, the efficiency improvement of the method of this invention is most significant under light load conditions, reaching 88.5%, while the traditional method only achieves 82.3%. This fully demonstrates the superiority of the dynamic load power allocation strategy of this invention, especially the significant effect of the strategy that primarily employs the Dickson rectification mode under light load conditions.

[0126] Finally, in terms of load adaptability, the method of this invention exhibits high efficiency under light, medium, and heavy load conditions, with a flatter efficiency curve. This indicates that the method of this invention can maintain efficient operation over a wide load range, which is particularly advantageous in practical applications where the load frequently changes.

[0127] In summary, these test results fully demonstrate the superiority of the method of this invention. It not only improves the overall efficiency of the system but also significantly enhances dynamic response performance and output stability. These advantages mainly stem from the innovative application of the Riemann-Zeta function in this invention. Mathematical tools such as transformations and Bessel functions are applied to the control algorithm, and a flexible dynamic load power allocation strategy is adopted.

[0128] The successful application of this method has opened up new avenues for efficient, stable, and adaptable power management systems. It is not only applicable to traditional power systems but can also be extended to emerging fields such as new energy power generation and electric vehicle charging, demonstrating broad application prospects.

[0129] 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 principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for power dynamic allocation of CC-Dickson fusion rectifier with self-evolutionary fuzzy control, characterized by, The method comprises the following steps: acquiring actual operation parameters of the rectifier system; determining a load power dynamic distribution coefficient; establishing a load characteristic model; constructing fuzzy control rules; generating a membership function relationship matrix; establishing a proportional integral control algorithm model; executing a multi-level adaptive control algorithm based on a Riemann-Zeta function; adjusting the working mode and parameters of the CC-Dickson fusion rectifier based on the output of the multi-level adaptive control algorithm; continuously monitoring the system performance and cyclically executing the multi-level adaptive control algorithm.

2. The method of claim 1, wherein, The multi-level adaptive control algorithm based on the Riemann-Zeta function includes: performing state space projection; performing transform state mapping; designing a Bessel function controller; performing Lyapunov function stability analysis; implementing a parameter adaptive law.

3. The method of claim 2, wherein, In the state space projection step, the system state is nonlinearly projected using the following Riemann Zeta function: where Z(s) is the projected state value, x n is the nth component of the original state vector x = [U i n, I out , ω, T em , ρ], s is a complex parameter, U i n is the input voltage, I out is the output current, ω is the load characteristic, T em is the temperature, and ρ is the load power dynamic distribution coefficient.

4. The method of claim 3, wherein, The In the transformation state mapping step, the following is used The transformation non-linearly maps the projected state values: The In the transformation state mapping step, the following is used The transformation non-linearly maps the projected state value: where M(Z) is the mapped state value, Z is the projected state value, a, b, c, d are real numbers and satisfy ad-bc≠0.

5. The method of claim 4, wherein, In the Bessel function controller design step, the following first-type Bessel function is used to design a nonlinear controller: u(t) = J α (M(Z)), where u(t) is a control input, J α is a first kind a-th order Bessel function, M(Z) is a mapped state value, and a is an order of the Bessel function; further, the first kind a-th order Bessel function is defined as: where Γ is a gamma function and m is a summation index.

6. The method of claim 5, wherein, In the Lyapunov function stability analysis step, a Lyapunov function is constructed as follows: V(e) = e T Pe, where V(e) is a Lyapunov function, e is a system error defined as e = M(Z) - M(Z desired ), M(Z) is a current mapped state value, M(Z desired ) is an expected mapped state value, and P is a positive definite symmetric matrix; further, a time derivative of the Lyapunov function is calculated as follows: where is a time derivative of the Lyapunov function, A and B are matrices in a system state space representation, and u(t) is a control input.

7. The method of claim 6, wherein, In the parameter adaptive law step, the following parameter adaptive law is designed based on Lyapunov stability theory: where, is the rate of change of the controller parameter vector, θ is the controller parameter vector, γ is a positive adaptive gain, e is the system error, P is a positive definite symmetric matrix, B is a matrix in the state space representation of the system, and φ(M(Z)) is the basis function vector.

8. The method of claim 1, wherein, The step of acquiring actual operation parameters of the rectifying system comprises: acquiring input voltage U in ; acquiring output current I out ; acquiring load characteristic ω; acquiring temperature T em ; and acquiring load power dynamic distribution coefficient ρ.

9. The method of claim 1, wherein, The step of determining the load power dynamic distribution coefficient comprises: determining the value of the load power dynamic distribution coefficient ρ according to the current operation state of the system; and dividing the load operation state into three intervals of light load, medium load and heavy load based on the load power dynamic distribution coefficient ρ.

10. The method of claim 1, wherein, The step of adjusting the working mode and parameters of the CC-Dickson fusion rectifier comprises: When the load is light, mainly using the Dickson rectification mode; When the load is medium, dynamically switching the Dickson rectification and CC rectification modes; When the load is heavy, mainly using the CC rectification mode; Based on the output of the multi-level adaptive control algorithm, the duty cycle, switching frequency and other related parameters of the rectifier are adjusted in real time.

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