Buck converter dynamic performance weight adjustment method based on particle swarm optimization

Abstract

The invention provides a step-down converter dynamic performance weight adjustment method based on particle swarm optimization. The step-down converter dynamic performance weight adjustment method comprises the following steps of 1, initializing control parameters, a population and the number of iterations; step 2, performing outer-layer circulation, and finding an optimal weight coefficient combination which minimizes a contribution ratio error; step 3, performing inner layer circulation: optimizing proportional gain and integral gain through a differential evolution algorithm, and realizing global optimization through reverse feedback of the outer layer circulation and the inner layer circulation; 4, judging whether a stop condition is met or not; and 5, inputting the optimal controller parameters into a proportional integral controller of the step-down circuit, and outputting an optimized voltage curve. According to the method, bidirectional interaction of dynamic weight adjustment and controller parameter optimization is innovatively achieved, the method has high adaptability and wide application prospects, especially under the complex load condition, the optimization strategy can be automatically adjusted, and it is ensured that the converter can keep excellent control performance under various working conditions.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent control, and in particular relates to a method for adjusting the dynamic performance weight of a buck converter based on particle swarm optimization. Background Art

[0002] With the continuous development of power electronics technology, switching power supply, as one of the important technologies for power conversion, has been widely used in power electronic equipment. As a typical step-down switching power supply, buck converter circuit has been widely used in communication equipment, consumer electronics, industrial control and other fields due to its advantages of high efficiency, small size and low cost. However, in practical applications, buck converter circuit faces the challenge of how to improve its performance, reduce loss and ensure stability.

[0003] Traditional buck converter circuit control system optimization methods often use fixed control strategies and parameter settings, which are difficult to cope with dynamic changes caused by load changes and power supply fluctuations. Especially in multi-objective optimization problems, how to balance performance indicators such as efficiency, response time, output stability and system robustness has become a key issue restricting the performance improvement of buck circuits.

[0004] In recent years, with the development of intelligent control technology and optimization algorithms, more and more research has focused on optimizing the performance of buck converter circuits through intelligent control algorithms. Particle swarm optimization (PSO) algorithm, as a global optimization method, can effectively adjust the fitness function weights of various performance indicators in the control system. By dynamically optimizing the weight ratio of the fitness function, it can better meet the needs of the system under different working conditions. Through the particle swarm algorithm, combined with dynamic ratio allocation, the weight coefficient of the fitness function can be more efficiently adjusted dynamically, thereby solving the design problem of the fitness function more quickly.

[0005] As an important part of power electronics technology, buck converter circuits are widely used in various DC power supply systems to achieve efficient voltage reduction and voltage regulation functions. In practical applications, due to factors such as load changes and input voltage fluctuations, the dynamic response capability, output stability and robustness of the buck converter circuit face many challenges. Therefore, optimizing the control strategy of the buck converter circuit to improve its overall performance is an important research direction in the field of power electronics technology. In order to meet the performance requirements under different load conditions, it is usually necessary to optimize the controller parameters. The existing technology uses differential evolution (DE) algorithm for parameter optimization. Although the effect is good, it requires manual design of the objective function and adjustment of the weights to balance the performance indicators. The operation is complicated and it is difficult to accurately coordinate various indicators. Summary of the invention

[0006] Purpose of the invention: In view of the shortcomings of the prior art, the present invention provides a method for adjusting the dynamic performance weight of a buck converter based on particle swarm optimization, aiming to improve the dynamic performance and stability of the power system.

[0007] The method of the present invention comprises the following steps: Step 1, initializing control parameters; initializing the population and number of iterations of the evolutionary algorithm; establishing a fitness function, wherein the fitness function includes performance indicators; Step 2, perform an outer loop: randomly generate a set of weight coefficients, and calculate and output the circuit state and performance indicators at the current moment according to the real-time operation data of the buck converter circuit, and calculate the actual contribution ratio of the circuit state and performance indicators at the current moment, compare the target contribution ratio with the actual contribution ratio, and perform normalized error calculation based on the target contribution ratio and the actual contribution ratio, and dynamically adjust the target contribution ratio based on the calculated error result; use the particle swarm algorithm to adjust each weight coefficient again; repeat step 2 until the optimal weight coefficient combination that minimizes the contribution ratio error is found; finally, feed the optimized weight coefficient back to the inner loop of step 3; Step 3: Perform inner loop: Optimize the proportional gain K through differential evolution algorithm p and integral gain K i , and reversely correct the search direction of the outer loop according to the optimization result of the inner loop, and achieve global optimization through reverse feedback between the outer loop and the inner loop; Step 4: Determine whether the following conditions are met: The voltage overshoot is less than 0.1, the steady-state error is less than 0.001, and the undershoot is less than 0.5.

[0008] If all are satisfied, the optimized controller parameter K is retained. p ,K i The voltage stabilization time of the current optimal result is compared with the voltage stabilization time of the previously obtained optimal result, and the smaller voltage stabilization time is saved as the optimal result, and the optimized K p ,K i Substitute it into the outer loop of step 2 again, according to the parameter K p ,K i Optimize k2, k4, k6, k8 again; otherwise, directly use the optimized K p ,K i Substitute into the outer loop of step 2 until the iteration is completed and the optimal solution is found; Step 5, input the obtained optimal controller parameters into the proportional-integral controller of the buck circuit, and output the optimized voltage curve.

[0009] Step 1 includes: Step 1.1, according to the experimental requirements, initialize the range and parameters, give fixed weight coefficients k1, k3, k5, k7; initialize the dynamically adjusted weight coefficients k2, k4, k6, k8; initialize the controller parameter K p and K i ; Set the number of dimensions, mutation factor and crossover probability of the Differential Evolution (DE) algorithm and initialize the population; set the range of controller parameters; set the number of iterations of the outer loop and inner loop; Step 1.2, design fitness function , and the fitness function Encapsulated in the evaluation function, the fitness function Defined as: (1), Among them, k1, k2 are the weight coefficients of the performance indicator stabilization time ST, k3, k4 are the weight coefficients of the performance indicator overshoot OV, k5, k6 are the weight coefficients of the performance indicator steady-state error SSE, k7, k8 are the weight coefficients of the performance indicator undershoot UV; e is a natural constant; Will Denoted as the intermediate parameter f1, Denoted as the intermediate parameter f2, Denoted as the intermediate parameter f3, Denoted as intermediate parameter f4.

[0010] Step 2 includes: Step 2.1, design a function optimize_weights to optimize the weight coefficient of the fitness function. The function optimize_weights uses a particle swarm optimization algorithm (PSO) to perform dynamic weight optimization, specifically including: The current position of each particle is updated using the following formula: (2) in, is the current position of the ith particle at the kth iteration, is the velocity of the ith particle at the k+1th iteration; The velocity update formula for each particle is: (3) in is the inertia weight; and is the learning factor; and is a random number uniformly distributed between [0,1]; is the historical optimal position of the i-th particle at the k-th iteration; is the global optimal position of the population; Step 2.2, design the main function compute_dynamic_error for dynamic weight optimization, wherein the main function compute_dynamic_error is based on the proportional gain K by analyzing p and integral gain K i The obtained voltage image is optimized based on the target contribution ratio given initially, and the optimal ratio is automatically obtained through dynamic adjustment. Each iteration updates the ratio according to the error, and performs particle swarm optimization. The particle swarm optimization algorithm PSO is used to find the minimum error value as the solution with the minimum error, so that the actual contribution ratio current_ratios is close to the target contribution ratio target_ratios. Step 2.3, calculate the error again using the updated target contribution ratio and the actual contribution ratio obtained after the next iteration until the iteration ends; In step 2.4, the optimal weight coefficient combination that minimizes the contribution ratio error is fed back to the inner loop of step 3.

[0011] Step 2.2 includes: Step 2.2.1, initialize the target contribution ratio: The target contribution ratio is a dynamically adjusted value, which is initialized as: target_ratios=[0.25.0.25,0.25,0.25] (4), Step 2.2.2, calculate the current actual contribution ratio: First calculate the sum of the total performance indicators : (5) Then calculate the actual contribution ratio of each indicator: (6) in Recorded as the actual contribution ratio of the first performance indicator , Recorded as the actual contribution ratio of the second performance indicator , Recorded as the actual contribution ratio of the third performance indicator , Recorded as the actual contribution ratio of the fourth performance indicator ; Step 2.2.3, dynamically adjust the target ratio; Step 2.2.4, calculate the contribution ratio error.

[0012] In step 2.2.3, the target ratio is dynamically adjusted using the following formula: (7), in is the actual contribution ratio of the i-th performance indicator, i ranges from 1 to 4, is the target contribution ratio of the i-th performance indicator, and adjust_rate is the dynamic adjustment rate.

[0013] In step 2.2.4, the contribution ratio error is calculated using the following formula: (8).

[0014] Step 3 includes: Step 3.1, initialize the population and initialize the proportional gain K for each individual p and integral gain K i ; Step 3.2: Use the differential evolution algorithm to optimize. Through the mutation and crossover operations in the differential evolution algorithm, K is continuously optimized. p ,K i And output the fitness value; Step 3.3, update the current global optimal solution according to the fitness value; Step 3.4: The optimized K p ,K i Substitute it into the outer loop of step 2 again, and according to the optimized K p ,K i Optimize k2, k4, k6, k8, and cycle in sequence until the optimal controller parameters are obtained.

[0015] The present invention also provides an electronic device, comprising a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor executes the steps of the described method.

[0016] The present invention also provides a storage medium storing a computer program or instruction. When the computer program or instruction is run on a computer, the steps of the method described are executed.

[0017] The present invention also provides a buck converter, which uses the method to adjust dynamic performance.

[0018] The present invention proposes a method for dynamically allocating weight ratios and adaptively adjusting the weight coefficients of the fitness function. The weight parameters of each performance index in the fitness function are dynamically adjusted by the PSO algorithm, and these weight parameters are placed in the fitness function. The DE algorithm is further used to optimize the controller's Kp and K i Parameters. This method can flexibly respond to load changes and input power fluctuations, and improve the overall performance of the buck converter circuit. Compared with the traditional fitness function parameter setting method, this method can obtain better weight parameters through the PSO algorithm when designing the fitness function, providing an innovative technical solution for the optimization of the buck converter circuit.

[0019] The present invention solves this problem by introducing the particle swarm optimization (PSO) algorithm. As a global optimization method based on swarm intelligence, the PSO algorithm has the advantages of simple implementation, few parameters, and fast convergence speed. The PSO algorithm is applied to the dynamic adjustment of the weights of each performance indicator in the fitness function of the buck converter, so that the system can automatically optimize the weight distribution under different operating conditions, thereby improving the overall performance of the system. In particular, the present invention solves the problem that the weight coefficient is difficult to accurately coordinate in the past by analyzing the voltage curve and dynamically adjusting the indicator weight ratio. By dynamically adjusting the weight ratio in the fitness function through the PSO algorithm and combining the differential evolution algorithm to optimize the controller parameters, the present invention significantly improves the dynamic performance, steady-state accuracy and overall stability of the buck converter. The method has strong adaptability and broad application prospects. Especially under complex load conditions, it can automatically adjust the optimization strategy to ensure that the converter can maintain excellent control performance under various working conditions.

[0020] The present invention has the following beneficial effects: (1) Dynamic weight adjustment mechanism: The core of the present invention uses a particle swarm algorithm (PSO) to achieve dynamic weight adjustment. This mechanism is based on controller parameters, dynamically allocates the weight ratio of performance indicators through normalization processing, and adjusts the weight of each indicator in the fitness function according to the actual control effect. Specifically, a method of dynamically updating the weight based on the error between the target ratio and the actual ratio is adopted, so that the components of the fitness function are automatically adjusted during the optimization process to ensure that various performance indicators (such as overshoot, steady-state error, etc.) are balanced during the optimization. Through this mechanism, the fitness function can be designed automatically, which solves the difficulty of manually assigning weight coefficients in traditional methods and greatly simplifies the fitness function design process. In the specific implementation, the particle swarm algorithm is used to optimize the controller parameters, and the target ratio is dynamically adjusted according to the error calculation, so that in each optimization iteration, the weight allocation gradually tends to the optimal state, further improving the stability and response performance of the control system. Through this method, the performance of the controller is significantly improved, avoiding the empirical dependence of manually designed controllers, achieving end-to-end optimization, and the weight adjustment during the optimization process is more in line with actual needs.

[0021] (2) The fitness function setting of the global objective of the buck circuit system and the double-layer closed-loop coordination of the controller system parameters: after completing N iterations, the full parameter update of the inner loop is triggered, and the search direction of the outer loop is reversely corrected according to the optimization result of the inner loop, so as to achieve global optimization through the reverse feedback of the outer loop and the inner loop.

[0022] (3) Exponential fitness function: Using an exponential form in the fitness function can quickly eliminate poor solutions during the search process, while the fitness value of the better solution is quickly amplified. Due to the sensitivity of the exponential function, the fitness value of the poor solution usually becomes very low quickly, which accelerates the algorithm to focus on the better area in the search space and improves the efficiency of the search.

[0023] (4) Multi-objective optimization strategy: The present invention adopts a multi-objective optimization strategy to achieve comprehensive optimization of the energy storage converter control system by comprehensively considering multiple performance indicators such as settling time (ST), overshoot (OV), steady-state error (SSE) and undershoot (UV). BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flow chart of the method of the present invention.

[0025] Figure 2 It is a control system optimization diagram of the buck conversion circuit in the embodiment of the present invention.

[0026] Figure 3 This is a tracking comparison diagram of the output voltage. DETAILED DESCRIPTION

[0027] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, and the above and other advantages of the present invention will become more clear.

[0028] like Figure 1 As shown, an embodiment of the present invention provides a method for adjusting the dynamic performance weight of a buck converter based on particle swarm optimization, comprising the following steps: Step 1, initialization, the fitness function includes multiple performance indicators, including the following steps: Step 1.1, according to the experimental requirements, initialize the range and parameters, and give fixed weight coefficients k1, k3, k5, k7. Set the number of dimensions, mutation factors and crossover probability of the DE algorithm; set the range of PI parameters; set the number of iterations of the inner and outer loops; Step 1.2, design the fitness function and encapsulate it in the evaluation function, which is defined as: (1) k1, k2 are the weight coefficients of the performance index ST, k3, k4 are the weight coefficients of the performance index OV, k5, k6 are the weight coefficients of the performance index SSE, k7, k8 are the weight coefficients of the performance index UV, where k1=50, k3=50, k5=50, k7=50. Denoted as f1, Denoted as f2, Denoted as f3, Denoted as f4; Step 1.3, design the optimize_weights function, that is, the function that optimizes the weight coefficient of the fitness function, and use the PSO algorithm for optimization. This optimization process is achieved through the particle velocity update formula. The specific update formula is as follows: Position update formula: The current position of each particle is updated using the following formula: (2) in, is the current position of the ith particle at the kth iteration, is the velocity of the ith particle at the (k+1)th iteration.

[0029] Speed ​​update formula: The speed update formula for each particle is: (3) in is the inertia weight, which controls how much the particle velocity is preserved. and are learning factors, which control the attraction of particles to their own historical optimal positions and the global optimal position of the population respectively. and is a random number uniformly distributed between [0,1]. is the historical optimal position of the i-th particle at the k-th iteration. is the global optimal position of the population.

[0030] Step 1.4, design the compute_dynamic_error function, which is the main function of PSO optimization. Its main function is to analyze the given K p ,K i The obtained voltage image is optimized based on the initially given contribution ratio, and the optimal ratio is automatically obtained through dynamic adjustment. Each iteration will update the ratio according to the error, and perform particle swarm optimization to find the set of solutions with the smallest error. That is, the minimum error value found by the particle swarm optimization algorithm is the final optimized objective function value, that is, the error value when the current contribution ratio (current_ratios) is closest to the target contribution ratio (target_ratios).

[0031] The details are as follows: Initialize the target contribution ratio: The target contribution ratio is a dynamically adjusted value, which is initialized as: target_ratios=[0.25.0.25,0.25,0.25] (4), Calculate the current actual contribution ratio: (5) (6) Dynamically adjust the target ratio: (7) Where adjust_rate is the dynamic adjustment rate.

[0032] Error calculation: (8) The final error is the sum of the squared errors between the target ratio and the current ratio.

[0033] Step 1.5, initialize the container and create a storage for the optimal K for each outer loop p ,K i and simulation data so that the output voltage image can be plotted and compared after the cycle is completed.

[0034] Step 2: According to the real-time operation data of the buck conversion circuit, the circuit state and performance indicators at the current moment are evaluated, and the outer loop is performed, the ratio is dynamically allocated through the PSO algorithm, the weight coefficient is dynamically adjusted, and the adjusted k2, k4, k6, k8 are output to the inner loop. The closed-loop buck circuit of the present invention is composed of the following key components: Input: Vin: DC input voltage source, positive input terminal; Switching elements: Q1: Power switch tube, its drain is connected to Vin, and its gate receives PWM control signal; Energy transfer elements: L1: energy storage inductor, one end of which is connected to the source of Q1 and the other end is connected to the output terminal Vout; D1: freewheeling diode, with the anode connected to the common node of Q1 source and L1, and the cathode grounded; Filter and load network: C1: output filter capacitor, connected between Vout and ground; R1: load resistor, connected in parallel across C1; Control and feedback network: PWM: Pulse width modulation signal source, whose output is connected to the gate of Q1, and the frequency and duty cycle are adjusted by the control logic; Gc(s): compensator transfer function module (PI controller), which receives the feedback signal and generates the error compensation signal; Vref: reference voltage source, compared with the feedback voltage signal to generate an error signal input to Gc(s); Vout: Output voltage node, fed back to the voltage divider network or voltage sampling module of the control loop.

[0035] The specific circuit diagram is as follows Figure 2 shown.

[0036] Step 3: Perform inner loop and optimize K by DE algorithm p ,K i Parameters, the specific steps are as follows:

[0037] Step 3.1, initialize the population, initialize the PI parameters (K p and K i ).

[0038] Step 3.2: Use evolutionary algorithm to optimize K through mutation, crossover and other operations in DE algorithm. p ,K i And output the fitness value.

[0039] Step 3.3, update the current global optimal solution according to the fitness value.

[0040] Step 3.4: The optimized K p ,K i Substitute it into the outer loop again, and optimize k2, k4, k6, k8 again based on this set of parameters. Compare the optimal result of the current loop with the optimal result obtained before, and retain the optimal value. p ,K i The specific requirements for the corresponding performance indicators are as follows: OV is less than 0.1, SSE is less than 0.001, and UV is less than 0.1 to meet the standards. On this basis, ST is compared. The smaller the ST, the better.

[0041] Step 4: Substitute the obtained optimal controller parameter combination into the system and draw a comparison chart of various performance indicators compared with static optimization, such as Figure 3 shown.

[0042] The present invention provides a method for adjusting the dynamic performance weight of a buck converter based on particle swarm optimization. There are many methods and ways to implement the technical solution. The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the protection scope of the present invention. All components not specified in this embodiment can be implemented by existing technologies.

Claims

1. A method for adjusting the dynamic performance weight of a buck converter based on particle swarm optimization, characterized in that: The following steps are involved: Step 1, initializing control parameters; initializing the population and number of iterations of the evolutionary algorithm; establishing a fitness function, wherein the fitness function includes performance indicators; Step 2, perform an outer loop: randomly generate a set of weight coefficients, and calculate and output the circuit state and performance indicators at the current moment according to the real-time operation data of the buck converter circuit, and calculate the actual contribution ratio of the circuit state and performance indicators at the current moment, compare the target contribution ratio with the actual contribution ratio, and perform normalized error calculation based on the target contribution ratio and the actual contribution ratio, and dynamically adjust the target contribution ratio based on the calculated error result; use the particle swarm algorithm to adjust each weight coefficient again; repeat step 2 until the optimal weight coefficient combination that minimizes the contribution ratio error is found; finally, feed the optimized weight coefficient back to the inner loop of step 3; Step 3: Perform inner loop: Optimize the proportional gain K through differential evolution algorithm p and integral gain K i , and reversely correct the search direction of the outer loop according to the optimization result of the inner loop, and achieve global optimization through reverse feedback between the outer loop and the inner loop; Step 4: Determine whether the following conditions are met: The voltage overshoot is less than 0.1, the steady-state error is less than 0.001, and the undershoot is less than 0.5; If all are satisfied, the optimized controller parameter K is retained. p ,K i The voltage stabilization time of the current optimal result is compared with the voltage stabilization time of the previously obtained optimal result, and the smaller voltage stabilization time is saved as the optimal result. p ,K i Substitute it into the outer loop of step 2 again, according to the parameter K p ,K i Optimize k2, k4, k6, k8 again; otherwise, directly use the optimized K p ,K i Substitute into the outer loop of step 2 until the iteration is completed and the optimal solution is found; Step 5, input the obtained optimal controller parameters into the proportional-integral controller of the buck circuit, and output the optimized voltage curve.

2. The method according to claim 1, characterized in that Step 1 includes: Step 1.1, according to the experimental requirements, initialize the range and parameters, give fixed weight coefficients k1, k3, k5, k7; initialize the dynamically adjusted weight coefficients k2, k4, k6, k8; initialize the controller parameter K p and K i ; Set the number of dimensions, mutation factor and crossover probability of the differential evolution algorithm and initialize the population; Set the range of controller parameters; Set the number of iterations of the outer loop and inner loop; Step 1.2, design fitness function , and the fitness function Encapsulated in the evaluation function, the fitness function Defined as: （1）， Among them, k1, k2 are the weight coefficients of the performance indicator stabilization time ST, k3, k4 are the weight coefficients of the performance indicator overshoot OV, k5, k6 are the weight coefficients of the performance indicator steady-state error SSE, k7, k8 are the weight coefficients of the performance indicator undershoot UV; e is a natural constant; Will Denoted as the intermediate parameter f1, Denoted as the intermediate parameter f2, Denoted as the intermediate parameter f3, Denoted as intermediate parameter f4.

3. The method according to claim 2, characterized in that Step 2 includes: Step 2.1, design a function optimize_weights for optimizing the weight coefficient of the fitness function, wherein the function optimize_weights uses a particle swarm optimization algorithm for dynamic weight optimization, specifically including: The current position of each particle is updated using the following formula: (2)， in, is the current position of the ith particle at the kth iteration, is the velocity of the ith particle at the k+1th iteration; The velocity update formula for each particle is: (3)， in is the inertia weight; and is the learning factor; and is a random number uniformly distributed between [0,1]; is the historical optimal position of the i-th particle at the k-th iteration; is the global optimal position of the population; Step 2.2, design the main function compute_dynamic_error for dynamic weight optimization, wherein the main function compute_dynamic_error is calculated by analyzing the proportional gain K p And the integral gain K i The obtained voltage image is optimized based on the target contribution ratio given initially, and the optimal ratio is automatically obtained through dynamic adjustment. Each iteration updates the ratio according to the error, and performs particle swarm optimization. The particle swarm optimization algorithm is used to find the minimum error value as the solution with the minimum error, so that the actual contribution ratio current_ratios is close to the target contribution ratio target_ratios. Step 2.3, calculate the error again using the updated target contribution ratio and the actual contribution ratio obtained after the next iteration until the iteration ends; In step 2.4, the optimal weight coefficient combination that minimizes the contribution ratio error is fed back to the inner loop of step 3.

4. The method according to claim 3, characterized in that Step 2.2 includes: Step 2.2.1, initialize the target contribution ratio: The target contribution ratio is a dynamically adjusted value, which is initialized as: target_ratios=[0.25.0.25,0.25,0.25] (4), Step 2.2.2, calculate the current actual contribution ratio: First calculate the sum of the total performance indicators : (5)， Then calculate the actual contribution ratio of each indicator: (6)， in Recorded as the actual contribution ratio of the first performance indicator , Recorded as the actual contribution ratio of the second performance indicator , Recorded as the actual contribution ratio of the third performance indicator , Recorded as the actual contribution ratio of the fourth performance indicator ; Step 2.2.3, dynamically adjust the target ratio; Step 2.2.4, calculate the contribution ratio error.

5. The method according to claim 4, characterized in that In step 2.2.3, the target ratio is dynamically adjusted using the following formula: （7）， in is the actual contribution ratio of the i-th performance indicator; is the target contribution ratio of the i-th performance indicator, and adjust_rate is the dynamic adjustment rate.

6. The method according to claim 5, characterized in that In step 2.2.4, the contribution ratio error is calculated using the following formula: (8)。 7. The method according to claim 6, characterized in that Step 3 includes: Step 3.1, initialize the population and initialize the proportional gain K for each individual p And the integral gain K i ; Step 3.2: Use the differential evolution algorithm to optimize. Through the mutation and crossover operations in the differential evolution algorithm, K is continuously optimized. p ,K i And output the fitness value; Step 3.3, update the current global optimal solution according to the fitness value; Step 3.4: The optimized K p ,K i Substitute it into the outer loop of step 2 again, and according to the optimized K p ,K i Optimize k2, k4, k6, k8, and cycle in sequence until the optimal controller parameters are obtained.

8. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores program codes, and when the program codes are executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 7.

9. A storage medium, characterized in that: A computer program or instruction is stored, and when the computer program or instruction is run on a computer, the steps of the method according to any one of claims 1 to 7 are executed.

10. A buck converter, characterized in that: The buck converter uses the method according to any one of claims 1 to 7 to perform dynamic performance adjustment.

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