Fuzzy PID fusion control method for GaN-based power circuit

Abstract

The invention discloses a fuzzy PID fusion control method for a GaN-based power circuit, and the method at least comprises the following steps: 1, designing an initial PID controller which has a high proportional term, a high integral term and a small differential term at an initial moment; step 2, on-line adjustment is carried out on parameters of the initial PID controller based on the output current error and the output current error change rate of the GaN-based power circuit by using a fuzzy logic adaptive method; and step 3, through iterative adjustment, an optimal PID control parameter suitable for the GaN-based power circuit is finally determined. According to the method, the parameters of the PID controller are dynamically optimized through a fuzzy logic self-adaptive method, so that the inherent defect that the dynamic performance of different working points is difficult to consider by a traditional PID controller is overcome, and the transient response performance and the operation stability of a GaN-based power circuit under the wide-range load condition are improved.

Description

Technical Field

[0001] This invention relates to the field of laser payload equipment driving source technology, and in particular to a fuzzy PID fusion control method for GaN-based power circuits. Background Technology

[0002] With the widespread application of laser payload equipment such as individual soldier, vehicle-mounted, and airborne systems, more stringent requirements are being placed on the high power density and high-frequency operation of their drive sources. As the core power unit, the performance of the drive source directly determines the output stability and response speed of the laser payload. Existing pump source drive systems based on silicon (Si) materials generally suffer from problems such as low power density, high switching losses, and efficiency degradation under high-frequency conditions, becoming a key bottleneck restricting the performance improvement of laser payload equipment.

[0003] Gallium nitride high electron mobility transistors (GaN HEMTs), with their inherent characteristics of low on-resistance, high breakdown voltage, and high switching speed, have shown great application potential in high-frequency, high-power-density switching power supply designs and are considered a core direction for next-generation power semiconductor devices. However, the fast switching characteristics and high-frequency response of GaN devices also present challenges in the control of switching power supply circuits. Influenced by factors such as nonlinear parasitic capacitance and transconductance fluctuations, GaN devices require extremely high precision in the drive signal. The narrow drive voltage range and low threshold voltage characteristics further increase the difficulty of precise control; improper control will seriously affect the system's dynamic response and operational stability.

[0004] Traditional PID control strategies require manual adjustment of the proportional parameter (K). p ), integral parameter (K) i ), differential parameter (K) d This approach is not only time-consuming and labor-intensive, but also prone to parameter matching limitations due to experience, making it difficult to achieve optimal control performance under complex operating conditions and failing to meet the dynamic adjustment accuracy requirements of GaN drive systems. Therefore, developing a high-precision control method adapted to the characteristics of GaN devices to achieve rapid response and stable output of the drive system has become a core problem urgently needing to be solved in the field of laser load drive technology.

[0005] The statements herein provide only background information in relation to this invention and do not necessarily constitute prior art. Summary of the Invention

[0006] The purpose of this invention is to dynamically optimize the parameters of a PID controller using a fuzzy logic adaptive method, thereby overcoming the inherent defect of traditional PID controllers that are difficult to balance the dynamic performance at different operating points, and thus improving the transient response performance and operational stability of GaN-based power circuits under a wide range of load conditions.

[0007] To achieve the above objectives, this invention provides a fuzzy PID fusion control method for GaN-based power circuits, comprising at least the following steps: Step 1: Design an initial PID controller, which has a high proportional term, a high integral term, and a small derivative term at the initial moment; Step 2: Using a fuzzy logic adaptive method, the parameters of the initial PID controller are adjusted online based on the output current error and the rate of change of the output current error of the GaN-based power circuit. Step 3: Through iterative adjustments, the optimal PID control parameters suitable for the GaN-based power circuit are finally determined.

[0008] Optionally, the GaN-based power circuit is a synchronous rectified Buck circuit with soft-switching function, and the switching device of the GaN-based power circuit is a GaN-based MOSFET.

[0009] Optionally, before step 1, the method further includes: performing small-signal modeling on the GaN-based power circuit to obtain a control-output transfer function.

[0010] Optionally, the control-output transfer function is specifically: ;in, i o For output current, d Duty cycle, V in Input voltage, L For the original inductor of Buck circuit, L r The inductor added to achieve the soft-switching function C For circuit capacitors, R For circuit load, V D For output voltage, s It is a complex variable.

[0011] Optionally, designing the initial PID controller includes determining the zeros and poles of the compensation network, specifically: 1) Set the first zero-point frequency at 1 / 4 of the original open-loop system corner frequency; 2) Set the second zero-point frequency at half the corner frequency of the original open-loop system; Set the pole frequency to at least 1.5 times the crossover frequency of the corrected open-loop system.

[0012] Optionally, the input variables of the fuzzy logic adaptive method are the output current error and the rate of change of the output current error, and the output variable is the adjustment amount of the parameters of the initial PID controller; the parameters of the initial PID controller include proportional parameters, integral parameters, and derivative parameters.

[0013] Optionally, the fuzzy subsets of the output current error, the output current error change rate, and the adjustment amount of the initial PID controller parameters are all defined as {negative large NB, negative medium NM, negative small NS, zero ZR, positive small PS, positive medium PM, positive large PB}, and a triangular membership function is adopted.

[0014] Optionally, the fuzzy logic adaptive method includes: 1) Fuzzification process: The output current error and the rate of change of the output current error are converted to the fuzzy domain by the first quantization factor and the second quantization factor, respectively; 2) Defuzzification process: The adjustment amount of the parameters of the initial PID controller obtained by fuzzy inference is converted back to the accurate output amount through the scaling factor.

[0015] Optionally, the defuzzification process employs the centroid method and generates a control variable lookup table based on offline calculations, which is used to adjust the parameters of the initial PID controller online in real time.

[0016] Optionally, the optimal PID control parameters are used as a calculation engine by the initial PID controller to generate the duty cycle of the switching transistor in the next cycle in real time.

[0017] Compared with the prior art, the technical solution of the present invention has at least the following beneficial effects: 1) By monitoring the output current error and its rate of change in real time, and using fuzzy inference to dynamically adjust the parameters of the PID controller, the control system can automatically adapt to a wide range of working conditions from no-load to full-load. This effectively solves the problem that traditional PID controllers may have optimal parameters at a certain load point, but may experience overshoot, oscillation, or sluggishness at other load points. As a result, faster and smoother transient response is achieved across the entire operating range.

[0018] 2) Furthermore, the fuzzy logic adaptive method transforms experience into executable inference rules, enabling refined and nonlinear adjustment of PID controller parameters. This rule-based intelligent adjustment method is more targeted than traditional linear or trial-and-error parameter tuning methods, effectively suppressing disturbances, reducing steady-state errors, and significantly improving the accuracy of output voltage / current in GaN-based power circuits and the stability of the system under complex operating conditions.

[0019] 3) Furthermore, the conversion between precise and fuzzy quantities is achieved through quantization factors and scaling factors. Its core algorithm has moderate requirements for the processor's computing power, is easy to implement on existing digital signal processors or microcontrollers and integrate into existing circuit control architectures, without increasing expensive hardware costs, and has good prospects for engineering applications and promotion value. Attached Figure Description

[0020] Figure 1 This is a flowchart of the fuzzy PID fusion control method for GaN-based power circuits according to the present invention.

[0021] Figure 2 This is a schematic diagram of a synchronous rectifier Buck circuit with soft-switching function according to the present invention.

[0022] Figure 3 The fuzzy inference scaling parameter K of this invention p Change diagram.

[0023] Figure 4 K is the fuzzy inference integral parameter of this invention. i Change diagram.

[0024] Figure 5 K is the fuzzy inference differential parameter of this invention. d Change diagram.

[0025] Figure 6 The figures show the step response curves of the fuzzy PID and the traditional PID of this invention; where a is the step response curve of the fuzzy PID and b is the step response curve of the traditional PID. Detailed Implementation

[0026] The following detailed description, in conjunction with the accompanying drawings and specific embodiments, provides a further detailed explanation of the fuzzy PID fusion control method for GaN-based power circuits proposed in this invention. The advantages and features of this invention will become clearer from the following description. It should be noted that the accompanying drawings are in a very simplified form and use non-precise scales, used only to facilitate and clearly illustrate the embodiments of this invention. Please refer to the accompanying drawings to make the objectives, features, and advantages of this invention more apparent and understandable. It should be understood that the structures, scales, sizes, etc., depicted in the accompanying drawings are only for illustrative purposes to aid those skilled in the art and are not intended to limit the implementation conditions of this invention. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportional relationships, or adjustments to the size, without affecting the effects and objectives achieved by this invention, should still fall within the scope of the technical content disclosed in this invention.

[0027] Because GaN devices have strict requirements for drive signals, their narrow drive voltage range and low threshold make precise control particularly important. To ensure dynamic response speed and operational stability, precise control strategies are necessary. Traditional PID controllers require manual adjustment of the proportional parameter (K). p ), integral parameter (K) i ), differential parameter (K) dThis method can achieve better control performance, but it is time-consuming and cannot guarantee optimal control performance.

[0028] To address the aforementioned issues, this invention monitors the output current error and its rate of change in real time and dynamically adjusts the parameters of the PID controller using fuzzy inference. This enables the control system to automatically adapt to a wide range of operating conditions, from no-load to full-load. This effectively solves the problem that traditional PID controllers may have optimal parameters at one load point but exhibit overshoot, oscillation, or sluggishness at other load points. As a result, it achieves a faster and smoother transient response across the entire operating range, significantly improving the accuracy of the output voltage / current of GaN-based power circuits and the stability of the system under complex operating conditions.

[0029] like Figure 1 As shown, this invention provides a fuzzy PID fusion control method for GaN-based power circuits, comprising at least the following steps: Step 1: Design an initial PID controller, which has a high proportional term, a high integral term, and a small derivative term at the initial moment.

[0030] When designing this control method, the issues to be considered include the power circuit topology, fuzzy inference method, and voltage drive signal threshold range. Before designing the initial PID controller, the GaN-based power circuit is first determined to be a synchronous rectifier Buck circuit with soft-switching function (circuit diagram shown). Figure 2 As shown, Q1 is the main switch, Q2 is the synchronous switch, D1 is the drain of Q1, D2 is the drain of Q2, S1 is the source of Q1, S2 is the source of Q2, G1 is the gate of Q1, G2 is the gate of Q2, Cr1 is the resonant capacitor 1, Cr2 is the resonant capacitor 2), and the switching device is a GaN-based MOSFET. Small-signal modeling is then performed on the GaN-based power circuit to obtain the control-output transfer function. The control-output transfer function is shown in equation (1-1): (1-1) in, i o For output current, d Duty cycle, V in Input voltage, L For the original inductor of Buck circuit, L r The inductor added to achieve the soft-switching function C For circuit capacitors, R For circuit load, V D For output voltage, s It is a complex variable.

[0031] Based on this, the design of a PID controller with high proportional, high integral, and small derivative terms at the initial moment is completed. The principles and specific design methods for setting the zeros and poles of the PID compensation network are as follows: 1) Determine the crossover frequency of the corrected open-loop system. The higher the system crossover frequency, the better the system's dynamic characteristics. However, an excessively high crossover frequency can lead to high-frequency switching frequencies and their harmonic noise, as well as high-frequency components caused by parasitic oscillations. Effective suppression of these negative effects needs to be considered.

[0032] 2) Determine the zero-pole frequencies of the compensation network. Since the poles of a PID controller can adversely affect the stability of the system, a PID compensation network can be designed, such that the first zero frequency is the pole of the PID controller. The transition frequency of the original open-loop system located at the origin This is done to mitigate the adverse effects. Generally, the first zero-point frequency can be set at 1 / 4 to 1 / 2 of the corner frequency of the original open-loop system, as shown in equation (1-2).

[0033] (1-2) Therefore, setting the first zero-point frequency at 1 / 4 of the original open-loop system's corner frequency yields the following result. .

[0034] Second zero frequency Set the turn-off frequency of the original open-loop system Nearby, as shown in equation (1-3), the influence of one of the two poles at the corner frequency of the original open-loop system can be canceled, thereby improving the phase margin of the system.

[0035] (1-3) Therefore, setting the second zero frequency at half the corner frequency of the original open-loop system yields the following result. .

[0036] Determining the pole frequencies in the compensation network improves the system's high-frequency suppression capability. In some embodiments, the pole frequencies of the PID compensation network can be determined. Set the crossover frequency of the corrected open-loop system More than 1.5 times that.

[0037] Determining the PID compensation network G c ( s After setting all zeros and poles, the gain of the PID compensation network is affected. KTo determine. Let. K When = 1, the open-loop circuit of the system after compensation G c ( s G id ( s )| K=1 Crossing frequency f c The gain is shown in equation (1-4).

[0038] (1-4) in, To compensate the network G c ( s The Fourier transform form of ) For the transfer function G id ( s The Fourier transform of ), where A is the gain constant.

[0039] To ensure the compensated open-loop circuit G c ( s G id ( s ) at crossing frequency f c The gain is 0dB, which requires satisfying the formula shown in equation (1-5).

[0040] (1-5) Step 2: Using a fuzzy logic adaptive method, the parameters of the initial PID controller are adjusted online based on the output current error and the rate of change of the output current error of the GaN-based power circuit.

[0041] After the initial PID controller design is completed, the output current needs to be monitored. I o The precise quantity is obtained and scaling is performed. A fuzzy logic adaptive method is used to transform the input variables into output variables. The input variables are the output current error e and the rate of change of the output current error ec, and the output variables are the adjustment amounts of the PID controller parameters. The initial PID controller parameters include the proportional parameter K. p Integral parameter K i Differential parameter K d The adjustment amounts are ΔK p ΔK i ΔK d The specific steps of the fuzzy logic adaptive method are as follows: 1) Fuzzification process: The output current error e and the output current error change rate ec are respectively quantized by the first quantization factor K.e Second quantization factor K ec Transition to a fuzzy domain; 2) Defuzzification process: The adjustment amount (ΔK) of the initial PID controller parameters obtained from fuzzy inference is... p ΔK i ΔK d (Through the scaling factor P) K(m) Convert back to precise output. Fuzzy inference scaling parameter K p Variation, integral parameter K i Changes, differential parameter K d The changes are as follows Figure 3 , Figure 4 , Figure 5 As shown.

[0042] In the above process, let the fundamental universe of discourse for the input and output variables be [-n, n]. Based on the actual dynamic range of the system [e...] min e max ]、[ec min , ec max ]、[Δk min(m) Δk max(m) (m=p,i,d), Select and determine the first quantization factor K e Second quantification factor K ec and the scaling factor P K(m) The transformation formula for the universe of discourse is shown in equation (1-6).

[0043] (1-6) Among them, the current error e, the rate of change of output current error ec, and the adjustment amount Δ of the PID controller parameters are... K p , ΔK i Δ K d The same fuzzy subsets are used, namely {negative large NB, negative medium NM, negative small NS, zero ZR, positive small PS, positive medium PM, positive PB}, and the membership functions are all triangular membership functions.

[0044] The defuzzification process employs the centroid method and generates a control variable lookup table based on offline calculations to obtain the adjustment amount of the PID controller parameters. ,in, K p0 , K i0 , K d0 These are the initial values ​​for the PID controller parameters. Kp , K i , K d These are the parameters of the calibrated PID controller.

[0045] Step 3: Through iterative adjustments, the optimal PID control parameters suitable for the GaN-based power circuit are finally determined.

[0046] The parameters of the PID controller, which have been tuned as described above, are iterated continuously to finally determine the optimal PID control parameters suitable for GaN-based power circuits. The optimal PID control parameters are used as the calculation engine by the initial PID controller to generate the duty cycle of the switching transistor in the next cycle in real time, as shown in Equation (1-7), and then enter the DPWM module to generate a DPWM signal for loop control.

[0047] (1-7) In the formula, This is the control output for the nth sample. The error of the nth sampling is... Let be the error of the i-th sampling. This represents the change in error.

[0048] The step responses of the fuzzy PID of this invention and the traditional PID were tested respectively, and the results are as follows: Figure 6 As shown in the figure, for the fuzzy PID, the time for the current to rise to a steady value is 2ms, indicating a relatively fast current rise rate; while for the traditional PID, the time for the current to rise to a steady value is 61ms, indicating a significantly slower current rise rate. This demonstrates that the fuzzy PID of the present invention has a faster step response speed.

[0049] In summary, this invention, by real-time monitoring of the output current error and its rate of change, and by dynamically adjusting the parameters of the PID controller using fuzzy inference, enables the control system to automatically adapt to a wide range of operating conditions from no-load to full-load. This effectively solves the problem that traditional PID controllers may have optimal parameters at one load point, but may exhibit overshoot, oscillation, or sluggishness at other load points. Thus, it achieves a faster and smoother transient response across the entire operating range, significantly improving the accuracy of the output voltage / current of GaN-based power circuits and the stability of the system under complex operating conditions.

[0050] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0051] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the present invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above description. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A GaN-based power circuit fuzzy PID fusion control method, characterized in that, Include at least the following steps: Step 1: Design an initial PID controller, which has a high proportional term, a high integral term, and a small derivative term at the initial moment; Step 2: Using a fuzzy logic adaptive method, the parameters of the initial PID controller are adjusted online based on the output current error and the rate of change of the output current error of the GaN-based power circuit. Step 3: Through iterative adjustments, the optimal PID control parameters suitable for the GaN-based power circuit are finally determined.

2. The method of claim 1, wherein, The GaN-based power circuit is a synchronous rectified Buck circuit with soft-switching function, and the switching device of the GaN-based power circuit is a GaN-based MOSFET.

3. The method of claim 1, wherein, Before step 1, the process also includes: performing small-signal modeling on the GaN-based power circuit to obtain the control-output transfer function.

4. The method as described in claim 3, characterized in that, The control-output transfer function is specifically as follows: ;in, i o For output current, d Duty cycle, V in Input voltage, L For the original inductor of Buck circuit, L r The inductor added to achieve the soft-switching function C For circuit capacitors, R For circuit load, V D For output voltage, s It is a complex variable.

5. The method as described in claim 1, characterized in that, Designing the initial PID controller involves determining the zeros and poles of the compensation network, specifically: 1) Set the first zero-point frequency at 1 / 4 of the original open-loop system corner frequency; 2) Set the second zero-point frequency at half the corner frequency of the original open-loop system; 3) Set the pole frequency to at least 1.5 times the crossover frequency of the corrected open-loop system.

6. The method as described in claim 1, characterized in that, The input variables of the fuzzy logic adaptive method are the output current error and the rate of change of the output current error, and the output variable is the adjustment amount of the parameters of the initial PID controller; the parameters of the initial PID controller include proportional parameters, integral parameters, and derivative parameters.

7. The method as described in claim 6, characterized in that, The fuzzy subsets of the output current error, the rate of change of the output current error, and the adjustment amount of the initial PID controller parameters are all defined as {negative large NB, negative medium NM, negative small NS, zero ZR, positive small PS, positive medium PM, positive large PB}, and a triangular membership function is used.

8. The method as described in claim 6, characterized in that, The fuzzy logic adaptive method includes: 1) Fuzzification process: The output current error and the rate of change of the output current error are converted to the fuzzy domain by the first quantization factor and the second quantization factor, respectively; 2) Defuzzification process: The adjustment amount of the parameters of the initial PID controller obtained by fuzzy inference is converted back to the accurate output amount through the scaling factor.

9. The method as described in claim 8, characterized in that, The defuzzification process employs the centroid method and generates a control variable lookup table based on offline calculations, which is used to adjust the parameters of the initial PID controller online in real time.

10. The method as described in claim 1, characterized in that, The optimal PID control parameters are used as a calculation engine by the initial PID controller to generate the duty cycle of the switching transistor in real time for controlling the next cycle.

Images