Research on static variable power supply control technology based on fuzzy PID

In recent years, with the development of power electronics technology, static variable power supplies are increasingly widely used in the fields of industry, military, medical treatment, aerospace, etc. In a certain type of surface-to-air missile weapon system, the static variable power supply is one of the main equipment and the source of electrical energy for the entire weapon system. Whether it can provide reliable and uninterrupted power supply directly affects the performance of the weapon system. Designing high-performance variable frequency power supplies is one of the current trends. The high performance of static variable power supplies is mainly reflected in good voltage regulation performance, high output voltage waveform quality, strong load adaptability, and good dynamic characteristics. In order to obtain high-quality sinusoidal output voltage waveforms, people apply modern control theory to the control of static variable power supply systems and propose many control methods based on modulation strategies.
The PID controller has a simple structure and strong robustness. It is currently widely used in many aspects. However, with the advancement of science and technology, the controlled objects are becoming more and more complex, and the traditional PID controller often cannot achieve good control effects. In order to improve the control effect of conventional PID and enhance the adaptability of the system, this paper designs a fuzzy PID controller that adjusts the control quantity of the system. Fuzzy control has certain advantages in overcoming the nonlinearity and time-varying nature of the system. In this paper, combined with the characteristics of the static variable power supply control system, the fuzzy PID control algorithm is used to improve the quality of the output voltage waveform of the static variable power supply, so that the system has good dynamic and static performance.



Once the parameters of the incremental PID controller are determined, they will no longer change, and the parameters have no ability to adapt to environmental changes. However, in the actual industrial production process, the output voltage of the static variable power supply is nonlinear, time-varying and uncertain, and the load it carries often changes, which makes the control object and the model mismatch. The parameters of the traditional PID controller are often poorly optimized and the control effect is poor. In order to overcome the defects of the traditional PID control system, a method combining fuzzy control and PID control is introduced to improve the tracking effect of the system and obtain the expected output.
1.2 Fuzzy PID control strategy
According to past experience, the quality of the output voltage waveform of the static variable power supply is closely related to the modulation wave signal. When the output voltage fluctuates greatly, if a conventional PID controller is used, its control performance may deteriorate or even become unstable. Therefore, in order to realize the adaptive ability of the controller, a direct voltage control method of the static variable power supply based on the fuzzy PID algorithm is proposed.
The fuzzy PID control principle of the static variable power supply is shown in Figure 1. After the error signal between the expected value and the actual output value is adjusted by fuzzy PID, the error signal is analyzed to generate a modulation wave, which is then modulated by a triangular carrier to generate a PWM signal to control the inverter bridge so that the system output signal approaches the expected value. The fuzzy PID control principle is shown in Figure 2.

The implementation of the fuzzy controller should first define the fuzzy set of input and output variables, determine the domain of each variable, and establish a fuzzy variable assignment table, that is, fuzzification; then, based on the experience accumulated in practice and learning, summarize several control rules, perform fuzzy reasoning based on the control rules, and use the maximum membership method to clarify the output.
1.2.1 Fuzzification
Single-phase inverter power supply adopts two-dimensional fuzzy control. There are three domains that need to be considered: output voltage deviation, deviation change rate and control quantity. Voltage deviation and deviation change rate are selected as input. Among them:

1.2.2 Determination of membership function and control rules of fuzzy control
Fuzzy control rules should be determined according to the dynamic and static characteristics expected by the system, that is, when the deviation is large, the main task of the control system is to eliminate the deviation. At this time, the weight coefficient of the deviation should be large; when the deviation is small, in order to reduce overshoot and make the system stable as soon as possible, the control quantity should be changed mainly according to the deviation change rate. At this time, it is required to increase the weight of the deviation change rate. The following explains the formulation of the fuzzy control rule table.
(1) According to the previous practical experience in the control process, a set of fuzzy conditional statements can be obtained. The language variable values ​​of the deviation and the deviation change rate are divided into 7 levels, and 7×7=49 fuzzy conditional statements can be summarized, which are described as follows:
if E=PB and EC=NB then U=ZO
if E=PB and EC=NM then U=ZO
if E=PB and EC=NS then U=NS
if E=PB and EC=ZO then U=NM
if E=PB and EC=PS then U=NB
…
(2) According to previous experience and repeated experiments, the triangular membership function form can be used to obtain the membership function of the deviation e, the deviation change rate ec and the control quantity U as shown in Figure 3. Based on this, the control rules that work in the corresponding domain are determined, and the fuzzy control state table shown in Table 1 is formulated.

1.2.3 Defuzzification and Implementation of Fuzzy PID Controller
In this design, when using CRI rule reasoning, the control process is to query the control rule table to generate the control quantity. By calculating all combinations of all elements in the domain of error signal e and error change rate ec, the output U of the fuzzy control quantity can be calculated, and the maximum membership rule is used for fuzzy decision-making, and U is converted into the corresponding determined quantity after clarification. The output control quantity obtained by looking up the table needs to be multiplied by the proportional factor Ku to obtain the modulation wave.
The characteristic of designing this fuzzy PID controller is that the control quantity of the system is adjusted by the fuzzy reasoning method according to the maximum deviation range of the input, and converted into PID control within the minimum deviation range. The conversion between the two is automatically realized according to the deviation range given in advance, so as to realize the automatic adjustment of the system control quantity.
2 Analysis of system simulation results
According to the above analysis, a control model is established in the Matlab/Simulink7.1 environment. Among them, the sampling period T is 0.001; the basic domain of the error is verified to be [-35, 35], the basic domain of the error change rate is [-5, 5], the basic domain of the control output is [-40, 40], and the fuzzification factors are inferred to be ke=0.2, ke=0.02, and kU=6.5; the PID parameters Kp=1.2, Ki=10, and Kd=0.000 5; the switching frequency is 3 kHz, and the input AC voltage is 380 V; the AC load voltage is 220 V/50 Hz, 120 000 kVA, and the output filter capacitor and inductor are 3 mH and 5 000 μF respectively; the output transformer parameters are 380 V/120 V, 250 000 kVA.
In the circuit simulation process, ordinary PID and fuzzy PID control are used to control the static variable power supply, the switch threshold is selected as 5 V, and three voltmeters measure the corresponding single-phase output voltage respectively. The simulation time is 0.5 s. The simulation results are shown in Figure 4.

According to the simulation results, the fuzzy PID control effect is obviously better than the PID control effect when the static variable power supply is started to stabilize the voltage, and the voltage can quickly recover to stability when the load is suddenly added for 0.2 s and disconnected for 0.3 s; the voltage fluctuation of PID control is large when the load is suddenly added and disconnected, and it takes a long time to recover to stability. It can be seen that the strategy of fuzzy PID control of static variable power supply combines the dynamic characteristics of fuzzy control and the steady-state performance of PID control, which greatly improves the steady-state and overshoot of the system, and improves the response speed and control accuracy of the system.
This paper introduces the fuzzy reasoning algorithm into the PID controller to solve the problem of slow convergence speed and low error accuracy of the PID controller in nonlinear systems. Through simulation experiments, the fuzzy PID controller controls the static variable power supply online, and its robustness and adaptability are strong, and it also has good interference suppression and regulation capabilities, which meets the requirements for the output of the static variable power supply. The simulation results of the entire system verify the correctness and feasibility of the fuzzy PID control algorithm applied to the static variable power supply.
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