Ji Yanhua, Sun Yukun (College of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China) Designed and applied adaptive fuzzy PID control theory in the design of speed ring. A three-phase 6/4-pole SRM was used as a control object and tested. The test results show that the system has good speed control performance and control characteristics.
The Switched Reluctance Motor Speed ​​Regulating System (SRD) is a new type of speed governing motor system composed of a double salient-pole switched reluctance motor (SRM), a power converter, a controller, and a detector. Compared with traditional DC and AC speed control systems, SRM not only maintains all the advantages of asynchronous motors, but also has simple motor structure, convenient control, reliable operation, low cost and high efficiency. In particular, the switched reluctance motor is increasingly widely used for the traction of electric vehicles because of its characteristics such as large starting torque, good speed control and control performance, and direct power supply from the DC power supply to the drive system.
Due to the non-linearity of the SRD system, the control performance is not ideal when using an ordinary fixed-parameter PID regulator for speed closed-loop regulation. Fuzzy control is an intelligent control method that is widely used at present, and a better control effect can be achieved without an accurate mathematical model of the controlled object. Its characteristic is that when the working conditions change in a wide range, the dynamic response is fast, the overshoot is small, and it has good robustness. However, in the speed regulation of an electric vehicle, it is required that the response is faster and more stable, so that the steady state error of the entire controlled object can not be minimized, and thus it is difficult to perform the task. This paper designs a self-adaptive fuzzy PID controller, which has the advantages of flexible control and flexible PID control.
The basic composition of the SRD is as shown. Where S is a three-phase 6/4-pole double salient structure. The current detection uses a resistive sampling isolation method and PI followers improve follow-up. The position signal obtained by the photoelectric rotor position sensor is given by the frequency converter I speed as the pro-controller! one! Power - for * 1I logic control hj | current detection | Basic composition of the SRD LM2917 indirectly measured speed, and the position signal by logic conversion state accuracy is higher, the general fuzzy controller due to variable domain set ic touch power switch Control letter ht. Speed ​​closed-loop adoption / adaptive model i.net paste PID control algorithm.
In the SRD, the power converter is an important part of the entire system. By operating the control output signal in the on-off state, the power of the power supply is provided to each phase winding in a suitable period to drive the rotation of the rotor. Therefore, the design of the power converter must be considered together with the motor and controller so that they can work together. This article uses the main circuit of the double-switch power converter as shown, with K-BT as the main switching device. The freewheeling diode of the switched reluctance motor converter requires a good switching characteristic and a high practical working efficiency, so a fast recovery diode is used. There are many dedicated driving circuits for IGBTs. This article uses IR's floating driver IC IR2130.IR2130 requires only one power supply, which overcomes the disadvantages of conventional drive circuits that require multiple isolated power supplies, and has overcurrent and undervoltage protection, greatly simplifying Hardware design improves reliability.
The main circuit of the power converter switch reluctance motor control is a reasonable change in the control parameters to achieve operational requirements. The traditional method is to limit the phase current by the current chopping control mode (CCC) at the start and low speed, and to use the fixed angle adjustment control (APC) of the Hof adjusting Hn at high speed. However, due to the existence of mode conversion, the control is complicated and the cost is high. PWM technology is used to fix the conduction angle, and the main switch of the speed regulation system is chopped to realize voltage regulation and speed regulation, and single mode control is realized in the entire speed range. PWM can introduce the control pole of a main switch of each phase, namely so-called single-pipe, can also introduce the control pole of two main switch tubes of each phase at the same time, namely so-called double-pipe. The single-tube operation is superior to the dual-channel, so the single-channel is used here. In the main circuit of each phase of the main circuit, the trigger signals of the lower switches S4, S5, and S6 are modulated by the PWM signal to realize the speed regulation of the system. control.
Self-adaptive fuzzy PID controller design The self-adaptive fuzzy PID controller takes the error e and error change ec as input, which can meet the requirements of different time offset e and deviation change ec to PID parameter self-tuning. The use of fuzzy control from the stability of the system, response speed, overshoot and steady-state accuracy to consider all aspects of characteristics, and Kd each parameter has its own different role. The role of the proportional coefficient is to speed up the system's response speed and improve the system adjustment accuracy. However, if KP is too large, overshoot will occur and even lead to system instability. The role of the integral action coefficient Ki is to eliminate the system's steady state error. However, if K/ is too large, the saturation of the integral will occur in the initial stage of the response process, causing a large overshoot of the response process. The role of the derivative action coefficient Kd is to improve the dynamic characteristics of the system. Its role is mainly to suppress the deviation in any direction during the response process, and predict the deviation change in advance. However, if Kd is too large, the response process will be braked prematurely and the adjustment time will be extended.
According to the influence of the parameters Kp, Ki and Kd on the output characteristics of the system, it can be concluded that for different Iel and Iecl, the self-tuning requirements of the controlled process on the parameters KP, Ki and Kd are that when the Iel is large, in order to speed up the system The response speed should be larger Kp and smaller Kd. At the same time, in order to prevent the saturation of the integration and to avoid a large overshoot of the system response, the integral action should be removed and the overshoot of Ki=0 should be reduced. Take a smaller value of Ki, the size of Kp and Kd should be moderate to ensure the system's response speed.
When Iel is small, in order to make the system have a good steady-state performance, a larger Kp and Ki should be taken, and in order to avoid the oscillation of the system near the set value, the value of Kd should be properly selected, the principle is: when Iecl In hours, the Kd value can be larger, usually taken as medium size; when Iecl is larger, Kd should be smaller.
According to the rotor position signal, the frequency converter is used to obtain the rotation speed of the rotor, and then the rotation speed is compared with the given rotation speed to obtain the deviation e of the rotation speed and the rate of change of the rotation speed deviation ec. The control quantity is then obtained by the fuzzy control algorithm according to e and ec. KP, Ki, and Kd to achieve parameter adjustment of the PID. Sampling calculation of the current speed deviation e and the rate of change of speed deviation ec as input to the fuzzy controller. PB denotes concepts such as negative large, negative medium, negative small, zero, positive small, median, and positive, and their universes are all selected from 13 quantitative levels. That is, e and ec can be determined based on experience and the actual relationship between parameters and deviations. The degree of membership assignment, as shown in Table 1.e = Table 1 Fuzzy membership values ​​of the e and ec membership tables. Based on the self-tuning requirements of parameters and Kd, the following state combinations = M and 丨ec丨 = are used. Therefore, the following fuzzy inference forms are used ( A total of five fuzzy inference rules) Weighting of e丨 = and Kd in different states. Kp/, Ki/, and Kd/ are the setting values ​​obtained by the conventional PID parameter setting method for the parameters Kp, Ki, and Kd, respectively, in different states.
The degree of membership of each fuzzy inference rule can be obtained by the following formula: The three parameters of the PID are calculated according to the following reasoning formula. The speed response curve shows that the speed regulation system with adaptive fuzzy PID controller has good dynamic speed control performance. 3003000r/min reliable adjustment. Ube, Ume, Use and Ubc, Umc, use are e and ec respectively. This paper applies the adaptive fuzzy PID control algorithm with strong robustness to the control of the low-power switched reluctance motor for electric vehicles. , And achieved a good control effect, indicating that fuzzy PID has the advantages of both fuzzy control and PID control, is an effective control algorithm, with a wide range of application value.
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