In order to meet the requirements of ground radar, the equipment is small in size, easy to disassemble and light in weight. In this paper, the core of the system is PICI8F2431 single chip computer and the servo motor is brushless DC motor.
Aiming at the related problems, a software servo method is proposed to form a full digital high precision servo system. Because of the influence of non-linear factors such as mechanical resonance, moment coupling and electrical parameter fluctuation, the improved anti-integral saturation PID controller and fuzzy rules are adopted. It has been proved that the system has high control accuracy and fast response speed. It has important reference value for designing X-type servo system with strict weight restriction. Radar, also known as radio positioning, uses radio methods to find targets and determine their spatial location.
Radar is an electronic device that detects targets by means of electromagnetic waves. The core component of radar system is target tracking system, which can search, capture and track targets. Most of the radar servo controllers in China are DSP, but these servo controllers have relatively complex structure, large volume, lack of scalability and other issues, which can not match the requirements of radar system under current conditions. The brushless DC motor control system based on single chip has the characteristics of small size, light weight, simple structure, fast start-up, high reliability and convenient maintenance. This paper mainly introduces X-type radar servo platform based on ST company and PIC18F2431 intelligent power driver chip L6235, and designs hardware and software for the system. Whether it is the choice of mechanical structure, servo components or the choice of main control chip, the design process strives to be simple, compact and reliable. According to the characteristics and accuracy requirements of X-type radar servo system controller, an improved anti-integral saturation PID controller and a fuzzy rule PID controller are designed. Radar antenna servo system controls the movement of antenna and accomplishes various dynamic and static performance indicators. The main body of radar antenna consists of two degrees of freedom, namely the pitch axis and the azimuth axis. The two degrees of freedom have different ranges of action. The function of the pitch axis is mainly to achieve position servo in the range of accuracy. The azimuth axis has a larger range, which requires not only the function required in the pitch axis, but also the fan sweep in a certain range of accuracy at different speeds and in different ranges. The structure of the dual-axis antenna servo platform is shown in Figure 1. Because there are some non-linear and uncertain factors in the system, such as fluctuation of electrical parameters, mechanical resonance and moment coupling, we mainly adopt an improved PID controller with anti-integral saturation, and the controller effectively combines fuzzy rules. Fig. 2 describes the main principle of the system servo system. The control strategy is mainly three-loop control, in which the feedback of position is set to the feedback position of photoelectric encoder, the feedback of speed is set to the differential of the feedback position of photoelectric encoder, and the feedback of current is set to the bus current induced by intelligent power module L6235. In the first part, we describe the overall structure of the controller hardware. The core part of this kind of controller is PIC18F2431, which drives the motor to rotate mainly through related driving chips. The linear potentiometer provides the analog signal of the pitch axis position of the antenna to the controller, which is converted into digital quantity by AD. The absolute photoelectric encoder provides the digital signal of the azimuth position of the antenna for the controller. The azimuth scanning speed is calculated by measuring the Hall signal period of BLDCM. The D/A conversion module outputs reference voltage control proportional to the azimuth scanning speed of antenna speed. Through digital isolation, RS422 and antenna controller communicate, and some control can be achieved by manual keyboard. The two driver chips of the controller include the main power supply circuit and the related drive control logic. The built-in over-current protection circuit makes the motor control simple and convenient. In the design of hardware circuit, PIC18F2413 single-chip computer is used. The origin of the single-chip computer is micro-core company. Brushless DC motor is chosen as hardware circuit motor. The driver mainly adopts intelligent power driver chip L6235, which is produced by ST company. The control input of the chip is compatible with CMOS/TTL logic microprocessor. The output level is DMOS three-phase bridge circuit.
It has PWM, forward and backward control, braking control and other control functions. It also has over-current, over-heat and under-voltage. Protection functions such as cross-conduction. By introducing the PID controller which combines the fuzzy rules and the improved anti-integral saturation into the design of the PID software, on the one hand, the flexibility of program debugging can be further improved, on the other hand, the system performance can be greatly improved. The control principle is shown in Figure 4. The determination of the fuzzy rule table mainly depends on the error and error variation, and then the proportion, integral and differential parameters of the PID and the change trend are determined by the data shown in the fuzzy rule table. Most of the traditional radar servo systems are controlled by PID (proportional, integral and differential), which has the advantages of simple control principle, high precision, easy to use and strong adaptability. It can provide satisfactory control performance for many control objects. However, with the gradual development of modern warfare, the complexity of radar antenna servo system is greatly improved, but its accurate mathematical model is difficult to obtain, and the process of selecting the optimal parameters of the PID controller becomes more and more troublesome; and with the gradual increase of the complexity of the battlefield environment, the adaptive parameters of the fixed controller become worse and worse. Therefore, to combine the advantages of fuzzy control and traditional PID control, a fuzzy adaptive PID controller with high fastness, robustness and control precision is designed. It can not only modify the parameters of the PID controller online, but also adapt to the changes of the parameters of the servo system. In order to improve the performance of radar effectively, the closed-loop control structure composed of speed loop, position loop and current loop is mostly used as the main control structure in radar servo system. The main structure is shown in Fig. 5. The current loop is the inner loop of the servo system, and its regulator is needed to track the change of the given current quickly. Because the parameters of the internal circulation system are clearer and the system operation changes little, the traditional PID control is chosen as the control algorithm of the current regulator.
The position of the speed loop is in the secondary outer loop of the servo system. In order to improve the smoothness of operation, thermostatic element traditional PID control can be used. The position of the position loop is located in the outer loop of the servo system, and its regulator can track the given position quickly and accurately. The influence of system parameters variation has good robustness. Therefore, a fuzzy adaptive PID controller is designed in the position loop to improve the performance of the system. Fuzzy adaptive PID controller requires self-tuning of PID parameters by error E and error variation Ec, and uses fuzzy control rules to correct PID parameters online. The structure is shown in Fig.
6. The design of the fuzzy adaptive PID controller is to find out the relationship between Kp, Ki, Kd and E and the Ec of the PID parameters. By continuously detecting E and Ec during operation, three parameters are calibrated online according to the principle of fuzzy control. In order to meet the different requirements of different E and Ec control parameters, the control object has good dynamic and static performance. The function parameters of the PID controller are as follows: The function of the proportional coefficient Kp is to speed up the response speed of the system and improve the adjustment accuracy of the system. The higher the Kp value is, the faster the response time of the system is, and the higher the adjustment precision is. However, the higher the Kp value is, the more unstable the system may be. If the Kp value is too small, the adjustment precision will be reduced, the response speed of the system will be slowed down, and the adjustment time will be prolonged, so that the steady-state and dynamic characteristics of the system will be worse. The integral coefficient Ki is used to eliminate the steady-state error of the system. The larger the Ki, the faster the static error of the system. But if the Ki is too large, the integral saturation may occur at the beginning of the response process, which results in the overshoot response process being too large. If the Ki is too small, the static error of the system will be difficult to eliminate and the adjustment accuracy of the system will be affected. The effect of differential coefficient Kd is to improve the dynamic characteristics of the system, mainly by changing the prediction deviation in advance in the course of deviation in any direction. But the Kd is too large, which will lead the braking response process ahead, thus prolonging the adjustment time and reducing the anti-interference performance of the system. According to the above characteristics, the control rules of the fuzzy inference controller are reasoned as follows: first, if the deviation E value is too large, the larger proportion coefficient Kp and the smaller differential coefficient Kd should be selected in practice; second, when the deviation E value is medium, the proportion coefficient Kp is used to help the system achieve relatively small overshoot response. In this case, the differential coefficient Kd will have a greater impact on the system, and its value should be appropriate to ensure that the system has a good response speed; Third, when the deviation E value is close to the set value or the deviation value is small, the value of the integral coefficient Ki should be reduced as much as possible, while the value of the proportional coefficient Kd should be increased as much as possible, when the deviation variation Ec is small. When the numerical value is small, the value of the proportional coefficient Kd should be as large as possible, whereas the value of the proportional coefficient Kd should be as small as possible.
Fuzzy logic toolbox is used in Matlab. Fuzzy controller is designed by editing FIS files. The fuzzy controller is a dual-input three-output controller with input variables E, Ec and output variables Kp, Ki and Kd. The input and output variables take seven linguistic values, and their fuzzy subsets are {NB, NM, NS, ZO, PS, PM, PB}. E and Ec are the same as [3,3]. Since the parameter value is determined only by the absolute deviation, if the absolute deviation is the input language variable, the accuracy and sensitivity of variable classification can be improved. In the design of Mamdani type fuzzy controller, the MAX-MIN method is chosen for the fuzzy reasoning, the anti-fuzzy method of weighted average method is adopted, the membership function of variables is triangle, and each value adopts the same width range.