1. Introduction Flutter flight testing has always been a crucial aspect of aircraft flight testing because it directly impacts flight safety. In flutter flight testing experiments, the flutter excitation system is one of the most important pieces of equipment. The DC servo system, as the drive unit, is a technically challenging and risky component in the development of the flutter excitation and analysis system, involving a series of issues such as synchronous control and small-scale special permanent magnet brushless DC servo motor technology. This paper uses LabVIEW 7 software as the development platform and leverages LabVIEW's powerful data acquisition capabilities and its PID and Fuzzy logic toolboxes to design a virtual instrument-based controller for this servo system, achieving synchronous control of the two motors. [b]2 Design of a Virtual Instrument-Based Synchronous Servo System Controller[/b] 2.1 Composition of the Synchronous Servo System Position-Velocity Dual-Closed-Loop DC Servo System Block Diagram The block diagram of the entire flutter exciter's DC servo system is shown in Figure 1. This DC servo system mainly realizes synchronous control of the two motors, including real-time position synchronization, speed synchronization, differential synchronization, and speed following functions, employing dual-closed-loop control. The outer loop is a position closed loop, which uses the counter of NI's PCI6221 data acquisition card combined with a photoelectric encoder to detect the position of the motor rotor. Introducing a position closed loop allows for convenient adoption of advanced control algorithms and enables the position difference to be converted into a control signal through a synchronization control algorithm to ensure synchronization accuracy. The inner loop is a speed closed loop, which uses an Mc33039 chip to detect the rotor speed. Introducing a speed closed loop improves the response speed of the DC servo system and significantly reduces the adverse effects of system parameter changes, suppressing the negative effects of nonlinearity such as friction and backlash, resulting in high anti-interference performance. 2.2 Controller Design 2.2.1 Overall Design As the core of the servo system, the controller centralizes, analyzes, and processes the detection information from various sensors and external input commands, issuing corresponding instructions according to a certain program to control the orderly operation of the entire system. Therefore, it undoubtedly plays a very important role in the overall system performance. PID control algorithm is a widely used control strategy in industrial control. Traditional PID controllers have advantages such as simple principle, easy design, easy adjustment, and good steady-state performance. They are easy to tune to the optimal control effect for systems with linear and deterministic models. However, the synchronous servo system of the chatter excitation system in this paper consists of two brushless DC motors, both of which are PWM speed-controlled. The relationship between the speed and the speed control voltage is obviously nonlinear. Therefore, a large number of experiments were conducted under different speed control voltages to determine the rotational speed. Then, a basic mathematical model was established using the speed control voltage and speed data. Secondly, the control signal formed by the difference between the two position signals was sent into the control algorithm. Finally, based on the traditional PID controller, fuzzy set theory was applied to design a fuzzy PID controller based on a simple model, which can easily realize online self-tuning of parameters to achieve a more ideal control effect. 2.2.2 Mathematical Model of Motor 2.2.3 Synchronization Algorithm Implementation (1) Piecewise Linearization of Speed-Voltage Relationship The two motors, A and B, in this synchronous servo system are both brushless DC motors with basically the same design parameters. The motor power supply voltage is 15V DC, and PWM speed control is used. Under normal power supply conditions, when the input voltage signal at the PWM terminal is greater than 1.4V, the motor starts to rotate. As the speed control voltage signal increases, the motor speed begins to increase, but it is obvious that the speed and speed control voltage are not linearly related. To this end, the relationship between motor speed and speed control voltage was first determined, and then piecewise linearized to establish an approximate linear relationship between motor speed and speed control voltage within a narrow range. Based on experimental data, the approximate linear relationship between speed control voltage (V) and speed was calculated for both groups of motors (A and B) at different speeds (r/s), as follows: Using this approximate relationship, the voltage setpoints for motors A and B at a fixed speed can be determined. Then, using the counter of the PCI6221 data acquisition card, the number of photoelectric encoder pulses (1024 square wave pulses per motor revolution) was acquired through LabVIEW programming, and the exact motor speed at this time could be calculated. This speed was then used to obtain the corresponding actual speed control voltage value through the above approximate relationship. Finally, the difference between the set voltage value and the actual voltage value was used for PID control. Since the proportional gain coefficient and integral gain coefficient corresponding to different stages of motor speed are different, and the proportional gain coefficient and integral gain coefficient corresponding to the rising and steady stages of the same speed stage are also different. In order to achieve the ideal control effect, the proportional gain coefficient and integral gain coefficient are initially determined by using the motor model and simulation experiment results; then, the proportional gain coefficient and integral gain coefficient are continuously adjusted through experiments to determine the most suitable proportional gain coefficient and integral gain coefficient for different stages; finally, a fuzzy rule base is established using fuzzy set theory to realize parameter self-tuning. (2) Establishing a fuzzy PID controller. In the LabVIEW front panel or control panel, the fuzzy logic controller design sub-option can be opened under the tools menu to easily design and modify the membership function, rule base, inference rules, etc. of the fuzzy controller. The design results are saved in a file ending in .fc for use in the application. The Fuzzy controller subroutine under Control is used to implement the fuzzy control algorithm in the program. The Load fuzzy controller under Control loads the file ending in .fc into the application and loads the PID parameters of the specified file into the fuzzy controller of the application. The three are closely connected and interlocked, which can conveniently and intuitively complete the design, editing and loading of the fuzzy controller. (3) Although a preliminary model for synchronous control has been established and fuzzy PID is used for adjustment, in order to further improve the synchronization of the servo system, the position or speed difference between the two motors is multiplied by an appropriate coefficient to form a small value. The voltage setpoint is reduced for the faster motor and increased for the slower motor to achieve synchronous control of the two motors. Since the motor is more sensitive to voltage signals at lower speeds, the coefficient can be set smaller at this time, and larger at high speeds, but it cannot be set too large in either case, otherwise it will lead to motor instability. [b]3. Experimental Results[/b] This synchronous servo system completed four experiments: speed following, speed synchronization, position synchronization, and differential synchronization. Speed ​​following includes three types: constant speed following, linear following, and linear following with step signals. In Figure 3 below, the white lines in a, b, and c represent the setpoint, the red ● represents the experimental result of motor A, and the green × represents the experimental result of motor B; the white lines in d and e represent the actual difference; the white line in f represents the setpoint, and the red ● represents the actual difference. [b]4. Experimental Data Processing and Analysis[/b] (1) For the input digital signal, the servo system can achieve real-time speed following of the waveforms shown in Figure 3a, b, and c. (2) It can achieve real-time speed synchronization of two motors, closed-loop control, as shown in Figure 3d, where the absolute error does not accumulate, the relative speed difference does not accumulate, and it can be controlled within 10 r/min. (3) It can achieve real-time position synchronization of two motors, closed-loop control, as shown in Figure 3e, where the absolute error does not accumulate, the relative angle difference does not accumulate, and it can be controlled within ±6°. (4) It can achieve that after one motor rotates to a given angle in advance, the other motor starts to rotate, and then the two motors maintain the constant phase difference and run synchronously, as shown in Figure 3f, with the error controlled within ±4°. [b]5. Conclusion[/b] The controller designed using the combination of the piecewise linear method, fuzzy PID control theory, and synchronous control method mentioned in this paper can achieve the control requirements of the synchronous servo system. Using these methods and theories, we can continue to develop towards lower or higher speeds, thus achieving synchronous control of dual motors even in a wider speed range. In addition, since the algorithm of the controller in this paper is relatively complex, the program size is large, and it runs on the platform of the operating system (accuracy can only reach the millisecond level), the execution cycle alone is close to 10 milliseconds. Therefore, our adjustment frequency is low at 20Hz. If we can find a way to increase the adjustment frequency, we believe that the control effect will be further improved. References: [1] Zhou Ziquan. Flight test of modern fighter jets [J]. Journal of Beijing University of Aeronautics and Astronautics, 2003, 12: 1110-1114 [2] Lu Ning, Zhou Wei. Design of intelligent PID controller based on LABVIEW [J]. Microcomputer Development 2005, 4: 66-68 [3] Chen Boshi. Electric drive automatic control system - motion control system [M]. Beijing, Machinery Industry Press, 2005 [4] National Instrument Corporation. PID Control toolset user Manual [M]. 2001 Editor: He Shiping