# A Fast Calibration Method for Sensors of Atmospheric Detection System  
[PERSON]  
1 Research Institute for Frontier Science, Beihang University, Beijing 100191, China 12 Sch  
To test the performance of temperature sensors in ADS, we often place the sensor in a known environment where the temperature can be accurately measured. By testing the error of the sensor output with the known ambient temperature, the sensors' accuracy performance can be obtained [14; 15]. However, ADS operates over a wide range between \(-80\)\({}^{\circ}\)C to \(50\)\({}^{\circ}\)C [16]. The traditional calibration instrument adopts the thermostat open-loop calibration method, which has low calibration efficiency, low safety, and high energy consumption [17; 18]. It cannot meet the large-scale requirement nor the fast and high precision of ADS. A common way to increase the speed of sensor calibration is to replace manual calibration with automated equipment [19; 20]. In [21], an automatic and fast calibration method for sensors was proposed. However, this method only optimizes the calibration process and does not analyze the dynamic performance of the calibration system. In [22], an active control sampling method was proposed, which trains the system before sensor data collection. However, this method is unsuitable for situations with little prior knowledge of the sensor [23; 24; 25]. In [26], a fast dynamic calibration method was proposed, which predicts the sensor's transfer function according to the dynamic data during the calibration process and improves the efficiency of the sensor calibration process. However, this method has high requirements for the stability of the calibration system and low resistance to external interference. The above cases show that using automated procedures to calibrate the sensor can improve efficiency. However, there are still significant challenges in analyzing system dynamic performance and control strategies [27; 28]. To realize the automatic control of temperature sensor calibration, we must obtain the dynamic transfer function of the temperature sensor to complete the design of the system control strategy. In [29], dynamic models of temperature sensors in benchmark tubular reactors were built, and their sensitivity to external perturbations was analyzed. In [30], a dynamic model of a bridge temperature sensor powered by dual constant current sources was developed. The sensor was calibrated, and the results showed promising results in high-temperature areas. In [31], a dynamic model of a hybrid system for measuring temperature using an optical fiber and a bimetal element was developed, and the sensor was calibrated. Existing research has established a relatively complete dynamic model of the temperature sensor. However, the system environment in which the sensor is located is different. In ADS, the dynamic model established before is no longer applicable. In many essential projects related to people's livelihood, such as smart energy management [32], techno-economic feasibility of photovoltaic solar generation systems [33], and permanent magnet synchronous motor [34], many advanced control algorithms have been used to obtain more satisfactory results. For the calibration system, to control the cost, the control computer in the system can often only provide the classical PID control method [35; 36]. The key to the success of the PID control method is how to design the parameters of the control system [37]. The Ziegler-Nichols (Z-N) tuning method in [38], which can design the controller's parameters, requires some prior knowledge and manual tuning. The Transfer-Function-Based (TFB) method in [39] can adjust the controller's parameters according to the transfer function's characteristics to obtain better performance. The Fuzzy PID method in [40] can dynamically adjust the parameters of the PID controller during the system response phase. Many excellent PID controller design methods have been proposed in the existing research.