# A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode
## 1 Introduction  
In recent years, with the development of space technology both domestically and internationally, satellite technology has matured significantly. Its application fields have been expanding, playing an increasingly important role in national defense security [1], space exploration [2], and socio-economic fields [3]. With the development of the space industry and the complexity of satellite missions, there is an increasingly urgent need to accurately assess and monitor the health status of satellite components in real time [4, 5]. The study of satellite system effectiveness assessment can understand the satellite operation status in real time, and provide information support for the reasonable allocation of spaceborne resources and space-mission planning, which is also the basis for improving satellite work efficiency and optimizing mission design. The comprehensive performance evaluation of satellite systems has been of wide concern at home and abroad. Early research mainly focused on the entire satellite platform and established different performance evaluation models through various methods. [PERSON] et al. combined fuzzy theory with the analytic hierarchy process in order to establish an evaluation criteria framework for satellite performance and implemented this assessment method [6]. [PERSON] et al. derived performance indicators from the perspectives of primary functional performance, regulatory system performance and cost performance, and proposed a comprehensive methodology for assessing navigation satellite effectiveness [7]. [PERSON] et al. achieved the performance evaluation of geostationary orbit remote sensing satellites by establishing a solar illumination model that integrated with the ADCperformance evaluation model and satellite system characteristics [8]. [PERSON] et al. proposed an enhanced fuzzy reasoning performance evaluation method, which was based on the original single-factor evaluation index fuzzy reasoning. The method incorporated multi-factor comparison and analytic hierarchy process, achieving the performance evaluation of navigation satellites [9]. [PERSON] et al. developed a communication satellite performance evaluation model based on the GA-BP (genetic algorithm-back propagation) neural network method [10]. With the deepening of research work, in recent years, the performance evaluation research carried out by scholars has gradually focused on satellite structure, power supply, attitude control and other subsystems. [PERSON] et al. examined the effectiveness of remote sensing image segmentation for remote sensing satellites, and evaluated the performance of remote sensing satellite images by determining the correlation between the subjective and objective segmentation quality indexes [11]. Based on fuzzy mathematics and hierarchical analysis, [PERSON] established an index system for evaluating the performance of satellite navigation systems and illustrated the evaluation process and methodology with examples [12]. [PERSON] et al. used grey system theory to construct a multi-state system performance evaluation model for the inaccurate and insufficient data of the communication satellite system [13]. [PERSON] et al. used the hierarchical analysis method to calculate the weights of the indicators, established the ADC model and simulated the indicator values, and finally completed the comprehensive performance evaluation of the satellite system [14]. At the present stage, most of the performance evaluation methods of satellite systems are subjective, such as hierarchical analysis and ADC model, while the uncertainty of sensor measurement and the multi-source disturbance of the space environment are ignored. In order to solve the above problems, this paper takes the attitude control system of an earth observation satellite as an example. Firstly, it establishes the performance evaluation indicator set under the attitude tracking mode and characterizes the uncertainty of the performance index through the generalized grey number.