## 1 Introduction  
With the rapid advancement of photoelectric technology, electro-optical telescopes are increasingly being integrated into vehicles, ships, aircraft, and spacecraft for diverse missions, such as optical communication, aerial photography, astronomical observation, and antenna stabilization [1; 2; 3; 4]. Control systems of moving-platform electro-optical telescopes (MPEOTs) require high-performance pointing directions to achieve accurate target acquisition, tracking, and positioning [5]. Once given the position of a target, the control system performs a series of coordinate transformations, which computes the angles necessary to point the telescope at the object. However, the actual line-of-sight (LOS) of MPEOT deviates inevitably from its desired pointing in practice [2], namely a pointing error, which impairs the efficiency of the target acquisition and tracking as well as the imaging quality. Therefore, to improve the MPEOT's overall performance, error modeling and pointing correction techniques should be investigated thoroughly. Errors affecting pointing arise during the manufacture, assembly, installation, and operation of the telescope, including machining errors in components, geometrical variations induced by misalignment and improper installation, inaccuracy of encoders, servo errors, environmental errors, etc. [6]. In general, errors from a particular source have two components: a deterministic part and a random part [7]. The paper contributes to understanding the calibration of the repeatable systematic errors, while the remaining nonlinear errors are not covered in this work. Pointing error correction is generally classified into two types: hardware calibration and software correction [8]. The former acquires the angular deviation and coordinatetransformation matrix between the components using external devices, such as calibration methods with the use of theodolites [9; 10] and other measuring equipment [11], so as to be compensated in the control system. However, the resources needed are significant and not necessarily effective. Based on error analysis and modeling, the latter estimates the systematic errors and deducts them from the current pointing value in real time, then it updates position commands that control the telescope axes to compensate for pointing errors. In contrast to hardware adjustment, software rectification significantly increases pointing accuracy while reducing the development and cost; it is the research focus of this article. Considerable progress has been made in the field of error modeling by directly considering parameters that are highly related to performance. [PERSON] et al. investigated the relationship between pointing error and temperature distribution for inter-satellite communication [12]. In some optical communication systems, pointing errors are avoided or reduced by focusing on system-wide performance metrics, such as the received signal power and Strehl ratio [13]. [PERSON] et al. emulated the effects of pointing error angles on the signal-to-noise ratio to optimize the pointing process [14]. For ground-based telescopes, the pointing error model can be expressed traditionally as a nonlinear function of azimuth and elevation [15], which can be further divided into two categories. One approach relies solely on numerical analysis and adopts an empirical model to explain the observed differences. A single function can be used to fit the error-correction surface across the entire sky or the sky can be broken down into regions and different functions used for each, such as the spherical harmonics function model and generalized extended approximation model [16; 17]. Analogously, [PERSON] [18] and [PERSON] [19] proposed the use of the backpropagation (BP) neural network model and radial basis function neural network model, respectively, tested and applied in the mobile laser ranging station. Although this strategy is simple to apply, the stability of the model is poor due to excessive parameters and the high correlation between them.