Real-Time Short-Term Delay Compensation for Optoelectronic Tracking Control System Under Uncertainty of Target Model

To address various short-term delay issues caused by detectors, fast steering mirrors (FSMs), and lasers in the photoelectric tracking system (PTS) based on a moving platform, this article presents a prediction method that integrates an improved tracking differentiator (TD), an adaptive zero-order hold (AZOH), and a Markov probability correction model. The method utilizes the tracking differentiator with speed feed forward and rapid exponential integral function (SFREITD) to extract velocity information from delayed data and enables initial prediction of global stochastic data. Additionally, the AZOH is introduced to address the issue of data prediction failure when the velocity is zero. Finally, the Markov probability model is applied to correct the prediction results. The superior performance of SFREITD has been fully verified through time-domain simulations in MATLAB. Meanwhile, based on a single-detector photoelectric platform, the prediction trajectories and prediction errors of inertial spatial angles are presented. The results demonstrate that the proposed scheme offers greater practical application value than traditional prediction methods such as segmented data fitting and long short-term memory (LSTM) networks and can effectively resolve the delay problem of global stochastic data.