In this paper, a real-time optimal attitude controller is designed for staring imaging, and the output command is based on future prediction. First, the mathematical model of staring imaging is established. Then, the structure of the optimal attitude controller is designed. The controller consists of a preprocessing algorithm and a neural network. Constructing the neural network requires training samples generated by optimization. The objective function in the optimization method takes the future control effect into account. The neural network is trained after sample creation to achieve real-time optimal control. Compared with the PID (proportional-integral-derivative) controller with the best combination of parameters, the neural network controller achieves better attitude pointing accuracy and pointing stability.

中文翻译：

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基于神经网络的凝视成像实时最优控制

本文针对凝视成像设计了一种实时最优姿态控制器，其输出命令基于未来的预测。首先，建立凝视成像的数学模型。然后，设计了最优姿态控制器的结构。控制器由预处理算法和神经网络组成。构建神经网络需要训练通过优化生成的样本。优化方法中的目标函数考虑了未来的控制效果。在创建样本后对神经网络进行训练，以实现实时最佳控制。与具有最佳参数组合的PID（比例-积分-微分）控制器相比，神经网络控制器具有更好的姿态指向精度和指向稳定性。