The article outlines the methodology of structural and parametric synthesis of neural network controllers for controlling objects with limiters under incomplete information about the controlled object. Artificial neural networks are used to create controllers that are sequentially integrated into a control system with control objects. Reinforcement learning and pre-building a neural network imitator of the control object are used to synthesize the neural network controller. This approach is particularly effective when classical control system synthesis methods are not applicable due to significant nonlinearity and the difficulty in forming a mathematical model of the control object with the required accuracy. The proposed methods expand the class of technical systems for which direct synthesis of near-optimal control laws is possible. The robustness, adaptability and technical feasibility of neural network controllers make them interesting for practical applications. The main attention in the article is paid to the choice of neural network structure in the imitator and controller, formation of training samples taking into account the limitations of the control object.

翻译：本文概述了在控制对象信息不完整且存在限制条件下，用于控制对象的神经网络控制器的结构与参数综合方法。采用人工神经网络构建控制器，并将其顺序集成至包含控制对象的控制系统中。通过强化学习及预先构建控制对象的神经网络模拟器来实现神经网络控制器的综合。当经典控制系统综合方法因对象显著非线性及难以按所需精度建立数学模型而无法适用时，该方法尤为有效。所提方法扩展了可直接综合准最优控制律的技术系统类别。神经网络控制器的鲁棒性、适应性与工程可行性使其具有实际应用价值。本文重点研究了模拟器与控制器中神经网络结构的选择，以及考虑对象限制条件的训练样本生成方法。