  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.
