Physical rigidity and mathematical correctness of the intelligent robot model: adequacy to a physical object and accuracy of motion dynamic system equations - method of deep machine learning based on Lagrangian neural networks

On the example of a well-known laboratory system represented as a mechanical, dynamically unstable control object - cart-pole, the main approaches and simplifications in creating a nonlinear mathematical model and the structure of computer modeling of its control system are considered. Methods for creating a mathematical description using simplifications of the physical model and its parameters, as well as aspects related to the mathematical accuracy and physical rigor of the dynamic object mathematical description and use of technologies for creating intelligent self-learning control systems are discussed.