А. N. Karapeev, Е.Y. Kosenko, М. Y. Medvedev, V. K. Pshikhopov
2025-04-27
Abstract ▼
An algorithm for adaptive control of a DC motor based on the use of machine learning technology with reinforcement is proposed and investigated. An overview and brief analysis of the state of affairs in the field of intelligent motor control systems is given. A mathematical model of the DC motor is presented, and a structural scheme for training an intellectual agent is presented. An intelligent adaptive motor speed control system is proposed. The DC motor is represented as a black box with the limited input and output. The control system is based on a zero-order Q-learning algorithm. It is assumed that the output of the intelligent agent is a control applied to the motor input. The intelligent system uses a tabular approximation of the value of each of the control action. In this article, we study the effect of the discreteness of the representation of state, the set of control effects used, the applied rewards, and the parameters of the learning algorithm on the control error. The sensitivity of the control system to the parameters of the motor and an unmeasured moment is investigated. Based on the results of the study, a modified algorithm is proposed, which assumes the measurement or evaluation of the current of the motor stator. The control algorithm provides robustness to parameters and external disturbance. Additionally, the approximation of the control value function using polynomials and using a neural network are investigated
N.О. Luzhevsky , V. F. Lubentsov , Е.V. Lubentsova
2026-02-27
Abstract ▼
A methodology for analyzing a robust automatic control system for linear dynamic control objects with a delay in the uncertainty of information about the parameters of the mathematical model and interference in the measured signals is described. The system implements a widely used PID controller and a dynamic compensator (DC) for the inertial part of the control object, implemented using estimates of state variables obtained on the basis of a state observer (SO). It is noted that any stability criterion can be used for the asymptotic stability of the controller and observer, but to ensure the maximum degree of stability and the required quality indicators of the transient process, it is convenient to use the maximum degree of stability criterion. In this paper, instead of derivatives obtained by differentiation, it is proposed to use in the dynamic compensator of the inertia of the object estimates of the state variables of the object obtained with the help of a state observer. Another difference from the known ones is the use of sigmoid functions in the corrective feedback of the state observer and the implementation of an additional effect to the main one based on the estimation error, compensating for external disturbance at the input of the object. The parametric synthesis of a state observer was used to study the impact of disturbances and noise on the performance of an automated control system (ACS) for an industrial interval-defined plant with various parameters. A robust typical PID controller with optimal parameters for maximum stability was calculated, taking into account the compensation of the inertial part of the object. For this purpose, a dynamic compensator (DC) was implemented using the parameters of a nominal (calculated) model of the object with the worst combination of parameters obtained on the basis of the interval model of the control object with a delay. The conducted research has established that the structure of the ACS with a typical PID controller with a sequential dynamic compensator for the inertia of the object and a state observer with corrective feedback based on sigma functions ensures the simplicity of the ACS synthesis methodology that is robust to changes in the parameters of the object and the action of unmeasured disturbances and uncontrolled interference.
