Search

Search Results

• CONTROL SYSTEM DESIGN AND AUTONOMY FOR TWO-WHEELED MOBILE ROBOT

  А. А. Tkachenko, D.D. Devyatkin

  2022-04-21

  Abstract ▼

  Model Predictive Control is an advanced process control method that used while meeting a
  set of constraints. From an engineering point of view, the MPC method of designing control systems
  is attractive, because is relatively simple in design, including for solving complex production
  problems. This method is similar to the classical synthesis of a control system based on a linearquadratic
  controller (LQR). The key difference between MPC and LQR is that predictive control
  solves the optimization problem within a sliding time horizon, while the linear quadratic method
  used to solve the same problem over a fixed time window. The paper considers a method for constructing
  two-wheeled mobile robot control system using Model Predictive Control. The process of
  building a mathematical model of the mechanical system of the robot is given, as well as the linearization
  of the resulting model is performed. The basic principles of constructing a control system
  based on MPC for linear systems without external disturbances, as well as using an observer to
  assess the state of the model under the influence of additive white Gaussian noises, are presented.
  A variant of the synthesis of a control system with imposed restrictions on the input signal is considered.
  Also presented is a method for determining the position of a two-wheeled robot in space
  using a vision system, which is based on the use of a neural network. The architecture of the used
  model is given, as well as a stereo camera, which used to build an image depth map. In addition to
  the above, the work describes in detail the principle of the deep learning model – YOLOv3, which
  based on several blocks of input data processing. A detailed description of the implementation of a
  stereo camera in conjunction with an artificial neural network model using the Python programming
  language and libraries for working with video data and a stereo camera is presented.

• ANALYSIS OF TRADITIONAL AND NEURAL NETWORK-BASED CONTROL METHODS FOR ELECTRIC DRIVES IN ROBOTICS AND PERSPECTIVES OF HYBRID APPROACHES

  А. I. Tataurov , V.Е. Vavilov

  287-298

  2025-12-30

  Abstract ▼

  The objective of this study is to conduct a comparative analysis of traditional and neural network-based control methods for electric drives in robotics, with an emphasis on identifying their strengths and weaknesses, determining their areas of application, and assessing the prospects for the development of hybrid approaches. Effective control of electric drives is critically important for modern robotic systems, which must demonstrate high performance, reliability, and versatility in various application domains. Specifically, key challenges include high-precision trajectory tracking, energy-efficient control, robust control under uncertainties and disturbances, constraint-aware control, as well as synchronized and coordinated control of multiple electric drives. In this regard, optimizing the control of electric drives to ensure motion accuracy, energy efficiency, and adaptation to changing conditions becomes a top priority. To achieve this goal, the study systematizes and analyzes the characteristics and applications of traditional electric drive control methods, such as PID controllers, Kalman filters, sliding mode control, and model predictive control. It also examines key neural network-based approaches to electric drive control, including feedforward neural networks, recurrent neural networks, radial basis functions, neuro-fuzzy systems, and reinforcement learning. A comparative analysis of these methods is conducted to identify their advantages and limitations based on key parameters such as trajectory tracking accuracy, robustness to disturbances and uncertainties, adaptability to changing operating conditions, and computational complexity. Additionally, the study investigates and assesses the prospects for hybrid electric drive control methods that combine the reliability and control quality of traditional methods in linear and structured environments with the flexibility and adaptability of neural network-based methods in complex and dynamic robotic systems. The study’s key findings indicate that traditional electric drive control methods, such as PID controllers and sliding mode control, remain effective and preferable in linear and well-defined systems due to their simplicity and reliability. At the same time, neural network-based approaches demonstrate significant advantages in controlling complex nonlinear systems, as well as in uncertain conditions requiring adaptation to changing environments. Special attention is given to hybrid control methods, which integrate the strengths of both traditional and neural network-based approaches. These methods are regarded as the most promising and advanced direction, enabling the development of intelligent and robust electric drive control systems capable of operating efficiently in complex and dynamic environments.

• NEURAL NETWORK APPROXIMATION OF MODEL-PREDICTIVE CONTROL FOR A DYNAMIC OBJECT STABILIZATION SYSTEM

  B.А. Komarov , S.V. Leonov , Т.Е. Mamonova

  276-287

  2025-12-30

  Abstract ▼

  Relevance. When solving problems of stabilization of dynamic objects, classical model predictive control is widely used. It provides high quality control by solving the optimization problem at each step, but it has significant computing costs, which limits its application in real-time systems with high requirements for update frequency. Therefore, the question of investigating the applicability of a neural network regulator trained on a model predictive regulator (MPC) when solving the problem of stabilizing the position of a dynamic object with a limited computational and time resource is relevant. Goal.  The purpose of the presented work was to develop and study a neural network regulator trained on the basis of an MPC regulator to stabilize the position of a dynamic object on a mobile platform. Methods. When performing the work, methods of system analysis, simulation modeling, as well as experimental tests on the bench were used. Results and conclusions. As part of the study, a neural network regulator was developed and trained that approximates the behavior of MPC based on data obtained when controlling a real balancing platform. The training was conducted on the input and output data of the MPC without using the internal model of the system, which made it possible to reproduce the dynamics of the regulator at significantly lower computational costs. Experimental results showed that the neural network model provides a stabilization quality comparable to the original MPC, while the calculation time was reduced from 47 ms to
  1.6 ms, which amounted to an acceleration value of 29 times. The proposed approach demonstrates the potential of neural network control methods in the problems of replacing complex optimization regulators for systems with limited computing resources.