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• 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.

• RESEARCH OF MARCHING PROPULSIONS THRUST CONTROL METHODS OF UNMANNED UNDERWATER VEHICLES

  V.V. Kostenko, N.A. Naidenko, I.G. Mokeeva, A.Y. Tolstonogov

  2020-07-10

  Abstract ▼

  The aim of the study is to assess advantages and disadvantages of existing methods for con-trolling thrust of main propulsions (MP) of unmanned underwater vehicles (UUV). The mathemat-ical model of the MP developed by IMTP FEB RAS was adopted as the object of study. It’s consist-ing of a set of models of an electric motor, propeller and thruster control unit. During the research the following tasks were solved: development of the mathematical model of a brushless motor with refine parameters based on results of its load tests; development of the mathematical model of a propeller based on its action curves determined in accordance with the PROPS model test regres-sion base; development of the mathematical model of an thruster control unit (TCU); simulation of reaction of the thruster for stepwise change of desired thrust with the open-loop regulation of elec-tromotive torque, with feedback on the frequency of rotation and on measured thrust. As the result of simulation main propulsion reaction on stepwise change of desired thrust in bollard pull mode it has been established that different types of thrust control are only differed in transient response time and static control error is almost non-existent for all types of control. Herewith, twofold de-crease in transient response time with thrust and frequency control was found over torque control. This is due to increased power consumption of the motor in the transition process. Modeling of the MP control at the counter flow caused by the movement of the underwater vehicle showed that the control with thrust feedback has the minimum static error and transient response time is compara-ble with the speed control.