Peer-reviewed articles 17,970 +



Title: CONTROL SYSTEM REDUCING ENERGY CONSUMPTION OF MANIPULATOR DRIVES BASED ON REINFORCEMENT LEARNING

CONTROL SYSTEM REDUCING ENERGY CONSUMPTION OF MANIPULATOR DRIVES BASED ON REINFORCEMENT LEARNING
Patryk Balazy; Pawel Knap; Tymoteusz Turlej; Artur Stefanczyk
10.5593/sgem2023/2.1
1314-2704
English
23
2.1
•    Prof. DSc. Oleksandr Trofymchuk, UKRAINE 
•    Prof. Dr. hab. oec. Baiba Rivza, LATVIA
Determining the optimal trajectory of a manipulator is a mathematically complex part of control due to the time-consuming determination of inverse kinematics, the complexity of which increases as the manipulator's degrees of freedom increase. The control of manipulator actuators is based on algorithms focusing on the shortest possible control time and zero position deviation. Values such as power consumption, which affect the growth of the carbon footprint, are not taken into account. For this reason, alternative control methods are being investigated, some of which can be used to determine the optimal trajectory in an uncertain and variable environment paying attention to instantaneous power consumption. This paper examines the capabilities of an artificial neural network algorithm for determining the trajectory of a four-axis manipulator with special emphasis on power consumption. The chosen method for learning artificial neural networks was reinforcement learning. Unlike the classical approach to determining inverse kinematics, the solution presented in the paper is based on model output signals. The artificial neural network determines the control policy based on the angular position, angular velocities, power consumption of the manipulator's actuators, and feedback, which is the reward function. The network was trained to reach a desired point from a fixed and random initial state of the robotic arm in optimal time and effective power requirements in such a way as to maintain a set distance from periodically moving obstacles. A three-dimensional representation of the manipulator designed in a CAD environment and mathematical models of the manipulator's drives were used for training. This approach accelerated the learning process of the algorithm, and the steps taken guarantee very similar, if not identical, performance on a real object.
[1] Q-learning neural controller for steam generator station in micro cogeneration systems / Krzysztof LALIK, Mateusz KOZEK, Szymon PODLASEK, Rafal FIGAJ, Pawel Gut Energies - Czasopismo elektroniczne ; ISSN 1996-1073. — 2021 vol. 14 iss. 17 art. no. 5334, s. 1–13. Bibliogr. s. 11–13
[2] Autonomous machine learning algorithm for stress monitoring in concrete using elastoacoustical effect - Krzysztof LALIK, Mateusz KOZEK, Ireneusz DOMINIK Materials [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1996-1944. — 2021 vol. 14 iss. 15 art. no. 4116, s. 1–14. Bibliogr. s. 12–14
[3] Oludare Isaac Abiodun, Aman Jantan, Abiodun Esther Omolara, Kemi Victoria Dada, Nachaat AbdElatif Mohamed, and Humaira Arshad. State-of-the-art in artificial neural network applications: Asurvey. Heliyon, 4(11):e00938, 2018.
[4] Ahmed A Hassan, Mohamed El-Habrouk, and Samir Deghedie. In-verse kinematics of redundant manipulators formulated as quadratic programming optimization problem solved using recurrent neural networks: A review. Robotica, 38(8):1495-1512, 2020.
[5] SWETHA Danthala, SEERAMSRINIVASA Rao, KASIPRASAD Mannepalli, and Dhantala Shilpa. Robotic manipulator control by using machine learning algorithms: A review. International Journal of Mechanical and Production Engineering Research and Development, 8(5):305-310, 2018
This project was possible due to the second edition of the IDUB/2022/3870 grant support, which was created to support Polish students’ research groups in participation in international events. This project has been developed with New-Tech students’ research group located in Faculty of Mechanical Engineering and Robotic, AGH University of Science and Technology in Krakow, Poland.
conference
Proceedings of 23rd International Multidisciplinary Scientific GeoConference SGEM 2023
23rd International Multidisciplinary Scientific GeoConference SGEM 2023, 03 - 09 July, 2023
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference SGEM
SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Russian Acad Arts; Turkish Acad Sci.
49-56
03 - 09 July, 2023
website
9087
Energy consumption, Reinforcement learning, Green control system

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