Evolution of Robust Control Methods for Robots: From Traditional Modeling to Intelligent Collaboration

DOI

10.2991/978-94-6463-823-3_3How to use a DOI?

Keywords

Robust Control; Intelligent Algorithms; Reinforcement Learning; Time-varying Disturbance; Uncertainty

Abstract

With the widespread use of robotics in complex dynamic environments, traditional robust control methods face great limitations when dealing with nonlinear time-varying loads, modeling errors, and other uncertainties. This paper systematically review the evolution path of robot robust control from traditional modeling to intelligent collaboration, focusing on the fusion mechanism of frequency and time-domain control methods with intelligent algorithms, and the application potential of data-driven end-to-end control strategies. This study reveals how model-driven and data-driven collaborative control can enhance the robustness of robots in dynamic environments by dynamically optimizing decision-making policies through intelligent algorithms such as reinforcement learning. Furthermore, future applications of digital twin, bio-inspired intelligence, and quantum computing technologies for robust control are explored, analyzing their potential to revolutionize robot control in complex scenarios. By summarizing the theoretical support and technical approaches of robust control of robots in complex dynamic environments, it provides a systematic reference for researchers in related fields.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Junyou Zhuo
PY  - 2025
DA  - 2025/08/31
TI  - Evolution of Robust Control Methods for Robots: From Traditional Modeling to Intelligent Collaboration
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 22
EP  - 33
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_3
DO  - 10.2991/978-94-6463-823-3_3
ID  - Zhuo2025
ER  -