Adaptive Control Systems in Humanoid Robots

Authors

  • Muhammad Nasiruddin Mahyuddin Department of Electrical Engineering, Lahore University of Management Sciences (LUMS), Lahore, Pakistan
  • Said G. Khan Department of Electrical Engineering, University of Engineering and Technology (UET), Lahore, Pakistan
  • Mukhtiar Ali Unar Department of Electronic Engineering,Mehran University of Engineering and Technology (MUET), Jamshoro, Sindh, Pakistan

Keywords:

adaptive control, humanoid robots, model-reference adaptive control, sliding mode control, reinforcement learning, real-time parameter estimation, disturbance rejection, trajectory tracking

Abstract

Adaptive control systems are pivotal in enhancing the autonomy and performance of humanoid robots, enabling them to operate effectively in dynamic and uncertain environments. This paper explores the integration of adaptive control mechanisms in humanoid robots, focusing on real-time parameter estimation, disturbance rejection, and trajectory tracking. The study examines various adaptive control strategies, including model-reference adaptive control (MRAC), sliding mode control (SMC), and reinforcement learning-based approaches. Through simulation and experimental validation, the paper demonstrates the efficacy of these methods in improving the robustness and adaptability of humanoid robots. The findings contribute to the advancement of autonomous robotic systems capable of performing complex tasks in unstructured settings

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Published

2024-12-31

Issue

Jul-Dec 2024

Section

Articles

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