Adaptive Control Systems in Robotics for Unstructured Environments

DOI: https://doi.org/10.63345/ijrmeet.org.v10.i2.1

Dr. Shakeb Khan

Maharaja Agrasen Himalayan Garhwal University

Uttarakhand, India

shakebkhan2011@gmail.com

Abstract

In the pursuit of deploying robotic systems in unstructured and dynamic environments—such as disaster sites, agricultural fields, and crowded urban spaces—traditional fixed-parameter controllers often fail to maintain performance and stability due to unforeseen disturbances and model uncertainties. Adaptive control, which dynamically adjusts controller parameters in real time, offers a promising solution by compensating for unknown system dynamics and external perturbations. This manuscript presents a comprehensive examination of adaptive control strategies applied to robotic manipulators operating in unstructured settings, focusing on model reference adaptive control (MRAC), self-tuning regulators (STR), sliding mode adaptive control (SMAC), and neural network–based adaptive schemes, all developed through 2022. A statistical comparison of these methods under standardized test conditions illustrates their relative efficacy in terms of tracking error, response time, and robustness. A methodology is then described for designing a hybrid adaptive controller that integrates MRAC with a fuzzy-logic supervisor, tailored for a 6‑degree-of-freedom manipulator. Experimental results demonstrate an average reduction in tracking error by 40% compared to classical PID control. Finally, the paper discusses the scope of application and limitations inherent to adaptive approaches, including computational complexity, requirement for persistent excitation, and sensitivity to noise. This work aims to guide robotics engineers in selecting and implementing adaptive control solutions that ensure reliable operation in real‑world, unstructured environments.

Keywords

Adaptive control; robotics; unstructured environments; model reference adaptive control; neural network adaptive control; fuzzy logic supervisor

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