Adaptive Motion Control for Autonomous Vehicles : Literature review

Abstract

Adaptive control plays a crucial role in the evaluation of autonomous vehicles and robotics, providing systems with the flexibility and robustness needed to operate safely and efficiently in dynamic and uncertain environments. This literature review explores the key concepts, current methods, and practical applications of adaptive control. It focuses particularly on feedback and model-based adaptive strategies such as Model Reference Adaptive Control (MRAC), Self-Tuning Control (STC), Robust Adaptive Control, and Dual Adaptive Control. Recent trends, including the integration of deep learning, reinforcement learning, edge computing, and secure system design, are discussed for their potential to further enhance adaptability and safety. Adaptive control has demonstrated significant impact across a range of real-world applications. In autonomous driving, it enables critical capabilities such as real-time lane changes, smooth intersection navigation, and safe manoeuvring through complex urban environments. In robotics, it supports advanced functions like force-sensitive manipulation and coordinated motion planning in dynamic settings. Despite these advancements, several key challenges occur. High computational demands, safety assurance under uncertainty, and long-term reliability. Overcoming these challenges will be essential for establishing adaptive control as a foundational technology in the future of intelligent autonomous systems.