An integrative review of control strategies in robotics

Abstract

This paper presents an integrative review of control strategies in robotics, covering classical control methods (linear quadratic regulator, proportional-integral-derivative), modern methods (adaptive, sliding mode, model predictive, and H-infinity), intelligent control methods (neural network, fuzzy logic, and machine learning), and hybrid control methods (integration of classical, modern, and intelligent control methods) to identify the advantages, limitations and gaps for future. A brief comparison of control methods between the types of control strategies is conducted with respect to robustness, stability, and complexity of implementation on 3 different levels of evaluation criteria: high, average, and low; advantages; limitations; and robotic applications, including examples. This paper discusses the theoretical and practical advancements and the classification of control strategies according to controller types (linear, nonlinear, and learning-based), approaches (model-based and model-free), and classifications (centralized, decentralized, and modal control). The review highlights the strengths, limitations, and potential research directions in bridging classical, modern, intelligent, and hybrid control paradigms to achieve safe, efficient, and adaptive robotic behavior in complex, uncertain environments. We discuss the future direction: autonomy, human-robot collaboration, and enhanced learning and challenges: cost, reliability, safety of control strategies, concluding with recommendations for future research.