From classical to intelligent control: Evolving trends in robotic manipulator technology

Numerous industrial, medical, and scientific applications currently depend on robotic manipulators, which are essential to the expanding robotics sector. These manipulators cannot operate well without advanced control algorithms that consider precision, adaptability, and flexibility. This work is focused on listing many types, applications, and functions of control strategies for robotic manipulators. The review commences with a broad overview of manipulators, including subjects such as cable-driven, parallel, and serial models. It subsequently contextualizes the unique mechanical and functional characteristics of each type. The next sections encompass a range of control procedures, from conventional techniques such as Proportional-Integral-Derivative (PID) and linear control to complex systems that integrate hybrid control frameworks like machine learning (ML), and artificial intelligence (AI). This comprehensive analysis addresses some enduring issues like task flexibility, computational efficiency, and environmental uncertainty, together with the advantages and disadvantages of each approach. This study also emphasizes emergent domains, including federated learning (FL), blockchain integration, and quantum computing, while also identifying prospective future research topics. By combining current information and outlining potential developments, the study provides researchers and engineers with a reference for improving the control of robotic manipulators, hence improving performance and reliability in challenging working environments. The findings of this work demonstrate the efficacy of advanced control methodologies and pave the way for advances that could transform robotic manipulation in the future.