Modern Control System Architectures and Methods for Collaborative Manipulators

Collaborative manipulators are often deployed as robotic systems that are intended to operate safely and intuitively within shared workspaces alongside human users. Their effectiveness depends not only on mechanical design, but also on the reliability of the underlying control architecture, the quality of sensor feedback, and the system’s ability to adapt to human interaction in real time. This study provides a structured review of modern control strategies used in collaborative manipulators, examining the theoretical principles and practical implementation of impedance, admittance, hybrid, and sensor-based approaches. An analysis of the existing literature shows that impedance and admittance control techniques are particularly well suited for human–robot collaboration, as they help to maintain stability during contact while enabling compliant and responsive motion that aligns with human intent.

This paper introduces a conceptual multi-layer control architecture that integrates safety supervision, trajectory planning, and sensor fusion. Within this architecture, a hybrid control scheme that combines force and motion sensing is highlighted as a promising direction for achieving adaptive behavior. Such an approach supports real-time adjustment of dynamic parameters and aligns with the safety limits defined in ISO/TS 15066, ensuring controlled contact forces and safe motion near human operators.

Overall, the presented framework offers a comprehensive theoretical foundation for further development of adaptive and sensor-rich control systems. These insights are expected to contribute to subsequent simulation studies, prototype testing, and the broader implementation of collaborative robots in industrial, medical, and human-assistive applications.