Isura
Papers
1
Total Citations
19
H-Index
1
About
Isura’s research lies at the intersection of robotics, control theory, and human-robot interaction, with a focus on developing adaptive systems that enhance collaboration between humans and machines. Their most cited work, “Model reference adaptive impedance control for physical human-robot interaction” (2016, 19 citations), introduces a novel dual-loop control framework that significantly improves task performance and stability in physical human-robot teams. The inner neuroadaptive loop learns robot dynamics in real time, enabling the robot to behave like a prescribed impedance model without relying on task-specific trajectories. The outer loop adapts this impedance to account for human operator dynamics, creating a unified system with desirable performance characteristics. This non-standard application of model reference adaptive control yields a controller that simultaneously provides assistive input and adaptive impedance features. Simulation results demonstrate enhanced task performance in repetitive point-to-point motions, showcasing Isura’s ability to bridge theoretical control advances with practical human-robot collaboration challenges. Their work contributes foundational insights for safer, more intuitive robotic assistants in manufacturing, rehabilitation, and service robotics.
Research Focus
Key Achievements
Top Papers
- 1