Papers
26
Total Citations
255
H-Index
8
About
Kamen Delchev is a prominent researcher whose work spans two deeply interconnected domains: advanced iterative learning control (ILC) for robotic systems and the automation of orthopedic surgical procedures. His contributions have significantly advanced the theoretical and practical foundations of robot trajectory tracking, particularly through his development of nonlinear and bounded-error ILC algorithms that guarantee constrained, high-precision performance in complex, repetitive tasks. Papers such as "Iterative Learning Control for Nonlinear Systems: A Bounded-Error Algorithm" (29 citations) and "State Space Constrained Iterative Learning Control for Robotic Manipulators" (31 citations) demonstrate his sustained effort to make ILC both mathematically rigorous and practically viable for industrial manipulators. Equally notable is Delchev's pioneering work in surgical robotics, where he has tackled critical challenges in orthopedic bone drilling, including automatic far cortex detection, feed rate control, and bone structure identification — problems with direct patient safety implications. His robotized drilling module (30 citations) stands as a landmark contribution bridging control engineering and medicine. Across more than a decade of research, his cumulative citation record reflects steady and meaningful influence on both the robotics control community and the emerging field of surgical automation.
Research Focus
Key Achievements
Top Papers
- 1State Space Constrained Iterative Learning Control for Robotic Manipulators31 citations · 2017
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- 3Iterative Learning Control for Nonlinear Systems: A Bounded‐Error Algorithm29 citations · 2012
- 4Feed rate control in robotic bone drilling process17 citations · 2020
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- 9Constrained Output Iterative Learning Control8 citations · 2020
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