Papers
3
Total Citations
53
H-Index
3
About
Sabit Ibadulla is a researcher specializing in autonomous robotics, evolutionary computation, and optimal control system synthesis. His work sits at the intersection of symbolic regression and artificial intelligence, with a particular focus on developing automated methods for designing control systems for mobile and aerial robotic platforms. Ibadulla's most significant contributions center on variational approaches to genetic and analytic programming. His 2015 papers introduced variational genetic programming and variational analytic programming as novel numerical frameworks for automatically constructing optimal feedback control functions — eliminating the need for manually designed controllers in complex robotic systems. These works, garnering 21 and 20 citations respectively, demonstrated that evolutionary symbolic regression could reliably synthesize mathematically explicit control laws directly from system dynamics, a meaningful advance in autonomous robotics research. Building on this foundation, his 2019 work broadened the perspective by systematically reviewing evolutionary symbolic regression methods and examining their applicability to artificial intelligence development in robotic technical systems, accumulating 12 citations. Together, his publications reflect a consistent research trajectory aimed at making robot control synthesis more automated, adaptive, and mathematically rigorous — contributing practical tools that hold value for researchers working in autonomous systems, swarm robotics, and intelligent control engineering.
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