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About

Abdelkrim Boukabou is a researcher whose work sits at the intersection of intelligent control systems, robotics, and computational intelligence. His research has made notable contributions to the development of advanced motion control strategies for robotic systems, with a particular focus on nonholonomic mobile robots and robot manipulators. Boukabou has distinguished himself through the design of neural network-based and fuzzy logic control architectures that enable high-precision trajectory tracking without reliance on cumbersome real-time tuning methods. His 2017 paper on robust adaptive neural network-based trajectory tracking for electrically driven mobile robots has garnered 89 citations, establishing it as his most influential contribution to the field. Building on this, his 2016 work on intelligent optimal neural network tracking controllers has attracted 49 citations, further cementing his reputation in autonomous robotics control. A particularly innovative contribution is his 2018 self-tuning motion controller, which integrates ant colony optimization with fuzzy systems to achieve adaptive, real-time precision — accumulating 36 citations. His earlier explorations into PD-fuzzy compensator control and neuro-optimal controllers for manipulators reflect a consistent research vision: making robotic systems smarter, more autonomous, and industrially viable through biologically inspired computational methods.