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H. Chekireb’s research focuses on the control of redundant robotic manipulators, with key contributions in trajectory tracking, obstacle avoidance, and adaptive fuzzy control. Their work addresses the fundamental challenge of enabling robots with extra degrees of freedom to perform primary tasks while satisfying additional constraints, such as avoiding obstacles or maintaining joint motion cyclicity. Chekireb’s most cited paper, "Trajectory tracking with obstacle avoidance of redundant manipulator based on fuzzy inference systems" (2016, 59 citations), demonstrates a novel approach that integrates fuzzy logic to enhance real-time obstacle avoidance during trajectory tracking. Earlier foundational work, "Redundant robot manipulator control with obstacle avoidance using extended Jacobian method" (2010, 20 citations), introduced the use of self-motion vectors within inverse kinematics to achieve safe, efficient motion. Chekireb also contributed to adaptive control theory with "Direct Adaptive Fuzzy Control of Nonlinear System Class with Applications" (2003, 18 citations), which proposed a stable, convergent method for MIMO nonlinear systems, particularly in robotics. Additionally, their study "Cyclic control of redundant robot under constraint with the self-motion method" (2008) compared techniques to preserve joint motion cyclicity, a critical issue for repetitive tasks. With over 100 total citations, Chekireb’s work has advanced the practical deployment of intelligent, constraint-aware robotic systems.