Papers
12
Total Citations
643
H-Index
10
About
Mohammad Hassan Khooban is a prominent researcher specializing in intelligent control systems, robotics, and advanced optimization techniques. His work centers on developing sophisticated control strategies for nonholonomic wheeled mobile robots and robotic manipulators, with a particular emphasis on bridging classical control theory with modern computational intelligence. Khooban has made substantial contributions through his pioneering integration of fuzzy logic, sliding mode control, and optimization algorithms to address real-world challenges such as parametric uncertainties, unmodeled dynamics, and measurement noise in robotic systems. His 2013 work on optimal Mamdani-type fuzzy controllers and particle swarm optimization-based fuzzy sliding mode control established foundational approaches that continue to influence the field, earning over 80 and 98 citations respectively. His more recent work has pushed boundaries further by incorporating deep reinforcement learning into fractional-order PID control design, demonstrating both theoretical rigor and experimental validation, accumulating over 100 citations. Throughout his career, Khooban has consistently employed bio-inspired and population-based optimization methods — including teaching-learning-based optimization and bat algorithms — to fine-tune complex controllers. With collectively hundreds of citations across his top publications, his research has meaningfully advanced trajectory tracking performance, robustness, and adaptability in modern robotic control systems, making his work essential reading for researchers in intelligent control and autonomous robotics.
Research Focus
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Top Papers
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