Sharifullah Khan

National University of Sciences and Technology

Papers

Total Citations

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About

Sharifullah Khan’s research centers on robotics, control systems, and artificial intelligence, with a particular focus on the dynamics and stabilization of agile mobile platforms. His most notable contribution is the development of a novel control strategy for a ball-bot—an inherently unstable, highly maneuverable robot—using a particle swarm optimization (PSO) trained neural network. By modeling the ball-bot as two decoupled, two-degree-of-freedom pendulum-on-a-cart systems, Khan demonstrated how machine learning can effectively handle complex, nonlinear control challenges that classical methods struggle to address. His 2016 paper on this work has garnered 2 citations, reflecting its niche but foundational role in advancing neural network-based control for unstable robotics. Khan’s approach bridges theoretical control theory and practical robotic design, offering a scalable solution for high-speed maneuvering. His work is particularly valuable for researchers exploring bio-inspired optimization and autonomous systems. Through this achievement, Khan has contributed a key stepping stone for future innovations in agile robotics, inspiring students and engineers to tackle the intersection of AI and mechanical stability.