Muhammad Salman Shaheer

National University of Sciences and Technology

Papers

Total Citations

H-Index

About

Muhammad Salman Shaheer is a robotics researcher whose work focuses on the control and stabilization of agile, non-linear robotic systems. His most-cited paper, "Control of a ball-bot using a PSO trained neural network" (2016, 2 citations), addresses a fundamental challenge in mobile robotics: maneuvering an inherently unstable ball-bot platform at high speeds. Shaheer’s key contribution lies in modeling the ball-bot as two decoupled, 2-DOF pendulum-on-cart systems—a classical yet complex control problem—and then applying a Particle Swarm Optimization (PSO) trained neural network to achieve robust, adaptive control. This approach demonstrates a novel integration of bio-inspired optimization with neural control for real-time stabilization, offering a computationally efficient alternative to traditional model-based methods. While his citation count is modest, his work is notable for tackling a difficult, high-impact problem in robotics: enabling fast, agile locomotion through intelligent control. Shaheer’s research bridges theoretical control theory and practical robotics, providing a foundation for future work on unstable platforms like ball-bots, segways, and other inverted-pendulum systems. His contributions are particularly valuable for students and researchers interested in neural control, swarm intelligence, and the challenges of dynamic stabilization in mobile robotics.