Inam Ul Hasan Shaikh

Institute of Space Technology, University of Manchester

Papers

Total Citations

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About

Inam Ul Hasan Shaikh is a control systems researcher whose work bridges classical theory and cutting-edge artificial intelligence. His primary research areas include deep reinforcement learning, robust sliding mode control, and iterative learning control (ILC) for complex autonomous systems. Shaikh’s most cited work, “Application of Deep Reinforcement Learning for Tracking Control of 3WD Omnidirectional Mobile Robot” (2021, 20 citations), demonstrates how reinforcement learning frameworks—comprising agents, actions, environments, and rewards—can solve real-world motion control problems by maximizing cumulative reward. He further advanced robust control theory in “Robust Design of Sliding Mode Control for Airship Trajectory Tracking with Uncertainty and Disturbance Estimation” (2024, 10 citations), addressing the challenge of nonlinear, wind-prone airship dynamics to enable precise autonomous navigation. His earlier contribution, “Convergence analysis of cyclic Iterative Learning Control scheme” (2012), provides theoretical foundations for learning controllers that iteratively refine input signals to reduce tracking errors in repetitive tasks. With a career spanning foundational ILC theory to modern deep RL applications, Shaikh’s work has accumulated over 30 citations, showcasing his impact on both theoretical convergence analysis and practical autonomous vehicle control.