Muhammad Aseer Khan

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About

Muhammad Aseer Khan is a robotics and control systems researcher whose work centers on the data-driven modeling and system identification of nonlinear dynamical systems, with a particular focus on two-wheeled robots (TWRs). His major contributions lie in bridging the gap between fundamental nonlinear kinematics and practical, efficient modeling techniques. In his most cited work (17 citations), Khan developed a dynamic model of a nonlinear TWR using a data-driven approach, implementing the system in Simulink and testing it across various input/output conditions to capture the robot’s complex behavior. Expanding on this, he introduced an artificial neural network (ANN) as a kinematic estimator, enabling efficient prediction of the TWR’s translational movement and rotational angle—a significant step toward real-time control applications. Khan’s research demonstrates how machine learning can replace or augment traditional physics-based modeling, offering a scalable path for autonomous system design. His work is particularly notable for its practical orientation, directly addressing the challenges of nonlinear dynamics in underactuated robots. With a growing citation footprint, Khan is establishing himself as a promising voice in the intersection of robotics, control theory, and data science.