Karanam Deepak

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About

Karanam Deepak is a control systems researcher whose work centers on the modeling and real-time implementation of nonlinear dynamical systems, with a particular focus on underactuated mechanical platforms. His most-cited contribution, "Model Predictive Control for rotary inverted pendulum using LabVIEW" (2019), addresses the challenge of stabilizing an inherently unstable, nonlinear rotary inverted pendulum—a classic benchmark in control theory. By integrating Model Predictive Control (MPC) with LabVIEW’s hardware-in-the-loop capabilities, Deepak demonstrated effective disturbance rejection and parameter estimation, offering a practical framework for advanced control strategies. This work has garnered 3 citations and holds significance for applications ranging from robotics and aerospace to self-balancing vehicles like the Segway. Deepak’s research bridges theoretical control algorithms with experimental validation, providing students and engineers with accessible methodologies for tackling complex, real-world control problems. His contributions underscore the importance of combining robust mathematical modeling with user-friendly software platforms to advance the study of nonlinear systems.