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About

Komal Rani is a robotics and control systems researcher whose work centers on intelligent control strategies for robot manipulators, with a particular focus on hybrid force and position control in uncertain and dynamically complex environments. Her research addresses one of the most challenging problems in robotic systems: maintaining precise simultaneous control of both motion and contact forces when robots interact with constrained surfaces in the presence of external disturbances and structural uncertainties. Rani's most significant contributions involve the innovative integration of Radial Basis Function (RBF) neural networks with model-based and adaptive control frameworks, enabling robots to learn and compensate for system uncertainties in real time. Her 2018 publications on intelligent hybrid force and position control have collectively garnered nearly 50 citations, establishing her as a notable contributor to adaptive robotics control. Her more recent work extends these foundations into optimal control territory, combining sliding mode control, quadratic optimization, and neural networks — supported by rigorous stability analysis grounded in Hamilton-Jacobi-Bellman theory. Her research is particularly valuable for students and engineers working on robotic manipulation, human-robot interaction, and industrial automation, offering mathematically robust and practically implementable solutions to real-world control challenges.