Dr. Ritu Raj’s research contributions lie at the intersection of fuzzy logic theory, controller simplification, and robust optimization for complex, nonlinear, and uncertain systems. Focusing extensively on Mamdani/Takagi–Sugeno and interval Type-2 fuzzy control frameworks, his work advances both theoretical and applied aspects of control engineering. A core theme is the development of simplified analytical models for fuzzy PID, PI, and PD controllers—reducing input dimensions while maintaining precision, thereby enhancing implementability and analytical tractability. This includes innovative designs such as one-dimensional input models, modified rule bases, and closed-form solutions that preserve robustness. His studies also address uncertainty handling through interval Type-2 fuzzy controllers and hybrid optimization methods, including Genetic Algorithms, Grey Wolf Optimization, and Artificial Bee Colony techniques, ensuring adaptive performance in dynamic environments. Applications of these advancements span benchmark systems and robotics, such as the planar snake robot, Twin Rotor MIMO systems, magnetic levitation setups, and the ball-and-beam system—earning recognition with a best-paper award in 2024. Dr. Raj’s publications also extend to structural and stability analysis of fuzzy control systems, offering fundamental insights into their operational properties. Beyond journal papers, his book chapters expand on modeling strategies, optimization-based tuning, and precompensated fuzzy control schemes. Overall, his research bridges rigorous control theory with practical engineering solutions, enabling more efficient, stable, and resilient fuzzy logic–based controllers across diverse applications, and contributing significantly to the advancement of intelligent control under uncertainty.

Google scholar - https://scholar.google.com/citations?user=vseeVWQAAAAJ&hl=en

Research Gate - https://www.researchgate.net/profile/Ritu-Raj-2