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About

Debashisha Jena is a control systems researcher whose work centers on the intelligent control of robotic manipulators, with particular expertise in sliding mode control (SMC), neural networks, and bio-inspired optimization techniques. His research consistently bridges classical control theory with modern computational intelligence, producing hybrid frameworks that enhance the precision, stability, and adaptability of robotic systems. Jena's most influential contributions include the development of a backstepping terminal sliding mode controller augmented with radial basis functional neural networks (2017, 53 citations) and a PSO-based neuro-fuzzy sliding mode control strategy for two-degree-of-freedom robot manipulators (2016, 49 citations). These works demonstrate his commitment to combining metaheuristic optimization — including Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) — with advanced control architectures like ANFIS and backstepping methods to overcome nonlinearity and uncertainty in robotic systems. His application of these techniques to real-world challenges, such as controlling overhead transmission line de-icing robot manipulators, underscores the practical relevance of his research. With cumulative citations exceeding 130, Jena has established a meaningful presence in the intelligent robotics control community, offering graduate students and fellow researchers a robust body of work connecting theoretical rigor with engineering application.