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About

Bibhrajit Halder is a robotics and control systems researcher whose work has made meaningful contributions to the field of fault detection and isolation in robotic systems. His research centers on developing robust methodologies for identifying sensor and actuator failures in complex, real-world robotic environments where model uncertainties and process disturbances are unavoidable challenges. Halder is perhaps best known for pioneering the Robust Nonlinear Analytic Redundancy (RNLAR) technique, a novel approach capable of detecting both sensor and actuator faults in input-affine nonlinear multivariable dynamic systems — a class that encompasses most robotic platforms. Applied extensively to wheeled mobile robots and robotic manipulators, this framework addresses the critical problem of model-plant mismatch, making fault detection viable under realistic operating conditions. His most cited work, published in 2007, has accumulated 20 citations and reflects sustained interest from the robotics community. Beyond fault diagnostics, Halder has explored rehabilitation robotics, contributing a discrete event system-based high-level supervisory controller for robot-assisted rehabilitation devices, demonstrating the breadth of his applied research. His early work on anti-swing control for robotic cranes further illustrates his range across control theory applications. With a body of work accumulating over 80 citations, Halder's research offers valuable tools for engineers designing safer, more dependable autonomous and assistive robotic systems.