B.T. Costic

Boeing (United States), Clemson University

Papers

Total Citations

233

H-Index

About

B.T. Costic’s research lies at the intersection of nonlinear control theory and adaptive systems, with a particular focus on repetitive learning control for systems plagued by unknown, periodic dynamics. His most significant contribution is the development of a Lyapunov-based approach to repetitive learning control, a framework that elegantly generates a learning-based feedforward term through a straightforward stability analysis. This innovation allows control designers to systematically address the challenge of compensating for unknown, periodic nonlinearities without requiring an explicit system model. The seminal paper detailing this work, published in 2002, has garnered 225 citations, underscoring its profound impact on the field of learning-based control. By bridging the gap between rigorous Lyapunov theory and practical repetitive learning, Costic’s work has provided a foundational tool for engineers tackling problems in robotics, manufacturing, and other domains where periodic disturbances are prevalent. His approach stands out for its simplicity and theoretical rigor, making it accessible to both researchers and practitioners. Through this achievement, Costic has cemented his reputation as a key contributor to the advancement of intelligent control systems, offering a powerful methodology that continues to inspire new generations of control engineers.