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About

Karl Worthmann is a control systems researcher whose work spans model predictive control (MPC), mobile robotics, and surgical robotics. His most influential contributions lie in advancing MPC methodologies for nonholonomic and holonomic mobile robots, particularly in proving that robust asymptotic stability can be achieved without stabilizing terminal constraints or costs — a significant theoretical breakthrough that simplifies practical implementation. His 2015 paper on nonholonomic mobile robot control has garnered 117 citations, establishing him as a leading voice in this specialized field. Worthmann has consistently extended these foundational results, exploring continuous-time settings for differential drive robots, distributed MPC with occupancy grid communication strategies, and data-driven predictive control leveraging Koopman operator theory. His 2025 work provocatively argues that geometric insight remains indispensable even in data-rich environments — a timely contribution amid the machine learning revolution. More recently, Worthmann has applied optimal control expertise to concentric tube robots in stereotactic neurosurgery, developing path-planning toolchains that navigate complex anatomical constraints to assist precision brain tumor targeting. This interdisciplinary expansion demonstrates his ability to translate rigorous mathematical theory into clinically meaningful applications, making his research portfolio both theoretically substantive and practically impactful across robotics and medical engineering.