معرفی
Claus Danielson serves as Assistant Professor in the Department of Mechanical Engineering at the University of New Mexico, specializing in constrained planning, control, and optimization for safety-critical systems. His work bridges theoretical control theory with practical applications across robotics, autonomous vehicles, and energy infrastructure.
His academic credentials include:
- PhD in Mechanical Engineering from University of California, Berkeley
- MS in Mechanical Engineering from Rensselaer Polytechnic Institute
- BS in Mechanical Engineering from University of Washington
Danielson's research centers on Model Predictive Control (MPC), motion planning, and reference governors. He develops algorithms exploiting structural properties in large-scale systems to ensure safety and robustness. His lab creates provably safe motion planning solutions using invariant set theory and non-convex optimization, with applications spanning autonomous vehicles, spacecraft, HVAC, and energy storage networks. Current projects emphasize data-driven approaches for complex constrained systems.
Analysis of his recent publications reveals dominant trends in data-driven invariant set methods (2023-2025), robust adaptive MPC for aerospace applications, and safety-guaranteed motion planning. His work increasingly integrates machine learning with control theory, particularly in randomized path planning and extremum seeking control for energy systems like concentrated solar power and HVAC networks.
No scientific awards are documented in the provided materials.
Danielson teaches Robot Engineering (ME-482/582) and Introduction to Feedback Control (ME-380/ECE-345), mentoring students in non-convex optimization, dynamic system modeling, and control algorithm design. His research collaborations include the UNM-AFRL Agile Manufacturing Laboratory and New Mexico EPSCoR Smart Grid Center, though specific grant details remain undisclosed.
He directs the Model Predictive Control Lab, which focuses on developing motion planning algorithms for robotics and autonomous vehicles. The lab also advances control solutions for photovoltaics, concentrated solar, and smart grid technologies, emphasizing scalable algorithms for large-scale energy systems with multi-timescale dynamics.
