Peter Seiler

Peter Seiler is an Associate Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan. His work focuses on robust control theory, particularly analyzing the impact of model uncertainty on system design. He contributes to the Robust Control Toolbox in MATLAB and develops algorithms for finite-time horizon robustness assessment. His research spans wind turbine control, fault detection in safety-critical systems, and disk drive stabilization, with prior industry experience at Honeywell Research Labs on aerospace and automotive systems.

• Education: Not explicitly mentioned

Seiler's research interests center on robust control theory, covering applications in aerospace (Boeing 787 redundancy management, NASA Orion flight control), wind energy (turbine and farm control), and safety-critical systems (UAVs, disk drives). He explores connections between robust control and optimization algorithms, including model-free reinforcement learning. His work often employs Integral Quadratic Constraints (IQCs) and Linear Parameter-Varying (LPV) techniques for stability and performance analysis.

His recent publications (2019-2018) emphasize robust control of time-varying systems, optimization algorithms, wind energy systems, and fault-tolerant UAVs. Trends include IQC applications for stability analysis, LPV synthesis for aerospace and wind turbines, and robustness in machine learning.

• Scientific Awards
  • Best Energy Paper Award, ASME Dynamic Systems and Control Conference (2016)

Seiler has advised numerous PhD, MS, and undergraduate researchers across topics including wind turbines, UAVs, disk drives, and supercavitating vehicles. His collaborations span institutions like the University of Minnesota, German Aerospace Center, and University of Campinas.

His lab at the University of Michigan integrates robust control theory with applications in aerospace, renewable energy, and fault-tolerant systems, working with teams on projects like UAV reliability, wind farm modeling, and adaptive control algorithms.