Koeln, Justin P.

Permanent URI for this collectionhttps://hdl.handle.net/10735.1/6795

Justin Koeln joined the faculty of the Jonsson School as an Assistant Professor of Mechanical Engineering in 2018 after completing a postdoc at the University of Illinois at Urbana-Champaign. He is also the founder and director of the Energy Systems Control Laboratory. His research interests include:

  • Dynamic Systems and Control
  • Distributed and Hierarchical Control
  • Energy Systems

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Recent Submissions

Now showing 1 - 2 of 2
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    Two-Level Hierarchical Mission-Based Model Predictive Control
    (Institute of Electrical and Electronics Engineers Inc.) Koeln, Justin P.; Alleyne, A. G.; Koeln, Justin P.
    A two-level hierarchical model predictive control (MPC) formulation is presented for constrained linear systems operating over a mission. Mission-based MPC is applicable to many control applications where the system operates for a finite time and stability about an equilibrium is not the primary objective. Instead, the primary control objective is to guarantee constraint satisfaction during operation as well as terminal constraints imposed on the final state of the system at the end of the mission. The secondary control objective is reference tracking, where references determine the desired operation for the system. A hierarchical control formulation permits the upper level controller to plan state trajectories over the entire mission, while a lower level controller modifies these trajectories to improve reference tracking. This decomposition of the control problem reduces computational cost, enabling real-time implementation for large systems with long missions. Feasibility proofs guarantee the constraint satisfaction while a numerical example demonstrates the efficacy of the approach.
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    Robust Hierarchical Model Predictive Control of Graph-Based Power Flow Systems
    (Elsevier Ltd) Koeln, Justin P.; Alleyne, A. G.; Koeln, Justin P.
    A robust hierarchical model predictive control framework is presented for controlling a linear system of dynamically coupled subsystems. A graph-based modeling framework captures the conservation laws of power flow systems, for which control optimizes the storage and routing of energy to maximize transient and steady-state power throughput. A constructive approach is presented for developing an N-level hierarchical controller, which guarantees satisfaction of state and input constraints in the presence of signal and model uncertainty.

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