Optimal Control of Switched Systems for MCM

Online (i.e., real-time) adaptive optimal controllers for maritime systems that operate in complex environments where intermittent sensing and actuation render modern online optimal control methods unsuitable.

ONR N00014-21-1-2481 (ongoing)

Details and publications

Operator Theory for Identification and Verification

We seek the development of new operator theoretic kernel methods that can harness data obtained from black and gray box dynamical systems.

NSF 2027999 (ongoing)

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Operator Theory for Control Synthesis

We are developing novel operator theoretic techniques for data and model-driven synthesis of control via control Lyapunov functions and solution of optimal control problems.

AFOSR FA9550-20-1-0127 (ongoing)

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Wind-Aware UAV Navigation

Development of theoretical and computational tools that enable prediction and exploitation of low-altitude gust information for autonomous aircraft operations.

NSF 1925147 (ongoing)

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Model-Aware Reinforcement Learning

Methods for online real-time learning that are robust to modeling errors and abrupt changes in the dynamic models using a model-aware reinforcement learning framework.

AFRL FA8651-19-2-0009 (ongoing)

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Scale Separation for Wind-aware Guidance and Control

In this project we developed a hierarchical control architecture that exploited the spatiotemporal scale separation in the atmospheric boundary layer wind flow dynamics.

NASA - EPSCoR - RIG 2019-33 (completed)

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Neuromuscular retraining to relieve joint contact forces

Alteration of gait to minimize joint contact forces, thereby delaying the onset and slow down the progression of knee osteoarthritis.

Unfunded (ongoing)

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Mine Countermeasures and Optimal Control

This project developed computational methods for reinforcement learning in continuous-time dynamic systems.

ONR N00014-16-1-2091 (completed)

Details and publications

Get in touch

rushikesh.kamalapurkar@okstate.edu
(405) 744-5900