David Copp

Papers

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About

David Copp is a researcher whose work lies at the intersection of control theory, robotics, and multi-agent systems, with a particular focus on enabling robust, real-time coordination for small, resource-constrained platforms. His most notable contribution is a novel output-feedback Model Predictive Control (MPC) approach combined with Moving Horizon Estimation (MHE), designed for the vision-based target tracking of small UAVs. This work, published in 2017 and cited 11 times, addresses a critical challenge: achieving robust, optimal control when full state information is unavailable, a common hurdle in field robotics. By emphasizing output-feedback strategies, Copp’s research provides a practical framework for coordinating multiple agents under uncertainty, advancing the state of the art in autonomous systems. Beyond his technical contributions, Copp is also dedicated to education, as evidenced by his 2021 paper on bringing programming, robotics, and control concepts to high school students. This outreach effort, while early in its citation impact, underscores his commitment to broadening participation in STEM and inspiring the next generation of engineers.