Ryan Johnson

Short Bio

Ryan S. Johnson received his B.S. and M.S. degrees in Aerospace Engineering from the University of California, Los Angeles in 2014 and 2017, respectively. From 2014 to 2018, he worked at Moog Aircraft Group as a Systems Engineer for the Airbus A350, Gulfstream G600, and special programs. In 2018, he interned at the Jet Propulsion Laboratory during Summer 2018, 2019, and 2020. His research interests include: parameter estimation, adaptive control, stability, robustness, modeling, and simulation of hybrid systems with applications to aerospace, robotics, and power systems.

Projects

-Hybrid control algorithm which permits operation of a DC to DC boost converter circuit under uncertainty in the circuit parameters

Publications

Publications here

[1] [2] [3] [4]

References

  1. [279] Hybrid Concurrent Learning for Hybrid Linear Regression, Johnson, R. S., and Sanfelice R. G. , Proceedings of the 61st IEEE Conference on Decision and Control, December, (2022)
  2. [256] Robust Finite-Time Parameter Estimation for Linear Dynamical Systems, Johnson, R. S., Saoud A., and Sanfelice R. G. , Proceedings of the 60th IEEE Conference on Decision and Control, December, (2021)
  3. [255] Parameter Estimation for Hybrid Dynamical Systems using Hybrid Gradient Descent, Johnson, R. S., Di Cairano S., and Sanfelice R. G. , Proceedings of the 60th IEEE Conference on Decision and Control, December, (2021)
  4. [244] Hybrid Adaptive Control for the DC-DC Boost Converter, Johnson, R. S., Altin B., and Sanfelice R. G. , Proceedings of the 7th Analysis and Design of Hybrid Systems, July, (2021)