AM 273System Identification for Aerial Robotic Systems
Introduces data-driven system identification methods for dynamical modeling of atmospheric, ocean, and robotic vehicles from experimental data. Topics include experiment design; model structure selection; parameter and state estimation; regression-based and maximum-likelihood approaches; and data analysis in both time and frequency domains. Emphasis is placed on integrating physical modeling with data-driven techniques and on assessing model validity and uncertainty.
Prerequisite(s): Enrollment is restricted to graduate students. Seniors may enroll by instructor permission. AM 100, AM 114, AM 147, or equivalent courses are expected but not required.
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When it runs
Spring 2026Open Spring 2026
| Section | Type | When | Where | Instructor | Seats |
|---|---|---|---|---|---|
| 01 | Lecture | Tue Thu 3:20pm–4:55pm | Crown Clrm 104 | J. Gonzalez-Rocha | Open 10/15 |
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