ME 220: OPTIMAL CONTROL AND ESTIMATION
University of California, Riverside
4 Units, Lecture, 4 hour; term paper, 1 hour. Prerequisite(s): ME 120, ME 121 or equivalent; or consent of instructor. Introduces optimal control and estimation with specific focus on discrete time linear systems. Topics include analysis of discrete Riccati equations; asymptotic properties of optimal controllers; optimal tracking; an introduction to Receding Horizon control; derivation of the Kalman filter; Extended Kalman Filter; and Unscented Kalman filter. May be taken Satisfactory (S) or No Credit (NC) with consent of instructor and graduate advisor. Cross-listed with EE 233.
Average GPA: 3.82
Grade distribution records: 13 students across 1 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 6 | 46.2% |
| A | 3 | 23.1% |
| A- | 2 | 15.4% |
| B+ | 1 | 7.7% |
| B | 1 | 7.7% |
Based on 13 student grade records across 1 term and 1 professor.
Instructors
- Jun Sheng 13 students, Average GPA 3.82