CDS 110b: State Estimation: Difference between revisions
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== Lecture Outline == | == Lecture Outline == | ||
<ol type=I> | <ol type=I> | ||
<li> | <li> Observability | ||
* Definition of observability (full nonlinear system) | |||
* Observability conditions for linear processes | |||
** Intuition: Derivation via differentiation | |||
** Formal proof | |||
<li> State Estimation | |||
* Intuition: offline estimation | |||
* Luenberger observer | |||
* Example | |||
<li> Separation Principle | |||
</ol> | </ol> | ||
Revision as of 02:23, 22 January 2006
See current course homepage to find most recent page available. |
| Course Home | L7-2: Sensitivity | L8-1: Robust Stability | L9-1: Robust Perf | Schedule |
This lecture presents an introduction to state estimation and observers. Beginning with a definition of observability, we provide conditions under which a linear system is observable and show how to construct an observer in the case where there is no noise. We then prove the separation principle, which shows how to combine state regulation with state estimation.
Lecture Outline
- Observability
- Definition of observability (full nonlinear system)
- Observability conditions for linear processes
- Intuition: Derivation via differentiation
- Formal proof
- State Estimation
- Intuition: offline estimation
- Luenberger observer
- Example
- Separation Principle
Lecture Materials
- Lecture Presentation (MP3)
- Optional reading: AM05, Sections 6.x
- Homework 4
