CDS 110b: State Estimation: Difference between revisions

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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. __NOTOC__
This set of lectures presents an introduction to modern (optimization-based) control design and introduces the concepts of 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. __NOTOC__


== Lecture Outline ==
== Course Materials ==
<ol type=I>
{| width=100%
<li> Observability
|-
* Definition of observability (full nonlinear system)
|
* Observability conditions for linear processes: intuition + proof
=== Lecture 1 ===
<li> State Estimation
| {{cds110b-pdfs|L1-1_introduction.pdf|Lecture Presentation}} (MP3)
* Luenberger observer
| {{cds110b-pdfs|hw1.pdf|HW #1}}
* Example: ducted fan
=== Lecture 2 ===
<li> Separation Principle
* {{cds110b-pdfs|L1-2_observability.pdf|Lecture Presentation}} (MP3)
* Proof of the separation principle
* Transfer function representation
* Example: ducted fan
</ol>
 
== Lecture Materials ==
* {{cds110b-pdfs|L4-1_observability.pdf|Lecture Presentation}} ({{cds110b-pdfs|L4-1_observers.mp3|MP3}})
* Reading: {{am05|Chapter_6_-_Output_Feedback|Sec 6.1-6.3}}
* Reading: {{am05|Chapter_6_-_Output_Feedback|Sec 6.1-6.3}}
* {{cds110b-pdfs|obs_dfan.m|obs_dfan.m}} - sample computations for Caltech ducted fan
* {{cds110b-pdfs|obs_dfan.m|obs_dfan.m}} - sample computations for Caltech ducted fan
|}


== References and Further Reading ==
== References and Further Reading ==
Line 25: Line 19:


== Frequently Asked Questions ==
== Frequently Asked Questions ==
'''Q: Is there any relationship between observability and differential flatness?'''
<blockquote>
<p>There is indeed a relationship, of sorts.  Strictly speaking, when we talk about differential flatness, we do so independent of the specific outputs for the system.  A system is flat if ''there exist'' outputs such that we can characterize the trajectories of the system (states and inputs) as a function of the flat outputs and their derivatives.  As one might imagine, with this choice of outputs, the system is ''always'' observable.</p>
<p>On the other hand, a system might be observable with a certain set of outputs, but those outputs are not necessarily differentially flat outputs.  For example, consider the system
<center><math>
\begin{matrix}
\dot x &= \begin{bmatrix} 0 & 1 \\ 0 & 0 \end{bmatrix} x & + \begin{bmatrix}0 \\ 1 \end{bmatrix} u \\
y &= \begin{bmatrix} 1 & 1 \end{bmatrix} x &
\end{matrix}
</math></center>
This matrix is observable (easy to check by computing the observability matrix), but with the given output, it is not possible to compute the input and state.  (Note that you can compute the state given the input; just not the state ''and'' input given the output.)</p>
<p> The basic issue in this example is that the process has a zero and hence there is an input such that the corresponding output is identically zero. </p>
</blockquote>

Revision as of 00:16, 27 December 2006

WARNING: This page is for a previous year.
See current course homepage to find most recent page available.
CDS 110b Schedule Project FAQ Reading

This set of lectures presents an introduction to modern (optimization-based) control design and introduces the concepts of 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.

Course Materials

Lecture 1

 Lecture Presentation (MP3) HW #1

Lecture 2

References and Further Reading

  • Friedland, Chapters 7 and 8

Frequently Asked Questions

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