CDS 110b: Receding Horizon Control

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L7-2: Sensitivity

L8-1: Robust Stability

L9-1: Robust Perf

Schedule

This lecture presents an overview of receding horizon control (RHC). In addition to providing a summary of the available theoretical results, we introduce the concept of differential flatness for simplifying RHC problems and provide an example of RHC control on the Caltech ducted fan. .

Lecture Outline

1. Receding Horizon Control
   • Problem Formulation
   • Stability theorems
2. Differential Flatness and Trajectory Generation
   • Definitions
   • Properties
   • Examples
3. Examples: Caltech ducted fan, satellite formation flight, multi-vehicle testbed

Lecture Materials

• Lecture Presentation (no MP3)
• Lecture notes: Online Control Customization via Optimization-Based Control, R. Murray et al, 2003.
• Homework 3

Frequently Asked Questions

Q: How is differential flatness defined?

A system of the form

${\dot {x}}=f(x,u)$

is said to be differentially flat if there exists an integer $p$ and a (smooth) function $h$ of the form

$z=h(x,u,{\dot {u}},{\ddot {u}},\dots ,u^{(p)})$

such that all solutions of the differential equation can be written in terms of $z$ and a finite number of its derivatives with respect to time. In other words, $x$ and $u$ satisfying the dynamics of the system have the form

${\begin{matrix}x&=&a(z,{\dot {z}},{\ddot {z}},\dots ,z^{(q)})\\u&=&b(z,{\dot {z}},{\ddot {z}},\dots ,z^{(q)})\end{matrix}}$

for some integer $q$ and smooth functions $a$ and $b$. The variable $z$ is often called the flat output and if a system is differentially flat then the number of flat outputs is equal to the number of inputs to the system.

Checking a system for flatness is difficult, but there are certain classes of systems for which there are necessary and sufficient conditions. Usually you find the flat outputs by a combination of physical insight and trial and error.

References:

• Flat systems, equivalence and trajectory generation, Phillipe Martin, Richard Murray, Pierre Rouchon, CDS Technical Report, 2003.

Q: How do you do trajectory optimization using differential flatness

The basic idea in using flatness for optimal trajectory generation is to rewrite the cost function and constraints in terms of the flat outputs and then parameterize the flat outputs in terms of a set of basis functions:

$z(t)=\sum _{i}\alpha _{i}\psi _{i}(t)$

Here, $\psi _{i}$, $i=1,\dots ,N$ are the basis functions (eg, $\psi _{i}(t)=t^{i}$) and $\alpha _{i}$ are constant coefficients.

Once you have parameterized the flat outputs by $\alpha$, you can convert all expressions involving $z$ into functions involving $\alpha$. This process is described in a more detail in the lectures notes (Section 4).