<p>Computer science is not my major, hence my question is two folded :</p>

<ul>
<li>how is defined the problem I have (if I can name it, it will be easier for me to look for references) ?</li>
<li>what kind algorithm would be more suited to solve it ?</li>
</ul>

<p>Let's take a wheeled remote controlled turtle (the <strong>real turtle</strong>) and a numerical model of it (the <strong>model turtle</strong>).</p>

<p>We first play once with the <strong>real turtle</strong>, record the commands and the resulting trajectory.</p>

<p>The problem is to find the right commands for the <strong>model turtle</strong> in order to fit (as much as possible) with the trajectory obtained previously with the <strong>real turtle</strong>.</p>

<p>With a perfectly representative model, it won't be an issue at all: for similar commands, one would get similar trajectory. But the model turtle will never be perfect, and commands will need to be tweaked.</p>

<p>Extra constraints are :</p>

<ul>
<li><strong>model turtle</strong> can only be initialized (at t=0) and run forward (in time) to the end of the trajectory (no reverse play, snapshot/recall, etc.). Simulation can be run many times though.</li>
<li><strong>model turtle</strong> is in fact a complex black box system, with no known equations of it and a bunch of internal states (mostly filters).</li>
</ul>

<p>Thanks in advance for the leads</p>

CC BY-SA 3.0
2012-07-06T17:33:52.417Z
It seems that you need a control loop, although normally control loops are used to make the physical system behave like the model system rather than vice versa
Vanessa
CC BY-SA 3.0
2012-07-11T10:01:34.923Z
With a PID feedback we couldn't obtain good results. Mostly because the link between controls and trajectory are difficult to assess (behavior is deterministic but unknown a priori). Hopefully, I can re-play many times the model (use genetic algorithm ? but how to converge fast ?). I could also use a neural network as an input for a PID, but I have little idea on the best inputs and outputs for the NN.
yota
<ds.algorithms><optimization>
Control optimization for a black box model
yota
# Control optimization for a black box model

(Asked by: username_0 on 2012-07-06)

Computer science is not my major, hence my question is two folded :

* how is defined the problem I have (if I can name it, it will be easier for me to look for references) ?
* what kind algorithm would be more suited to solve it ?

Let's take a wheeled remote controlled turtle (the **real turtle**) and a numerical model of it (the **model turtle**).

We first play once with the **real turtle**, record the commands and the resulting trajectory.

The problem is to find the right commands for the **model turtle** in order to fit (as much as possible) with the trajectory obtained previously with the **real turtle**.

With a perfectly representative model, it won't be an issue at all: for similar commands, one would get similar trajectory. But the model turtle will never be perfect, and commands will need to be tweaked.

Extra constraints are :

* **model turtle** can only be initialized (at t=0) and run forward (in time) to the end of the trajectory (no reverse play, snapshot/recall, etc.). Simulation can be run many times though.
* **model turtle** is in fact a complex black box system, with no known equations of it and a bunch of internal states (mostly filters).

Thanks in advance for the leads

**Tags:** ds.algorithms, optimization

> **Comments:**
> **username_1**: It seems that you need a control loop, although normally control loops are used to make the physical system behave like the model system rather than vice versa  
> **username_0**: With a PID feedback we couldn't obtain good results. Mostly because the link between controls and trajectory are difficult to assess (behavior is deterministic but unknown a priori). Hopefully, I can re-play many times the model (use genetic algorithm ? but how to converge fast ?). I could also use a neural network as an input for a PID, but I have little idea on the best inputs and outputs for the NN.  

---

## 0 answers

