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Add notes about sensors/actuators

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Thomas Dehaeze
2020-06-16 18:33:50 +02:00
parent e5e6f1aa7a
commit a2a8b3103d
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content/phdthesis/monkhorst04_dynam_error_budget.md
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@@ -8,17 +8,7 @@ Tags
: [Dynamic Error Budgeting]({{< relref "dynamic_error_budgeting" >}})
Reference
: <sup id="651e626e040250ee71a0847aec41b60c"><a class="reference-link" href="#monkhorst04_dynam_error_budget" title="@phdthesis{monkhorst04_dynam_error_budget,
author = {Wouter Monkhorst},
school = {Delft University},
title = {Dynamic Error Budgeting, a design approach},
year = 2004,
}">@phdthesis{monkhorst04_dynam_error_budget,
author = {Wouter Monkhorst},
school = {Delft University},
title = {Dynamic Error Budgeting, a design approach},
year = 2004,
}</a></sup>
: <sup id="651e626e040250ee71a0847aec41b60c"><a class="reference-link" href="#monkhorst04_dynam_error_budget" title="Wouter Monkhorst, Dynamic Error Budgeting, a design approach (2004).">(Wouter Monkhorst, 2004)</a></sup>
Author(s)
: Monkhorst, W.
@@ -105,9 +95,9 @@ Find a controller \\(C\_{\mathcal{H}\_2}\\) which minimizes the \\(\mathcal{H}\_
In order to synthesize an \\(\mathcal{H}\_2\\) controller that will minimize the output error, the total system including disturbances needs to be modeled as a system with zero mean white noise inputs.
This is done by using weighting filter \\(V\_w\\), of which the output signal has a PSD \\(S\_w(f)\\) when the input is zero mean white noise (Figure [1](#org7f8d04e)).
This is done by using weighting filter \\(V\_w\\), of which the output signal has a PSD \\(S\_w(f)\\) when the input is zero mean white noise (Figure [1](#org76ddb2c)).
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{{< figure src="/ox-hugo/monkhorst04_weighting_filter.png" caption="Figure 1: The use of a weighting filter \\(V\_w(f)\,[SI]\\) to give the weighted signal \\(\bar{w}(t)\\) a certain PSD \\(S\_w(f)\\)." >}}
@@ -118,23 +108,23 @@ The PSD \\(S\_w(f)\\) of the weighted signal is:
Given \\(S\_w(f)\\), \\(V\_w(f)\\) can be obtained using a technique called _spectral factorization_.
However, this can be avoided if the modelling of the disturbances is directly done in terms of weighting filters.
Output weighting filters can also be used to scale different outputs relative to each other (Figure [2](#org4f416df)).
Output weighting filters can also be used to scale different outputs relative to each other (Figure [2](#org425ff37)).
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<a id="org425ff37"></a>
{{< figure src="/ox-hugo/monkhorst04_general_weighted_plant.png" caption="Figure 2: The open loop system \\(\bar{G}\\) in series with the diagonal input weightin filter \\(V\_w\\) and diagonal output scaling iflter \\(W\_z\\) defining the generalized plant \\(G\\)" >}}
#### Output scaling and the Pareto curve {#output-scaling-and-the-pareto-curve}
In this research, the outputs of the closed loop system (Figure [3](#orgc347ae6)) are:
In this research, the outputs of the closed loop system (Figure [3](#orgba842f3)) are:
- the performance (error) signal \\(e\\)
- the controller output \\(u\\)
In this way, the designer can analyze how much control effort is used to achieve the performance level at the performance output.
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{{< figure src="/ox-hugo/monkhorst04_closed_loop_H2.png" caption="Figure 3: The closed loop system with weighting filters included. The system has \\(n\\) disturbance inputs and two outputs: the error \\(e\\) and the control signal \\(u\\). The \\(\mathcal{H}\_2\\) minimized the \\(\mathcal{H}\_2\\) norm of this system." >}}