Update all files with new citeproc-org package
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@@ -4,11 +4,15 @@ author = ["Thomas Dehaeze"]
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### Backlinks {#backlinks}
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- [Dynamic Error Budgeting]({{< relref "dynamic_error_budgeting" >}})
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Tags
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: [Dynamic Error Budgeting]({{< relref "dynamic_error_budgeting" >}})
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Reference
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: <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>
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: ([Monkhorst 2004](#org7d7f3f5))
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Author(s)
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: Monkhorst, W.
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@@ -95,9 +99,9 @@ Find a controller \\(C\_{\mathcal{H}\_2}\\) which minimizes the \\(\mathcal{H}\_
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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.
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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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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](#org7321040)).
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<a id="org76ddb2c"></a>
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<a id="org7321040"></a>
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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)\\)." >}}
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@@ -108,23 +112,23 @@ The PSD \\(S\_w(f)\\) of the weighted signal is:
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Given \\(S\_w(f)\\), \\(V\_w(f)\\) can be obtained using a technique called _spectral factorization_.
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However, this can be avoided if the modelling of the disturbances is directly done in terms of weighting filters.
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Output weighting filters can also be used to scale different outputs relative to each other (Figure [2](#org425ff37)).
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Output weighting filters can also be used to scale different outputs relative to each other (Figure [2](#orgea16f0b)).
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<a id="org425ff37"></a>
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<a id="orgea16f0b"></a>
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{{< 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\\)" >}}
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#### Output scaling and the Pareto curve {#output-scaling-and-the-pareto-curve}
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In this research, the outputs of the closed loop system (Figure [3](#orgba842f3)) are:
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In this research, the outputs of the closed loop system (Figure [3](#org95f6ca6)) are:
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- the performance (error) signal \\(e\\)
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- the controller output \\(u\\)
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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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<a id="orgba842f3"></a>
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<a id="org95f6ca6"></a>
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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." >}}
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@@ -147,10 +151,7 @@ To achieve the highest degree of prediction accuracy, it is recommended to use t
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When an \\(\mathcal{H}\_2\\) controller is synthesized for a particular system, it can give the control designer useful hints about how to control the system best for optimal performance.
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Drawbacks however are, that no robustness guarantees can be given and that the order of the \\(\mathcal{H}\_2\\) controller will generally be too high for implementation.
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# Bibliography
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<a class="bibtex-entry" id="monkhorst04_dynam_error_budget">Monkhorst, W., *Dynamic error budgeting, a design approach* (2004). Delft University.</a> [↩](#651e626e040250ee71a0847aec41b60c)
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## Bibliography {#bibliography}
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## Backlinks {#backlinks}
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- [Dynamic Error Budgeting]({{< relref "dynamic_error_budgeting" >}})
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<a id="org7d7f3f5"></a>Monkhorst, Wouter. 2004. “Dynamic Error Budgeting, a Design Approach.” Delft University.
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