Add year and author to phdthesis list

content/phdthesis/jabben07_mechat.md

@@ -2,10 +2,15 @@
 title = "Mechatronic design of a magnetically suspended rotating platform"
 author = ["Thomas Dehaeze"]
 draft = false
+ref_author = "Jabben, L."
+ref_year = 2007
 +++
 
 Tags
-: [Dynamic Error Budgeting]({{< relref "dynamic_error_budgeting" >}})
+: [Dynamic Error Budgeting]({{<relref "dynamic_error_budgeting.md#" >}})
+
+Reference
+: ([Jabben 2007](#org6250919))
 
 Author
 : Jabben, L.
@@ -13,9 +18,6 @@ Author
 Year
 : 2007
 
-DOI
-:
-
 
 ## Dynamic Error Budgeting {#dynamic-error-budgeting}
 
@@ -161,21 +163,21 @@ Three factors influence the performance:
 The DEB helps identifying which disturbance is the limiting factor, and it should be investigated if the controller can deal with this disturbance before re-designing the plant.
 
 The modelling of disturbance as stochastic variables, is by excellence suitable for the optimal stochastic control framework.
-In Figure [1](#orgbf22b5e), the generalized plant maps the disturbances to the performance channels.
+In Figure [1](#orgcc56194), the generalized plant maps the disturbances to the performance channels.
 By minimizing the \\(\mathcal{H}\_2\\) system norm of the generalized plant, the variance of the performance channels is minimized.
 
-<a id="orgbf22b5e"></a>
+<a id="orgcc56194"></a>
 
 {{< figure src="/ox-hugo/jabben07_general_plant.png" caption="Figure 1: Control system with the generalized plant \\(G\\). The performance channels are stacked in \\(z\\), while the controller input is denoted with \\(y\\)" >}}
 
 
 #### Using Weighting Filters for Disturbance Modelling {#using-weighting-filters-for-disturbance-modelling}
 
-Since disturbances are generally not white, the system of Figure [1](#orgbf22b5e) needs to be augmented with so called **disturbance weighting filters**.
+Since disturbances are generally not white, the system of Figure [1](#orgcc56194) needs to be augmented with so called **disturbance weighting filters**.
 
 A disturbance weighting filter gives the disturbance PSD when white noise as input is applied.
 
-This is illustrated in Figure [2](#org27e9aeb) where a vector of white noise time signals \\(\underbar{w}(t)\\) is filtered through a weighting filter to obtain the colored physical disturbances \\(w(t)\\) with the desired PSD \\(S\_w\\) .
+This is illustrated in Figure [2](#org772dfb7) where a vector of white noise time signals \\(\underbar{w}(t)\\) is filtered through a weighting filter to obtain the colored physical disturbances \\(w(t)\\) with the desired PSD \\(S\_w\\) .
 
 The generalized plant framework also allows to include **weighting filters for the performance channels**.
 This is useful for three reasons:
@@ -184,7 +186,7 @@ This is useful for three reasons:
 -   some performance channels may be of more importance than others
 -   by using dynamic weighting filters, one can emphasize the performance in a certain frequency range
 
-<a id="org27e9aeb"></a>
+<a id="org772dfb7"></a>
 
 {{< figure src="/ox-hugo/jabben07_weighting_functions.png" caption="Figure 2: Control system with the generalized plant \\(G\\) and weighting functions" >}}
 
@@ -209,9 +211,9 @@ So, to obtain feasible controllers, the performance channel is a combination of
 By choosing suitable weighting filters for \\(y\\) and \\(u\\), the performance can be optimized while keeping the controller effort limited:
 \\[ \\|z\\|\_{rms}^2 = \left\\| \begin{bmatrix} y \\ \alpha u \end{bmatrix} \right\\|\_{rms}^2 = \\|y\\|\_{rms}^2 + \alpha^2 \\|u\\|\_{rms}^2 \\]
 
-By calculation \\(\mathcal{H}\_2\\) optimal controllers for increasing \\(\alpha\\) and plotting the performance \\(\\|y\\|\\) vs the controller effort \\(\\|u\\|\\), the curve as depicted in Figure [3](#org5ae58f0) is obtained.
+By calculation \\(\mathcal{H}\_2\\) optimal controllers for increasing \\(\alpha\\) and plotting the performance \\(\\|y\\|\\) vs the controller effort \\(\\|u\\|\\), the curve as depicted in Figure [3](#orgeab38dd) is obtained.
 
-<a id="org5ae58f0"></a>
+<a id="orgeab38dd"></a>
 
 {{< figure src="/ox-hugo/jabben07_pareto_curve_H2.png" caption="Figure 3: An illustration of a Pareto curve. Each point of the curve represents the performance obtained with an optimal controller. The curve is obtained by varying \\(\alpha\\) and calculating an \\(\mathcal{H}\_2\\) optimal controller for each \\(\alpha\\)." >}}
 
@@ -235,3 +237,8 @@ By calculation \\(\mathcal{H}\_2\\) optimal controllers for increasing \\(\alpha
 > To use the measured PSDs in an optimal control design, such as H2-control, the disturbances must be modelled using linear time invariant models with multiple white noise input.
 > To derive such models, spectral factorization is used.
 > It is recommended to investigate which methods for spectral factorization are currently available and numerically robust.
+
+
+## Bibliography {#bibliography}
+
+<a id="org6250919"></a>Jabben, Leon. 2007. “Mechatronic Design of a Magnetically Suspended Rotating Platform.” Delft University.