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Oprava PF a MPF + jejich implementace pro pmsm system

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1\section{EKF\_\-unQ Class Reference}
2\label{classEKF__unQ}\index{EKF_unQ@{EKF\_\-unQ}}
3Extended \doxyref{Kalman}{p.}{classKalman} filter with unknown {\tt Q}. 
4
5
6Inheritance diagram for EKF\_\-unQ:\nopagebreak
7\begin{figure}[H]
8\begin{center}
9\leavevmode
10\includegraphics[width=101pt]{classEKF__unQ__inherit__graph}
11\end{center}
12\end{figure}
13Collaboration diagram for EKF\_\-unQ:\nopagebreak
14\begin{figure}[H]
15\begin{center}
16\leavevmode
17\includegraphics[width=400pt]{classEKF__unQ__coll__graph}
18\end{center}
19\end{figure}
20\subsection*{Public Member Functions}
21\begin{CompactItemize}
22\item 
23{\bf EKF\_\-unQ} ({\bf RV} rx, {\bf RV} ry, {\bf RV} ru, {\bf RV} rQ)\label{classEKF__unQ_159eaaa5a05c5ceecdaa20956a307244}
24
25\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
26void {\bf condition} (const vec \&Q0)\label{classEKF__unQ_cd06a8c662da244cf61bb5bd39688c99}
27
28\begin{CompactList}\small\item\em Substitute {\tt val} for {\tt rvc}. \item\end{CompactList}\item 
29void {\bf set\_\-parameters} ({\bf diffbifn} $\ast$pfxu, {\bf diffbifn} $\ast$phxu, const ldmatQ0, const ldmatR0)\label{classEKF_28d058ae4d24d992d2f055419a06ee66}
30
31\begin{CompactList}\small\item\em Set nonlinear functions for mean values and covariance matrices. \item\end{CompactList}\item 
32void {\bf set\_\-parameters} (const mat \&A0, const mat \&B0, const mat \&C0, const mat \&D0, const {\bf ldmat} \&R0, const {\bf ldmat} \&Q0)\label{classKalman_239b28a0380946f5749b2f8d2807f93a}
33
34\begin{CompactList}\small\item\em Set parameters with check of relevance. \item\end{CompactList}\item 
35void {\bf bayes} (const vec \&dt)\label{classEKF_c79c62c9b3e0b56b3aaa1b6f1d9a7af7}
36
37\begin{CompactList}\small\item\em Here dt = [yt;ut] of appropriate dimensions. \item\end{CompactList}\item 
38void {\bf bayes} (mat Dt)\label{classBM_87b07867fd4c133aa89a18543f68d9f9}
39
40\begin{CompactList}\small\item\em Batch Bayes rule (columns of Dt are observations). \item\end{CompactList}\item 
41void {\bf set\_\-est} (const vec \&mu0, const {\bf ldmat} \&P0)\label{classKalman_80bcf29466d9a9dd2b8f74699807d0c0}
42
43\begin{CompactList}\small\item\em Set estimate values, used e.g. in initialization. \item\end{CompactList}\item 
44{\bf epdf} \& {\bf \_\-epdf} ()\label{classKalman_a213c57aef55b2645e550bed81cfc0d4}
45
46\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
47const {\bf RV} \& {\bf \_\-rv} () const \label{classBM_126bd2595c48e311fc2a7ab72876092a}
48
49\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
50double {\bf \_\-ll} () const \label{classBM_87f4a547d2c29180be88175e5eab9c88}
51
52\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
53const {\bf RV} \& {\bf \_\-rvc} () const \label{classBMcond_3fa60348b2da6b4208bb95b8d146900a}
54
55\begin{CompactList}\small\item\em access function \item\end{CompactList}\end{CompactItemize}
56\subsection*{Protected Attributes}
57\begin{CompactItemize}
58\item 
59{\bf RV} {\bf rvy}\label{classKalman_7501230c2fafa3655887d2da23b3184c}
60
61\begin{CompactList}\small\item\em Indetifier of output rv. \item\end{CompactList}\item 
62{\bf RV} {\bf rvu}\label{classKalman_44a16ffd5ac1e6e39bae34fea9e1e498}
63
64\begin{CompactList}\small\item\em Indetifier of exogeneous rv. \item\end{CompactList}\item 
65int {\bf dimx}\label{classKalman_39c8c403b46fa3b8c7da77cb2e3729eb}
66
67\begin{CompactList}\small\item\em cache of rv.count() \item\end{CompactList}\item 
68int {\bf dimy}\label{classKalman_ba17b956df1e38b31fbbc299c8213b6a}
69
70\begin{CompactList}\small\item\em cache of rvy.count() \item\end{CompactList}\item 
71int {\bf dimu}\label{classKalman_b0153795a1444b6968a86409c778d9ce}
72
73\begin{CompactList}\small\item\em cache of rvu.count() \item\end{CompactList}\item 
74mat {\bf A}\label{classKalman_5e02efe86ee91e9c74b93b425fe060b9}
75
76\begin{CompactList}\small\item\em Matrix A. \item\end{CompactList}\item 
77mat {\bf B}\label{classKalman_dc87704284a6c0bca13bf51f4345a50a}
78
79\begin{CompactList}\small\item\em Matrix B. \item\end{CompactList}\item 
80mat {\bf C}\label{classKalman_86a805cd6515872d1132ad0d6eb5dc13}
81
82\begin{CompactList}\small\item\em Matrix C. \item\end{CompactList}\item 
83mat {\bf D}\label{classKalman_d69f774ba3335c970c1c5b1d182f4dd1}
84
85\begin{CompactList}\small\item\em Matrix D. \item\end{CompactList}\item 
86{\bf ldmat} {\bf Q}\label{classKalman_9b69015c800eb93f3ee49da23a6f55d9}
87
88\begin{CompactList}\small\item\em Matrix Q in square-root form. \item\end{CompactList}\item 
89{\bf ldmat} {\bf R}\label{classKalman_11d171dc0e0ab111c56a70f98b97b3ec}
90
91\begin{CompactList}\small\item\em Matrix R in square-root form. \item\end{CompactList}\item 
92{\bf enorm}$<$ {\bf ldmat} $>$ {\bf est}\label{classKalman_5568c74bac67ae6d3b1061dba60c9424}
93
94\begin{CompactList}\small\item\em posterior density on \$x\_\-t\$ \item\end{CompactList}\item 
95{\bf enorm}$<$ {\bf ldmat} $>$ {\bf fy}\label{classKalman_e580ab06483952bd03f2e651763e184f}
96
97\begin{CompactList}\small\item\em preditive density on \$y\_\-t\$ \item\end{CompactList}\item 
98mat {\bf \_\-K}\label{classKalman_d422f51467c7a06174af2476d2826132}
99
100\begin{CompactList}\small\item\em placeholder for \doxyref{Kalman}{p.}{classKalman} gain \item\end{CompactList}\item 
101vec $\ast$ {\bf \_\-yp}\label{classKalman_5188eb0329f8561f0b357af329769bf8}
102
103\begin{CompactList}\small\item\em cache of fy.mu \item\end{CompactList}\item 
104{\bf ldmat} $\ast$ {\bf \_\-Ry}\label{classKalman_e17dd745daa8a958035a334a56fa4674}
105
106\begin{CompactList}\small\item\em cache of fy.R \item\end{CompactList}\item 
107{\bf ldmat} $\ast$ {\bf \_\-iRy}\label{classKalman_8a35bd14afa5a2d9bbd23ad333bec874}
108
109\begin{CompactList}\small\item\em cache of fy.iR \item\end{CompactList}\item 
110vec $\ast$ {\bf \_\-mu}\label{classKalman_d1f669b5b3421a070cc75d77b55ba734}
111
112\begin{CompactList}\small\item\em cache of est.mu \item\end{CompactList}\item 
113{\bf ldmat} $\ast$ {\bf \_\-P}\label{classKalman_b3388218567128a797e69b109138271d}
114
115\begin{CompactList}\small\item\em cache of est.R \item\end{CompactList}\item 
116{\bf ldmat} $\ast$ {\bf \_\-iP}\label{classKalman_13fec2c93d8a132201e28b70270acf5c}
117
118\begin{CompactList}\small\item\em cache of est.iR \item\end{CompactList}\item 
119{\bf RV} {\bf rv}\label{classBM_af00f0612fabe66241dd507188cdbf88}
120
121\begin{CompactList}\small\item\em Random variable of the posterior. \item\end{CompactList}\item 
122double {\bf ll}\label{classBM_5623fef6572a08c2b53b8c87b82dc979}
123
124\begin{CompactList}\small\item\em Logarithm of marginalized data likelihood. \item\end{CompactList}\item 
125bool {\bf evalll}\label{classBM_bf6fb59b30141074f8ee1e2f43d03129}
126
127\begin{CompactList}\small\item\em If true, the filter will compute likelihood of the data record and store it in {\tt ll} . Set to false if you want to save time. \item\end{CompactList}\item 
128{\bf RV} {\bf rvc}\label{classBMcond_9ba793c8ec453f04d372d17195ed8dec}
129
130\begin{CompactList}\small\item\em Identificator of the conditioning variable. \item\end{CompactList}\end{CompactItemize}
131
132
133\subsection{Detailed Description}
134Extended \doxyref{Kalman}{p.}{classKalman} filter with unknown {\tt Q}.
135
136The documentation for this class was generated from the following file:\begin{CompactItemize}
137\item 
138work/mixpp/tests/pmsm\_\-unkQpf.cpp\end{CompactItemize}
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