Calibration
7.2 Least Squares Formulation
Least squares formulation has become one of the major tools for parameters estimation on experimental data. Although there are other methods available, this one provides good results and is the most widely known as it is easier to comprehend and provide better results. In fact, this method was found to have optimal statistical properties as it is consistent, unbiased and efficient [39].
Regarding equation (7.3), one can express de error vector as
=f−CR, (7.4)
remembering that the purpose of this formulation is to obtainC, as a calibration matrix.
By choosing a certain C, considered to be an approximation of the realb C, a minimization of the parameterJ, defined as
J=T, (7.5)
is carried out, where the superscriptT indicates the matrix transposition.
By substituting equation (7.4) in equation (7.5), one obtains
J= (f−RC)T(f−RC)
=fTf−CTRTf−fTRC+CTRTRC.
(7.6)
Now that the parameterJis fully defined, a differentiation of this parameter in order toChas to be carried out. The result has to be equated to zero in order to find out the relation by which the estimate Cb minimizesJ,
∂J
∂C
C=bC
=−2RTf+ 2RTRCb = 0 RTRCb =RTf.
(7.7)
The result of this differentiation is
Cb = (RTR)−1RTf (7.8)
and the parameterCb is calledleast-squares estimator (LSE) ofC.
Cb was estimated by assuming that the error is constant during all the performed tests, that is, 1=2=...=k. This assumption , however, is far from reality since it is not possible to guarantee that the same physical conditions are constant and affect equally the set of tests as the calibration process is mainly manual.
To obtain the newweighted least-squares estimator, an analogous process has to be carried out.
The new weighted error criterion is
JW =TW
= (f−RC)TW(f−RC)
, (7.9)
whereWstands for the desired weighting matrix.
By restrictingW to being a symmetric positive definite matrix and carrying out the minimization of the parameter JW with respect to C using the same methodology as in equation (7.7), the weighted least-squares estimator,CbW, is obtained, as
CbW = (RTWR)−1RTWf. (7.10)
7.2.1 Weighting Matrix Definition
As said previously, several factors may affect the calibration environment and thus, it is not reasonable to assume that in each observation the transducers and the loading errors are the same. This reason leads to considerate a new factor in the equation, the weighting matrix,W.
Since the calibration is a manual process of applying a pre-defined load to the balance by means of dead weights, pulleys and strings, there are uncertainties that would not be taken into account using the ordinary LS method. Each dead weight has a characteristic fabrication mass whose measurement is
made by a balance that induces an uncertainty, so the combination of several dead weights induces an error that is very hard to quantify. As for the pulleys and strings, there are friction forces between both this components that can interfere with the results and whose behaviour is impossible to predict. These are the main sources of error in terms of the calibration process itself.
Regarding the full bridges used to measure the sensing bars deformation, they can also be a source of error as the extensometers are very sensitive to the experimental conditions that can interfere with the output voltages and thus the calibration results.
For this reason, the weighting matrix has to be defined considering the two main sources of error described previously: the one related to the calibration process itself and the other related to the full bridges measurements.
Regarding equation (7.10), the term (RTWR)−1 provides information about uncertainties of the estimated parameters, being its diagonal elements called variances (squared uncertainties -u2
Cb) and its off-diagonal elements are the covariances of the fitted parameters.
As for W, it can be regarded as the inverse of the covariance matrix (V) which is related to the uncertainties of the applied loads (uf). It reflects both sources of error stated above, as represented in equation 7.11 where the first term is related with the weighting process and the other is related to the bridges readings.
W= (VW +SVRST)−1 (7.11)
Regarding the matrices that are used to obtainW, it is necessary to fully explain them.
MatrixVW
VW is a square matrix whose dimension is the number of loading conditions (N×N) and is considered to be diagonal. As stated previously, this matrix is related to the uncertainties of the calibration process itself that have two main sources: the first one is due to the uncertainties of the applied weights and the other encompasses the remaining sources of error that can affect the behaviour of the calibration system like friction forces between the strings and the pulleys or misalignments.
MatrixVR
VR is a square matrix with dimension6N ×6N that is related with the uncertainties on the full bridges readings. To obtain this matrix it is necessary to repeat the calibration process with the same loading conditions in the shortest possible time in order to avoid interferences. The diagonal elements of the ma- trix represent the variance between the same bridges outputs and the off-diagonal elements correspond to the covariant elements.
VR=
uR1,1;R1,1 uR1,1;R1,2 . . . uR1,1;R1,N uR1,1;R2,1 . . . uR1,1;R6,N uR1,2;R1,1 uR1,2;R1,2 . . . uR1,2;R1,N uR1,2;R2,1 . . . uR1,2;R6,N
... ... . .. ... ... . .. ...
uR1,N;R1,1 uR1,N;R1,2 . . . uR1,N;R1,N uR1,N;R2,1 . . . uR1,N;R6,N
uR2,1;R1,1 uR2,1;R1,2 . . . uR2,1;R1,N uR2,1;R2,1 . . . uR2,1;R6,N
... ... . .. ... ... . .. ...
uR6,N;R1,1 uR6,N;R1,2 . . . uR6,N;R1,N uR6,N;R2,1 . . . uR6,N;R6,N
(7.12)
In order to obtain the covariance matrix (VR), it is necessary to obtain a matrixR with dimension 6N×3. In this case we are assuming that three similar calibration processes are carried on. Regarding this matrix, the firstN elements of column1 are the readings of the full bridge number 1 from the first calibration process, the nextN elements of column 1 are the reading of the full bridge number 2 from the first calibration process and so on. Similarly for the columns 2 and 3, using the results of the second and third calibration processes, respectively. The matrixVRis then obtained via MATLABR code.
MatrixS
Matrix Sis the sensitivity matrix and has a dimension of N ×6N and its components correspond to the partial derivatives of equation (7.2). Considering the component of drag force (f1), the matrixSis represented as
S=
∂f1,1
∂R1,1
∂f1,1
∂R1,2 . . . ∂R∂f1,1
1,N
∂f1,1
∂R2,1 . . . ∂R∂f1,1
6,N
∂f1,2
∂R1,1
∂f1,2
∂R1,2 . . . ∂R∂f1,2
1,N
∂f1,2
∂R2,1 . . . ∂R∂f1,2
6,N
... ... . .. ... ... . .. ...
∂f1,N−1
∂R1,1
∂f1,N−1
∂R1,2 . . . ∂f∂R1,N−1
1,N
∂f1,N−1
∂R2,1 . . . ∂f∂R1,N−1
6,N
∂f1,N
∂R1,1
∂f1,N
∂R1,2 . . . ∂R∂f1,N
1,N
∂f1,N
∂R2,1 . . . ∂R∂f1,N
6,N
. (7.13)
Regarding the aerodynamic loads (f), the first subscript indicates the component (i= 1, ...,6) and the second subscript stands for the calibration loading condition (j = 1, ..., N). As for the bridges readings (R), the first subscript indicates the bridge number (i= 1, ...6) while the second component stands also for the loading condition (j = 1, ..., N). Matrix Scan be simplified as only the partial derivatives that correspond to the same calibration loading condition are different from 0 and so it can be written as
S=
∂f1,1
∂R1,1 0 . . . 0 ∂R∂f1,1
2,1 . . . 0 0 ∂R∂f1,2
1,2 . . . 0 0 . . . 0 ... ... . .. ... ... . .. ...
0 0 . . . 0 0 . . . 0
0 0 . . . ∂R∂f1,N
1,N 0 . . . ∂R∂f1,N
6,N
. (7.14)
After having presented matricesVRandS, it is possible to conclude that the termSVRSTis related to the uncertainties of the full bridges readings that affect the measurements and has a dimension of
N×N. The first estimation of the matrixSis found in a preliminary analysis where the weighting matrix is considered to be the identity matrix (I), then it is possible to estimateVW and assumingW=V−1W, it is possible to obtain the matrixCbW from which the components ofScan be estimated.
Now that all the components of the weighting matrix are defined, it is possible to obtain the least- squares estimator parameter (C) and thus the values of forces and moments estimated by the least-b squares formulation, whose vector is represented asbf, calculated as
bf =RC.b (7.15)