Tangle matlab scripts
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119
matlab/meas_transformation.m
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119
matlab/meas_transformation.m
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%% Clear Workspace and Close figures
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clear; close all; clc;
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%% Intialize Laplace variable
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s = zpk('s');
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% Define accelerometers positions/orientations
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% <<sec:accelerometer_pos>>
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% Let's first define the position and orientation of all measured accelerations with respect to a defined frame $\{O\}$.
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Opm = [-0.1875, -0.1875, -0.245;
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-0.1875, -0.1875, -0.245;
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0.1875, -0.1875, -0.245;
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0.1875, -0.1875, -0.245;
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0.1875, 0.1875, -0.245;
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0.1875, 0.1875, -0.245]';
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% #+name: tab:accelerometers_table_positions
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% #+caption: Positions of the accelerometers fixed to the vibration table with respect to $\{O\}$
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% #+attr_latex: :environment tabularx :width 0.55\linewidth :align Xcccccc
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% #+attr_latex: :center t :booktabs t :float t
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% #+RESULTS:
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% | | $a_1$ | $a_2$ | $a_3$ | $a_4$ | $a_5$ | $a_6$ |
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% |---+--------+--------+--------+--------+--------+--------|
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% | x | -0.188 | -0.188 | 0.188 | 0.188 | 0.188 | 0.188 |
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% | y | -0.188 | -0.188 | -0.188 | -0.188 | 0.188 | 0.188 |
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% | z | -0.245 | -0.245 | -0.245 | -0.245 | -0.245 | -0.245 |
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% We then define the direction of the measured accelerations (unit vectors):
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Osm = [0, 1, 0;
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0, 0, 1;
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1, 0, 0;
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0, 0, 1;
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1, 0, 0;
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0, 0, 1;]';
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% Transformation matrix from motion of the solid body to accelerometer measurements
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% <<sec:transformation_motion_to_acc>>
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% Let's try to estimate the x-y-z acceleration of any point of the solid body from the acceleration/angular acceleration of the solid body expressed in $\{O\}$.
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% For any point $p_i$ of the solid body (corresponding to an accelerometer), we can write:
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% \begin{equation}
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% \begin{bmatrix}
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% a_{i,x} \\ a_{i,y} \\ a_{i,z}
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% \end{bmatrix} = \begin{bmatrix}
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% \dot{v}_x \\ \dot{v}_y \\ \dot{v}_z
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% \end{bmatrix} + p_i \times \begin{bmatrix}
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% \dot{\omega}_x \\ \dot{\omega}_y \\ \dot{\omega}_z
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% \end{bmatrix}
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% \end{equation}
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% We can write the cross product as a matrix product using the skew-symmetric transformation:
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% \begin{equation}
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% \begin{bmatrix}
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% a_{i,x} \\ a_{i,y} \\ a_{i,z}
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% \end{bmatrix} = \begin{bmatrix}
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% \dot{v}_x \\ \dot{v}_y \\ \dot{v}_z
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% \end{bmatrix} + \underbrace{\begin{bmatrix}
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% 0 & p_{i,z} & -p_{i,y} \\
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% -p_{i,z} & 0 & p_{i,x} \\
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% p_{i,y} & -p_{i,x} & 0
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% \end{bmatrix}}_{P_{i,[\times]}} \cdot \begin{bmatrix}
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% \dot{\omega}_x \\ \dot{\omega}_y \\ \dot{\omega}_z
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% \end{bmatrix}
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% \end{equation}
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% If we now want to know the (scalar) acceleration $a_i$ of the point $p_i$ in the direction of the accelerometer direction $\hat{s}_i$, we can just project the 3d acceleration on $\hat{s}_i$:
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% \begin{equation}
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% a_i = \hat{s}_i^T \cdot \begin{bmatrix}
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% a_{i,x} \\ a_{i,y} \\ a_{i,z}
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% \end{bmatrix} = \hat{s}_i^T \cdot \begin{bmatrix}
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% \dot{v}_x \\ \dot{v}_y \\ \dot{v}_z
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% \end{bmatrix} + \left( \hat{s}_i^T \cdot P_{i,[\times]} \right) \cdot \begin{bmatrix}
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% \dot{\omega}_x \\ \dot{\omega}_y \\ \dot{\omega}_z
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% \end{bmatrix}
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% \end{equation}
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% Which is equivalent as a simple vector multiplication:
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% \begin{equation}
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% a_i = \begin{bmatrix}
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% \hat{s}_i^T & \hat{s}_i^T \cdot P_{i,[\times]}
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% \end{bmatrix}
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% \begin{bmatrix}
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% \dot{v}_x \\ \dot{v}_y \\ \dot{v}_z \\ \dot{\omega}_x \\ \dot{\omega}_y \\ \dot{\omega}_z
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% \end{bmatrix} = \begin{bmatrix}
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% \hat{s}_i^T & \hat{s}_i^T \cdot P_{i,[\times]}
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% \end{bmatrix} {}^O\vec{x}
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% \end{equation}
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% And finally we can combine the 6 (line) vectors for the 6 accelerometers to write that in a matrix form.
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% We obtain Eq. eqref:eq:M_matrix.
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% #+begin_important
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% The transformation from solid body acceleration ${}^O\vec{x}$ from sensor measured acceleration $\vec{a}$ is:
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% \begin{equation} \label{eq:M_matrix}
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% \vec{a} = \underbrace{\begin{bmatrix}
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% \hat{s}_1^T & \hat{s}_1^T \cdot P_{1,[\times]} \\
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% \vdots & \vdots \\
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% \hat{s}_6^T & \hat{s}_6^T \cdot P_{6,[\times]}
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% \end{bmatrix}}_{M} {}^O\vec{x}
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% \end{equation}
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% with $\hat{s}_i$ the unit vector representing the measured direction of the i'th accelerometer expressed in frame $\{O\}$ and $P_{i,[\times]}$ the skew-symmetric matrix representing the cross product of the position of the i'th accelerometer expressed in frame $\{O\}$.
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% #+end_important
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% Let's define such matrix using matlab:
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M = zeros(length(Opm), 6);
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for i = 1:length(Opm)
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Ri = [0, Opm(3,i), -Opm(2,i);
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-Opm(3,i), 0, Opm(1,i);
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Opm(2,i), -Opm(1,i), 0];
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M(i, 1:3) = Osm(:,i)';
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M(i, 4:6) = Osm(:,i)'*Ri;
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end
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238
matlab/simscape_model.m
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238
matlab/simscape_model.m
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%% Clear Workspace and Close figures
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clear; close all; clc;
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%% Intialize Laplace variable
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s = zpk('s');
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% Run meas_transformation.m script in order to get the tranformation (jacobian) matrix
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run('meas_transformation.m')
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% Open the Simulink File
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open('vibration_table.slx')
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% Springs
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% <<sec:simscape_springs>>
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% The 4 springs supporting the suspended optical table are modelled with "bushing joints" having stiffness and damping in the x-y-z directions:
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spring.kx = 1e4; % X- Stiffness [N/m]
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spring.cx = 1e1; % X- Damping [N/(m/s)]
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spring.ky = 1e4; % Y- Stiffness [N/m]
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spring.cy = 1e1; % Y- Damping [N/(m/s)]
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spring.kz = 1e4; % Z- Stiffness [N/m]
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spring.cz = 1e1; % Z- Damping [N/(m/s)]
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spring.z0 = 32e-3; % Equilibrium z-length [m]
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% Inertial Shaker (IS20)
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% <<sec:simscape_inertial_shaker>>
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% The inertial shaker is defined as two solid bodies:
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% - the "housing" that is fixed to the element that we want to excite
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% - the "inertial mass" that is suspended inside the housing
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% The inertial mass is guided inside the housing and an actuator (coil and magnet) can be used to apply a force between the inertial mass and the support.
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% The "reacting" force on the support is then used as an excitation.
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% #+name: tab:is20_characteristics
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% #+caption: Summary of the IS20 datasheet
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% #+attr_latex: :environment tabularx :width 0.4\linewidth :align lX
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% #+attr_latex: :center t :booktabs t :float t
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% | Characteristic | Value |
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% |-----------------+------------|
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% | Output Force | 20 N |
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% | Frequency Range | 10-3000 Hz |
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% | Moving Mass | 0.1 kg |
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% | Total Mass | 0.3 kg |
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% From the datasheet in Table [[tab:is20_characteristics]], we can estimate the parameters of the physical shaker.
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% These parameters are defined below
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shaker.w0 = 2*pi*10; % Resonance frequency of moving mass [rad/s]
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shaker.m = 0.1; % Moving mass [m]
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shaker.m_tot = 0.3; % Total mass [m]
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shaker.k = shaker.m*shaker.w0^2; % Spring constant [N/m]
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shaker.c = 0.2*sqrt(shaker.k*shaker.m); % Damping [N/(m/s)]
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% 3D accelerometer (356B18)
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% <<sec:simscape_accelerometers>>
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% An accelerometer consists of 2 solids:
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% - a "housing" rigidly fixed to the measured body
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% - an "inertial mass" suspended inside the housing by springs and guided in the measured direction
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% The relative motion between the housing and the inertial mass gives a measurement of the acceleration of the measured body (up to the suspension mode of the inertial mass).
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% #+name: tab:356b18_characteristics
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% #+caption: Summary of the 356B18 datasheet
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% #+attr_latex: :environment tabularx :width 0.5\linewidth :align lX
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% #+attr_latex: :center t :booktabs t :float t
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% | Characteristic | Value |
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% |---------------------+---------------------|
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% | Sensitivity | 0.102 V/(m/s2) |
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% | Frequency Range | 0.5 to 3000 Hz |
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% | Resonance Frequency | > 20 kHz |
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% | Resolution | 0.0005 m/s2 rms |
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% | Weight | 0.025 kg |
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% | Size | 20.3x26.1x20.3 [mm] |
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% Here are defined the parameters for the triaxial accelerometer:
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acc_3d.m = 0.005; % Inertial mass [kg]
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acc_3d.m_tot = 0.025; % Total mass [m]
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acc_3d.w0 = 2*pi*20e3; % Resonance frequency [rad/s]
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acc_3d.kx = acc_3d.m*acc_3d.w0^2; % Spring constant [N/m]
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acc_3d.ky = acc_3d.m*acc_3d.w0^2; % Spring constant [N/m]
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acc_3d.kz = acc_3d.m*acc_3d.w0^2; % Spring constant [N/m]
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acc_3d.cx = 1e2; % Damping [N/(m/s)]
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acc_3d.cy = 1e2; % Damping [N/(m/s)]
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acc_3d.cz = 1e2; % Damping [N/(m/s)]
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% DC gain between support acceleration and inertial mass displacement is $-m/k$:
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acc_3d.g_x = 1/(-acc_3d.m/acc_3d.kx); % [m/s^2/m]
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acc_3d.g_y = 1/(-acc_3d.m/acc_3d.ky); % [m/s^2/m]
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acc_3d.g_z = 1/(-acc_3d.m/acc_3d.kz); % [m/s^2/m]
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% We also define the sensitivity in order to have the outputs in volts.
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acc_3d.gV_x = 0.102; % [V/(m/s^2)]
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acc_3d.gV_y = 0.102; % [V/(m/s^2)]
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acc_3d.gV_z = 0.102; % [V/(m/s^2)]
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% The problem with using such model for accelerometers is that this adds states to the identified models (2x3 states for each triaxial accelerometer).
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% These states represents the dynamics of the suspended inertial mass.
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% In the frequency band of interest (few Hz up to ~1 kHz), the dynamics of the inertial mass can be ignore (its resonance is way above 1kHz).
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% Therefore, we might as well use idealized "transform sensors" blocks as they will give the same result up to ~20kHz while allowing to reduce the number of identified states.
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% The accelerometer model can be chosen by setting the =type= property:
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acc_3d.type = 2; % 1: inertial mass, 2: perfect
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% Number of states
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% Let's first use perfect 3d accelerometers:
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acc_3d.type = 2; % 1: inertial mass, 2: perfect
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% And identify the dynamics from the shaker force to the measured accelerations:
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%% Name of the Simulink File
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mdl = 'vibration_table';
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%% Input/Output definition
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clear io; io_i = 1;
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io(io_i) = linio([mdl, '/F'], 1, 'openinput'); io_i = io_i + 1;
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io(io_i) = linio([mdl, '/acc'], 1, 'openoutput'); io_i = io_i + 1;
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%% Run the linearization
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Gp = linearize(mdl, io);
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Gp.InputName = {'F'};
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Gp.OutputName = {'a1', 'a2', 'a3', 'a4', 'a5', 'a6'};
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% #+RESULTS:
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% : size(Gp)
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% : State-space model with 6 outputs, 1 inputs, and 12 states.
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% We indeed have the 12 states corresponding to the 6 DoF of the suspended optical table.
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% Let's now consider the inertial masses for the triaxial accelerometers:
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acc_3d.type = 1; % 1: inertial mass, 2: perfect
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%% Name of the Simulink File
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mdl = 'vibration_table';
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%% Input/Output definition
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clear io; io_i = 1;
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io(io_i) = linio([mdl, '/F'], 1, 'openinput'); io_i = io_i + 1;
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io(io_i) = linio([mdl, '/acc'], 1, 'openoutput'); io_i = io_i + 1;
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%% Run the linearization
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Ga = linearize(mdl, io);
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Ga.InputName = {'F'};
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Ga.OutputName = {'a1', 'a2', 'a3', 'a4', 'a5', 'a6'};
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% Resonance frequencies and mode shapes
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% Let's now identify the resonance frequency and mode shapes associated with the suspension modes of the optical table.
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acc_3d.type = 2; % 1: inertial mass, 2: perfect
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%% Name of the Simulink File
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mdl = 'vibration_table';
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%% Input/Output definition
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clear io; io_i = 1;
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io(io_i) = linio([mdl, '/F'], 1, 'openinput'); io_i = io_i + 1;
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io(io_i) = linio([mdl, '/acc_O'], 1, 'openoutput'); io_i = io_i + 1;
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%% Run the linearization
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G = linearize(mdl, io);
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G.InputName = {'F'};
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G.OutputName = {'ax', 'ay', 'az', 'wx', 'wy', 'wz'};
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% Compute the resonance frequencies
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ws = eig(G.A);
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ws = ws(imag(ws) > 0);
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% And the associated response of the optical table
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x_mod = zeros(6, 6);
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for i = 1:length(ws)
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xi = evalfr(G, ws(i));
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x_mod(:,i) = xi./norm(xi);
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end
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% Verify transformation
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%% Options for Linearized
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options = linearizeOptions;
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options.SampleTime = 0;
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%% Name of the Simulink File
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mdl = 'vibration_table';
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%% Input/Output definition
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clear io; io_i = 1;
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io(io_i) = linio([mdl, '/F'], 1, 'openinput'); io_i = io_i + 1;
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io(io_i) = linio([mdl, '/acc'], 1, 'openoutput'); io_i = io_i + 1;
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io(io_i) = linio([mdl, '/Absolute_Accelerometer'], 1, 'openoutput'); io_i = io_i + 1;
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%% Run the linearization
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G = linearize(mdl, io, 0.0, options);
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G.InputName = {'F'};
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G.OutputName = {'a1', 'a2', 'a3', 'a4', 'a5', 'a6', ...
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'Dx', 'Dy', 'Dz', 'Rx', 'Ry', 'Rz'};
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G_acc = inv(M)*G(1:6, 1);
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G_id = G(7:12, 1);
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bodeFig({G_acc(1), G_id(1)})
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bodeFig({G_acc(2), G_id(2)})
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bodeFig({G_acc(3), G_id(3)})
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bodeFig({G_acc(4), G_id(4)})
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bodeFig({G_acc(5), G_id(5)})
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bodeFig({G_acc(6), G_id(6)})
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