dehaeze20_optim_robus_compl_filte/matlab/index.org

Robust and Optimal Sensor Fusion - Matlab Computation

• Introduction
• Optimal Sensor Fusion for noise characteristics
  • Introduction
  • ZIP file containing the data and matlab files
  • Architecture
  • Noise of the sensors
  • H-Two Synthesis
  • H-Infinity Synthesis - method A
  • H-Infinity Synthesis - method B
  • Comparison of the methods
  • Conclusion
• Robustness to sensor dynamics uncertainty
  • Introduction
  • ZIP file containing the data and matlab files
  • Unknown sensor dynamics dynamics
  • Design the complementary filters in order to limit the phase and gain uncertainty of the super sensor
  • First Basic Example with gain mismatch
  • More Complete example with model uncertainty
• Equivalent Super Sensor
  • Sensor Fusion Architecture
  • Equivalent Configuration
  • Model the uncertainty of the super sensor
  • Model the noise of the super sensor
  • First guess
• Optimal And Robust Sensor Fusion in Practice
  • Measurement of the noise characteristics of the sensors
    • Huddle Test
    • Weights that represents the noises' PSD
    • Comparison of the noises' PSD
    • Computation of the coherence, power spectral density and cross spectral density of signals
  • Estimate the dynamic uncertainty of the sensors
  • Optimal and Robust synthesis of the complementary filters
• Methods of complementary filter synthesis
  • Complementary filters using analytical formula
    • Introduction
    • ZIP file containing the data and matlab files
    • Analytical 1st order complementary filters
    • Second Order Complementary Filters
    • Third Order Complementary Filters
  • H-Infinity synthesis of complementary filters
    • Introduction
    • ZIP file containing the data and matlab files
    • Synthesis Architecture
    • Weights
    • H-Infinity Synthesis
    • Obtained Complementary Filters
  • Feedback Control Architecture to generate Complementary Filters
    • Introduction
    • ZIP file containing the data and matlab files
    • Architecture
    • Loop Gain Design
    • Complementary Filters Obtained
  • Analytical Formula found in the literature
    • Analytical Formula
    • Matlab
    • Discussion
  • Comparison of the different methods of synthesis
• Bibliography

Introduction   ignore

In this document, the design of complementary filters is studied.

One use of complementary filter is described below:

The basic idea of a complementary filter involves taking two or more sensors, filtering out unreliable frequencies for each sensor, and combining the filtered outputs to get a better estimate throughout the entire bandwidth of the system. To achieve this, the sensors included in the filter should complement one another by performing better over specific parts of the system bandwidth.

• in section sec:optimal_comp_filters, the optimal design of the complementary filters in order to obtain the lowest resulting "super sensor" noise is studied

When blending two sensors using complementary filters with unknown dynamics, phase lag may be introduced that renders the close-loop system unstable.

• in section sec:comp_filter_robustness, the blending robustness to sensor dynamic uncertainty is studied.

Then, three design methods for generating two complementary filters are proposed:

• in section sec:comp_filters_analytical, analytical formulas are proposed
• in section sec:h_inf_synthesis_complementary_filters, the $\mathcal{H}_\infty$ synthesis is used
• in section sec:feedback_generate_comp_filters, the classical feedback architecture is used
• in section sec:analytical_formula_literature, analytical formulas found in the literature are listed

Optimal Sensor Fusion for noise characteristics

<<sec:optimal_comp_filters>>

Introduction   ignore

The idea is to combine sensors that works in different frequency range using complementary filters.

Doing so, one "super sensor" is obtained that can have better noise characteristics than the individual sensors over a large frequency range.

The complementary filters have to be designed in order to minimize the effect noise of each sensor on the super sensor noise.

ZIP file containing the data and matlab files   ignore

All the files (data and Matlab scripts) are accessible here.

Architecture

Let's consider the sensor fusion architecture shown on figure fig:fusion_two_noisy_sensors_weights where two sensors (sensor 1 and sensor 2) are measuring the same quantity $x$ with different noise characteristics determined by $N_1(s)$ and $N_2(s)$.

$\tilde{n}_1$ and $\tilde{n}_2$ are normalized white noise:

\begin{equation} \Phi_{\tilde{n}_1}(\omega) = \Phi_{\tilde{n}_1}(\omega) = 1 \end{equation}

Fusion of two sensors

We consider that the two sensor dynamics $G_1(s)$ and $G_2(s)$ are ideal:

\begin{equation} G_1(s) = G_2(s) = 1 \end{equation}

We obtain the architecture of figure fig:sensor_fusion_noisy_perfect_dyn.

Fusion of two sensors with ideal dynamics

$H_1(s)$ and $H_2(s)$ are complementary filters:

\begin{equation} H_1(s) + H_2(s) = 1 \end{equation}

The goal is to design $H_1(s)$ and $H_2(s)$ such that the effect of the noise sources $\tilde{n}_1$ and $\tilde{n}_2$ has the smallest possible effect on the estimation $\hat{x}$.

We have that the Power Spectral Density (PSD) of $\hat{x}$ is: \[ \Phi_{\hat{x}}(\omega) = |H_1(j\omega) N_1(j\omega)|^2 \Phi_{\tilde{n}_1}(\omega) + |H_2(j\omega) N_2(j\omega)|^2 \Phi_{\tilde{n}_2}(\omega), \quad \forall \omega \]

And the goal is the minimize the Root Mean Square (RMS) value of $\hat{x}$:

\begin{equation} \sigma_{\hat{x}} = \sqrt{\int_0^\infty \Phi_{\hat{x}}(\omega) d\omega} \end{equation}

Noise of the sensors

Let's define the noise characteristics of the two sensors by choosing $N_1$ and $N_2$:

• Sensor 1 characterized by $N_1(s)$ has low noise at low frequency (for instance a geophone)
• Sensor 2 characterized by $N_2(s)$ has low noise at high frequency (for instance an accelerometer)

  omegac = 100*2*pi; G0 = 1e-5; Ginf = 1e-4;
  N1 = (Ginf*s/omegac + G0)/(s/omegac + 1)/(1 + s/2/pi/100);

  omegac = 1*2*pi; G0 = 1e-3; Ginf = 1e-8;
  N2 = ((sqrt(Ginf)*s/omegac + sqrt(G0))/(s/omegac + 1))^2/(1 + s/2/pi/4000)^2;

  <<plt-matlab>>

Noise Characteristics of the two sensors (png, pdf)

H-Two Synthesis

As $\tilde{n}_1$ and $\tilde{n}_2$ are normalized white noise: $\Phi_{\tilde{n}_1}(\omega) = \Phi_{\tilde{n}_2}(\omega) = 1$ and we have: \[ \sigma_{\hat{x}} = \sqrt{\int_0^\infty |H_1 N_1|^2(\omega) + |H_2 N_2|^2(\omega) d\omega} = \left\| \begin{matrix} H_1 N_1 \\ H_2 N_2 \end{matrix} \right\|_2 \] Thus, the goal is to design $H_1(s)$ and $H_2(s)$ such that $H_1(s) + H_2(s) = 1$ and such that $\left\| \begin{matrix} H_1 N_1 \\ H_2 N_2 \end{matrix} \right\|_2$ is minimized.

For that, we use the $\mathcal{H}_2$ Synthesis.

We use the generalized plant architecture shown on figure fig:h_infinity_optimal_comp_filters.

$\mathcal{H}_2$ Synthesis - Generalized plant used for the optimal generation of complementary filters

The transfer function from $[n_1, n_2]$ to $\hat{x}$ is: \[ \begin{bmatrix} N_1 H_1 \\ N_2 (1 - H_1) \end{bmatrix} \] If we define $H_2 = 1 - H_1$, we obtain: \[ \begin{bmatrix} N_1 H_1 \\ N_2 H_2 \end{bmatrix} \]

Thus, if we minimize the $\mathcal{H}_2$ norm of this transfer function, we minimize the RMS value of $\hat{x}$.

We define the generalized plant $P$ on matlab as shown on figure fig:h_infinity_optimal_comp_filters.

  P = [0   N2  1;
       N1 -N2  0];

And we do the $\mathcal{H}_2$ synthesis using the h2syn command.

  [H1, ~, gamma] = h2syn(P, 1, 1);

Finally, we define $H_2(s) = 1 - H_1(s)$.

  H2 = 1 - H1;

The complementary filters obtained are shown on figure fig:htwo_comp_filters.

The PSD of the noise of the individual sensor and of the super sensor are shown in Fig. fig:psd_sensors_htwo_synthesis.

The Cumulative Power Spectrum (CPS) is shown on Fig. fig:cps_h2_synthesis.

The obtained RMS value of the super sensor is lower than the RMS value of the individual sensors.

  <<plt-matlab>>

Obtained complementary filters using the $\mathcal{H}_2$ Synthesis (png, pdf)

  PSD_S1 = abs(squeeze(freqresp(N1, freqs, 'Hz'))).^2;
  PSD_S2 = abs(squeeze(freqresp(N2, freqs, 'Hz'))).^2;
  PSD_H2 = abs(squeeze(freqresp(N1*H1, freqs, 'Hz'))).^2+abs(squeeze(freqresp(N2*H2, freqs, 'Hz'))).^2;

  <<plt-matlab>>

Power Spectral Density of the estimated $\hat{x}$ using the two sensors alone and using the optimally fused signal (png, pdf)

  CPS_S1 = 1/pi*cumtrapz(2*pi*freqs, PSD_S1);
  CPS_S2 = 1/pi*cumtrapz(2*pi*freqs, PSD_S2);
  CPS_H2 = 1/pi*cumtrapz(2*pi*freqs, PSD_H2);

  <<plt-matlab>>

Cumulative Power Spectrum of individual sensors and super sensor using the $\mathcal{H}_2$ synthesis (png, pdf)

H-Infinity Synthesis - method A

Another objective that we may have is that the noise of the super sensor $n_{SS}$ is following the minimum of the noise of the two sensors $n_1$ and $n_2$: \[ \Gamma_{n_{ss}}(\omega) = \min(\Gamma_{n_1}(\omega),\ \Gamma_{n_2}(\omega)) \]

In order to obtain that ideal case, we need that the complementary filters be designed such that:

\begin{align*} & |H_1(j\omega)| = 1 \text{ and } |H_2(j\omega)| = 0 \text{ at frequencies where } \Gamma_{n_1}(\omega) < \Gamma_{n_2}(\omega) \\ & |H_1(j\omega)| = 0 \text{ and } |H_2(j\omega)| = 1 \text{ at frequencies where } \Gamma_{n_1}(\omega) > \Gamma_{n_2}(\omega) \end{align*}

Which is indeed impossible in practice.

We could try to approach that with the $\mathcal{H}_\infty$ synthesis by using high order filters.

As shown on Fig. fig:noise_characteristics_sensors, the frequency where the two sensors have the same noise level is around 9Hz. We will thus choose weighting functions such that the merging frequency is around 9Hz.

The weighting functions used as well as the obtained complementary filters are shown in Fig. fig:weights_comp_filters_Hinfa.

  n = 5; w0 = 2*pi*10; G0 = 1/10; G1 = 10000; Gc = 1/2;
  W1a = (((1/w0)*sqrt((1-(G0/Gc)^(2/n))/(1-(Gc/G1)^(2/n)))*s + (G0/Gc)^(1/n))/((1/G1)^(1/n)*(1/w0)*sqrt((1-(G0/Gc)^(2/n))/(1-(Gc/G1)^(2/n)))*s + (1/Gc)^(1/n)))^n;

  n = 5; w0 = 2*pi*8; G0 = 1000; G1 = 0.1; Gc = 1/2;
  W2a = (((1/w0)*sqrt((1-(G0/Gc)^(2/n))/(1-(Gc/G1)^(2/n)))*s + (G0/Gc)^(1/n))/((1/G1)^(1/n)*(1/w0)*sqrt((1-(G0/Gc)^(2/n))/(1-(Gc/G1)^(2/n)))*s + (1/Gc)^(1/n)))^n;

  P = [W1a -W1a;
       0    W2a;
       1    0];

And we do the $\mathcal{H}_\infty$ synthesis using the hinfsyn command.

  [H2a, ~, gamma, ~] = hinfsyn(P, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'on');

[H2a, ~, gamma, ~] = hinfsyn(P, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'on');
Resetting value of Gamma min based on D_11, D_12, D_21 terms

Test bounds:      0.1000 <  gamma  <=  10500.0000

  gamma    hamx_eig  xinf_eig  hamy_eig   yinf_eig   nrho_xy   p/f
1.050e+04   2.1e+01 -3.0e-07   7.8e+00   -1.3e-15    0.0000    p
5.250e+03   2.1e+01 -1.5e-08   7.8e+00   -5.8e-14    0.0000    p
2.625e+03   2.1e+01   2.5e-10   7.8e+00   -3.7e-12    0.0000    p
1.313e+03   2.1e+01 -3.2e-11   7.8e+00   -7.3e-14    0.0000    p
  656.344   2.1e+01 -2.2e-10   7.8e+00   -1.1e-15    0.0000    p
  328.222   2.1e+01 -1.1e-10   7.8e+00   -1.2e-15    0.0000    p
  164.161   2.1e+01 -2.4e-08   7.8e+00   -8.9e-16    0.0000    p
   82.130   2.1e+01   2.0e-10   7.8e+00   -9.1e-31    0.0000    p
   41.115   2.1e+01 -6.8e-09   7.8e+00   -4.1e-13    0.0000    p
   20.608   2.1e+01   3.3e-10   7.8e+00   -1.4e-12    0.0000    p
   10.354   2.1e+01 -9.8e-09   7.8e+00   -1.8e-15    0.0000    p
    5.227   2.1e+01 -4.1e-09   7.8e+00   -2.5e-12    0.0000    p
    2.663   2.1e+01   2.7e-10   7.8e+00   -4.0e-14    0.0000    p
    1.382   2.1e+01 -3.2e+05#  7.8e+00   -3.5e-14    0.0000    f
    2.023   2.1e+01 -5.0e-10   7.8e+00    0.0e+00    0.0000    p
    1.702   2.1e+01 -2.4e+07#  7.8e+00   -1.6e-13    0.0000    f
    1.862   2.1e+01 -6.0e+08#  7.8e+00   -1.0e-12    0.0000    f
    1.942   2.1e+01 -2.8e-09   7.8e+00   -8.1e-14    0.0000    p
    1.902   2.1e+01 -2.5e-09   7.8e+00   -1.1e-13    0.0000    p
    1.882   2.1e+01 -9.3e-09   7.8e+00   -2.0e-15    0.0001    p
    1.872   2.1e+01 -1.3e+09#  7.8e+00   -3.6e-22    0.0000    f
    1.877   2.1e+01 -2.6e+09#  7.8e+00   -1.2e-13    0.0000    f
    1.880   2.1e+01 -5.6e+09#  7.8e+00   -1.4e-13    0.0000    f
    1.881   2.1e+01 -1.2e+10#  7.8e+00   -3.3e-12    0.0000    f
    1.882   2.1e+01 -3.2e+10#  7.8e+00   -8.5e-14    0.0001    f

 Gamma value achieved:     1.8824

  H1a = 1 - H2a;

  <<plt-matlab>>

Weights and Complementary Fitlers obtained (png, pdf)

We then compute the Power Spectral Density as well as the Cumulative Power Spectrum.

  PSD_Ha = abs(squeeze(freqresp(N1*H1a, freqs, 'Hz'))).^2+abs(squeeze(freqresp(N2*H2a, freqs, 'Hz'))).^2;
  CPS_Ha = 1/pi*cumtrapz(2*pi*freqs, PSD_Ha);

H-Infinity Synthesis - method B

We have that: \[ \Phi_{\hat{x}}(\omega) = \left|H_1(j\omega) N_1(j\omega)\right|^2 + \left|H_2(j\omega) N_2(j\omega)\right|^2 \]

Then, at frequencies where $|H_1(j\omega)| < |H_2(j\omega)|$ we would like that $|N_1(j\omega)| = 1$ and $|N_2(j\omega)| = 0$ as we discussed before. Then $|H_1 N_1|^2 + |H_2 N_2|^2 = |N_1|^2$.

We know that this is impossible in practice. A more realistic choice is to design $H_2(s)$ such that when $|N_2(j\omega)| > |N_1(j\omega)|$, we have that: \[ |H_2 N_2|^2 = \epsilon |H_1 N_1|^2 \]

Which is equivalent to have (by supposing $|H_1| \approx 1$): \[ |H_2| = \sqrt{\epsilon} \frac{|N_1|}{|N_2|} \]

And we have:

\begin{align*} \Phi_{\hat{x}} &= \left|H_1 N_1\right|^2 + |H_2 N_2|^2 \\ &= (1 + \epsilon) \left| H_1 N_1 \right|^2 \\ &\approx \left|N_1\right|^2 \end{align*}

Similarly, we design $H_1(s)$ such that at frequencies where $|N_1| > |N_2|$: \[ |H_1| = \sqrt{\epsilon} \frac{|N_2|}{|N_1|} \]

For instance, is we take $\epsilon = 1$, then the PSD of $\hat{x}$ is increased by just by a factor $\sqrt{2}$ over the all frequencies from the idea case.

We use this as the weighting functions for the $\mathcal{H}_\infty$ synthesis of the complementary filters.

The weighting function and the obtained complementary filters are shown in Fig. fig:weights_comp_filters_Hinfb.

  epsilon = 2;

  W1b = 1/epsilon*N1/N2;
  W2b = 1/epsilon*N2/N1;

  W1b = W1b/(1 + s/2/pi/1000); % this is added so that it is proper

  P = [W1b -W1b;
       0    W2b;
       1    0];

And we do the $\mathcal{H}_\infty$ synthesis using the hinfsyn command.

  [H2b, ~, gamma, ~] = hinfsyn(P, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'on');

[H2b, ~, gamma, ~] = hinfsyn(P, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'on');
Test bounds:      0.0000 <  gamma  <=     32.8125

  gamma    hamx_eig  xinf_eig  hamy_eig   yinf_eig   nrho_xy   p/f
   32.812   1.8e+01   3.4e-10   6.3e+00   -2.9e-13    0.0000    p
   16.406   1.8e+01   3.4e-10   6.3e+00   -1.2e-15    0.0000    p
    8.203   1.8e+01   3.3e-10   6.3e+00   -2.6e-13    0.0000    p
    4.102   1.8e+01   3.3e-10   6.3e+00   -2.1e-13    0.0000    p
    2.051   1.7e+01   3.4e-10   6.3e+00   -7.2e-16    0.0000    p
    1.025   1.6e+01 -1.3e+06#  6.3e+00   -8.3e-14    0.0000    f
    1.538   1.7e+01   3.4e-10   6.3e+00   -2.0e-13    0.0000    p
    1.282   1.7e+01   3.4e-10   6.3e+00   -7.9e-17    0.0000    p
    1.154   1.7e+01   3.6e-10   6.3e+00   -1.8e-13    0.0000    p
    1.089   1.7e+01 -3.4e+06#  6.3e+00   -1.7e-13    0.0000    f
    1.122   1.7e+01 -1.0e+07#  6.3e+00   -3.2e-13    0.0000    f
    1.138   1.7e+01 -1.3e+08#  6.3e+00   -1.8e-13    0.0000    f
    1.146   1.7e+01   3.2e-10   6.3e+00   -3.0e-13    0.0000    p
    1.142   1.7e+01   5.5e-10   6.3e+00   -2.8e-13    0.0000    p
    1.140   1.7e+01 -1.5e-10   6.3e+00   -2.3e-13    0.0000    p
    1.139   1.7e+01 -4.8e+08#  6.3e+00   -6.2e-14    0.0000    f
    1.139   1.7e+01   1.3e-09   6.3e+00   -8.9e-17    0.0000    p

 Gamma value achieved:     1.1390

  H1b = 1 - H2b;

  <<plt-matlab>>

Weights and Complementary Fitlers obtained (png, pdf)

  PSD_Hb = abs(squeeze(freqresp(N1*H1b, freqs, 'Hz'))).^2+abs(squeeze(freqresp(N2*H2b, freqs, 'Hz'))).^2;
  CPS_Hb = 1/pi*cumtrapz(2*pi*freqs, PSD_Hb);

Comparison of the methods

The three methods are now compared.

The Power Spectral Density of the super sensors obtained with the complementary filters designed using the three methods are shown in Fig. fig:comparison_psd_noise.

The Cumulative Power Spectrum for the same sensors are shown on Fig. fig:comparison_cps_noise.

The RMS value of the obtained super sensors are shown on table tab:rms_results.

	rms value
Sensor 1	1.3e-03
Sensor 2	1.3e-03
H2 Fusion	1.2e-04
H-Infinity a	2.4e-04
H-Infinity b	1.4e-04

RMS value of the estimation error when using the sensor individually and when using the two sensor merged using the optimal complementary filters

  <<plt-matlab>>

Comparison of the obtained Power Spectral Density using the three methods (png, pdf)

  <<plt-matlab>>

Comparison of the obtained Cumulative Power Spectrum using the three methods (png, pdf)

Conclusion

From the above complementary filter design with the $\mathcal{H}_2$ and $\mathcal{H}_\infty$ synthesis, it still seems that the $\mathcal{H}_2$ synthesis gives the complementary filters that permits to obtain the minimal super sensor noise (when measuring with the $\mathcal{H}_2$ norm).

Robustness to sensor dynamics uncertainty

<<sec:comp_filter_robustness>>

Introduction   ignore

Let's first consider ideal sensors where $G_1 = 1$ and $G_2 = 1$ (figure fig:fusion_two_noisy_sensors_with_dyn_bis).

Fusion of two sensors

We then have:

\begin{align*} \hat{x} &= (x + n_1) H_1 + (x + n_2) H_2 \\ &= x + n_1 H_1 + n_2 H_2 \end{align*}

So the estimation error is \[ \delta_x = \hat{x} - x = n_1 H_1 + n_2 H_2 \]

And we see that the complementary filters are only shaping the noise and that they do not impact the transfer function from $x$ to $\hat{x}$ that is in the feedback path.

ZIP file containing the data and matlab files   ignore

All the files (data and Matlab scripts) are accessible here.

Unknown sensor dynamics dynamics

In practical systems, the sensor dynamics has always some level of uncertainty. Let's represent that with multiplicative input uncertainty as shown on figure fig:fusion_gain_mismatch.

Fusion of two sensors with input multiplicative uncertainty

We have:

\begin{align*} \frac{\hat{x}}{x} &= (1 + W_1 \Delta_1) H_1 + (1 + W_2 \Delta_2) H_2 \\ &= 1 + W_1 H_1 \Delta_1 + W_2 H_2 \Delta_2 \end{align*}

With $\Delta_i$ is any transfer function satisfying $\| \Delta_i \|_\infty < 1$.

We see that as soon as we have some uncertainty in the sensor dynamics, we have that the complementary filters have some effect on the transfer function from $x$ to $\hat{x}$.

We want that the super sensor transfer function has a gain of 1 and no phase variation over all the frequencies: \[ \frac{\hat{x}}{x} \approx 1 \]

Thus, we want that

\begin{align*} & |W_1 H_1 \Delta_1 + W_2 H_2 \Delta_2| < \epsilon \quad \forall \omega, \forall \Delta_i, \|\Delta_i\|_\infty < 1 \\ \Longleftrightarrow & |W_1 H_1| + |W_2 H_2| < \epsilon \quad \forall \omega \end{align*}

Which is approximately the same as requiring \[ \left\| \begin{matrix} W_1 H_1 \\ W_2 H_2 \end{matrix} \right\|_\infty < \epsilon \]

How small should we choose $\epsilon$?

The uncertainty set of the transfer function from $\hat{x}$ to $x$ is bounded in the complex plane by a circle centered on 1 and with a radius equal to $\epsilon$ (figure fig:uncertainty_gain_phase_variation).

We then have that the angle introduced by the super sensor is bounded by $\arcsin(\epsilon)$: \[ \angle \frac{\hat{x}}{x} \le \arcsin (\epsilon) \quad \forall \omega \]

Maximum phase variation

Thus, we choose should choose $\epsilon$ so that the maximum phase uncertainty introduced by the sensors is of an acceptable value.

Design the complementary filters in order to limit the phase and gain uncertainty of the super sensor

Let's say the two sensors dynamics $H_1$ and $H_2$ have been identified with the associated uncertainty weights $W_1$ and $W_2$.

If we want to have a maximum phase introduced by the sensors of 20 degrees, we have to design $H_1$ and $H_2$ such that:

\begin{align*} & arcsin(|H_1 W_1| + |H_2 W_2|) < 20 \text{ deg} \\ \Longleftrightarrow & |H_1 W_1| + |H_2 W_2| < 0.34 \end{align*}

We can do that with the $\mathcal{H}_\infty$ synthesis by setting upper bounds on the complementary filters using weights that corresponds to the sensor dynamics uncertainty.

For simplicity, let's suppose $W_1(s) = W_2(s) = 0.1$ ($10\%$ uncertainty in the sensor gain). \[ |H_1 W_1| + |H_2 W_2| < 3.4 \]

Thus, by limiting the norm of the complementary filters, we can limit the maximum unwanted phase introduced by the uncertainty on the sensors dynamics.

This is of primary importance in order to ensure the stability of the feedback loop using the super sensor signal.

First Basic Example with gain mismatch

Let's consider two ideal sensors except one sensor has not an expected gain of one but a gain of $0.6$.

  G1 = 1;
  G2 = 0.6;

Let's design two complementary filters as shown on figure fig:comp_filters_robustness_test. The complementary filters shown in blue does not present a bump as the red ones but provides less sensor separation at high and low frequencies.

  <<plt-matlab>>

The two complementary filters designed for the robustness test (png, pdf)

We then compute the bode plot of the super sensor transfer function $H_1*G_1 + H_2*G_2$ for both complementary filters pair (figure fig:tf_super_sensor_comp).

We see that the blue complementary filters with a lower maximum norm permits to limit the phase lag introduced by the gain mismatch.

  <<plt-matlab>>

Comparison of the obtained super sensor transfer functions (png, pdf)

TODO More Complete example with model uncertainty

Equivalent Super Sensor

The goal here is to find the parameters of a single sensor that would best represent a super sensor.

Sensor Fusion Architecture

Let consider figure fig:sensor_fusion_full where two sensors are merged. The dynamic uncertainty of each sensor is represented by a weight $w_i(s)$, the frequency characteristics each of the sensor noise is represented by the weights $N_i(s)$. The noise sources $\tilde{n}_i$ are considered to be white noise: $\Phi_{\tilde{n}_i}(\omega) = 1, \ \forall\omega$.

Sensor fusion architecture (png, pdf).

\begin{align*} \hat{x} &= H_1(s) N_1(s) \tilde{n}_1 + H_2(s) N_2(s) \tilde{n}_2 \\ &\quad \quad + \Big(H_1(s) \big(1 + w_1(s) \Delta_1(s)\big) + H_2(s) \big(1 + w_2(s) \Delta_2(s)\big)\Big) x \\ &= H_1(s) N_1(s) \tilde{n}_1 + H_2(s) N_2(s) \tilde{n}_2 \\ &\quad \quad + \big(1 + H_1(s) w_1(s) \Delta_1(s) + H_2(s) w_2(s) \Delta_2(s)\big) x \end{align*}

To the dynamics of the super sensor is:

\begin{equation} \frac{\hat{x}}{x} = 1 + H_1(s) w_1(s) \Delta_1(s) + H_2(s) w_2(s) \Delta_2(s) \end{equation}

And the noise of the super sensor is:

\begin{equation} n_{ss} = H_1(s) N_1(s) \tilde{n}_1 + H_2(s) N_2(s) \tilde{n}_2 \end{equation}

Equivalent Configuration

We try to determine $w_{ss}(s)$ and $N_{ss}(s)$ such that the sensor on figure fig:sensor_fusion_equivalent is equivalent to the super sensor of figure fig:sensor_fusion_full.

Equivalent Super Sensor (png, pdf).

Model the uncertainty of the super sensor

At each frequency $\omega$, the uncertainty set of the super sensor shown on figure fig:sensor_fusion_full is a circle centered on $1$ with a radius equal to $|H_1(j\omega) w_1(j\omega)| + |H_2(j\omega) w_2(j\omega)|$ on the complex plane. The uncertainty set of the sensor shown on figure fig:sensor_fusion_equivalent is a circle centered on $1$ with a radius equal to $|w_{ss}(j\omega)|$ on the complex plane.

Ideally, we want to find a weight $w_{ss}(s)$ so that:

\[ |w_{ss}(j\omega)| = |H_1(j\omega) w_1(j\omega)| + |H_2(j\omega) w_2(j\omega)|, \quad \forall\omega \]

Model the noise of the super sensor

The PSD of the estimation $\hat{x}$ when $x = 0$ of the configuration shown on figure fig:sensor_fusion_full is:

\begin{align*} \Phi_{\hat{x}}(\omega) &= | H_1(j\omega) N_1(j\omega) |^2 \Phi_{\tilde{n}_1} + | H_2(j\omega) N_2(j\omega) |^2 \Phi_{\tilde{n}_2} \\ &= | H_1(j\omega) N_1(j\omega) |^2 + | H_2(j\omega) N_2(j\omega) |^2 \end{align*}

The PSD of the estimation $\hat{x}$ when $x = 0$ of the configuration shown on figure fig:sensor_fusion_equivalent is:

\begin{align*} \Phi_{\hat{x}}(\omega) &= | N_{ss}(j\omega) |^2 \Phi_{\tilde{n}} \\ &= | N_{ss}(j\omega) |^2 \end{align*}

Ideally, we want to find a weight $N_{ss}(s)$ such that:

\[ |N_{ss}(j\omega)|^2 = | H_1(j\omega) N_1(j\omega) |^2 + | H_2(j\omega) N_2(j\omega) |^2 \quad \forall\omega \]

First guess

We could choose

\begin{align*} w_{ss}(s) &= H_1(s) w_1(s) + H_2(s) w_2(s) \\ N_{ss}(s) &= H_1(s) N_1(s) + H_2(s) N_2(s) \end{align*}

But we would have:

\begin{align*} |w_{ss}(j\omega)| &= |H_1(j\omega) w_1(j\omega) + H_2(j\omega) w_2(j\omega)|, \quad \forall\omega \\ &\neq |H_1(j\omega) w_1(j\omega)| + |H_2(j\omega) w_2(j\omega)|, \quad \forall\omega \end{align*}

and

\begin{align*} |N_{ss}(j\omega)|^2 &= | H_1(j\omega) N_1(j\omega) + H_2(j\omega) N_2(j\omega) |^2 \quad \forall\omega \\ &\neq | H_1(j\omega) N_1(j\omega)|^2 + |H_2(j\omega) N_2(j\omega) |^2 \quad \forall\omega \\ \end{align*}

Optimal And Robust Sensor Fusion in Practice

Here are the steps in order to apply optimal and robust sensor fusion:

• Measure the noise characteristics of the sensors to be merged (necessary for "optimal" part of the fusion)
• Measure/Estimate the dynamic uncertainty of the sensors (necessary for "robust" part of the fusion)
• Apply H2/H-infinity synthesis of the complementary filters

Measurement of the noise characteristics of the sensors

Huddle Test

The technique to estimate the sensor noise is taken from cite:barzilai98_techn_measur_noise_sensor_presen.

Let's consider two sensors (sensor 1 and sensor 2) that are measuring the same quantity $x$ as shown in figure fig:huddle_test.

Huddle test block diagram

Each sensor has uncorrelated noise $n_1$ and $n_2$ and internal dynamics $G_1(s)$ and $G_2(s)$ respectively.

We here suppose that each sensor has the same magnitude of instrumental noise: $n_1 = n_2 = n$. We also assume that their dynamics is ideal: $G_1(s) = G_2(s) = 1$.

We then have:

\begin{equation} \gamma_{\hat{x}_1\hat{x}_2}^2(\omega) = \frac{1}{1 + 2 \left( \frac{|\Phi_n(\omega)|}{|\Phi_{\hat{x}}(\omega)|} \right) + \left( \frac{|\Phi_n(\omega)|}{|\Phi_{\hat{x}}(\omega)|} \right)^2} \end{equation}

Since the input signal $x$ and the instrumental noise $n$ are incoherent:

\begin{equation} |\Phi_{\hat{x}}(\omega)| = |\Phi_n(\omega)| + |\Phi_x(\omega)| \end{equation}

From equations eq:coh_bis and eq:incoherent_noise, we finally obtain

\begin{equation} |\Phi_n(\omega)| = |\Phi_{\hat{x}}(\omega)| \left( 1 - \sqrt{\gamma_{\hat{x}_1\hat{x}_2}^2(\omega)} \right) \end{equation}

Weights that represents the noises' PSD

For further complementary filter synthesis, it is preferred to consider a normalized noise source $\tilde{n}$ that has a PSD equal to one ($\Phi_{\tilde{n}}(\omega) = 1$) and to use a weighting filter $N(s)$ in order to represent the frequency dependence of the noise.

The weighting filter $N(s)$ should be designed such that:

\begin{align*} & \Phi_n(\omega) \approx |N(j\omega)|^2 \Phi_{\tilde{n}}(\omega) \quad \forall \omega \\ \Longleftrightarrow & |N(j\omega)| \approx \sqrt{\Phi_n(\omega)} \quad \forall \omega \end{align*}

These weighting filters can then be used to compare the noise level of sensors for the synthesis of complementary filters.

The sensor with a normalized noise input is shown in figure fig:one_sensor_normalized_noise.

One sensor with normalized noise

Comparison of the noises' PSD

Once the noise of the sensors to be merged have been characterized, the power spectral density of both sensors have to be compared.

Ideally, the PSD of the noise are such that:

\begin{align*} \Phi_{n_1}(\omega) &< \Phi_{n_2}(\omega) \text{ for } \omega < \omega_m \\ \Phi_{n_1}(\omega) &> \Phi_{n_2}(\omega) \text{ for } \omega > \omega_m \end{align*}

Computation of the coherence, power spectral density and cross spectral density of signals

The coherence between signals $x$ and $y$ is defined as follow \[ \gamma^2_{xy}(\omega) = \frac{|\Phi_{xy}(\omega)|^2}{|\Phi_{x}(\omega)| |\Phi_{y}(\omega)|} \] where $|\Phi_x(\omega)|$ is the output Power Spectral Density (PSD) of signal $x$ and $|\Phi_{xy}(\omega)|$ is the Cross Spectral Density (CSD) of signal $x$ and $y$.

The PSD and CSD are defined as follow:

\begin{align} |\Phi_x(\omega)| &= \frac{2}{n_d T} \sum^{n_d}_{n=1} \left| X_k(\omega, T) \right|^2 \\ |\Phi_{xy}(\omega)| &= \frac{2}{n_d T} \sum^{n_d}_{n=1} [ X_k^*(\omega, T) ] [ Y_k(\omega, T) ] \end{align}

where:

• $n_d$ is the number for records averaged
• $T$ is the length of each record
• $X_k(\omega, T)$ is the finite Fourier transform of the $k^{\text{th}}$ record
• $X_k^*(\omega, T)$ is its complex conjugate

Estimate the dynamic uncertainty of the sensors

Let's consider one sensor represented on figure fig:one_sensor_dyn_uncertainty.

The dynamic uncertainty is represented by an input multiplicative uncertainty where $w(s)$ is a weight that represents the level of the uncertainty.

The goal is to accurately determine $w(s)$ for the sensors that have to be merged.

Sensor with dynamic uncertainty

Optimal and Robust synthesis of the complementary filters

Once the noise characteristics and dynamic uncertainty of both sensors have been determined and we have determined the following weighting functions:

• $w_1(s)$ and $w_2(s)$ representing the dynamic uncertainty of both sensors
• $N_1(s)$ and $N_2(s)$ representing the noise characteristics of both sensors

The goal is to design complementary filters $H_1(s)$ and $H_2(s)$ shown in figure fig:sensor_fusion_full such that:

• the uncertainty on the super sensor dynamics is minimized
• the noise sources $\tilde{n}_1$ and $\tilde{n}_2$ has the lowest possible effect on the estimation $\hat{x}$

Sensor fusion architecture with sensor dynamics uncertainty

Methods of complementary filter synthesis

Complementary filters using analytical formula

<<sec:comp_filters_analytical>>

Introduction   ignore

ZIP file containing the data and matlab files   ignore

All the files (data and Matlab scripts) are accessible here.

Analytical 1st order complementary filters

First order complementary filters are defined with following equations:

\begin{align} H_L(s) = \frac{1}{1 + \frac{s}{\omega_0}}\\ H_H(s) = \frac{\frac{s}{\omega_0}}{1 + \frac{s}{\omega_0}} \end{align}

Their bode plot is shown figure fig:comp_filter_1st_order.

  w0 = 2*pi; % [rad/s]

  Hh1 = (s/w0)/((s/w0)+1);
  Hl1 = 1/((s/w0)+1);

  <<plt-matlab>>

Bode plot of first order complementary filter (png, pdf)

Second Order Complementary Filters

We here use analytical formula for the complementary filters $H_L$ and $H_H$.

The first two formulas that are used to generate complementary filters are:

\begin{align*} H_L(s) &= \frac{(1+\alpha) (\frac{s}{\omega_0})+1}{\left((\frac{s}{\omega_0})+1\right) \left((\frac{s}{\omega_0})^2 + \alpha (\frac{s}{\omega_0}) + 1\right)}\\ H_H(s) &= \frac{(\frac{s}{\omega_0})^2 \left((\frac{s}{\omega_0})+1+\alpha\right)}{\left((\frac{s}{\omega_0})+1\right) \left((\frac{s}{\omega_0})^2 + \alpha (\frac{s}{\omega_0}) + 1\right)} \end{align*}

where:

• $\omega_0$ is the blending frequency in rad/s.

• $\alpha$ is used to change the shape of the filters:

  • Small values for $\alpha$ will produce high magnitude of the filters $|H_L(j\omega)|$ and $|H_H(j\omega)|$ near $\omega_0$ but smaller value for $|H_L(j\omega)|$ above $\approx 1.5 \omega_0$ and for $|H_H(j\omega)|$ below $\approx 0.7 \omega_0$
  • A large $\alpha$ will do the opposite

This is illustrated on figure fig:comp_filters_param_alpha. The slope of those filters at high and low frequencies is $-2$ and $2$ respectively for $H_L$ and $H_H$.

  <<plt-matlab>>

Effect of the parameter $\alpha$ on the shape of the generated second order complementary filters (png, pdf)

We now study the maximum norm of the filters function of the parameter $\alpha$. As we saw that the maximum norm of the filters is important for the robust merging of filters.

  figure;
  plot(alphas, infnorms)
  set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log');
  xlabel('$\alpha$'); ylabel('$\|H_1\|_\infty$');

  <<plt-matlab>>

Evolution of the H-Infinity norm of the complementary filters with the parameter $\alpha$ (png, pdf)

Third Order Complementary Filters

The following formula gives complementary filters with slopes of $-3$ and $3$:

\begin{align*} H_L(s) &= \frac{\left(1+(\alpha+1)(\beta+1)\right) (\frac{s}{\omega_0})^2 + (1+\alpha+\beta)(\frac{s}{\omega_0}) + 1}{\left(\frac{s}{\omega_0} + 1\right) \left( (\frac{s}{\omega_0})^2 + \alpha (\frac{s}{\omega_0}) + 1 \right) \left( (\frac{s}{\omega_0})^2 + \beta (\frac{s}{\omega_0}) + 1 \right)}\\ H_H(s) &= \frac{(\frac{s}{\omega_0})^3 \left( (\frac{s}{\omega_0})^2 + (1+\alpha+\beta) (\frac{s}{\omega_0}) + (1+(\alpha+1)(\beta+1)) \right)}{\left(\frac{s}{\omega_0} + 1\right) \left( (\frac{s}{\omega_0})^2 + \alpha (\frac{s}{\omega_0}) + 1 \right) \left( (\frac{s}{\omega_0})^2 + \beta (\frac{s}{\omega_0}) + 1 \right)} \end{align*}

The parameters are:

• $\omega_0$ is the blending frequency in rad/s
• $\alpha$ and $\beta$ that are used to change the shape of the filters similarly to the parameter $\alpha$ for the second order complementary filters

The filters are defined below and the result is shown on figure fig:complementary_filters_third_order.

  alpha = 1;
  beta = 10;
  w0 = 2*pi*14;

  Hh3_ana = (s/w0)^3 * ((s/w0)^2 + (1+alpha+beta)*(s/w0) + (1+(alpha+1)*(beta+1)))/((s/w0 + 1)*((s/w0)^2+alpha*(s/w0)+1)*((s/w0)^2+beta*(s/w0)+1));
  Hl3_ana = ((1+(alpha+1)*(beta+1))*(s/w0)^2 + (1+alpha+beta)*(s/w0) + 1)/((s/w0 + 1)*((s/w0)^2+alpha*(s/w0)+1)*((s/w0)^2+beta*(s/w0)+1));

  <<plt-matlab>>

Third order complementary filters using the analytical formula (png, pdf)

H-Infinity synthesis of complementary filters

<<sec:h_inf_synthesis_complementary_filters>>

Introduction   ignore

ZIP file containing the data and matlab files   ignore

All the files (data and Matlab scripts) are accessible here.

Synthesis Architecture

We here synthesize the complementary filters using the $\mathcal{H}_\infty$ synthesis. The goal is to specify upper bounds on the norms of $H_L$ and $H_H$ while ensuring their complementary property ($H_L + H_H = 1$).

In order to do so, we use the generalized plant shown on figure fig:sf_hinf_filters_plant_b where $w_L$ and $w_H$ weighting transfer functions that will be used to shape $H_L$ and $H_H$ respectively.

Generalized plant used for the $\mathcal{H}_\infty$ synthesis of the complementary filters

The $\mathcal{H}_\infty$ synthesis applied on this generalized plant will give a transfer function $H_L$ (figure fig:sf_hinf_filters_b) such that the $\mathcal{H}_\infty$ norm of the transfer function from $w$ to $[z_H,\ z_L]$ is less than one: \[ \left\| \begin{array}{c} H_L w_L \\ (1 - H_L) w_H \end{array} \right\|_\infty < 1 \]

Thus, if the above condition is verified, we can define $H_H = 1 - H_L$ and we have that: \[ \left\| \begin{array}{c} H_L w_L \\ H_H w_H \end{array} \right\|_\infty < 1 \] Which is almost (with an maximum error of $\sqrt{2}$) equivalent to:

\begin{align*} |H_L| &< \frac{1}{|w_L|}, \quad \forall \omega \\ |H_H| &< \frac{1}{|w_H|}, \quad \forall \omega \end{align*}

We then see that $w_L$ and $w_H$ can be used to shape both $H_L$ and $H_H$ while ensuring (by definition of $H_H = 1 - H_L$) their complementary property.

$\mathcal{H}_\infty$ synthesis of the complementary filters

Weights

  omegab = 2*pi*9;
  wH = (omegab)^2/(s + omegab*sqrt(1e-5))^2;
  omegab = 2*pi*28;
  wL = (s + omegab/(4.5)^(1/3))^3/(s*(1e-4)^(1/3) + omegab)^3;

  <<plt-matlab>>

Weights on the complementary filters $w_L$ and $w_H$ and the associated performance weights (png, pdf)

H-Infinity Synthesis

We define the generalized plant $P$ on matlab.

  P = [0   wL;
       wH -wH;
       1   0];

And we do the $\mathcal{H}_\infty$ synthesis using the hinfsyn command.

  [Hl_hinf, ~, gamma, ~] = hinfsyn(P, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'on');

[Hl_hinf, ~, gamma, ~] = hinfsyn(P, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'on');
Test bounds:      0.0000 <  gamma  <=      1.7285

  gamma    hamx_eig  xinf_eig  hamy_eig   yinf_eig   nrho_xy   p/f
    1.729   4.1e+01   8.4e-12   1.8e-01    0.0e+00    0.0000    p
    0.864   3.9e+01 -5.8e-02#  1.8e-01    0.0e+00    0.0000    f
    1.296   4.0e+01   8.4e-12   1.8e-01    0.0e+00    0.0000    p
    1.080   4.0e+01   8.5e-12   1.8e-01    0.0e+00    0.0000    p
    0.972   3.9e+01 -4.2e-01#  1.8e-01    0.0e+00    0.0000    f
    1.026   4.0e+01   8.5e-12   1.8e-01    0.0e+00    0.0000    p
    0.999   3.9e+01   8.5e-12   1.8e-01    0.0e+00    0.0000    p
    0.986   3.9e+01 -1.2e+00#  1.8e-01    0.0e+00    0.0000    f
    0.993   3.9e+01 -8.2e+00#  1.8e-01    0.0e+00    0.0000    f
    0.996   3.9e+01   8.5e-12   1.8e-01    0.0e+00    0.0000    p
    0.994   3.9e+01   8.5e-12   1.8e-01    0.0e+00    0.0000    p
    0.993   3.9e+01 -3.2e+01#  1.8e-01    0.0e+00    0.0000    f

 Gamma value achieved:     0.9942

We then define the high pass filter $H_H = 1 - H_L$. The bode plot of both $H_L$ and $H_H$ is shown on figure fig:hinf_filters_results.

  Hh_hinf = 1 - Hl_hinf;

Obtained Complementary Filters

The obtained complementary filters are shown on figure fig:hinf_filters_results.

  <<plt-matlab>>

Obtained complementary filters using $\mathcal{H}_\infty$ synthesis (png, pdf)

Feedback Control Architecture to generate Complementary Filters

<<sec:feedback_generate_comp_filters>>

Introduction   ignore

The idea is here to use the fact that in a classical feedback architecture, $S + T = 1$, in order to design complementary filters.

Thus, all the tools that has been developed for classical feedback control can be used for complementary filter design.

ZIP file containing the data and matlab files   ignore

All the files (data and Matlab scripts) are accessible here.

Architecture

Architecture used to generate the complementary filters

We have: \[ y = \underbrace{\frac{L}{L + 1}}_{H_L} y_1 + \underbrace{\frac{1}{L + 1}}_{H_H} y_2 \] with $H_L + H_H = 1$.

The only thing to design is $L$ such that the complementary filters are stable with the wanted shape.

A simple choice is: \[ L = \left(\frac{\omega_c}{s}\right)^2 \frac{\frac{s}{\omega_c / \alpha} + 1}{\frac{s}{\omega_c} + \alpha} \]

Which contains two integrator and a lead. $\omega_c$ is used to tune the crossover frequency and $\alpha$ the trade-off "bump" around blending frequency and filtering away from blending frequency.

Loop Gain Design

Let's first define the loop gain $L$.

  wc = 2*pi*1;
  alpha = 2;

  L = (wc/s)^2 * (s/(wc/alpha) + 1)/(s/wc + alpha);

  <<plt-matlab>>

Bode plot of the loop gain $L$ (png, pdf)

Complementary Filters Obtained

We then compute the resulting low pass and high pass filters.

  Hl = L/(L + 1);
  Hh = 1/(L + 1);

  <<plt-matlab>>

Low pass and High pass filters $H_L$ and $H_H$ for different values of $\alpha$ (png, pdf)

Analytical Formula found in the literature

<<sec:analytical_formula_literature>>

Analytical Formula

cite:min15_compl_filter_desig_angle_estim

\begin{align*} H_L(s) = \frac{K_p s + K_i}{s^2 + K_p s + K_i} \\ H_H(s) = \frac{s^2}{s^2 + K_p s + K_i} \end{align*}

cite:corke04_inert_visual_sensin_system_small_auton_helic

\begin{align*} H_L(s) = \frac{1}{s/p + 1} \\ H_H(s) = \frac{s/p}{s/p + 1} \end{align*}

cite:jensen13_basic_uas

\begin{align*} H_L(s) = \frac{2 \omega_0 s + \omega_0^2}{(s + \omega_0)^2} \\ H_H(s) = \frac{s^2}{(s + \omega_0)^2} \end{align*} \begin{align*} H_L(s) = \frac{C(s)}{C(s) + s} \\ H_H(s) = \frac{s}{C(s) + s} \end{align*}

cite:shaw90_bandw_enhan_posit_measur_using_measur_accel

\begin{align*} H_L(s) = \frac{3 \tau s + 1}{(\tau s + 1)^3} \\ H_H(s) = \frac{\tau^3 s^3 + 3 \tau^2 s^2}{(\tau s + 1)^3} \end{align*}

cite:baerveldt97_low_cost_low_weigh_attit

\begin{align*} H_L(s) = \frac{2 \tau s + 1}{(\tau s + 1)^2} \\ H_H(s) = \frac{\tau^2 s^2}{(\tau s + 1)^2} \end{align*}

Matlab

  omega0 = 1*2*pi; % [rad/s]
  tau = 1/omega0; % [s]

  % From cite:corke04_inert_visual_sensin_system_small_auton_helic
  HL1 = 1/(s/omega0 + 1); HH1 = s/omega0/(s/omega0 + 1);

  % From cite:jensen13_basic_uas
  HL2 = (2*omega0*s + omega0^2)/(s+omega0)^2; HH2 = s^2/(s+omega0)^2;

  % From cite:shaw90_bandw_enhan_posit_measur_using_measur_accel
  HL3 = (3*tau*s + 1)/(tau*s + 1)^3; HH3 = (tau^3*s^3 + 3*tau^2*s^2)/(tau*s + 1)^3;

  <<plt-matlab>>

Comparison of some complementary filters found in the literature (png, pdf)

Discussion

Analytical Formula found in the literature provides either no parameter for tuning the robustness / performance trade-off.

Comparison of the different methods of synthesis

<<sec:discussion>> The generated complementary filters using $\mathcal{H}_\infty$ and the analytical formulas are very close to each other. However there is some difference to note here:

• the analytical formula provides a very simple way to generate the complementary filters (and thus the controller), they could even be used to tune the controller online using the parameters $\alpha$ and $\omega_0$. However, these formula have the property that $|H_H|$ and $|H_L|$ are symmetrical with the frequency $\omega_0$ which may not be desirable.
• while the $\mathcal{H}_\infty$ synthesis of the complementary filters is not as straightforward as using the analytical formula, it provides a more optimized procedure to obtain the complementary filters

Bibliography   ignore

bibliographystyle:unsrt bibliography:ref.bib