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# On the Design of Complementary Filters for Control - Computation with Matlab
Author: Thomas Dehaeze

## Table of Contents

- 1. Optimal Sensor Fusion for noise characteristics
  - 1.1. Architecture
  - 1.2. Noise of the sensors
  - 1.3. H-Two Synthesis
  - 1.4. Analysis
- 2. Robustness to sensor dynamics uncertainty
  - 2.1. Unknown sensor dynamics dynamics
  - 2.2. Design the complementary filters in order to limit the phase and gain uncertainty of the super sensor
  - 2.3. First Basic Example with gain mismatch
  - 2.4. TODO More Complete example with model uncertainty
- 3. Complementary filters using analytical formula
  - 3.1. Analytical 1st order complementary filters
  - 3.2. Second Order Complementary Filters
  - 3.3. Third Order Complementary Filters
- 4. H-Infinity synthesis of complementary filters
  - 4.1. Synthesis Architecture
  - 4.2. Weights
  - 4.3. H-Infinity Synthesis
  - 4.4. Obtained Complementary Filters
- 5. Feedback Control Architecture to generate Complementary Filters
  - 5.1. Architecture
  - 5.2. Loop Gain Design
  - 5.3. Complementary Filters Obtained
- 6. Analytical Formula found in the literature
  - 6.1. Analytical Formula
  - 6.2. Matlab
  - 6.3. Discussion
- 7. Comparison of the different methods of synthesis

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 1, 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 2, the blending robustness to sensor dynamic uncertainty is studied.

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

- in section 3, analytical formulas are proposed
- in section 4, the \(\mathcal{H}_\infty\) synthesis is used
- in section 5, the classical feedback architecture is used
- in section 6, analytical formulas found in the literature are listed

## 1 Optimal Sensor Fusion for noise characteristics

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.

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

### 1.1 Architecture

Let's consider the sensor fusion architecture shown on figure 1 where two sensors 1 and 2 are measuring the same quantity \(x\) with different noise characteristics determined by \(W_1\) and \(W_2\).

\(n_1\) and \(n_2\) are white noise (constant power spectral density over all frequencies).

Figure 1: Fusion of two sensors

We consider that the two sensor dynamics \(G_1\) and \(G_2\) are ideal (\(G_1 = G_2 = 1\)). We obtain the architecture of figure 2.

Figure 2: Fusion of two sensors with ideal dynamics

\(H_1\) and \(H_2\) are complementary filters (\(H_1 + H_2 = 1\)). The goal is to design \(H_1\) and \(H_2\) such that the effect of the noise sources \(n_1\) and \(n_2\) has the smallest possible effect on the estimation \(\hat{x}\).

We have that the Power Spectral Density (PSD) of \(\hat{x}\) is: \[ \Gamma_{\hat{x}} = |H_1 W_1|^2 \Gamma_{n_1} + |H_2 W_2|^2 \Gamma_{n_2} \]

And the goal is the minimize the Root Mean Square (RMS) value of \(\hat{x}\): \[ \sigma_{\hat{x}} = \sqrt{\int_0^\infty \Gamma_{\hat{x}}(\omega) d\omega} \]

As \(n_1\) and \(n_2\) are white noise: \(\Gamma_{n_1} = \Gamma_{n_2} = 1\) and we have: \[ \sigma_{\hat{x}} = \sqrt{\int_0^\infty |H_1 W_1|^2(\omega) + |H_2 W_2|^2(\omega) d\omega} = \left\| \begin{matrix} H_1 W_1 \\ H_2 W_2 \end{matrix} \right\|_2 \]

Thus, the goal is to design \(H_1\) and \(H_2\) such that \(H_1 + H_2 = 1\) and such that \(\left\| \begin{matrix} H_1 W_1 \\ H_2 W_2 \end{matrix} \right\|_2\) is minimized.

For that, we will use the \(\mathcal{H}_2\) Synthesis.

### 1.2 Noise of the sensors

Let's define the noise characteristics of the two sensors by choosing \(W_1\) and \(W_2\):

- Sensor 1 characterized by \(W_1\) has low noise at low frequency (for instance a geophone)
- Sensor 2 characterized by \(W_2\) has low noise at high frequency (for instance an accelerometer)

    omegac = 100*2*pi; G0 = 1e-5; Ginf = 1e-4;
    W1 = (Ginf*s/omegac + G0)/(s/omegac + 1)/(1 + s/2/pi/4000);

    omegac = 1*2*pi; G0 = 1e-3; Ginf = 1e-8;
    W2 = ((sqrt(Ginf)*s/omegac + sqrt(G0))/(s/omegac + 1))^2/(1 + s/2/pi/4000)^2;

Figure 3: Noise Characteristics of the two sensors (png, pdf)

### 1.3 H-Two Synthesis

We use the generalized plant architecture shown on figure 4.

Figure 4: \(\mathcal{H}_2\) Synthesis - Generalized plant used for the optimal generation of complementary filters

The transfer function from \([n_1, n_2]\) to \(\hat{x}\) is: \[ \begin{bmatrix} W_1 H_1 \\ W_2 (1 - H_1) \end{bmatrix} \] If we define \(H_2 = 1 - H_1\), we obtain: \[ \begin{bmatrix} W_1 H_1 \\ W_2 H_2 \end{bmatrix} \]

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 4.

    P = [0   W2  1;
         W1 -W2  0];

And we do the \(\mathcal{H}_2\) synthesis using the `h2syn` command.

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

What is minimized is `norm([W1*H1,W2*H2], 2)`.

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

    H2 = 1 - H1;

### 1.4 Analysis

The complementary filters obtained are shown on figure 5. The PSD of the 6. Finally, the RMS value of \(\hat{x}\) is shown on table 1. The optimal sensor fusion has permitted to reduced the RMS value of the estimation error by a factor 8 compare to when using only one sensor.

Figure 5: Obtained complementary filters using the \(\mathcal{H}_2\) Synthesis (png, pdf)

Figure 6: Power Spectral Density of the estimated \(\hat{x}\) using the two sensors alone and using the optimally fused signal (png, pdf)

Table 1: RMS value of the estimation error when using the sensor individually and when using the two sensor merged using the optimal complementary filters
| rms value
--- | ---
Sensor 1 | 1.1e-02
Sensor 2 | 1.3e-03
Optimal Sensor Fusion | 1.5e-04

## 2 Robustness to sensor dynamics uncertainty

Let's first consider ideal sensors where \(G_1 = 1\) and \(G_2 = 1\) (figure 7).

Figure 7: 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.

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

### 2.1 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 8.

Figure 8: 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 9).

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

Figure 9: Maximum phase variation

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

### 2.2 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.

### 2.3 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 10. 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.

Figure 10: 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 11).

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

Figure 11: Comparison of the obtained super sensor transfer functions (png, pdf)

### 2.4 TODO More Complete example with model uncertainty

## 3 Complementary filters using analytical formula

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

### 3.1 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 12.

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

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

Figure 12: Bode plot of first order complementary filter (png, pdf)

### 3.2 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 13. The slope of those filters at high and low frequencies is \(-2\) and \(2\) respectively for \(H_L\) and \(H_H\).

Figure 13: 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('$\|H_1\|_\infty$');

Figure 14: Evolution of the H-Infinity norm of the complementary filters with the parameter \(\alpha\) (png, pdf)

### 3.3 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 15.

    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));

Figure 15: Third order complementary filters using the analytical formula (png, pdf)

## 4 H-Infinity synthesis of complementary filters

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

### 4.1 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 16 where \(w_L\) and \(w_H\) weighting transfer functions that will be used to shape \(H_L\) and \(H_H\) respectively.

Figure 16: 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 17) 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.

Figure 17: \(\mathcal{H}_\infty\) synthesis of the complementary filters

### 4.2 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;

Figure 18: Weights on the complementary filters \(w_L\) and \(w_H\) and the associated performance weights (png, pdf)

### 4.3 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 19.

    Hh_hinf = 1 - Hl_hinf;

### 4.4 Obtained Complementary Filters

The obtained complementary filters are shown on figure 19.

Figure 19: Obtained complementary filters using \(\mathcal{H}_\infty\) synthesis (png, pdf)

## 5 Feedback Control Architecture to generate Complementary Filters

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.

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

### 5.1 Architecture

Figure 20: 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.

### 5.2 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);

Figure 21: Bode plot of the loop gain \(L\) (png, pdf)

### 5.3 Complementary Filters Obtained

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

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

Figure 22: Low pass and High pass filters \(H_L\) and \(H_H\) for different values of \(\alpha\) (png, pdf)

## 6 Analytical Formula found in the literature

### 6.1 Analytical Formula

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*}

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*}

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*}

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*}

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*}

### 6.2 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;

Figure 23: Comparison of some complementary filters found in the literature (png, pdf)

### 6.3 Discussion

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

## 7 Comparison of the different methods of synthesis

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

- [min15_compl_filter_desig_angle_estim] Min & Jeung, Complementary Filter Design for Angle Estimation Using Mems Accelerometer and Gyroscope, Department of Control and Instrumentation, Changwon National University, Changwon, Korea, 641-773 (2015).
- [corke04_inert_visual_sensin_system_small_auton_helic] Peter Corke, An Inertial and Visual Sensing System for a Small Autonomous Helicopter, Journal of Robotic Systems, 21(2), 43-51 (2004). link. doi.
- [jensen13_basic_uas] Austin Jensen, Cal Coopmans & YangQuan Chen, Basics and guidelines of complementary filters for small UAS navigation, nil, in in: 2013 International Conference on Unmanned Aircraft Systems (ICUAS), edited by (2013)
- [shaw90_bandw_enhan_posit_measur_using_measur_accel] Shaw & Srinivasan, Bandwidth Enhancement of Position Measurements Using Measured Acceleration, Mechanical Systems and Signal Processing, 4(1), 23-38 (1990). link. doi.
- [baerveldt97_low_cost_low_weigh_attit] Baerveldt & Klang, A Low-Cost and Low-Weight Attitude Estimation System for an Autonomous Helicopter, nil, in in: Proceedings of IEEE International Conference on Intelligent Engineering Systems, edited by (1997)

Author: Thomas Dehaeze

Created: 2019-08-21 mer. 13:17

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