I’ve written an article on the wiki on what I call controller fusion.  I refer to it as controller fusion becuase, like Sensor Fusion, I use filters to blend non-ideal outputs from more than 1 transfer function into 1 output which is closer to the desired output.

For a work proposal on a reaction cancellation mechanism I used a proportional controller for fast response and a PI-Lead controller to drive the steady-state error to 0.  In simple sensor fusion the sensor outputs are filtered and then added together to form a better single output.  In this form of controller fusion I use filters on the error signal to adjust the gain of the controller in real-time.  As a result, PI-Lead output is almost turned off for a step command and the proportional controller output is almost turned off when the system is holding a steady-state value.

I have not had the time to bring this idea to full maturity but I thought it was an interesting enough idea to share.

Sensor Fusion

As discussed in the previous blog entry, sensor fusion is used to create one good sensor from at least 2 sensors that are not good enough to meet specifications.  This can be done simply but when real sensors are involved it can also become a bit of a black art requiring a lot of skill and experience.

Simple Sensor Fusion Example

The wiki has an example showing the details of simple sensor fusion.  The simple example has a low frequency sensor with a bandwidth of 20 Hz.  It also included a high frequency sensor with a lower bandwidth of 1 Hz and an upper of 1 kHz.  The sensors are blended using a second order low pass and high pass filter.  Both filters have a bandwidth of 15 Hz.

I hope to eventually create another example with better filters.  I also hope to create another sensor fusion example for sensors with non-ideal transfer functions.

Sensor Fusion or Sensor Blending

Sensors are what provides feedback to a closed loop system.  Sometime you can’t get the sensor characteristics you need.  This happens a lot in the aerospace industry.

When any one sensor cannot provide the necessary feedack then it is time for sensor fusion or sensor blending.  The simplest form of sensor fusion is a matter of two or more sensors which are filtered so that their strengths (good responsivity and low noise) are used while their weaknesses are filtered out.

Often times sensor fusion is nothing more than simple second order low pass or high pass filters with their outputs added together.  This simple fusion allows for two sensors to provide the desired output.

Simple Example of Sensor Fusion

The most simple sensor fusion that I’ve come across is the combination of two angular rate gyroscopes.  The low frequency gyro was good out to a frequency of approximately 20 Hz.  The high frequency gyro was good between 1 and 1000 Hz.  Unfortunately this system was sensitive to frequencies around 5 Hz.

Normally the blending frequency of the sensor fusion would have happened between 1 Hz and 20 Hz based on an analysis of each sensor’s noise and responsivity.  This example system was sensitive to frequencies around 5 Hz which meant that we needed to avoid frequencies between 0.5 Hz and 50 Hz.

The main weakness of the high frequency sensor was phase loss below 1 Hz.  So we designed a filter to extend the low end of the high frequency sensor down to 0.5 Hz.  More difficult to implement than to conceptualize but it takes some practice to do it correctly.

Ideal Sensor vs. Real Sensor

The ideal sensor is typically modeled with a second order system that has a natural frequency equal to the spec bandwidth and a damping of 0.707 or 1.  I default to 0.707.  This leads to a nice flat, unity response for the sensor below the bandwidth.  Real sensors are non-unity below the bandwidth – i.e. the magnitude has some ripple to it.  Sensor ripple around the blending frequency can be very problematic and must be assessed based on the system needs.

Introduction to Sensor Fusion on the Wiki

Here is an article on the wiki on Sensor Fusion.  It is currently a small, simple article that I hope to expand and encourage anyone interested in Sensor Fusion to help me expand.