Expanding the operation range of 3D printed sensors in Control applications

MSc assignment

In the past decade Additive Manufacturing (AM) has shown great potential in fabricating prototypes in a plethora of fields, including robotics and biomedical engineering [1]. Recently we have shown the possibility to use Fused-Filament-Fabrication (FFF) AM to produce a medical device for use within an operating theatre [2]. With maturing of the technology and optimisation of the employed machines both in hardware and software, the realisation of ready to use products using AM is becoming more and more appealing. However, there are still several challenges to overcome before 3D printed sensors are widely adopted in more demanding fields and applications, like for example, to provide a control feedback signal in medium to high bandwidth application.

The main disadvantages of such sensors lie in their response. Especially for 3D printed sensors based on the principle of piezo-resistivity, the response is highly nonlinear, exhibiting hysteric behaviour, creep and drift [1]. Some of these characteristics may be attributed to the intrinsic properties of the employed polymers, and as such it is not possible to address them at a material level. There have been studies that show simple tricks, like the realisation of differential measurement [3], which can help reduce such affects. Albeit, it is not possible to completely compensate for them, and additionally, a differential measurement is not always feasible to realise. 

Within the NIFTy laboratory of the Robotics and Mechatronics group, there have been studies focused on model-based methods as to compensate for response hysteresis [4]. These techniques may prove instrumental in extending the operation range of 3D printed sensors. However, currently these models are only trained offline and have not been focused on capturing creep and/or drift. 

In this MSc assignment the student is asked to perform the following tasks:

  • Literature study on the current compensation methods and model-based control schemes.
  • Build upon the existing models, modify if necessary and evaluate the model under creep and drift.
  • Explore adaptive compensation methods and simulate model performance.
  • Apply the developed method on a provided experimental setup and sensor, run measurements and process the data.
  • Compile a detailed report of the development and findings.


Important prerequisites for the assignment are affinity with the following courses.

  • Optimal or Robust Control
  • Control System Design for Robotics
  • Nonlinear control (optional but beneficial)
  • Modelling, Dynamics and Kinematics (optional but beneficial) 


[1] https://ieeexplore.ieee.org/document/9279230

[2] https://ieeexplore.ieee.org/document/9639727

[3] https://ieeexplore.ieee.org/abstract/document/8956652

[4] https://ieeexplore.ieee.org/abstract/document/9278942