Closed-loop, neural network controlled accelerometer design

E.I. Gaura, N. Ferreira, R.J. Rider, N. Steele, Romanowicz B. Laudon M. (Editor)

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

3 Citations (Scopus)


In this paper, a closed-loop, smart transducer design is proposed, based on artificial neural network (ANN) techniques. The design aims to improve the performance of open-loop, off-the-shelf capacitive acceleration sensors and increase their robustness to manufacturing tolerances. A "model reference" control strategy was adopted for the design of the smart transducer. Multilayer perceptron (MLP) type networks were chosen for implementing the control strategy. While a static MLP was used for the feedback arrangement, a tap delayed lines MLP was necessary for implementing the controller due to the dynamic nonlinear behaviour exhibited by the sensing device. A dynamic version of the back-error propagation algorithm was used for training the networks. The resulting closed-loop transducer had a dynamic range of ±10g and a stable behaviour for input stimuli up to ±100g.
Original languageEnglish
Title of host publication2000 International Conference on Modeling and Simulation of Microsystems
Number of pages4
Publication statusPublished - 2000
EventInternational Conference on Modeling and Simulation of Microsystems - San Diego, United States
Duration: 27 Mar 200029 Mar 2000


ConferenceInternational Conference on Modeling and Simulation of Microsystems
Abbreviated titleMSM
Country/TerritoryUnited States
CitySan Diego
Internet address


  • Accelerometers
  • Closed loop control systems
  • Electrostatic actuators
  • Feedback control
  • Machine design
  • Microelectromechanical devices
  • Micromachining
  • Nonlinear systems
  • Robustness (control systems)
  • Transducers, Electrostatic actuation
  • Micromachined accelerometers
  • Model reference control
  • Multilayer perceptron (MLP) neural networks, Neural networks


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