Abstract
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 language | English |
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Title of host publication | 2000 International Conference on Modeling And Simulation of Microsystems, Technical Proceedings |
Editors | M Laudon, B Romanowicz |
Publisher | Computational Publications |
Pages | 513-516 |
Number of pages | 4 |
ISBN (Print) | 0966613570 |
Publication status | Published - 2000 |
Externally published | Yes |
Event | 3rd International Conference on Modeling and Simulation of Microsystems - San Diego, Canada Duration: 27 Mar 2000 → 29 Mar 2000 |
Conference
Conference | 3rd International Conference on Modeling and Simulation of Microsystems |
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Country/Territory | Canada |
City | San Diego |
Period | 27/03/00 → 29/03/00 |
Keywords
- micromachined accelerometer
- neural network
- model reference control