A model predictive control for an aggregate actuator with a self-tuning initial condition procedure in combustion engines
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Authors
This contribution deals with a hybrid actuator composed by a piezo and a hydraulic part and with a Robust Model Predictive Control (RMPC) structure combined with a feedforward control in camless engine motor applications. A combination between a feedforward control based on an inversion of the system and an MPC structure is considered. To perform a feedforward regulator an identification of the start condition of the piezo actuator is needed. This start condition of the piezo actuator is due to some structural constructive aspects which generate an offset into the piezo position. The feedforward regulator ends up being an affine function to compensate for this offset. A procedure for its identification is proposed. The idea behind the conception of the proposed new actuator is to use the advantages of both the high precision of the piezo and the force of the hydraulic part. In fact, piezoelectric actuators (PEAs) are commonly used for precision positionings, despite the fact that PEAs present nonlinearities, such as hysteresis, saturations, and creep. In the control problem such nonlinearities must be taken into account. In this paper the Preisach dynamic model with the abovemen-tioned nonlinearities is considered together with a feedforward control combined with a RMPC. Simulations of the implementation of the MPC structure together with the feedforward regulator and the abovementioned start condition of the piezo actuator with real data are shown.
Original language | English |
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Title of host publication | Model Predictive Control: Theory, Practices and Future Challenges |
Editors | Corrine Wade |
Number of pages | 19 |
Place of Publication | New York |
Publisher | Nova Science Publishers, Inc. |
Publication date | 2015 |
Pages | 41-59 |
Article number | 2 |
ISBN (print) | 9781634638593, 163463859X |
ISBN (electronic) | 9781634638876 |
Publication status | Published - 2015 |
- Engineering - Combustion engines, Model predictive control