The precise variation of the magneto-rhelogical (MR) damping force in a semi-active suspension is a key issue in order to assure the desired performances over a suspension control system. The open loop control of this force is a very common strategy. Other schemes propose adaptive controller alternatives while the automotive hardware is a constrained computation resource. This paper proposes the implementation of the damping force control system based on the MR damper using an internal model control approach. The controller and the internal model are proposed as artificial neural networks (ANN) trained and validated with realistic automotive datasets. The results shows good servo control and fast regulation to abrupt disturbances without on-line ANN tuning computations.
|Number of pages||6|
|Publication status||Published - 1 Dec 2009|
|Event||Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics - |
Duration: 1 Dec 2009 → …
|Conference||Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics|
|Period||1/12/09 → …|
Copyright 2010 Elsevier B.V., All rights reserved.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Human-Computer Interaction