Deep learning framework for controlling an active suspension system
In this paper, a feed-forward deep neural network (DNN) and automated search method for optimum network structure are developed to control an active suspension system (ASS). The network was trained through supervised learning using the backpropagation algorithm. The training data were generated from...
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Veröffentlicht in: | Journal of vibration and control 2019-09, Vol.25 (17), p.2316-2329 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | In this paper, a feed-forward deep neural network (DNN) and automated search method for optimum network structure are developed to control an active suspension system (ASS). The network was trained through supervised learning using the backpropagation algorithm. The training data were generated from an optimal proportional–integral–derivative controller tuned based on a full state feedback optimal controller. The trained network was implemented in an ASS test rig for a quarter-car model and was initially tested in simulation under parameter uncertainties. Experimental results showed that the developed DNN controller outperforms the optimal controller under uncertainties in terms of reducing the sprung mass acceleration and actuator energy consumption, with a 4% and 14% reduction, respectively. |
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ISSN: | 1077-5463 1741-2986 |
DOI: | 10.1177/1077546319853070 |