Deep learning framework for controlling an active suspension system

Aleksey Konoiko, Allan Kadhem, Islam Saiful, Navid Ghorbanian, Yahya Zweiri, M. Necip Sahinkaya

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


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.

Original languageBritish English
Pages (from-to)2316-2329
Number of pages14
JournalJVC/Journal of Vibration and Control
Issue number17
StatePublished - 1 Sep 2019


  • Active suspension system
  • backpropagation
  • control design
  • control systems
  • deep learning
  • deep neural networks
  • machine learning
  • optimal control
  • optimal proportional–integral–derivative controller


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