Machine learning of electro-hydraulic motor dynamics

Luca Baronti, Biao Zhang, Marco Castellani, Duc Pham

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
170 Downloads (Pure)


In this paper we propose an innovative machine learning approach to the hydraulic motor load balancing problem involving intelligent optimisation and neural networks. Two different nonlinear artificial neural network approaches are investigated, and their accuracy is compared to that of a linearised analytical model. The first neural network approach uses a multi-layer perceptron to reproduce the load simulator dynamics. The multi-layer perceptron is trained using the Rprop algorithm. The second approach uses a hybrid scheme featuring an analytical model to represent the main system behaviour, and a multi-layer perceptron to reproduce unmodelled nonlinear terms. Four techniques are tested for the optimisation of the parameters of the analytical model: random search, an evolutionary algorithm, particle swarm optimisation, and the Bees Algorithm. Experimental tests on 4500 real data samples from an electro-hydraulic load simulator rig reveal that the accuracy of the hybrid and the neural network models is comparable, and significantly superior to the accuracy of the analytical model. The results of the optimisation procedures suggest also that the inferior performance of the analytical model is likely due to the non-negligible magnitude of the unmodelled nonlinearities, rather than suboptimal setting of the parameters. Despite its limitations, the analytical linear model performs comparably to the state-of-the-art in the literature, whilst the neural and hybrid approaches compare favourably.
Original languageEnglish
Article number130
Number of pages12
JournalSN Applied Sciences
Early online date21 Dec 2019
Publication statusPublished - Jan 2020


  • regression
  • system identification
  • load simulator
  • electro-hydraulic motor
  • analytical modelling
  • optimisation
  • multi-layer perceptron

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering
  • Control and Systems Engineering


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