Learning exactly linearizable deep dynamics models

Ryuta Moriyasu

doi:10.1080/18824889.2025.2459429

Learning exactly linearizable deep dynamics models

2025-12-31

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DOI：10.1080/18824889.2025.2459429

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Research on control using models based on machine-learning methods has now shifted to the practical engineering stage. Achieving high performance and theoretically guaranteeing the safety of the system is critical for such applications. In this paper, we propose a learning method for exactly linearizable dynamical models that can easily apply various control theories to ensure stability, reliability, etc., and to provide a high degree of freedom of expression. As an example, we present a design that combines simple linear control and control barrier functions. The proposed model is employed for the real-time control of an automotive engine, and the results demonstrate good predictive performance and stable control under constraints.

Keywords:

Model predictive control

machine learning

control application

nonlinear control

Hammerstein-Wiener model

Learning exactly linearizable deep dynamics models

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