Learning Exactly Linearizable Deep Dynamics Models
Ryuta Moriyasu Masayuki Kusunoki Kenji Kashima
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.