Related papers: Learning Exactly Linearizable Deep Dynamics Models

Learning Exactly Linearizable Deep Dynamics Models

URL: http://arxiv.org/abs/2311.18261v1
Date: Thu, 30 Nov 2023 05:40:55 GMT
Title: Learning Exactly Linearizable Deep Dynamics Models
Authors: Ryuta Moriyasu, Masayuki Kusunoki, Kenji Kashima
Abstract summary: We propose a learning method for exactly linearizable dynamical models that can easily apply various control theories to ensure stability, reliability, etc. 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.
Score: 0.07366405857677226
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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