3 research outputs found
Learning Exactly Linearizable Deep Dynamics Models
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
Structured Hammerstein-Wiener Model Learning for Model Predictive Control
This paper aims to improve the reliability of optimal control using models
constructed by machine learning methods. Optimal control problems based on such
models are generally non-convex and difficult to solve online. In this paper,
we propose a model that combines the Hammerstein-Wiener model with input convex
neural networks, which have recently been proposed in the field of machine
learning. An important feature of the proposed model is that resulting optimal
control problems are effectively solvable exploiting their convexity and
partial linearity while retaining flexible modeling ability. The practical
usefulness of the method is examined through its application to the modeling
and control of an engine airpath system.Comment: 6 pages, 3 figure