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.

基于机器学习方法的控制研究现已过渡到实际工程阶段。本文提出了一种学习方法，用于确保系统的稳定性、可靠性等方面，为各种控制理论提供了高度的自由度，以实现高性能和理论上的安全性。通过将简单线性控制与控制屏障函数相结合，展示了一个设计示例，并将其应用于汽车发动机的实时控制，实验结果表明具有良好的预测性能和稳定的控制能力。