Near Boundary Control of Automotive Engine using Machine Learning
Near Boundary Control of Automotive Engine using Machine Learning
复制标题
使用机器学习的汽车发动机近边界控制
DOI：
10.1109/icamechs49982.2020.9310084
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Yasuyuki Satoh
中科院分区：
文献类型：
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作者：
Sho Fujiwara;Masami Iwase;Yasuyuki Satoh
我们的目标是开发一种控制方法，用于汽车发动机运行的边界内，目前sbetween允许和违反的操作域。这些边界可以用数学模型来表示。对于这些边界，我们利用状态依赖Riccati方程设计了一个近边界控制，当发动机运行在允许区域内时，使发动机工作以满足驾驶员的需求;当驾驶员的需求指示在违规区域内时，使发动机不超过边界。这样的控制器需要高计算性能，因此实际上不可能在真实的机器中实现。在本文中，引入了一个深度神经网络来模拟预先设计的近边界控制的行为，它可以以大大减少的计算负载工作。
We aim to develop a control method for an automotive engine to operate inside the boundary that present sbetween the admissible and violated operation domain. These boundaries can be represented by a mathematical model. For those boundaries, we design a near-boundary control using State-Dependent Riccati Equation, which performs to make the engine work to satisfy the driver demand when the engine operates inside the admissible zone, and which performs to make the engine not exceed the boundaries when the driver demand indicates inside the violated zone. Such a controller requires high computational performance so that it is practically impossible to be implemented into a real machine. In this paper, a deep neural network is introduced to mimic the behavior of the predesigned near-boundary control, which can work with a much-reduced computational load.
利用汽车发动机边界模型的控制方法
DOI：
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发表时间：
2017
期刊：
影响因子：
--
作者：
Nagaosa Katsuaki;Serizawa Takuya;Sato Kotoru;Iwase Masami
通讯作者：
Iwase Masami