Deep reinforcement learning in smart manufacturing: A review and prospects
To facilitate the personalized smart manufacturing paradigm with cognitive automation
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …
非平稳间歇过程数据解析与状态监控—回顾与展望
赵春晖， 余万科， 高福荣 - 自动化学报, 2020 - aas.net.cn
间歇过程作为制造业的重要生产方式之一, 其高效运行是智能制造的优先主题.
为了保障生产过程的高效运行, 面向间歇生产的过程数据解析与状态监控算法在最近三十年间 …
为了保障生产过程的高效运行, 面向间歇生产的过程数据解析与状态监控算法在最近三十年间 …
A multiphase information fusion strategy for data-driven quality prediction of industrial batch processes
As one of the most important modes of industrial production, the batch process often
involves complex and continuous physicochemical reactions, making it challenging to …
involves complex and continuous physicochemical reactions, making it challenging to …
Online reinforcement learning with passivity-based stabilizing term for real time overhead crane control without knowledge of the system model
Due to the existing uncertainties such as the payload mass and unmodeled dynamics in the
overhead crane system, classical model-based control methods yielding fixed control gain …
overhead crane system, classical model-based control methods yielding fixed control gain …
A survey and comparative evaluation of actor‐critic methods in process control
D Dutta, SR Upreti - The Canadian Journal of Chemical …, 2022 - Wiley Online Library
Actor‐critic (AC) methods have emerged as an important class of reinforcement learning
(RL) paradigm that enables model‐free control by acting on a process and learning from the …
(RL) paradigm that enables model‐free control by acting on a process and learning from the …
Robust safe reinforcement learning control of unknown continuous-time nonlinear systems with state constraints and disturbances
Industrial processes must operate safely and optimally in practice, which typically entails
solving a constrained optimal control (COC) problem. Finding a control policy for such a …
solving a constrained optimal control (COC) problem. Finding a control policy for such a …
Deep neural network based recursive feature learning for nonlinear dynamic process monitoring
J Zhu, H Shi, B Song, S Tan… - The Canadian Journal of …, 2020 - Wiley Online Library
The data collected from modern industrial processes always have nonlinear and dynamic
characteristics. The recently developed deep neural network method, stacked denoising …
characteristics. The recently developed deep neural network method, stacked denoising …
A novel echo state network and its application in temperature prediction of exhaust gas from hot blast stove
Y Yang, X Zhao, X Liu - IEEE Transactions on Instrumentation …, 2020 - ieeexplore.ieee.org
Hot blast stove (HBS) provides hot air for the blast furnace and temperature prediction for its
exhaust gas is of vital importance to control the process. In this article, a novel deep memory …
exhaust gas is of vital importance to control the process. In this article, a novel deep memory …
Comprehensive process decomposition for closed-loop process monitoring with quality-relevant slow feature analysis
In modern industrial processes, quality-relevant process monitoring methods are important
to timely indicate abnormal product quality. Moreover, advanced closed-loop control …
to timely indicate abnormal product quality. Moreover, advanced closed-loop control …
Deep reinforcement learning with domain randomization for overhead crane control with payload mass variations
Overhead cranes, as an important tool for loading and transporting, play an important role in
modern industry. A key challenge in overhead crane control is payload mass variation: a …
modern industry. A key challenge in overhead crane control is payload mass variation: a …