.png){kind=icon}
返回
Approximate dynamic programming-based approaches for input-output data-driven control of nonlinear processes
.png){kind=icon}
PRE
AI
DOI：10.1016/j.automatica.2005.02.006.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We propose two approximate dynamic programming (ADP)-based strategies for control of nonlinear processes using input-output data. In the first strategy, which we term 'J-Iearning,' one builds an empirical nonlinear model using closed-loop test data and performs dynamic programming with it to derive an improved control policy. In the second strategy, called 'Q-learning,' one tries to learn an improved control policy in a model-less manner. Compared to the conventional model predictive control approach, the new approach offers some practical advantages in using nonlinear empirical models for process control. Besides the potential reduction in the on-line computational burden, it offers a convenient way to control the degree of model extrapolation in the calculation of optimal control moves. One major difficulty associated with using an empirical model within the multi-step predictive control setting is that the model can be excessively extrapolated into regions of the state space where identification data were scarce or nonexistent, leading to performances far worse than predicted by the model. Within the proposed ADP-based strategies, this problem is handled by imposing a penalty term designed on the basis of local data distribution. A CSTR example is provided to illustrate the proposed approaches. (c) 2005 Elsevier Ltd. All rights reserved.
Keyword:
nonlinear model identification
nonlinear model predictive control
approximate dynamic programming
NARX model
reinforcement learning
Q-learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.9
论文数:
1.2W
被引数:
5.2W
机构
暂无机构信息
引用论文
Second order asymptotics for the propagation speed of interfaces in the Allen‐Cahn phase field model for elastic solids.png)
PAMM
PAMMIF0
PREAI
分享收藏An additive-attack-proof watermarking mechanism for databases' copyrights protection using image
PREAI
分享收藏Dysfunctions of decision‐making and cognitive control as transdiagnostic mechanisms of mental disorders: advances, gaps, and needs in current research作为精神障碍的跨诊断机制的决策和认知控制功能障碍: 当前研究的进展，差距和需求
OAAI
OAAI分享收藏Developing a robust model predictive control architecture through regional knowledge analysis of artificial neural networks通过人工神经网络的区域知识分析开发鲁棒的模型预测控制体系结构
PREAI
分享收藏A process dissociation framework: Separating automatic from intentional uses of memory过程分离框架: 将自动与有意使用的内存分开
PREAI
分享收藏
查看更多内容