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Statistical machine-learning-based predictive control of uncertain nonlinear processes
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DOI：10.1002/aic.17642.png)
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摘要
En 中文
In this study, we present machine-learning-based predictive control schemes for nonlinear processes subject to disturbances, and establish closed-loop system stability properties using statistical machine learning theory. Specifically, we derive a generalization error bound via Rademacher complexity method for the recurrent neural networks (RNN) that are developed to capture the dynamics of the nominal system. Then, the RNN models are incorporated in Lyapunov-based model predictive controllers, under which we study closed-loop stability properties for the nonlinear systems subject to two types of disturbances: bounded disturbances and stochastic disturbances with unbounded variation. A chemical reactor example is used to demonstrate the implementation and evaluate the performance of the proposed approach.
Keyword:
generalization error
machine learning
model predictive control
recurrent neural networks
stochastic nonlinear systems
期刊
IF:
4
论文数:
1.1W
被引数:
2.9W
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引用论文
Lyapunov-based model predictive control of stochastic nonlinear systems基于Lyapunov的随机非线性系统模型预测控制.png)
AUTOMATICA
AUTOMATICAIF5.9
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Output-Feedback Lyapunov-Based Predictive Control of Stochastic Nonlinear Systems基于输出反馈Lyapunov的随机非线性系统预测控制
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