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Recurrent Neural Network-Based Joint Chance Constrained Stochastic Model Predictive Control*

作     者:Shu-Bo Yang Zukui Li 

作者机构:Department of Chemical and Materials Engineering University of Alberta Edmonton Canada 

出 版 物:《IFAC-PapersOnLine》 

年 卷 期:2022年第55卷第7期

页      面:780-785页

主  题:Recurrent neural network Stochastic model predictive control Stochastic optimal control Joint chance constraint Sample average approximation 

摘      要:A novel recurrent neural network (RNN)-based approach is proposed in this work to handle joint chance-constrained stochastic model predictive control (SMPC) problem. In the proposed approach, the joint chance constraint (JCC) is first reformulated as a quantile-based inequality to reduce the complexity in approximation. Then, the quantile function (QF) in the quantile-based inequality is replaced by the empirical QF using sample average approximation (SAA). Afterwards, the empirical QF is approximated via an RNN-based surrogate model, which is embedded into the SMPC problem formulation to predict quantile values at different sampling instants. By employing the RNN-based approximation, the resulting deterministic optimization problem is finally solved through a nonlinear optimization solver. The proposed approach is applied to a hydrodesulphurisation process to demonstrate its efficiency in handling the SMPC problem.

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