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

Abstract

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.