Machine learning has attracted an increased level of attention in model identification in recent years. Among many machine learning techniques, the recurrent neural network has been widely-used for modeling a general class of dynamical systems. In this work, we focus on the design of model predictive control (MPC) for nonlinear systems that utilizes an ensemble of well-fitting recurrent neural network (RNN) models to predict nonlinear dynamics. Specifically, RNN models are initially developed based on the dataset generated from extensive open-loop simulations within a certain operation region to capture process dynamics for a general class of nonlinear systems with a sufficiently small modeling error between the RNN model and the actual nonlinear process model [1,2,3]. Subsequently, the Lyapunov-based MPC (LMPC) that utilizes RNN models as the prediction model is developed in a sample-and-hold fashion to achieve closed-loop stability in the sense that the state of the closed-loop system is bounded in the stability region for all times and ultimately converges to a small neighborhood around the origin . Additionally, ensemble regression modeling tools are employed in the formulation of LMPC to improve prediction accuracy of RNN models and overall closed-loop performance while parallel computing is utilized to reduce computation time. Finally, a chemical reactor example demonstrates the effectiveness of the proposed LMPC design using ensemble regression models and the significant improvement of computational efficiency under parallel operation.
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