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Machine-Learning-based Predictive Control of Nonlinear Processes with Uncertainty

In this work, we present machine-learning-based predictive control schemes for nonlinear processes subject to disturbances, and investigate 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.