Online Learning-Based Predictive Control of Switched Nonlinear Systems With Disturbances

This work presents a model predictive control (MPC) scheme using online learning of recurrent neural network (RNN) models to approximate the dynamics of switched nonlinear systems subject to unknown but bounded disturbances, for which the mode transitions follow a prescribed switching schedule. A generalization error bound for online learning RNNs using non-independent and identically distributed (non-i.i.d.) data samples from the real-time operation of switched nonlinear systems is first derived. Subsequently, a Lyapunov-based MPC scheme using online learning RNNs is developed to stabilize the switched nonlinear system for each mode and guarantee the satisfaction of scheduled mode transitions, followed by closed-loop stability analysis that accounts for the RNN generalization error. Finally, the effectiveness of the proposed MPC scheme is demonstrated using a chemical process example switched between two modes.