Estimation and MPC control based on gated recurrent unit neural networks with unknown disturbances

This paper proposes a nonlinear model predictive control (NMPC) approach for incrementally input-to-state stable gated recurrent units (GRU) neural networks affected by state and output disturbances. In particular, a Luenbergerlike observer is designed for state and disturbance estimation with guaranteed convergence properties. This paves the way for the design of an NMPC regulator capable of rejecting unknown piecewise-constant disturbances. The method is tested in simulation on a nonlinear benchmark system, i.e., a chemical reaction process, showing promising results.