Dual-mode Explicit Output-feedback Predictive Control Based on Neural Network Models *

This paper applies an approximate multi-parametric Nonlinear Programming approach to explicitly solve output-feedback Nonlinear Model Predictive Control (NMPC) problems for constrained nonlinear systems described by black-box models. In particular, neural network models are used and the optimal regulation problem is considered. A dual-mode control strategy is employed in order to achieve an offset-free closed-loop response in the presence of bounded disturbances and/or model errors. The approach is applied to design an explicit NMPC for regulation of a pH maintaining system.