Extended Neural Model Predictive Control of Non-Linear Systems

A neural model-based predictive control scheme is proposed for dealing with steady-state offsets found in standard MPC schemes. This structure is based on a constrained local instantaneous linear model-based predictive control methodologies together with a static offset pre-filter for assuring free tracking errors and disturbance rejection features. A non-linear state-space neural network architecture trained offline is used for modelling purposes, from which linear models are extracted by Taylor series expansion at each sampling time. Results from experiments show that this extended MPC scheme ensures good tracking and disturbance rejection performances.