Experimental application of a neural constrained model predictive controller based on reference system

摘要

The proposed constrained model predictive control (MPC) is based on a successive linearization of a neural model at each sampling time and the closed loop response is subject to a first order reference system as set of equality constraints. In addition the system inputs are subject to hard constraints. In order to satisfy both types of constraints simultaneously it was needed to include a slack vector in the equality constraints. This slack vector provides more flexibility in the control moves in order to render the solution of the optimization problem feasible. The proposed MPC was implemented in an experimental pH neutralization plant. Results showed a very satisfactory performance of the proposed strategy.