Artificial neural network based model predictive control: Implementing achievable set‐points

Abstract This paper addresses the problem of determining achievable set‐points for artificial neural network (ANN)‐based model predictive control (MPC) designs. In particular, this work considers a case where a first‐principles model may not be readily available for a nonlinear process, while sufficient closed‐loop data containing possibly correlated outputs is available, such that an ANN‐based model that captures the nonlinear dynamics …