A Family of Model Predictive Control Algorithms With Artificial Neural Networks

TLDR

This paper presents nonlinear model‑based predictive control algorithms for MIMO processes modeled with feedforward neural networks. The authors develop two main MPC schemes—MPC‑NO, which solves a nonlinear optimisation online, and MPC‑NPL, which uses an online neural model for local linearisation and a nonlinear free trajectory, with single‑point and multi‑point linearisation options and a hybrid MPC‑NPL‑NO variant. MPC‑NPL proves to be more reliable and computationally efficient than MPC‑NO, while both schemes achieve comparable closed‑loop performance.

Abstract

A Family of Model Predictive Control Algorithms With Artificial Neural Networks This paper details nonlinear Model-based Predictive Control (MPC) algorithms for MIMO processes modelled by means of neural networks of a feedforward structure. Two general MPC techniques are considered: the one with Nonlinear Optimisation (MPC-NO) and the one with Nonlinear Prediction and Linearisation (MPC-NPL). In the first case a nonlinear optimisation problem is solved in real time on-line. In order to reduce the computational burden, in the second case a neural model of the process is used on-line to determine local linearisation and a nonlinear free trajectory. Single-point and multi-point linearisation methods are discussed. The MPC-NPL structure is far more reliable and less computationally demanding in comparison with the MPC-NO one because it solves a quadratic programming problem, which can be done efficiently within a foreseeable time frame. At the same time, closed-loop performance of both algorithm classes is similar. Finally, a hybrid MPC algorithm with Nonlinear Prediction, Linearisation and Nonlinear optimisation (MPC-NPL-NO) is discussed.