ResNet and PolyNet Based Identification and (MPC) Control of Dynamical Systems: A Promising Way

This paper deals with model predictive control synthesis which take benefits from artificial neural networks to model (non-linear) dynamical system. More precisely, thanks to a systematic and rigorous methodology, it is shown that residual networks (ResNet) and PolyInception networks (PolyNet) neura...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.20657-20672
Main Authors: Blaud, Pierre Clement, Chevrel, Philippe, Claveau, Fabien, Haurant, Pierrick, Mouraud, Anthony
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Back propagation networks
Benchmarks
Computer architecture
Computer Science
Control theory
Dynamical systems
Engineering Sciences
Feedforward control
feedforward neural networks
Fuzzy control
Mathematics
model predictive control
Neural networks
Neurons
Operations Research
Optimization
PolyNet
Predictive control
Predictive models
quadruple tank process
ResNet
Water levels
Water tanks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper deals with model predictive control synthesis which take benefits from artificial neural networks to model (non-linear) dynamical system. More precisely, thanks to a systematic and rigorous methodology, it is shown that residual networks (ResNet) and PolyInception networks (PolyNet) neural network architectures, developed initially for image recognition, are very good candidate for i) identification of dynamical systems, ii) being used as embedded model in a model predictive control laws. Concretely, the widely used non-linear dynamical system quadruple tank process is used as a benchmark. The neural network architectures studied are i) feedforward networks as a reference point, and the two other linked to Euler integration method ii) residual networks and iii) PolyInception networks. Networks training is performed by mixing classical back-propagation algorithm and hyperparameters optimisation through heuristics. The identification results provided show that neural networks of types ii) and iii) perform better than the classical one i), with a better generalisation capability. Finally, model predictive controllers are synthesized based on the various networks trained. The simulation results obtained for controlling water levels of a 4 tanks system benchmark give interesting insights. They show that residual networks based model predictive control is better suited than feedforward networks and PolyInception networks based ones, both taking into account computation time and set point errors.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3196920