Robustness of Model Predictive Control Using a Novel Tuning Approach Based on Artificial Neural Network

Houssam Moumouh (1, 2) , Nicolas Langlois (3) , Madjid Haddad (2)

1 NU - Normandie Université
2 SEGULA Technologies [Trappes]
3 Pôle Automatique et Systèmes

A successful implementation of Model Predictive Control (MPC) requires appropriately tuned parameters. This paper presents a novel tuning approach based on Artificial Neural Network (ANN). To build the data learning base of the ANN, we adopted the Particle Swarm Optimisation (PSO) method, and we used the reliable algorithm, Online Sequential Extreme-Learning-Machine (OS-ELM) to learn the ANN. The objective of this work is to show that good tuning of MPC parameters makes it possible to reach closed-loop stability and ensure robustness against disturbances and sensor noises, without using robustification approaches in addition to MPC. The effectiveness of our approach is brought to light by comparing the obtained performances to other MPC tuning approaches without disturbances, and also to a robustified Generalized Predictive Control (GPC) using Youla parametrisation in the presence of disturbances.

Mots clés: Predictive Control; model predictive control; artificial neural network; ANN learning; particle swarm optimisation method; MPC parameters; closed-loop stability; robustification; MPC tuning; robustified generalized predictive control; sequential extreme-learning-machine; robustness; data learning; sensor noises; Youla parametrisation

Mots clés: Commande prédictive

Domaines: Automatique / Robotique

Houssam Moumouh, Nicolas Langlois, Madjid Haddad. Robustness of Model Predictive Control Using a Novel Tuning Approach Based on Artificial Neural Network. 2020 28th Mediterranean Conference on Control and Automation (MED), Sep 2020, Saint-Raphaël, France. pp.127-132, ⟨10.1109/MED48518.2020.9183204⟩. ⟨hal-03044384⟩