Off-line Robustness Improvement of a Predictive Controller 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. In This paper an Artificial Neural Network (ANN) based approach is presented. To build the data learning base of the ANN, we adopted the Particle Swarm Optimisation (PSO) method, and we used an Online Sequential Extreme-Learning-Machine (OS-ELM) algorithm to learn the ANN. The objective of this work is to to ensure robustness of the closed-loop system against neglecting dynamics, disturbances and sensor noises, and also show that good tuning of MPC parameters allows to reach closed-loop stability without using robustification approaches in addition to MPC. The effectiveness of our approach has been emphasized by comparing the obtained performances to Generalized Predictive Control (GPC) approach without robustification approach, and also to a robustified (GPC) using Youla parametrisation in the presence of disturbances.

Mots clés: Tuning; Heuristic algorithms; Robustness; Artificial neural networks; Predictive control; Particle swarm optimization; State-space methods

Domaines: Automatique / Robotique

Houssam Moumouh, Nicolas Langlois, Madjid Haddad. Off-line Robustness Improvement of a Predictive Controller Using a Novel Tuning Approach Based on Artificial Neural Network. 2020 IEEE 16th International Conference on Control & Automation (ICCA), Oct 2020, Singapore, Singapore. pp.797-802, ⟨10.1109/ICCA51439.2020.9264476⟩. ⟨hal-03044368⟩