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A Novel Tuning Approach for MPC Parameters Based on Artificial Neural Network: An application to FOPDT System

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Houssam Moumouh
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Nicolas Langlois
Madjid Haddad

Abstract

A successful implementation of Model Predictive Control (MPC) requires appropriately tuned parameters. In this paper an Artificial-Neural-Network (ANN) based approach is presented and detailed in the case of a First Order Plus Dead Time (FOPDT) control-lable system. The original part of our approach lies in its capability to tune the MPC parameters using Particle-Swarm-Optimization (PSO) and Online-Sequential-Extreme-Learning-Machine(OS-ELM). This approach allows also to reach efficiently closed-loop stability. The effectiveness of our approach has been emphasized by comparing the obtained performances to other existing methods.
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Dates and versions

hal-02407974 , version 1 (12-12-2019)

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  • HAL Id : hal-02407974 , version 1

Cite

Houssam Moumouh, Nicolas Langlois, Madjid Haddad. A Novel Tuning Approach for MPC Parameters Based on Artificial Neural Network: An application to FOPDT System. 15th European Conference on Advanced Control and Diagnosis ACD'19, Nov 2019, Bologna, Italy. ⟨hal-02407974⟩
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