Intelligent Multi-Objective Nonlinear Model Predictive Control (iMO-NMPC): Towards the 'on-line' optimization of highly complex control problems

  • Authors:

  • Juan José Valera García;Vicente Gómez Garay;Eloy Irigoyen Gordo;Fernando Artaza Fano;Mikel Larrea Sukia

  • Affiliations:

  • Tecnalia Research & Innovation - Industry and Transport Division, Parque Tecnolóógico de Zamudio, Edificio 202, E-48170 Zamudio, Vizcaya, Spain and University of the Basque Country (UPV/ ...;University of the Basque Country (UPV/EHU), Intelligent Control Research Group, Department of System Engineering and Automatic Control, ETSI, 48013 Bilbao, Vizcaya, Spain;University of the Basque Country (UPV/EHU), Intelligent Control Research Group, Department of System Engineering and Automatic Control, ETSI, 48013 Bilbao, Vizcaya, Spain;University of the Basque Country (UPV/EHU), Intelligent Control Research Group, Department of System Engineering and Automatic Control, ETSI, 48013 Bilbao, Vizcaya, Spain;University of the Basque Country (UPV/EHU), Intelligent Control Research Group, Department of System Engineering and Automatic Control, ETSI, 48013 Bilbao, Vizcaya, Spain

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2012

Quantified Score

Hi-index 12.05

Visualization

Abstract

The benefits of using the Nonlinear Model Predictive Control (NMPC) for the response optimization of highly complex controlled plants are well known. Nevertheless the complexity and associated high computational cost make its implementation and reliability the focus of the discussion. This paper proposes an Intelligent and Multi-Objective NMPC (iMO-NMPC) scheme where several, and often conflicting, control objectives can be competing simultaneously in the control problem. In the iMO-NMPC, the combination of a Neural Network, a Multi-Objective Genetic Algorithm and a Fuzzy Inference System, help us in the nonlinear search for near-optimal control actions, aiming to fulfil all the specified control objectives simultaneously. The proposed scheme adds an expert stage that can adaptively change the degree of importance (weight) of each control objective according to the state of the plant. Therefore, once the nonlinear multi-objective optimization problem is solved at each sampling time and the non-inferior control solutions belonging to the set of Pareto are obtained, the most appropriate one is selected by using the control objectives weights inferred from the expert stage. Some experimental results showing the iMO-NMPC effectiveness and the details about its implementation over control systems with low sampling times are also presented and discussed in this paper.