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A Novel Discrete-time Nonlinear Model Predictive Control Based on State Space Model

  • Carlos Sotelo
  • Antonio Favela-Contreras
  • Francisco Beltrán-Carbajal
  • Graciano Dieck-Assad
  • Pedro Rodríguez-Cañedo
  • David Sotelo
Regular Papers Control Theory and Applications
  • 16 Downloads

Abstract

This paper proposes a novel finite dimensional discrete-time Nonlinear Model Predictive Control. This technique is based on discrete-time state-space models, Taylor series expansion for prediction and performance index optimization. Furthermore, the technique extends the concept of the Lie derivative for the discrete time case using Euler backwards method. The performance validation for the discrete-time Nonlinear Model Predictive Control uses the simulation of a single-link flexible joint robot and the inverted pendulum. Comparison of the proposed finite dimensional discrete-time Nonlinear Model Predictive Control technique with Feedback Linearization Control is also discussed. Analytical and numerical results show excellent performances for both, the single-link flexible joint and inverted pendulum controllers using the proposed discrete-time Nonlinear Model Predictive Control technique.

Keywords

Feedback linearization Lie derivatives nonlinear model predictive control relative degree 

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Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Carlos Sotelo
    • 1
  • Antonio Favela-Contreras
    • 1
  • Francisco Beltrán-Carbajal
    • 2
  • Graciano Dieck-Assad
    • 1
  • Pedro Rodríguez-Cañedo
    • 1
  • David Sotelo
    • 1
  1. 1.Tecnologico de Monterrey, Escuela de Ingenieria y CienciasMonterreyMéxico
  2. 2.Universidad Autónoma Metropolitana, Unidad Azcapotzalco, Departamento de EnergíaMexico CityMéxico

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