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Solving nonlinear MBPC through convex optimization: A comparative study using neural networks

  • Miguel Ayala Botto
  • Hubert A. B. te Braake
  • José Sá da Costa
Poster Presentations 1 Scientific Applications
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1112)

Abstract

Typical solutions based on nonlinear constrained optimization-based strategies are hard to find and usually demand for higher level of computation. In this paper two techniques for transforming the initial nonlinear optimization into an approximate convex optimization are presented and tested for a rigid manipulator modeled with a feedforward neural network. The results have shown that the overall performance is enhanced when performing an approximate feedback linearization.

Keywords

Model Predictive Control Feedforward Neural Network Prediction Horizon Linear Optimization Problem Predictive Control Scheme 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Miguel Ayala Botto
    • 1
  • Hubert A. B. te Braake
    • 2
  • José Sá da Costa
    • 1
  1. 1.Instituto Superior Técnico Department of Mechanical Engineering GCAR/IDMECTechnical University of LisbonLisboa CodexPortugal
  2. 2.Control Laboratory, Department Electrical EngineeringDelft University of TechnologyGA DelftThe Netherlands

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