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Adaptive Neurocontrol of a Certain Class of MIMO Discrete-Time Processes Based on Stability Theory

  • Jean-Michel Renders
  • Marco Saerens
  • Hugues Bersini
Part of the Advances in Industrial Control book series (AIC)

Abstract

In this chapter, we prove the stability of a certain class of nonlinear discrete MIMO (Multi-Input Multi-Output) systems controlled by a multilayer neural net with a simple weight adaptation strategy. The proof is based on the Lyapunov stability theory. However, the stability statement is only valid if the initial weight values are not too far from their optimal values which allow perfect model matching (local stability). We therefore propose to initialize the weights with values that solve the linear problem. This extends our previous work (Renders, 1993; Saerens, Renders & Bersini, 1994), where single-input single-output (SISO) systems were considered.

Keywords

Neural Network Adaptive Control Lyapunov Stability Theory Internal Model Control Neural Controller 
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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Copyright information

© Springer-Verlag London Limited 1995

Authors and Affiliations

  • Jean-Michel Renders
    • 1
  • Marco Saerens
    • 2
  • Hugues Bersini
    • 2
  1. 1.Laboratoire d’automatique (cp. 165)Université Libre de BruxellesBruxellesBelgium
  2. 2.IRIDIA Laboratory (cp. 194/6)Université Libre de BruxellesBruxellesBelgium

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