Adaptive inverse-lattice learning control

George Nikolakopoulos, Anthony Tzes

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this article the design framework for an Adaptive Inverse Lattice Controller(AILC) with learning attributes, applicable to linear Auto Regressive (AR) systems, is presented. The utilized controller structure relies on the principle of Inverse Model Control (IMC) and its topology resembles that of a lattice filter. The adaptation rules depend on the identified system dynamics through an adaptive lattice filter. The identification scheme is extended with a proposed algorithm for the model order selection. Within the employed IMC-structure, an inverse lattice controller is utilized in the forward path in cascade with a lowpass detuning filter. As time progresses, the lattice filter estimates more accurately the system dynamics, and the learning scheme adjusts accordingly the attributes of the detuning filter. Simulation studies are used to investigate the efficacy of the suggested scheme.

Original languageEnglish (US)
Title of host publicationIFAC Proceedings Volumes (IFAC-PapersOnline)
EditorsGabriel Ferrate, Eduardo F. Camacho, Luis Basanez, Juan. A. de la Puente
PublisherIFAC Secretariat
Number of pages6
ISBN (Print)9783902661746
StatePublished - 2002
Event15th World Congress of the International Federation of Automatic Control, 2002 - Barcelona, Spain
Duration: Jul 21 2002Jul 26 2002

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
ISSN (Print)1474-6670


Other15th World Congress of the International Federation of Automatic Control, 2002


  • Adaptive lattice filtering
  • Internal model control
  • Learning control

ASJC Scopus subject areas

  • Control and Systems Engineering


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