Approximation and complexity trade-off by TP model transformation in controller design: a case study of the TORA System

Zoltán Petres, P. Baranyi, Hideki Hashimoto

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

The main objective of the paper is to study the approximation and complexity trade-off capabilities of the recently proposed tensor product distributed compensation (TPDC) based control design framework. The TPDC is the combination of the TP model transformation and the parallel distributed compensation (PDC) framework. The Tensor Product (TP) model transformation includes an Higher Order Singular Value Decomposition (HOSVD)-based technique to solve the approximation and complexity tradeoff. In this paper we generate TP models with different complexity and approximation properties, and then we derive controllers for them. We analyze how the trade-off effects the model behavior and control performance. All these properties are studied via the state feedback controller design of the Translational Oscillations with an Eccentric Rotational Proof Mass Actuator (TORA) System.

Original languageEnglish
Pages (from-to)575-585
Number of pages11
JournalAsian Journal of Control
Volume12
Issue number5
DOIs
Publication statusPublished - Sep 2010

Fingerprint

Tensors
Controllers
Singular value decomposition
State feedback
Actuators
Compensation and Redress

Keywords

  • Linear Matrix Inequalities (LMI)
  • Nonlinear control
  • Tensor Product Distributed Compensation framework (TPDC)
  • TORA system

ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Approximation and complexity trade-off by TP model transformation in controller design : a case study of the TORA System. / Petres, Zoltán; Baranyi, P.; Hashimoto, Hideki.

In: Asian Journal of Control, Vol. 12, No. 5, 09.2010, p. 575-585.

Research output: Contribution to journalArticle

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