Approximation and Complexity Trade-off by TP model transformation in Controller Design: A case study of the TORA system

Zoltán Petres, Péter Baranyi

Research output: Paper

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 TP model transformation includes an HOSVD based technique to solve the approximation and complexity trade-off. 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
Publication statusPublished - dec. 1 2007
Event8th International Symposium of Hungarian Researchers on Computational Intelligence and Informatics, CINTI 2007 - Budapest, Hungary
Duration: nov. 15 2007nov. 17 2007

Other

Other8th International Symposium of Hungarian Researchers on Computational Intelligence and Informatics, CINTI 2007
CountryHungary
CityBudapest
Period11/15/0711/17/07

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

Cite this

Petres, Z., & Baranyi, P. (2007). Approximation and Complexity Trade-off by TP model transformation in Controller Design: A case study of the TORA system. Paper presented at 8th International Symposium of Hungarian Researchers on Computational Intelligence and Informatics, CINTI 2007, Budapest, Hungary.