Approximation and complexity trade‐off by TP model transformation in controller design: A case study of the TORA system

Abstract

AbstractThe 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 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.Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society