Multivariable Virtual Reference Feedback Tuning with Bayesian regularization

Emerson Christ Boeira¹; Diego Eckhard¹

¹ Universidade Federal do Rio Grande do Sul

Baixar PDF doi:10.20906/CPS/CBA2018-1060

Resumo

This paper proposes the use of regularization on the multivariable formulation of the Virtual Reference Feedback Tuning (VRFT). When the process to be controlled has a signicant amount of noise, the standard VRFT approach, that uses the instrumental variable technique, provides estimates with very poor statistical properties. To cope with that, this paper considers the use of regularization on the estimation procedure, reducing the covariance error at the cost of inserting a small bias. Also, this paper explains different types of regularization matrices and presents the methodology to tune these matrices. In order to demonstrate the benets of the proposed formulation, a numerical example is presented.

Palavras-chave: Control Theory and Applications; Data-driven Control; System Identification; Regularization