@conference {Boeira:Eckhard:2018,
	title = {Multivariable Virtual Reference Feedback Tuning with Bayesian regularization},
	booktitle = {XXII Congresso Brasileiro de Autom{\'a}tica},
	year = {2018},
	note = {n/a},
	pages = {1{\textendash}8},
	publisher = {{SBA} Sociedade Brasileira de Autom{\'a}tica},
	organization = {{SBA} Sociedade Brasileira de Autom{\'a}tica},
	address = {Jo{\~a}o Pessoa},
	abstract = {<p>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 significant 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 benefits of the proposed formulation, a numerical example is presented.</p>
},
	doi = {10.20906/CPS/CBA2018-1060},
	author = {E. C. Boeira and D. Eckhard}
}
