Particle filtering for state estimation in nonlinear industrial systems

Rigatos, GG

dc.contributor.author	Rigatos, GG	en
dc.date.accessioned	2014-03-01T01:31:37Z
dc.date.available	2014-03-01T01:31:37Z
dc.date.issued	2009	en
dc.identifier.issn	0018-9456	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19853
dc.subject	Extended Kalman filter (EKF)	en
dc.subject	Gaussian filters	en
dc.subject	Industrial robotic manipulator	en
dc.subject	Nonparametric filters	en
dc.subject	Particle filter (PF)	en
dc.subject	Sensor fusion	en
dc.subject	State estimation	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.classification	Instruments & Instrumentation	en
dc.subject.other	Gaussian filters	en
dc.subject.other	Industrial robotic manipulator	en
dc.subject.other	Nonparametric filters	en
dc.subject.other	Particle filter (PF)	en
dc.subject.other	Sensor fusion	en
dc.subject.other	Acoustic noise	en
dc.subject.other	Air filters	en
dc.subject.other	Cellular radio systems	en
dc.subject.other	Control theory	en
dc.subject.other	End effectors	en
dc.subject.other	Estimation	en
dc.subject.other	Extended Kalman filters	en
dc.subject.other	Flexible manipulators	en
dc.subject.other	Nonlinear filtering	en
dc.subject.other	Position control	en
dc.subject.other	Robotics	en
dc.subject.other	Sensors	en
dc.subject.other	Spurious signal noise	en
dc.subject.other	State estimation	en
dc.subject.other	Industry	en
dc.title	Particle filtering for state estimation in nonlinear industrial systems	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TIM.2009.2021212	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TIM.2009.2021212	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	State estimation is a major problem in industrial systems, particularly in industrial robotics. To this end, Gaussian and nonparametric filters have been developed. In this paper, the extended Kalman filter, which assumes Gaussian measurement noise, is compared with the particle filter, which does not make any assumption on the measurement noise distribution. As a case study, the estimation of the state vector of an industrial robot is used when measurements are available from an accelerometer that was mounted on the end effector of the robotic manipulator and from the encoders of the joints' motors. It is shown that, in this kind of sensor fusion problem, the particle filter outperforms the extended Kalman filter, at the cost of more demanding computations. © 2009 IEEE.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Instrumentation and Measurement	en
dc.identifier.doi	10.1109/TIM.2009.2021212	en
dc.identifier.isi	ISI:000270720000009	en
dc.identifier.volume	58	en
dc.identifier.issue	11	en
dc.identifier.spage	3885	en
dc.identifier.epage	3900	en