Bilateral teleoperation control using recursive least squares filter with forgetting factor

In this research, we have designed and implemented recursive least squares (RLS) algorithm in master slave tracking on Geomagic® Touch™ (Phantom Omni) haptic device. RLS algorithm enables us to achieve optimal tracking in a teleoperation system in which the system parameters vary with time and the n...

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Vydané v:2015 Annual IEEE India Conference (INDICON) s. 1 - 6
Hlavní autori: Bhardwaj, Akshay, Agarwal, Vijyant, Parthasarathy, Harish
Médium: Konferenčný príspevok.. Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 01.12.2015
Predmet:
adaptive filter
Algorithm design and analysis
Algorithms
Conferences
Dynamics
Error analysis
Errors
Force
Heuristic algorithms
Kalman Gain matrix
Least squares method
linear time varying systems
Master slave teleoperation
Mathematical model
nonlinear dynamical system
Phantoms
Recursive
recursive least squares algorithm
Robots
Tracking
Tracking (position)
ISSN:2325-9418
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Shrnutí:In this research, we have designed and implemented recursive least squares (RLS) algorithm in master slave tracking on Geomagic® Touch™ (Phantom Omni) haptic device. RLS algorithm enables us to achieve optimal tracking in a teleoperation system in which the system parameters vary with time and the noise is weakly non-stationary. In our previous work on teleoperation, we employed Widrow's least mean square algorithm instead of RLS algorithm and achieved satisfactorily high tracking accuracy. There, we employed instantaneous errors to update filter coefficients and hence slave positions. This study initiated with the idea that if we account all or some of the previous errors in updating filter coefficients and thus reducing current error, we might be probably able to achieve even higher tracking accuracy than that achieved with WLMS. Therefore, in order to understand this influence of older errors on tracking accuracy, we have applied RLS algorithm with forgetting factor. The use of forgetting factor in the least squares algorithm enables us to base our tracking on different weights of past errors that further helps us in understanding this influence at a broader level.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
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SourceType-Conference Papers & Proceedings-2
ISSN:2325-9418
DOI:10.1109/INDICON.2015.7443651