Bayesian state estimation of a flexible industrial robot [Elektronisk resurs] / Patrik Axelsson, Rickard Karlsson, Mikael Norrlöf
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Axelsson, Patrik (författare)
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Karlsson, Rickard (författare)
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- Norrlöf, Mikael, 1971- (författare)
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Alternativt namn: Norrlöf, Using iterative learning control to get better performance of robot control systems / Mikael Norrlöf,Svante Gunnarsson Place : Publisher,, 1971-
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- Linköpings universitet. Institutionen för systemteknik (utgivare)
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Alternativt namn: ISY
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Alternativt namn: Engelska: Linköping University. Department of Electrical Engineering
- Linköping : Department of Electrical Engineering, Linköpings universitet, 2011
- Engelska 9 s.
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Serie: LiTH-ISY-R, 1400-3902 ; 3027
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Läs hela texten (Sammanfattning och fulltext från Linköping University Electronic Press)
Sammanfattning
Ämnesord
Stäng
- A sensor fusion method for state estimation of a flexible industrial robot is developed to enhance the performance. By measuring the acceleration at the end-effector, the accuracy of the arm angular position, velocity, and acceleration estimates are improved. The problem is formulated in a Bayesian estimation framework and two solutions are proposed; one using the extended Kalman filter (EKF) and one using the particle filter (PF). In a simulation study on a realistic flexible industrial robot, the position performance is shown to be close to the fundamental Cramer-Rao lower bound (CRLB), outperforming the previous non-accelerometer method. The technique is also verified in experiments on the ABB IRB4600 robot, where the dynamic performance for the accelerometer method is significantly better, even when model errors are present.
Ämnesord
- Reglerteknik (sao)
- Automatic control (LCSH)
Indexterm och SAB-rubrik
- Industrial robot
- Positioning
- Estimation
- Particle filter
- Extended Kalman filter
- Cramér-Rao lower bound
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