Sensitivity-based adaptive SRUKF for online state, parameter, and process covariance estimation

Riva, Mauro Hernan; Wielitzka, Mark; Ortmaier, Tobias

doi:10.1109/cdc.2017.8263871

Cited by 2 publications

(2 citation statements)

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“…Maximum likelihood identification method (Young, 2015). The closed-loop output error method and its variants (Gautier et al , 2013; Janot et al , 2014a, 2014b; Brunot et al , 2020): the closed-loop input error and the direct and inverse dynamic identification model algorithm. The direct dynamics identification model with Kalman filtering method and its improved methods (Joukov et al , 2015; Riva et al , 2017), namely, the extended Kalman filter, the unscented Kalman filter, the central difference Kalman filter, the bootstrap particle filter, square-root extended Kalman filter, square-root unscented Kalman filter and square-root central difference Kalman filter. Neural networks methods (Leboutet et al , 2021), such as Adaline neural network and Hopfield–Tank recurrent neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…The direct dynamics identification model with Kalman filtering method and its improved methods (Joukov et al , 2015; Riva et al , 2017), namely, the extended Kalman filter, the unscented Kalman filter, the central difference Kalman filter, the bootstrap particle filter, square-root extended Kalman filter, square-root unscented Kalman filter and square-root central difference Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

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A nonlinear robot joint friction compensation method including stick and sliding characteristics

Feng

Zhang

et al. 2023

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Purpose Due to dynamic model is the basis of realizing various robot control functions, and it determines the robot control performance to a large extent, this paper aims to improve the accuracy of dynamic model for n-Degree of Freedom (DOF) serial robot. Design/methodology/approach This paper exploits a combination of the link dynamical system and the friction model to create robot dynamic behaviors. A practical approach to identify the nonlinear joint friction parameters including the slip properties in sliding phase and the stick characteristics in presliding phase is presented. Afterward, an adaptive variable-step moving average method is proposed to effectively reduce the noise impact on the collected data. Furthermore, a radial basis function neural network-based friction estimator for varying loads is trained to compensate the nonlinear effects of load on friction during robot joint moving. Findings Experiment validations are carried out on all the joints of a 6-DOF industrial robot. The experimental results of joint torque estimation demonstrate that the proposed strategy significantly improves the accuracy of the robot dynamic model, and the prediction effect of the proposed method is better than that of existing methods. Originality/value The proposed method extends the robot dynamic model with friction compensation, which includes the nonlinear effects of joint stick motion, joint sliding motion and load attached to the end-effector.

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