Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/92783
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Title: Adaptive Unscented Kalman Filter-based disturbance rejection with application to high precision hydraulic robotic control
Authors: Lu, P 
Sandy, T
Buchli, J
Issue Date: 2019
Source: IEEE International Conference on Intelligent Robots and Systems, 3-8 Nov. 2019, Macau, China, p. 4365-4372
Abstract: This paper presents a novel nonlinear disturbance rejection approach for high precision model-based control of hydraulic robots. While most disturbance rejection approaches make use of observers, we propose a novel adaptive Unscented Kalman Filter to estimate the disturbances in an unbiased minimum-variance sense. The filter is made adaptive such that there is no need to tune the covariance matrix for the disturbance estimation. Furthermore, whereas most model-based control approaches require the linearization of the system dynamics, our method is nonlinear which means that no linearization is required. Through extensive simulations as well as real hardware experiments, we demonstrate that our proposed approach can achieve high precision tracking and can be readily applied to most robotic systems even in the presence of uncertainties and external disturbances. The proposed approach is also compared to existing approaches which demonstrates its superior tracking performance.
Publisher: Institute of Electrical and Electronics Engineers
ISBN: 978-1-7281-4004-9 (Electronic ISBN)
978-1-7281-4003-2 (USB ISBN)
978-1-7281-4005-6 (Print on Demand(PoD) ISBN)
DOI: 10.1109/IROS40897.2019.8970476
Description: IEEE/RSJ International Conference on Intelligent Robots and Systems [IROS]
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The following publication Lu, P., Sandy, T., & Buchli, J. (2019, November). Adaptive unscented Kalman filter-based disturbance rejection with application to high precision hydraulic robotic control. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 4365-4372). IEEE is available at https://doi.org/10.1109/IROS40897.2019.8970476
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