Adaptive Unscented Kalman Filter-based Disturbance Rejection With Application to High Precision Hydraulic Robotic Control
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Date
2019
Publication Type
Conference Paper
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yes
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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.
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published
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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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Pages / Article No.
4365 - 4372
Publisher
IEEE
Event
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019)
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Methods
Software
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Organisational unit
02284 - NFS Digitale Fabrikation / NCCR Digital Fabrication