Contact Force Estimation of Robot Manipulators With Imperfect Dynamic Model: On Gaussian Process Adaptive Disturbance Kalman Filter

Wei, Yanran; Lyu, Shangke; Li, Wenshuo; Yu, Xiang; Wang, Zidong; Guo, Lei

doi:10.1109/tase.2023.3280750

Cited by 2 publications

(2 citation statements)

References 56 publications

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“…For example, the DO combined with the variational Bayes skewt filter is able to handle the joint effects of additive UDS and recessive statistical parameter of skew-t noises [65]. A composite DO+learning-based KF scheme is presented in [120] for the contact force estimation of robot manipulators. The uncertainties in the robot manipulator model and the force generative model are effectively separated by employing Gaussian process regression and variational Bayes inference techniques for statistical parameters learning.…”

Section: Composite Do+stochastic Filter 1) Composite Do+kalman-type F...mentioning

confidence: 99%

Composite Disturbance Filtering: A Novel State Estimation Scheme for Systems With Multisource, Heterogeneous, and Isomeric Disturbances

Guo,

Li,

Zhu

et al. 2023

IEEE Open J. Ind. Electron. Soc.

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State estimation has long been a fundamental problem in signal processing and control areas. The main challenge is to design filters with ability to reject or attenuate various disturbances. With the arrival of big data era, the disturbances of complicated systems are physically multi-source, mathematically heterogenous, affecting the system dynamics via isomeric (additive, multiplicative, and recessive) channels, and deeply coupled with each other. In traditional filtering schemes, the multi-source heterogenous disturbances are usually simplified as a lumped one so that the "single" disturbance can be either rejected or attenuated. Since the pioneering work in 2012 [42], a novel state estimation methodology called composite disturbance filtering (CDF) has been proposed, which deals with the multi-source, heterogenous, and isomeric disturbances based on their specific characteristics. With the CDF, enhanced anti-disturbance capability can be achieved via refined quantification, effective separation, and simultaneous rejection and attenuation of the disturbances. In this paper, an overview of the CDF scheme is provided, which includes the basic principle, general design procedure, application scenarios (e.g. alignment, localization and navigation), and future research directions. In summary, it is expected that the CDF offers an effective tool for state estimation, especially in the presence of multi-source heterogeneous disturbances.

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Section: Composite Do+stochastic Filter 1) Composite Do+kalman-type F...mentioning

confidence: 99%

Composite Disturbance Filtering: A Novel State Estimation Scheme for Systems With Multisource, Heterogeneous, and Isomeric Disturbances

Guo,

Li,

Zhu

et al. 2023

IEEE Open J. Ind. Electron. Soc.

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“…Previous knowledge of the kinematic and dynamic model of the robotic manipulator is required by most traditional controllers (Thuruthel et al, 2019 ). Several estimation methods such as parameter identification (Zhang et al, 2024 ) and state estimation (Wei et al, 2023 ) have been proposed to alleviate the tolerance of the robot model. However, it is still essential to have knowledge of the fundamental model and recalibrate the parameters for various types of robotic manipulators (Íñigo Elguea-Aguinaco et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Curiosity model policy optimization for robotic manipulator tracking control with input saturation in uncertain environment

Wang,

Xie

et al. 2024

Front. Neurorobot.

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In uncertain environments with robot input saturation, both model-based reinforcement learning (MBRL) and traditional controllers struggle to perform control tasks optimally. In this study, an algorithmic framework of Curiosity Model Policy Optimization (CMPO) is proposed by combining curiosity and model-based approach, where tracking errors are reduced via training agents on control gains for traditional model-free controllers. To begin with, a metric for judging positive and negative curiosity is proposed. Constrained optimization is employed to update the curiosity ratio, which improves the efficiency of agent training. Next, the novelty distance buffer ratio is defined to reduce bias between the environment and the model. Finally, CMPO is simulated with traditional controllers and baseline MBRL algorithms in the robotic environment designed with non-linear rewards. The experimental results illustrate that the algorithm achieves superior tracking performance and generalization capabilities.

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Contact Force Estimation of Robot Manipulators With Imperfect Dynamic Model: On Gaussian Process Adaptive Disturbance Kalman Filter

Cited by 2 publications

References 56 publications

Composite Disturbance Filtering: A Novel State Estimation Scheme for Systems With Multisource, Heterogeneous, and Isomeric Disturbances

Composite Disturbance Filtering: A Novel State Estimation Scheme for Systems With Multisource, Heterogeneous, and Isomeric Disturbances

Curiosity model policy optimization for robotic manipulator tracking control with input saturation in uncertain environment

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