Extended robust Kalman filter for attitude estimation

Inoue, Roberto S.; Terra, Marco H.; Cerri, Joao P.

doi:10.1049/iet-cta.2015.0235

Cited by 25 publications

(27 citation statements)

References 21 publications

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“…For example, a disturbance observer, as proposed in Kong et al ( 2009 ), could compensate the effect of human torque τ h in Equations (14) and (20). Optimal robust filter for DMJLS (Ishihara et al, 2015 ) and extended robust Kalman filter proposed in Inoue et al ( 2016 ) could better estimate the states of the SRPAR.…”

Section: Discussionmentioning

confidence: 99%

Impedance Control for Robotic Rehabilitation: A Robust Markovian Approach

et al. 2017

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The human-robot interaction has played an important role in rehabilitation robotics and impedance control has been used in the regulation of interaction forces between the robot actuator and human limbs. Series elastic actuators (SEAs) have been an efficient solution in the design of this kind of robotic application. Standard implementations of impedance control with SEAs require an internal force control loop for guaranteeing the desired impedance output. However, nonlinearities and uncertainties hamper such a guarantee of an accurate force level in this human-robot interaction. This paper addresses the dependence of the impedance control performance on the force control and proposes a control approach that improves the force control robustness. A unified model of the human-robot system that considers the ankle impedance by a second-order dynamics subject to uncertainties in the stiffness, damping, and inertia parameters has been developed. Fixed, resistive, and passive operation modes of the robotics system were defined, where transition probabilities among the modes were modeled through a Markov chain. A robust regulator for Markovian jump linear systems was used in the design of the force control. Experimental results show the approach improves the impedance control performance. For comparison purposes, a standard H∞ force controller based on the fixed operation mode has also been designed. The Markovian control approach outperformed the H∞ control when all operation modes were taken into account.

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Section: Discussionmentioning

confidence: 99%

Impedance Control for Robotic Rehabilitation: A Robust Markovian Approach

et al. 2017

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“…Based on the model and output equation, the KF can estimate the state, represented by

. In Algorithm 1 the discrete local KF is presented with following matrices [ 21 , 22 ]:…”

Section: Methodsmentioning

confidence: 99%

Inertial Sensor Error Reduction through Calibration and Sensor Fusion

Lambrecht

Nogueira

Bôrtole³

et al. 2016

Sensors

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This paper presents the comparison between cooperative and local Kalman Filters (KF) for estimating the absolute segment angle, under two calibration conditions. A simplified calibration, that can be replicated in most laboratories; and a complex calibration, similar to that applied by commercial vendors. The cooperative filters use information from either all inertial sensors attached to the body, Matricial KF; or use information from the inertial sensors and the potentiometers of an exoskeleton, Markovian KF. A one minute walking trial of a subject walking with a 6-DoF exoskeleton was used to assess the absolute segment angle of the trunk, thigh, shank, and foot. The results indicate that regardless of the segment and filter applied, the more complex calibration always results in a significantly better performance compared to the simplified calibration. The interaction between filter and calibration suggests that when the quality of the calibration is unknown the Markovian KF is recommended. Applying the complex calibration, the Matricial and Markovian KF perform similarly, with average RMSE below 1.22 degrees. Cooperative KFs perform better or at least equally good as Local KF, we therefore recommend to use cooperative KFs instead of local KFs for control or analysis of walking.

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“…In the next section, the robust control approaches we are proposing is given in terms of discrete‐time systems. In this sense, can be discretized according to , in the following way

x_{i + 1} = false(F_{i} + δ F_{i} false) x_{i} + false(G_{i} + δ G_{i} false) u_{i},

where x i + 1 and x i are the discrete states of

\overset{x}{true}

, u i is the discrete input of u 2 , F i ≃ I + Λ 2 T ,

G_{i} ≃ Ω_{2} T^{\frac{1}{2}}

and T is the sample time. The terms δF i and δG i represent parameter uncertainties of the WMR.…”

Section: Problem Formulationmentioning

confidence: 99%

“…In the next section, the robust control approaches we are proposing is given in terms of discrete-time systems. In this sense, (10) can be discretized according to [23], in the following way…”

Section: Problem Formulationmentioning

confidence: 99%

Robust recursive linear quadratic regulator for wheeled mobile robots based on optical motion capture cameras

Inoue

Terra

Leão

et al. 2019

Asian Journal of Control

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In this paper, we deal with nonholonomic wheeled mobile robots (WMR) modeled as uncertain nonlinear systems. Sources of uncertainties can be due to erroneous estimation of mass, inertia, and center of gravity and due to payload time-varying. They also can be considered as external disturbances generated from unstructured environments. We are proposing the use of a robust linear quadratic regulator (RLQR) to deal with tracking problems of WMR. In order to guarantee the effectiveness of this control approach, the robot posture is measured through a high-precision motion capture system. This RLQR encompasses in a unified framework all state and output uncertain parameters of the system and does not depend on any auxiliary parameter to be tuned. It is useful to be used in online applications. Experimental results are presented with a comparative study among the R-LQR, the nonlinear  ∞ control via game theory, and the standard proportional-derivative controller plus computed torque (PD+CT).

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Extended robust Kalman filter for attitude estimation

Cited by 25 publications

References 21 publications

Impedance Control for Robotic Rehabilitation: A Robust Markovian Approach

Impedance Control for Robotic Rehabilitation: A Robust Markovian Approach

Inertial Sensor Error Reduction through Calibration and Sensor Fusion

Robust recursive linear quadratic regulator for wheeled mobile robots based on optical motion capture cameras

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