6533b7cefe1ef96bd125707a
RESEARCH PRODUCT
Localization from inertial data and sporadic position measurements
Antonino SferlazzaFilippo D'ippolitoLuca ZaccarianGiovanni Garraffasubject
[INFO.INFO-SY] Computer Science [cs]/Systems and Control [cs.SY]0209 industrial biotechnologyInertial frame of referenceDynamical systems theoryObserver (quantum physics)Computer science020208 electrical & electronic engineeringsampled data observer02 engineering and technologyhybrid systems020901 industrial engineering & automationExponential stabilityControl and Systems EngineeringControl theoryPosition (vector)sporadic measurementsHybrid systemLocalization[INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering0202 electrical engineering electronic engineering information engineering[INFO.INFO-SY]Computer Science [cs]/Systems and Control [cs.SY]Representation (mathematics)[INFO.INFO-AU] Computer Science [cs]/Automatic Control EngineeringInertial navigation systemdescription
International audience; A novel estimation strategy for inertial navigation in indoor/outdoor environments is proposed with a specific attention to the sporadic nature of the non-periodic measurements. After introducing the inertial navigation model, we introduce an observer providing an asymptotic estimate of the plant state. We use a hybrid dynamical systems representation for our results, in order to provide an effective, and elegant theoretical framework. The estimation error dynamics with the proposed observer shows a peculiar cascaded interconnection of three subsystems (allowing for intuitive gain tuning), with perturbations occurring either on the jump or on the flow dynamics (depending on the specific subsystem under consideration). For this structure, we show global exponential stability of the error dynamics. Hardware-in-the-loop results confirm the effectiveness of the proposed solution.
year | journal | country | edition | language |
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2020-07-11 |