UAV state and parameter estimation in wind using calibration trajectories optimized for observability

Shastry, Animesh Kumar; Paley, Derek A.

doi:10.23919/acc50511.2021.9482853

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…The system is designed to work fully onboard with negligible intervention from the operator. The flight control software 25 has been made open-source by the authors. F I G U R E 3 Software architecture overview.…”

Section: Software Overviewmentioning

confidence: 99%

System identification for high‐performance UAV control in wind

Shastry

Paley

2023

Intl J Robust & Nonlinear

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SummaryThis article describes and experimentally evaluates a comprehensive system identification framework for high‐performance UAV control in wind. The framework incorporates both linear offline and nonlinear online methods to estimate model parameters in support of a nonlinear model‐based control implementation. Inertial parameters of the UAV are estimated using a frequency‐domain linear system identification program by incorporating control data obtained from motor‐speed sensing along with state estimates from an automated frequency sweep maneuver. The drag‐force coefficients and external wind are estimated recursively in flight with a square‐root unscented Kalman filter. A custom flight controller is developed to handle the computational demand of the online estimation and control. Flight experiments illustrate the nonlinear controller's tracking performance and enhanced gust rejection capability.

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Section: Software Overviewmentioning

confidence: 99%

System identification for high‐performance UAV control in wind

Shastry

Paley

2023

Intl J Robust & Nonlinear

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“…which is of the form (11). The measured output corresponds to fixed-frame measurement (12), with H H H x = I 2 .…”

Section: Link To the Second Examplementioning

confidence: 99%

“…The most widespread criterion to measure the extent of observability brought by a trajectory is the local OG, introduced by Krener and Ide [10]. In some simple cases, the optimal trajectories might be computed analytically, see [6], but this is generally not the case, and one needs to resort to numerical methods for trajectory planning [11] or trajectory modification for sensors calibration [1]. Despite the drawbacks of Gramian maximization based planning methods [7], the local OG or the closely related Constructibility Gramian [3] remains a relevant tool to establish the most informative trajectories, especially when the system's model is well-known, i.e., the process noise is low.…”

Section: Introductionmentioning

confidence: 99%

Optimal Active Sensing Control for Two-Frame Systems

Benhamou,

Bonnabel,

Chapdelaine

2023

2023 62nd IEEE Conference on Decision and Control (CDC)

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This paper provides a complete characterization of the trajectories that maximize the information collected by a moving vehicle, through sensors' measurements, for the recently introduced class of nonlinear "two-frame systems". The information is quantified in terms of the trace of the observability Gramian (OG) along a trajectory. In general, this quantity nontrivially depends on the control inputs and the state trajectory, resulting in a difficult optimal control problem. Herein, we leverage the property of invariant filtering that Jacobians are state-trajectory independent, that is, only depend on the control inputs, which enables us to mathematically derive optimal trajectories in closed form. We illustrate the results numerically on problems from robotics such as 3D robot localization, and 2D simultaneous localization and mapping.

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UAV state and parameter estimation in wind using calibration trajectories optimized for observability

Cited by 2 publications

References 19 publications

System identification for high‐performance UAV control in wind

System identification for high‐performance UAV control in wind

Optimal Active Sensing Control for Two-Frame Systems

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