UAV State and Parameter Estimation in Wind Using Calibration Trajectories Optimized for Observability

Shastry, Animesh Kumar; Paley, Derek A.

doi:10.1109/lcsys.2020.3044491

Cited by 11 publications

(14 citation statements)

References 19 publications

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“…The process model of each component of the state vector and the measurement model is the same as in the original formulation. 23 The orientation estimate mean values in the stereographic coordinates are combined with the yaw value from the VIO camera and transformed to SO(3) orientation before supplying it to the SE(3) controller.…”

Section: Methodsmentioning

confidence: 99%

“…A model-based feedback linearization controller operates on the Special Euclidean Group SE(3) 7 with necessary modifications to include aerodynamic interactions. 23 Nominal values for mass and inertia are obtained from a weight scale and a CAD model, respectively. Instead of manually tuning the position and orientation control gains, they were obtained by setting the damping ratio to 0.75 and natural frequency to 2 for translation control and 13 for orientation control.…”

Section: Introductionmentioning

confidence: 99%

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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: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

System identification for high‐performance UAV control in wind

Shastry

Paley

2023

Intl J Robust & Nonlinear

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“…In the current studies, Kazim et al [38] propose a fully nonlinear robust adaptive controller for tracking the trajectory of UAVs in the presence of realistic wind gusts. In related work [39], a selfcalibrated UAV control framework is proposed to compensate the changing wind conditions without operator intervention or manual tuning. A similar approach is proposed in [40].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Customer's satisfaction. The previous study [39] has indicated that it might be difficult to define equitable aid distribution among recipients. For modelling purposes we assume that the equitable aid distribution is measured by the Customer's Satisfaction Level ( CSL ).…”

Section: Constraintsmentioning

confidence: 99%

Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions

Radzki

Golińska-Dawson

Bocewicz

et al. 2021

J Intell Robot Syst

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Besides commercial and military applications, unmanned aerial vehicles (UAVs) are now used more commonly in disaster relief operations. This study proposes a novel model for proactive and reactive planning (different scenarios) that allow for a higher degree of realism, thus a higher likelihood for a mission of being executed according to the plan even when weather forecasts are changing. The novelty of this study results from the addition of a function of resistance of UAVs mission to changes in weather conditions. We link the influence of weather conditions on the UAV’s energy consumption. The goal is to ensure the completion of planned deliveries by a fleet of UAVs under changing weather conditions before their batteries discharge and to identify the emergency route for returned if the mission cannot be completed. An approach based on constraint programming is proposed, as it has proven to be effective in various contexts, especially related to the nonlinearity of the system’s characteristics. The proposed approach has been tested on several instances, which have allowed for analyzing how the plan of mission is robust to the changing weather conditions with different parameters, such as the fleet size, battery capacity, and distribution network layout.

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“…Kalman filters, including EKF, have been applied to estimate the disturbances [18], [19], however, it is a stochastic approach. The work in [20] proposed UAV state, external wind, and parameter estimation in windy conditions using unscented Kalman filter based on IMU and ground velocity measurements. However, these methods require numerous assumptions for noise.…”

Section: Introductionmentioning

confidence: 99%

Robust Motion Control of Nonlinear Quadrotor Model With Wind Disturbance Observer

et al. 2021

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This paper focuses on robust wind disturbance rejection for nonlinear quadrotor models. By leveraging on nonlinear unknown observer theory, it proposes a nonlinear dynamic filter that, using sensors already on-board the aircraft, can estimate in real-time wind gust signals in the three dimensions. The wind disturbance is then treated as input to the PD controller for a quick and robust flight pathway in presence of disturbances. With this scheme, the wind disturbance can be precisely estimated online and compensated in real-time. Hence, the quadrotor can successfully reach its desired attitude and position. To show the effective and desired performance of the method, simulation results are presented in Matlab/Simulink and ROS-enabled Gazebo platform.

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UAV State and Parameter Estimation in Wind Using Calibration Trajectories Optimized for Observability

Cited by 11 publications

References 19 publications

System identification for high‐performance UAV control in wind

System identification for high‐performance UAV control in wind

Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions

Robust Motion Control of Nonlinear Quadrotor Model With Wind Disturbance Observer

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