Optimal control and state estimation for unmanned aerial vehicle under random vibration and uncertainty

In the past decade, many approaches that attempted to solve the problem of optimal control and parameter estimation of an unmanned aerial vehicle with a priori uncertain parameters simply implied two ways to solve such problem. First, by the formation of optimal control based on a refined mathematical model of the unmanned aerial vehicle, and second, by using the estimation and identification methods of the model parameter of the unmanned aerial vehicle based on measured data from flight tests. However, the identification of the dynamic parameters of the unmanned aerial vehicle is a complicated task because of a number of factors such as random vibration noise, disturbance, and uncertainty of the sensor measurements. Due to the influence of random vibration noise, the problem of correlated vibration noises and uncertainty has encountered inevitably, and the accuracy of the state estimation for unmanned aerial vehicle is degraded. This study concentrates on the optimal control and state estimation for the unmanned aerial vehicle under the combination of both random vibration noise and uncertainty collected by the sensors. The effects of random vibrations at various stages of a large-scale flight that are a priori uncertain require the inclusion of identification algorithms in the optimal control loop. The results showed that the method used in the analysis had been able to provide accurate estimations.

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Recent Advances in Unmanned Aerial Vehicles Forest Remote Sensing—A Systematic Review. Part II: Research Applications

Forests ◽

10.3390/f12040397 ◽

2021 ◽

Vol 12 (4) ◽

pp. 397

Author(s):

Riccardo Dainelli ◽

Piero Toscano ◽

Salvatore Filippo Di Gennaro ◽

Alessandro Matese

Keyword(s):

Remote Sensing ◽

Unmanned Aerial Vehicle ◽

Sustainable Management ◽

Large Scale ◽

Spectral Signature ◽

Accurate Information ◽

Data Interoperability ◽

Critical Issues ◽

Management Context ◽

Aerial Vehicle

Forest sustainable management aims to maintain the income of woody goods for companies, together with preserving non-productive functions as a benefit for the community. Due to the progress in platforms and sensors and the opening of the dedicated market, unmanned aerial vehicle–remote sensing (UAV–RS) is improving its key role in the forestry sector as a tool for sustainable management. The use of UAV (Unmanned Aerial Vehicle) in precision forestry has exponentially increased in recent years, as demonstrated by more than 600 references published from 2018 until mid-2020 that were found in the Web of Science database by searching for “UAV”+“forest”. This result is even more surprising when compared with similar research for “UAV”+“agriculture”, from which emerge about 470 references. This shows how UAV–RS research forestry is gaining increasing popularity. In Part II of this review, analyzing the main findings of the reviewed papers (227), numerous strengths emerge concerning research technical issues. UAV–RS is fully applicated for obtaining accurate information from practical parameters (height, diameter at breast height (DBH), and biomass). Research effectiveness and soundness demonstrate that UAV–RS is now ready to be applied in a real management context. Some critical issues and barriers in transferring research products are also evident, namely,(1) hyperspectral sensors are poorly used, and their novel applications should be based on the capability of acquiring tree spectral signature especially for pest and diseases detection, (2) automatic processes for image analysis are poorly flexible or based on proprietary software at the expense of flexible and open-source tools that can foster researcher activities and support technology transfer among all forestry stakeholders, and (3) a clear lack exist in sensors and platforms interoperability for large-scale applications and for enabling data interoperability.

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Combined Optimal Control and Combinatorial Optimization for Searching and Tracking Using an Unmanned Aerial Vehicle

Journal of Intelligent & Robotic Systems ◽

10.1007/s10846-018-0915-4 ◽

2018 ◽

Vol 95 (2) ◽

pp. 691-706 ◽

Cited By ~ 2

Author(s):

Anders Albert ◽

Lars Imsland

Keyword(s):

Optimal Control ◽

Combinatorial Optimization ◽

Unmanned Aerial Vehicle ◽

Aerial Vehicle

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Optimal Control of a Multirotor Unmanned Aerial Vehicle Based on a Multiphysical Model

Volume 2: Intelligent Transportation/Vehicles; Manufacturing; Mechatronics; Engine/After-Treatment Systems; Soft Actuators/Manipulators; Modeling/Validation; Motion/Vibration Control Applications; Multi-Agent/Networked Systems; Path Planning/Motion Control; Renewable/Smart Energy Systems; Security/Privacy of Cyber-Physical Systems; Sensors/Actuators; Tracking Control Systems; Unmanned Ground/Aerial Vehicles; Vehicle Dynamics, Estimation, Control; Vibration/Control Systems; Vibrations ◽

10.1115/dscc2020-3239 ◽

2020 ◽

Author(s):

Nicolas Michel ◽

Zhaodan Kong ◽

Xinfan Lin

Keyword(s):

Optimal Control ◽

Unmanned Aerial Vehicle ◽

Operation Time ◽

Rigid Body Dynamics ◽

System Level ◽

Optimal Velocity ◽

Rotor Aerodynamics ◽

Time Optimal ◽

Aerial Vehicle ◽

Level Model

Abstract Electric multirotor aircraft with vertical-take-off-and-landing capabilities are emerging as a revolutionary transportation mode. This paper studies optimal control of a multirotor unmanned aerial vehicle based on a system-level multiphysical model. The model considers aerodynamics of the rotor-propeller assembly, electro-mechanical dynamics of the motor and motor controller, and rigid-body dynamics of the vehicle, as control based on a system-level model incorporating all these dynamics and their coupling is missing in literature. A forward flight operation is considered for time-optimal and energy-optimal control, as well as battery voltages of 25 V and 21 V. Energy-optimal control is shown to reduce the energy required for the operation by 38.5% at 25 V, while reducing the battery voltage increases the minimum operation time by 19.8%. The energy-optimal cruise velocity is also examined, demonstrating that the optimal velocity predicted without considering rotor aerodynamics uses 35.2% more energy per meter travelled than is required at the true optimal velocity.

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Influence of Propeller Overlap on Large-Scale Tandem UAV Performance

Unmanned Systems ◽

10.1142/s2301385019500092 ◽

2019 ◽

Vol 07 (04) ◽

pp. 245-260

Author(s):

Adrian B. Weishäupl ◽

Stephen D. Prior

Keyword(s):

Carbon Fiber ◽

Unmanned Aerial Vehicle ◽

Large Scale ◽

Vertical Separation ◽

Hovering Flight ◽

Wide Range ◽

Aerial Vehicle

This paper investigates the interference that arises from overlapping Unmanned Aerial Vehicle (UAV) propellers during hovering flight. The tests have been conducted on [Formula: see text] ultralight carbon fiber propellers using a bespoke mount and the RCBenchmark Series 1780 dynamometer at various degrees of overlap [Formula: see text] and vertical separation [Formula: see text]. A great deal of confusion regarding the losses that are associated with mounting propellers in a co-axial configuration is reported in the literature, with a summary of historical tandem helicopters having been conducted. The results highlight a region of beneficial overlap (0–20%), which has the potential to be advantageous to a wide range of UAVs.

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Real-Time Onboard 3D State Estimation of an Unmanned Aerial Vehicle in Multi-Environments Using Multi-Sensor Data Fusion

Sensors ◽

10.3390/s20030919 ◽

2020 ◽

Vol 20 (3) ◽

pp. 919 ◽

Cited By ~ 3

Author(s):

Hao Du ◽

Wei Wang ◽

Chaowen Xu ◽

Ran Xiao ◽

Changyin Sun

Keyword(s):

Data Fusion ◽

State Estimation ◽

Real Time ◽

Unmanned Aerial Vehicle ◽

Position Estimation ◽

Sensor Data ◽

Measurement Unit ◽

Sensor Data Fusion ◽

Multi Sensor Data Fusion ◽

Aerial Vehicle

The question of how to estimate the state of an unmanned aerial vehicle (UAV) in real time in multi-environments remains a challenge. Although the global navigation satellite system (GNSS) has been widely applied, drones cannot perform position estimation when a GNSS signal is not available or the GNSS is disturbed. In this paper, the problem of state estimation in multi-environments is solved by employing an Extended Kalman Filter (EKF) algorithm to fuse the data from multiple heterogeneous sensors (MHS), including an inertial measurement unit (IMU), a magnetometer, a barometer, a GNSS receiver, an optical flow sensor (OFS), Light Detection and Ranging (LiDAR), and an RGB-D camera. Finally, the robustness and effectiveness of the multi-sensor data fusion system based on the EKF algorithm are verified by field flights in unstructured, indoor, outdoor, and indoor and outdoor transition scenarios.

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Optimal Control of a Zero-Gravity Flight for an Unmanned Aerial Vehicle

AIAA Atmospheric Flight Mechanics Conference ◽

10.2514/6.2017-3731 ◽

2017 ◽

Author(s):

Mohammad Sadraey

Keyword(s):

Optimal Control ◽

Unmanned Aerial Vehicle ◽

Zero Gravity ◽

Aerial Vehicle

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Fast Data Acquisition of Aerial Images Using Unmanned Aerial Vehicle System

International Journal of Informatics and Communication Technology (IJ-ICT) ◽

10.11591/ijict.v3i3.pp162-170 ◽

2014 ◽

Vol 3 (3) ◽

pp. 162

Author(s):

Norhadija Darwin ◽

Anuar Ahmad

Keyword(s):

Data Acquisition ◽

Root Mean Square ◽

Unmanned Aerial Vehicle ◽

Large Scale ◽

Digital Camera ◽

Aerial Images ◽

Mean Square ◽

Fast Data Acquisition ◽

Vehicle System ◽

Aerial Vehicle

The present work discusses the technique and methodology of analysing the potential of fast data acquisition of aerial images using unmanned aerial vehicle system. This study utilizes UAV system for large scale mapping by using digital camera attached to the UAV. UAV is developed from the low-altitude photogrammetric mapping to perform the accuracy of the aerial photography and the resolution of the image. The Ground Control Points (GCPs) and Check Points (CPs) are established using Rapid Static techniques through GPS observation for registration purpose in photogrammetric process. The GCPs is used in the photogrammetric processes to produce photogrammetric output while the CP is employed for accuracy assessment. A Pentax Optio W90 consumer digital camera is also used in image acquisition of the aerial photograph. Besides, this study also involves image processing and map production using Erdas Imagine 8.6 software. The accuracy of the orthophoto is determined using the equation of Root Mean Square Error (RMSE). The final result from orthophoto is compared to the ground survey using total station to show the different accuracy of DEM and planimetric survey. It is discovered that root mean square errors obtained from UAV system are ± 0.510, ± 0.564 and ± 0.622 for coordinate x, y and z respectively. Hence, it can be concluded that the accuracy obtained from UAV system is achieved in sub meter. In a nutshell, UAV system has potential use for large scale mapping in field of surveying and other diversified environmental applications especially for small area which has limited time and less man power.

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