unmanned helicopter
9109991
Quadrotor Helicopter Flight Dynamics and Control: Theory and Experiment
Quadrotor helicopters are emerging as a popular platform for unmanned aerial vehicle (UAV) research, due to the simplicity of their construction and maintenance, their ability to hover, and their vertical take o and landing (VTOL) capability. Current designs have often considered only nominal operating conditions for vehicle control design. This work seeks to address issues that arise when deviating significantly from the hover flight regime. Aided by well established research for helicopter flight control, three separate aerodynamic eects are investigated as they pertain to quadrotor flight, due to vehicular velocity, angle of attack, and airframe design. They cause moments that aect attitude control, and thrust variation that aects altitude control. Where possible, a theoretical development is first presented, and is then validated through both thrust test stand measurements and vehicle flight tests using the Stanford Testbed of Autonomous Rotorcraft for Multi Agent Control (STARMAC) quadrotor helicopter. The results enabled improved controller performance.
Gabriel M Homann Haomiao Huang Steven L Waslander Claire J Tomlin
110294916
Principles of Helicopter Aerodynamics
Preface to the second edition Preface to the first edition Acknowledgements List of main symbols 1. Introduction: a history of helicopter flight 2. Fundamentals of rotor aerodynamics 3. Blade element analysis 4. Rotating blade motion 5. Helicopter performance 6. Aerodynamics design of helicopters 7. Aerodynamics of rotor airfoils 8. Unsteady airfoil behavior 9. Dynamic stall 10. Rotor wakes and blade tip vortices 11. Rotor airframe interaction aerodynamics 12. Autogiros and gyroplanes 13. Aerodynamics of wind turbines 14. Computational methods for helicopter aerodynamics Appendix Index.
John Gordon Leishman
17288868
Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle
Two critical limitations for using current satellite sensors in real time crop management are the lack of imagery with optimum spatial and spectral resolutions and an unfavorable revisit time for most crop stress detection applications. Alternatives based on manned airborne platforms are lacking due to their high operational costs. A fundamental requirement for providing useful remote sensing products in agriculture is the capacity to combine high spatial resolution and quick turnaround times. Remote sensing sensors placed on unmanned aerial vehicles (UAVs) could fill this gap, providing low cost approaches to meet the critical requirements of spatial, spectral, and temporal resolutions. This paper demonstrates the ability to generate quantitative remote sensing products by means of a helicopter based UAV equipped with inexpensive thermal and narrowband multispectral imaging sensors. During summer of 2007, the platform was flown over agricultural fields, obtaining thermal imagery in the 7.5 13 mum region (40 cm resolution) and narrowband multispectral imagery in the 400 800 nm spectral region (20 cm resolution) Surface reflectance and temperature imagery were obtained, after atmospheric corrections with MODTRAN. Biophysical parameters were estimated using vegetation indices, namely, normalized difference vegetation index, transformed chlorophyll absorption in reflectance index/optimized soil adjusted vegetation index, and photochemical reflectance index (PRI) coupled with SAILH and FLIGHT models. As a result, the image products of leaf area index, chlorophyll content (C ab) and water stress detection from PRI index and canopy temperature were produced and successfully validated. This paper demonstrates that results obtained with a low cost UAV system for agricultural applications yielded comparable estimations, if not better, than those obtained by traditional manned airborne sensors.
IEEE Transactions on Geoscience and Remote Sensing
Jose A Jimenez-Berni Pablo J Zarco-Tejada Lola Suarez Elias Fereres
11182760
System identification of small size unmanned helicopter dynamics
Abstmcf: Flight testing of a fully instrumented model scale unmanned helicopter (Yamaha R SO with loft. diameter rotor) was conducted for the purpose of dynamic model identification. This paper describes the application of CIFER' system identification techniques, which have been developed for full size helicopters, to this aircraft. An accurate, high bandwidth, linear state space model was derived for the hover condition. The model structure includes the explicit representation of regressive rotor flap dynamics, rigid body fuselage dynamics, and the yaw damper. The R 50 codiguration and identified dynamics are compared with those of a dynamically scaled UH 1H. The identified model shows excellent predictive capability and is well suited for flight control design and simulation applications.
Bernard Mettler Mark Tischler Takeo Kanade
55232381
DRELIO: An unmanned helicopter for imaging coastal areas
Very high spatial resolution remote sensing images and Digital Elevation Models (DEM) are widely used in coastal management applications. For example, they are used for the quantification of morphosedimentary changes of the coastal fringe, including cross shore and longshore sediment transport. They are also used as input in hydrodynamics numerical modelling Spatial resolution, precision and accuracy are critical parameters of the DEM. Presently, most of DEM are built using aerial or satellite images with a spatial resolution coarser than 50cm is not accurate enough for most of applications. An unmanned photogrammetric helicopter (DRELIO) has been developed. It is equipped with an autopilot system. After loading the flight plan, no ground communications are needed from take off to landing. The fly altitude can reach 100m above the ground. DRELIO can operate in windy conditions up to 50km/h and it is able to make stationary flights. A reflex camera with high quality interchangeable optics is onboard. Depending on the focal length and flying altitude, the resolution of the images varies from 1 to 5 cm with a ground coverage of 50 by 75m up to 250 by 375m. Due to specific flight conditions and image acquisitions, a photogrammetric toolbox has been developed. Using stereoscopic images and GPS positionning of reference points on the images, it allows to build DEM and an orthorectified image with a spatial resolution better than 5 cm. In this study, we present an example of an acquisition realzed on the beach of Porsmillin (French Brittany) and we discuss the precision and accuracy obtained by this method. The DRELIO system, which produces DEM concurrent to LIDARs, appears now to be more flexible and efficient than UAV (unmanned aerial vehicle) helicopters equipped with electric engines, UAV planes and less expensive than LIDAR.
Christophe Delacourt Pascal Allemand Marion Jaud Philippe Grandjean Anne Deschamps Jerome Ammann Veronique Cuq Serge Suanez
110844564
Remote sensing Technology for Vegetation Monitoring using an Unmanned Helicopter
The objective of this study is to develop a system that can generate a map regarding crop status obtained by mounting an imaging sensor on an unmanned helicopter. The flight characteristic of an unmanned helicopter is effective for remote sensing. A real time kinematic global positioning system (RTK GPS) was adopted, and an inertial sensor (INS) that provides posture (roll and pitch angles) was installed in the helicopter. In addition, a geomagnetic direction sensor (GDS) that outputs an absolute direction was also attached to the helicopter. While obtaining pictures using the imaging sensor on the unmanned helicopter, image distortions arose due to changes in helicopter posture. Converting from image coordinates to global coordinates removes this distortion. However, a geomagnetic warp surrounding the helicopter created errors in the posture data, particularly a large GDS error. By correcting GDS errors, it was possible to generate a field map from helicopter captured image data with a spatial error of 38 cm.
Ryo Sugiura Noboru Noguchi Kazunobu Ishii
16813915
Structured H Infinity Command and Control Loop Design for Unmanned Helicopters
The aim of this paper is to present rigorous and efficient methods for designing flight controllers for unmanned helicopters that have guaranteed performance, intuitive appeal for the flight control engineer, and prescribed multivariableloopstructures.Helicopterdynamicsdonotdecoupleastheydoforthe fixed wingaircraftcase,andso the design of helicopter flight controllers with a desirable and intuitive structure is not straightforward. We use an H1 output feedback design procedure thatis simplified in the sense that rigorous controller designs are obtained by solving only two coupled matrix design equations. An efficient algorithm is given for solving these that does not require initial stabilizing gains. An output feedback approach is given that allows one to selectively close prescribed multivariable feedbackloopsusing areducedsetof thestates ateach step.At eachstep,shaping filtersmay beadded thatimproveperformanceandyieldguaranteedrobustnessandspeedofresponse.ThenetresultyieldsanH1 design with a control structure that has been historically accepted in the flight control community. As an example, a design forstationkeepingandhoverofanunmannedhelicopterispresented.Theresult isastationkeeping hovercontroller withrobustperformanceinthepresenceofdisturbances(includingwindgusts),excellentdecoupling,andgoodspeed of response.
Jyotirmay Gadewadikar Frank L Lewis Kamesh Subbarao Ben M Chen
15279814
An Application of Reinforcement Learning to Aerobatic Helicopter Flight
Autonomous helicopter flight is widely regarded to be a highly challenging control problem. This paper presents the first successful autonomous completion on a real RC helicopter of the following four aerobatic maneuvers: forward flip and sideways roll at low speed, tail in funnel, and nose in funnel. Our experimental results significantly extend the state of the art in autonomous helicopter flight. We used the following approach: First we had a pilot fly the helicopter to help us find a helicopter dynamics model and a reward (cost) function. Then we used a reinforcement learning (optimal control) algorithm to find a controller that is optimized for the resulting model and reward function. More specifically, we used differential dynamic programming (DDP) an extension of the linear quadratic regulator (LQR)
NIPS
P Abbeel Adam Coates Morgan Quigley A Ng
106475094
Linear and Nonlinear Control of Small Scale Unmanned Helicopters
1 Introduction. 1.1 Background Information. 1.2 The Mathematical Problem 1.3 Controller Designs. 1.3.1 Linear Controller Design. 1.3.2 Nonlinear Controller Design. 1.4 Outline of the Book. 2 Review of Linear and Nonlinear Controller Designs. 2.1 Linear Controller Designs. 2.2 Nonlinear Controller Design. 2.3 Remarks. 3 Helicopter Basic Equations of Motion. 3.1 Helicopter Equations of Motion. 3.2 Position and Orientation of the Helicopter. 3.2.1 Helicopter Position Dynamics. 3.2.2 Helicopter Orientation Dynamics. 3.3 Complete Helicopter Dynamics. 3.4 Remarks. 4 Simplified Rotor Dynamics. 4.1 Introduction. 4.2 Blade Motion. 4.3 Swashplate Mechanism. 4.4 Fundamental Rotor Aerodynamics. 4.5 Flapping Equations of Motion. 4.6 Rotor Tip Path Plane Equation. 4.7 First Order Tip Path Plane Equations. 4.8 Main Rotor Forces and Moments. 4.9 Remarks. 5 Frequency Domain System Identification. 5.1 Mathematical Modeling. 5.1.1 First Principles Modeling. 5.1.2 System Identification Modeling. 5.2 Frequency Domain System Identification. 5.3 Advantages of the Frequency Domain Identification. 5.4 Helicopter Identification Challenges. 5.5 Frequency Response and the Coherence Function. 5.6 The CIFER c Package. 5.7 Time History Data and Excitation Inputs. 5.8 Linearization of the Equations of Motion. 5.9 Stability and Control Derivatives. 5.10 Model Identification. 5.10.1 Experimental Platform. 5.10.2 Parametrized State Space Model. 5.10.3 Identification Setup. 5.10.4 Time Domain Validation. 5.11 Remarks. 6 Linear Tracking Controller Design for Small Scale Unmanned Helicopters. 6.1 Helicopter Linear Model. 6.2 Linear Controller Design Outline. 6.3 Decomposing the System. 6.4 Velocity and Heading Tracking Controller Design. 6.4.1 Lateral Longitudinal Dynamics. 6.4.2 Yaw Heave Dynamics. 6.4.3 Stability of the Complete System Error Dynamics. 6.5 Position and Heading Tracking. 6.6 PID Controller Design. 6.7 Experimental Results. 6.8 Remarks. 7 Nonlinear Tracking Controller Design for Unmanned Helicopters. 7.1 Introduction. 7.2 Helicopter Nonlinear Model. 7.2.1 Rigid Body Dynamics. 7.2.2 ExternalWrench Model. 7.2.3 Complete Rigid Body Dynamics. 7.3 Translational Error Dynamics. 7.4 Attitude Error Dynamics. 7.4.1 Yaw Error Dynamics. 7.4.2 Orientation Error Dynamics. 7.4.3 Angular Velocity Error Dynamics. 7.5 Stability of the Attitude Error Dynamics. 7.6 Stability of the Translational Error Dynamics. 7.7 Numeric Simulation Results. 7.8 Remarks. 8 Time Domain Parameter Estimation and Applied Discrete Nonlinear Control for Small Scale Unmanned Helicopters. 8.1 Introduction. 8.2 Discrete System Dynamics. 8.3 Discrete Backstepping Algorithm. 8.3.1 Angular Velocity Dynamics. 8.3.2 Translational Dynamics. 8.3.3 Yaw Dynamics. 8.4 Parameter Estimation Using Recursive Least Squares. 8.5 Parametric Model. 8.6 Experimental Results. 8.6.1 Time History Data and Excitation Inputs. 8.6.2 Validation. 8.6.3 Control Design. 8.7 Remarks. 9 Time Domain System Identification for Small Scale Unmanned Helicopters Using Fuzzy Models. 9.1 Introduction. 9.2 Takagi Sugeno Fuzzy Models. 9.3 Proposed Takagi Sugeno System for Helicopters. 9.4 Experimental Results. 9.4.1 Tunning of the Membership Function Parameters. 9.4.2 Validation. 10 Comparison Studies. 10.1 Summary of the Controller Designs. 10.2 Experimental Results. 10.3 First Maneuver: Forward Flight. 10.4 Second Maneuver: Aggressive Forward Flight. 10.5 Third Maneuver: 8 Shaped Trajectory. 10.6 Fourth Maneuver: Pirouette Trajectory. 10.7 Remarks. 11 Epilogue. 11.1 Introduction. 11.2 Advantages and Novelties of the Designs. 11.3 Testing and Implementation. 11.4 Remarks. A Fundamentals of Backstepping Control. A.1 Integrator Backstepping. A.2 Example of a Recursive Backstepping Design. References.
Ioannis A Raptis Kimon P Valavanis
109113994
Small Unmanned Aircraft: Theory and Practice
Autonomous unmanned air vehicles (UAVs) are critical to current and future military, civil, and commercial operations. Despite their importance, no previous textbook has accessibly introduced UAVs to students in the engineering, computer, and science disciplines until now. Small Unmanned Aircraft provides a concise but comprehensive description of the key concepts and technologies underlying the dynamics, control, and guidance of fixed wing unmanned aircraft, and enables all students with an introductory level background in controls or robotics to enter this exciting and important area. The authors explore the essential underlying physics and sensors of UAV problems, including low level autopilot for stability and higher level autopilot functions of path planning. The textbook leads the student from rigid body dynamics through aerodynamics, stability augmentation, and state estimation using onboard sensors, to maneuvering through obstacles. To facilitate understanding, the authors have replaced traditional homework assignments with a simulation project using the MATLAB/Simulink environment. Students begin by modeling rigid body dynamics, then add aerodynamics and sensor models. They develop low level autopilot code, extended Kalman filters for state estimation, path following routines, and high level path planning algorithms. The final chapter of the book focuses on UAV guidance using machine vision. Designed for advanced undergraduate or graduate students in engineering or the sciences, this book offers a bridge to the aerodynamics and control of UAV flight.
Randal W Beard Timothy W McLain