ABSTRACT
Flying robots and unmanned aerial vehicles (UAVs) have received growing interest in industrial and academic research, and they may prove useful for many civilian missions. Furthermore, among miniature rotorcraft-based UAVs, the mini quadrotor helicopter gives rise to great interest because of its high maneuverability, its payload capacity, and its ability to hover, as explained in [1]. Such a vertical takeoff and landing (VTOL) vehicle has advantage over conventional helicopters: owing to symmetry, it is relatively simple to design and construct. The quadrotor is an underactuated dynamic system with four input forces and six output coordinates (attitude and position). However, this system can be broken down into two subsystems, one defining the translation movement and the other one the rotation movement. These subsystems are coupled in cascade because the translational subsystem depends on the rotational one, but the rotational subsystem is independent of the translational one. Self-governing flights require the generation of low-level control signals sent to actuators as well as decision making related to guidance and navigation. Low-level flight control is known as
23.1 Introduction .........................................................................................................................409 23.2 Mathematical Background .................................................................................................. 411
23.2.1 Unit Quaternions and Attitude Kinematics .......................................................... 411 23.2.2 Sensor Modeling ................................................................................................... 412
23.2.2.1 Angular Velocity Sensors ..................................................................... 412 23.2.2.2 Reference Vector Sensors ..................................................................... 412
23.3 Problem Statement .............................................................................................................. 413 23.4 Nonlinear Observer for Attitude Estimation ...................................................................... 414
23.4.1 Attitude Estimation Using Vector Observation .................................................... 414 23.4.2 Design of a Nonlinear Observer for Attitude Estimation ..................................... 415
23.5 Experimental Results .......................................................................................................... 418 23.5.1 Implementation of an AHRS Using the Attitude Observer .................................. 418 23.5.2 Using the AHRS for the Attitude Control of an MAV.......................................... 420
23.6 Conclusion ........................................................................................................................... 424 Acknowledgments ..........................................................................................................................424 References ......................................................................................................................................424
