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Meeting	ACGS Committee Meeting 94 - Reno - November 2004
Agenda Location	6 SUBCOMMITTEE A – Aeronautics and Surface Vehicles 6.3 Intelligent Fault-Tolerant Control and Path Planning for a Small-Scale Autonomous Helicopter: Theory and Flight Experiments
Title	Intelligent Fault-Tolerant Control and Path Planning for a Small-Scale Autonomous Helicopter: Theory and Flight Experiments
Presenter	Denis Garagić, Jovan D. Bošković, and Raman K. Mehra
Affiliation	Scientific Systems Company Inc. (SSCI)
Available Downloads*	presentation
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Abstract	In this paper we present advanced intelligent fault-tolerant control and path planning algorithms for unmanned rotorcraft vehicles. A multi-layer autonomous intelligent flight control architecture (AI-FCS) is presented consisting of Adaptive Robust Controller for Underactuated Systems (ARCUS), on-line Failure Detection, Identification and Reconfiguration (FDIR), Autonomous Trajectory Management (ATM), and Autonomous Mission Planning (AMP) subsystems. The overall autonomous intelligent controller is implemented on the GTMax autonomous small-scale Helicopter (RMax-Yamaha) and tested in flights under the Final Experiment for DARPA Software Enabled Control (SEC) Program. The flight experiment results regarding the performance evaluation of the proposed control architecture are presented and discussed. The novel robust nonlinear helicopter controller innovatively combines the principles of nonlinear feedback linearization, robust control, and Linear Quadratic Regulator (LQR) techniques in order to ensure asymptotic tracking of the helicopter position and heading and its recovery from unsafe modes of operation arising due to disturbances, while enforcing the asymptotic stability of the zero-dynamics. A medium- fidelity dynamic model of a rotorcraft, which accounts for helicopter rigid body dynamics, aero dynamic forces and moments, flapping and stabilizer bar dynamics, engine and power dynamics, is developed and its parameterized dynamic model is utilized for the controller design. A new on-line Autonomous Path Planning (APP) algorithms for multiple known/unknown and pop-up obstacles avoidance is developed and tested in flights. An important feature of the path planning algorithm is its capability to autonomously reconfigure the path plan to satisfy a new set of kinematic/dynamic vehicle constraints imposed on the vehicle due to actuator failures.