Adaptive Control of Aircraft Gianluca Di Muro, PhD Student Advisor: Silvia Ferrari, PhD

1. Stabilator Deflection Thrust Rudder Deflection NNC NNA Hx CF Training Aileron Deflection Adaptive Control of Aircraft • Gianluca Di Muro, PhD Student • Advisor: Silvia Ferrari, PhD • Laboratory for Intelligent Systems and Controls (LISC) • Department of Mechanical Engineering and Material Science, Duke University, Durham, NC, USA Typical aircraft missions include: • Transport / Surveillance • Steady, level flight • Small maneuvers • Combat • Extreme maneuvers • Exploratory • Unmodeled dynamic effects Potential Benefits of Nonlinear Adaptive Control Systems • Reconfigurable flight control • Failure-tolerant control • Safer and easier-to-fly General Aviation aircraft • Autonomous flight Neural Network Controller Business Jet Controls yc(t) ys(t) + (t) – + BUSINESS JET u(t) + • Aircraft Model • Decoupled dynamics • Linearization • Control law • Linear control • DHP neural network initialization • Online training Better over-all performance, through adapting neural controller On-going Research: Flight Testing on Raytheon Bonanza CJ-144 Capabilities: • Licensed experimental • Synthetic vision • Auto-land (in progress) • Full-envelope protection • Flight-path commands • Digital avionics processor • Potential for: CFIT protection, protected airspace avoidance, .... Research Bonanza With Advanced Displays & Controls [Courtesy of Raytheon Aircraft Company and Wichita State University] References: [1] S. Ferrari and R. F. Stengel, “On-line Adaptive Critic Flight Control”, Journal of Guidance, Control, and Dynamics, vol. 27, no.5, 2004, pp. 777-786 [2] M. Jensenius, Constrained Learning in Neural Control Systems, M. S. Thesis, Duke University, 2005