Model Predictive Neural Control for Aggressive Helicopter Maneuvers
 | Name : Model Predictive Neural Control for Aggressive Helicopter Maneuvers File Type : Size : 478 KB |
Model Predictive Neural Control for Aggressive Helicopter Maneuvers Eric A. Wan, Alexander A. Bogdanov, Richard Kieburtz Antonio Baptista, Magnus Carlsson, Yinglong Zhang, and Mike Zulauf OGI School of Science and Engineering, OHSU 20000 NW Walker Rd, Beaverton, Oregon 97006 Editors Summary This chapter shares with Chapter 9 the adoption of a model predictive control (MPC) framework for flight control applications, but the details differ substantially. In particular, the control feedback in this case is a superposition of a neural-network-based nonlinear mapping and a nonlinear state-dependent Riccati equation (SDRE) controller. The neural network is optimized (trained) online for high performance using a high-fidelity dynamic simulation model of the vehicle. The SDRE controller design, repeated at every sample time, provides initial local asymptotic stability. The relative contributions of each controller vary depending on the training error of the neural network. The application considered is maneuver control of autonomous helicopters. The controller is multivariable with five Ebook Relate: control ebook neural ebook model ebook controller ebook network ebook neural network ebook high fidelity ebook sdre controller ebook the neural ebook the neural network ebook for aggressive helicopter ebook predictive neural control ebook |
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