SNAC Convergence and Use in Adaptive Autopilot Design
In this paper, approximate dynamic programming (ADP)based design tools are developed for adaptive control of aircraft control under nominal and damaged conditions. Nominal control of the system is computed with a Single Network Adaptive Critic(SNAC) derived through principles of ADP. Convergence of SNAC training is shown by reducing it to solving a set of nonlinear algebraic equations in weights. Unlike many adaptive control approaches, we develop approximate optimal control expressions to handle uncertainties. Uncertainties are calculated with an online neural network with guaranteed convergence. Longitudinal dynamics of an aircraft is used to illustrate the working of the developed algorithms. ©2009 IEEE.
S. Chen et al., "SNAC Convergence and Use in Adaptive Autopilot Design," Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers (IEEE), Jan 2009.
The definitive version is available at http://dx.doi.org/10.1109/IJCNN.2009.5178706
Proceedings of the International Joint Conference on Neural Networks (2009, Atlanta, GA)
Mechanical and Aerospace Engineering
Article - Conference proceedings
© 2009 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.