Design and evaluation of a robust dynamic neurocontroller for a multivariable aircraft control problemThe design of a dynamic neurocontroller with good robustness properties is presented for a multivariable aircraft control problem. The internal dynamics of the neurocontroller are synthesized by a state estimator feedback loop. The neurocontrol is generated by a multilayer feedforward neural network which is trained through backpropagation to minimize an objective function that is a weighted sum of tracking errors, and control input commands and rates. The neurocontroller exhibits good robustness through stability margins in phase and vehicle output gains. By maintaining performance and stability in the presence of sensor failures in the error loops, the structure of the neurocontroller is also consistent with the classical approach of flight control design.
Document ID
19930053007
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Troudet, T. (NASA Lewis Research Center Cleveland, OH, United States)
Garg, S. (NASA Lewis Research Center Cleveland, OH, United States)
Merrill, W. (NASA Lewis Research Center Cleveland, OH, United States)
Date Acquired
August 16, 2013
Publication Date
January 1, 1992
Publication Information
Publication: In: IJCNN - International Joint Conference on Neural Networks, Baltimore, MD, June 7-11, 1992, Proceedings. Vol. 1 (A93-37001 14-63)
Publisher: Institute of Electrical and Electronics Engineers, Inc.