NTRS - NASA Technical Reports Server

Back to Results
Monitoring the Performance of a Neuro-Adaptive ControllerTraditional control has proven to be ineffective to deal with catastrophic changes or slow degradation of complex, highly nonlinear systems like aircraft or spacecraft, robotics, or flexible manufacturing systems. Control systems which can adapt toward changes in the plant have been proposed as they offer many advantages (e.g., better performance, controllability of aircraft despite of a damaged wing). In the last few years, use of neural networks in adaptive controllers (neuro-adaptive control) has been studied actively. Neural networks of various architectures have been used successfully for online learning adaptive controllers. In such a typical control architecture, the neural network receives as an input the current deviation between desired and actual plant behavior and, by on-line training, tries to minimize this discrepancy (e.g.; by producing a control augmentation signal). Even though neuro-adaptive controllers offer many advantages, they have not been used in mission- or safety-critical applications, because performance and safety guarantees cannot b e provided at development time-a major prerequisite for safety certification (e.g., by the FAA or NASA). Verification and Validation (V&V) of an adaptive controller requires the development of new analysis techniques which can demonstrate that the control system behaves safely under all operating conditions. Because of the requirement to adapt toward unforeseen changes during operation, i.e., in real time, design-time V&V is not sufficient.
Document ID
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Schumann, Johann
(Research Inst. for Advanced Computer Science Moffett Field, CA, United States)
Gupta, Pramod
(QSS Group, Inc. Moffett Field, CA, United States)
Date Acquired
August 21, 2013
Publication Date
January 1, 2004
Subject Category
Aircraft Stability And Control
Meeting Information
Meeting: 24th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MTNS2004)
Location: Garching
Country: Germany
Start Date: July 25, 2004
End Date: July 30, 2004
Distribution Limits
Work of the US Gov. Public Use Permitted.

Available Downloads

There are no available downloads for this record.
No Preview Available