Real-time stable adaptive control implementation using a neural network processor

Timothy Robinson; Mohammad Bodruzzaman; Kevin L. Priddy; Karl Mathia

doi:10.1117/12.205122

6 April 1995 Real-time stable adaptive control implementation using a neural network processor

Timothy Robinson, Mohammad Bodruzzaman, Kevin L. Priddy, Karl Mathia

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205122
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

Helicopters are highly non-linear systems that have dynamics that change significantly with respect to environmental conditions. The system parameters also vary heavily with respect to velocity. These nonlinearities limit the use of traditional fixed controllers, since they can make the aircraft unstable. The purpose of this paper is to make contributions to the development of an `intelligent' control system that can be applied to complex problems such as this in real- time. Using a slowly changing model and a simplified nonlinear model as examples, a neural network based controller is shown to have the ability to learn from these example plants and to generalize this knowledge for previously unseen plants. The adaptability comes from a neural network that adjusts coefficients of the controller in real-time while running on the accurate automation neural network processor.

Citation Download Citation

Timothy Robinson, Mohammad Bodruzzaman, Kevin L. Priddy, and Karl Mathia "Real-time stable adaptive control implementation using a neural network processor", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205122

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