Fail-safe stability for dynamic systems using neural-network controllers

The small gain theorem is used to consider the stability of a neural network controlled system under the condition that some of the neurons may fail with attenuated outputs and possibly with the addition of a constant output bias. Sufficient conditions based on the small gain theorem and the circle criterion are obtained for fail-safe stability. A loop-transformation technique is used to overcome the conservative nature of the small gain approach.