A training rule which guarantees finite-region stability for a class of closed-loop neural-network control systems.
A training method for a class of neural network controllers is presented which guarantees closed-loop system stability. The controllers are assumed to be nonlinear, feedforward, sampled-data, full-state regulators implemented as single hidden-layer neural networks. The controlled systems must be locally hermitian and observable. Stability of the closed-loop system is demonstrated by determining a Lyapunov function, which can be used to identify a finite stability region about the regulator point.
Kuntanapreeda
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Center for Self Organizing and Intelligent Syst., Utah State Univ., Logan, UT.
Fullmer
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