Abstract
In this paper, we present a design of a new adaptive control system and demonstrate its performance in a computer simulation of nonlinear synchronous machine control. The new design utilizes self-organization and the predictive estimation capabilities of neural-net computing. Real-time adaptation is facilitated by the error-based on-line learning scheme implemented on a cluster-wise segmented associative memory system. It is demonstrated that the neural network system is capable of modelling highly nonlinear systems, detecting changes in the dynamic process conditions and stabilizing the system.
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