Abstract
This paper addresses the design of controllers for systems modelled as recurrent neural networks (RNNs). A novel data-based procedure for the design of RNN-based regulators is proposed, guaranteeing closed-loop stability properties and desired performances, conferred by virtual reference feedback tuning. The approach is tested on a realistic nonlinear system.
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