NLq neural control theory: Case study for a ball and beam system

Abstract

In this paper we design a linear dynamic output feedback controller for a ball and beam system based on an identified neural state space model. This is done by applying dynamic backpropagation, constrained by NLq internal or I/O stability conditions. The performance of the controller has been tested on a real ball and beam set-up. 

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