Bounded neuro-control position regulation for a geared DC motor

The purpose of this paper is to present a simple neuro-control law in order to control a geared DC motor. The main advantage of this controller is that it does not require an exact knowledge of the values of the motor parameters. The proposed artificial neural network is characterized by two input synaptic weights, two output synaptic weights and one threshold; these parameters are used to define the performance of the closed loop system. The DC motor parameters, the synaptic weights and the ANN threshold are combined in order to construct an off-line learning condition. Such condition guarantees that the seminorm of the regulation error remains bounded (closed loop performance index) and it is constructed through a Lyapunov-like analysis. The neuro-controller is evaluated through numerical simulations and through small-scale laboratory experiments by implementing the neuro-controller with electronic hardware.