Laboratory implementation of a neural network trajectory controller for a DC motor

The laboratory implementation of a neural network controller for high performance DC drives is described. The objective is to control the rotor speed and/or position to follow an arbitrarily selected trajectory at all times. The control strategy is based on indirect model reference adaptive control (MRAC). The motor characteristics are explicitly identified through a multilayer perceptron type neural network. The output of the trained neural network is used to drive the motor in order to achieve a desired time trajectory of the controlled variable. The neural network controller is assembled in a commercially available PC-based real-time control system shell, using software subroutines. An H-bridge, DC/DC voltage converter is interfaced with the computer to generate the specified terminal voltage sequences for driving the motor. All software and hardware components are off the shelf. The versatility of the motor/controller arrangement is displayed through real-time plots of the controlled states. >

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