Abstract
A neural network based control scheme with an adaptive neural model reference structure is described. A neural net emulator is first trained to model the plant's behavior. The neural net controller is next trained to learn the plant's inverse dynamics by backpropagating the error at the output of the plant through the emulator. The proposed structure of this method allows both the neural network controller and emulator to be continuously trained online. Simulation results to control a nonlinear temperature control process showed that the proposed neural network control method is easily implemented for a wide variety of control problems. >
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