Additive feedforward control with neural networks

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This paper demonstrates a method to control a nonlinear, multivariable, noisy process using trained neural networks. The basis for the method is a trained neural network controller acting as the inverse process model. A training method for obtaining such an inverse process model is applied. A suitable 'shaped' (low-pass filtered) reference is used to overcome problems with excessive control action when using a controller acting as the inverse process model. The control concept is Additive Feedforward Control, where the trained neural network controller, acting as the inverse process model, is placed in a supplementary pure feedforward path to an existing feedback controller. This concept benefits from the fact, that an existing, traditional designed, feedback controller can be retained without any modifications, and after training the connection of the neural network feedforward controller to the feedback controlled process may happen gradually and controlled.