Learning to control: some practical experiments with neural networks

The author reports on a series of experiments for controlling a nonlinear industrial plant using various neural network controller architectures. The architectures studied include copying an existing controller, inverse dynamics control, and feedback linearizable learning control. These experiments have shown the capabilities and potentials of using neural networks for control. In addition, some shortcomings of the approach were revealed, such as the need to estimate the order of the plant