A regulator design method using multi-layered neural networks

The authors present a nonlinear regulator design method that uses multilayered neural networks (MNNs) for nonlinear systems operating in the presence of system uncertainties. The regulator consists of two MNNs for modeling and control in addition to the classical linear optimal regulator. The MNN for modeling is connected in parallel with the system equation to compensate for system uncertainties. The MNN for control is connected in parallel with the linear optimal regulator to compensate for the control errors caused by system uncertainties. Both MNNs are trained so that a desired response of the plant is obtained. A training signal of the MNN for control is acquired through the results of modeling. It is shown that the regulator can generate more sophisticated control inputs than the linear optimal regulator, even when the MNNs get stuck at local minima. A priori knowledge of the plant dynamics can be incorporated into the system equation and into the corresponding linear optimal regulator. The learning time is greatly reduced as compared with the case where MNN is used alone for modeling or control. Numerical simulations show that the regulator can stabilize a plant that is not stabilized by the linear regulator