Multilevel control of a dynamical systems using neural networks

The concepts related to multilevel control are investigated in the context of a greatly simplified hypothetical aircraft control problem. A general fault detection and control problem is considered. The problem deals with the case where a nonlinear plant is suitably parameterized and each of the different configurations contains an unknown parameter which lies in a compact interval. In this case, it is assumed that a nominal plant in each configuration can be identified off-line using neural networks. A stabilizing controller is assumed to exist for all the plants belonging to a configuration and was designed off-line using neural networks. The fault detection problem was carried out at the higher level by a neural network used as a pattern recognizer. The simulation results shown illustrate the operation of the entire system when a fault is assumed to occur. An adaptive controller was used online to compensate for the uncertainty in the parameters.<>