Decomposition and hierarchical control for discrete large scale system using neural networks

This paper presents a method to compute sub-optimal control strategies of discrete time large scale nonlinear systems by neural networks. The method is based on the principle of decomposition of the global system into interconnected subsystems for which we consider that non-linearities are located in the interconnection terms. Then, a mixed method is considered to coordinate between different subsystems in order to compute the optimal control. So, for each subsystem, local optimal feedback gains are expressed in terms of the interconnection vector. For this purpose, neural networks have been used in order to identify these gains. Simulation results of a rotary crane show that the proposed method yields to satisfactory performances. The robustness of the proposed approach is analysed.