A hierarchical optimization neural network for large-scale dynamic systems

• Published in: Automatica (Oxford) Vol. 37; no. 12; pp. 1931 - 1940
• Main Author: Hou, Zeng-Guang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2001
• Subjects: Algorithms; Hierarchical control; Hierarchical systems; Iterative methods; Large-scale systems; Neural networks; Optimal control systems; Optimization; Large-scale systems; Neural networks; Optimization; Hierarchical control
• ISSN: 0005-1098; 1873-2836
• DOI: 10.1016/S0005-1098(01)00158-3

A recurrent neural network for dynamical hierarchical optimization of nonlinear discrete large-scale systems is presented. The proposed neural network consists of hierarchically structured sub-networks: one coordination sub-network at the upper level and several local optimization sub-networks at the lower level. In particular, the coordination sub-network and the local optimization sub-networks work simultaneously. This feature makes the proposed method outperform in computational efficiency the conventional iterative algorithms where there usually exists an alternately waiting time during the coordination and local optimization processes. Moreover, the state equations of the subsystems of the large-scale system are imbedded into their corresponding local optimization sub-networks. This imbedding technique not only overcomes the difficulty in treating the constraints imposed by the state equations, but also leads to significant reduction in the network size. We present stability analysis to prove that the neural network is asymptotically stable and this stable state corresponds to the optimal solution to the original optimal control problem. Finally, we illustrate the performance of the proposed method by an example.