Hierarchical control for discrete large-scale complex systems by intelligent controllers

This paper presents a new method to approximate optimal control strategies of discrete time large-scale nonlinear systems using intelligent approaches. The idea is based on the decomposition principle of the global system into interconnected subsystems for which nonlinearities are located in the interconnection terms. Then, the mixed coordination procedure between different subsystems is formulated as a hierarchical method for the solution of large-scale optimal control problems. So, for each subsystem, local optimal feedback gains are expressed in terms of the interconnection vector. For this purpose, neural networks and fuzzy logic controllers have been constructed in order to identify these gains. A comparison with the differential dynamic programming procedure as a reference method is done. Simulation results of two numerical examples show that the proposed method yields to satisfactory performances, and the robustness of the proposed approaches has been tested.

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