Decentralized Stabilization for Nonlinear Systems with Unknown Mismatched Interconnections

This paper establishes a neural network and policy iteration based decentralized control scheme to stabilize large-scale nonlinear systems with unknown mismatched interconnections. For relaxing the common assumption of upper boundedness on interconnections when designing the decentralized optimal control, interconnections are approximated by neural networks with local signals of isolated subsystem and replaced reference signals of coupled subsystems. By using the adaptive estimation term, the performance index function is constructed to reflect the replacement error. Hereafter, it is proven that the developed decentralized optimal control policies can guarantee the closed-loop large-scale nonlinear system to be uniformly ultimately bounded. The effectiveness of the developed scheme is verified by a simulation example.