Decentralized adaptive approximation based control of a class of large-scale systems

This paper considers the design of a decentralized adaptive approximation based control scheme for a class of interconnected nonlinear systems. Linearly parameterized neural networks are used to adaptively approximate the unknown dynamics of each subsystem and the unknown interconnections. The feedback control and adaptation laws are based only on local measurements of the state. A dead-zone modification is used to address the issues of stability and robustness in the presence of residual approximation errors. A simulation example is used to illustrate the proposed control design methodology.