Abstract—Stable DIRECT and inDIRECT decentralized adaptive radial basis
neural network controllers are presented for a class of interconnected
nonlinear systems. The feedback and adaptation mechanisms for each
subsystem depend only upon local measurements to provide asymptotic
tracking of a reference trajectory. Due to the functional approximation
capabilities of radial basis neural networks, the dynamics for each
subsystem are not required to be linear in a set of unknown coeffi cients
as is typically required in decentralized adaptive schemes. In addition,
each subsystem is able to adaptively compensate for disturbances and
interconnections with unknown bounds.
