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.
標簽：
decentralized
controllers
Abstract
adaptive
上傳時間：
2017-08-17
上傳用戶：gdgzhym
