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Decentralized adaptive control of nonlinear systems using radial basis neural networks
1999
IEEE Transactions on Automatic Control
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 coefficients as is typically
doi:10.1109/9.802914
fatcat:phsxk43w65dndenhrr7kmyueyu