论文信息 - Identification and control experiments using neural designs

Identification and control experiments using neural designs

Neural designs are reported for system identification and control using static and dynamic gradient update schemes. Real-time implementation of the designs using a hardware example case system illustrates the inherent capability of neural networks to handle nonlinearities, learn, and perform control effectively for a real world system, based on minimal system information. The advantages of dynamic schemes over static ones are highlighted and a neural control design with feedforward and feedback components that facilitates incorporation of available knowledge about a system is described.<<ETX>>

S.S. Nair | S.I. Mistry

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