Identification of a two-link flexible manipulator using adaptive time delay neural networks

This paper deals with identification of a two-link flexible manipulator belonging to a class of multi-input, multi-output (MIMO) nonlinear systems, by using adaptive time delay neural networks (ATDNNs). Two neuro-dynamic identifiers are proposed. The capabilities of the proposed structures for representing the nonlinear input-output map of the flexible manipulator are shown analytically. Selection criteria for specifying the fixed structural parameters as well as the adaptation laws for updating the adjustable parameters of the networks are provided. During identification, the two-link flexible manipulator is under nonlinear control and the input-output data sets are generated for different desired trajectories. Simulation results reveal that the proposed neuro-dynamic structures are capable of successfully identifying a highly nonlinear system without any a priori information about the nonlinearities of the system and without any off-line training.

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