Abstract
The Lyapunov based theoretical development of a direct-drive neural-network robot controller is shown in the paper. Derived equations of the adaptive neural-network sliding-mode controller were verified on a real laboratory direct-drive 3.DOF PUMA like mechanism. The neural network continuous sliding-mode controller was successfully tested for algorithm’s adaptation capability for sudden changes in the manipulator dynamics (load).
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