| Popis: |
The Lyapunov based theoretical development of a neural-network sliding-mode based estimation of highly non-linear and variable robot plant for a direct-drive robot controller is shown in the paper. Derived adaptive control law was tested for four types of robot neuralnetwork sliding-mode controllers: centralized, simplified centralized, decentralized and simplified decentralized, which were verified on a real laboratory direct-drive 3 D.O.F. PUMA like mechanism. Centralized and decentralized control approaches estimate only a part of the variable robot dynamic model (torque model due to friction, Coriolis, centripetal and centrifugal forces) and use only the part of a dynamic plant model (the so called estimated inertia matrix M). Both simplified methods do not need any plant model parameter for an accurate estimation of the direct-drive robot plant, but need some more time to learn dynamic model parameters. All four types of the neural network continuous slidingmode controllers were successfully tested for algorithm’s adaptation capability for sudden changes in the manipulator dynamics (load). |
