Multilayer Neural-Net Robot Controller With Guaranteed Tracking Performance  

Oleh:

Lewis, F. L. ; Yegildirek, A. ; Liu, Kai

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 7 no. 2 (1996), page 388-399.
Topik: multilayer networks; multilayer; neural - net; robot; performance

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.1 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

A multilayer neural - net (NN) controller for a general serial - link rigid robot arm is developed. The structure of the NN controller is derived using a filtered error / passivity approach. No off - line learning phase is needed for the proposed NN controller and the weights are easily initialized. The non linear nature of the NN, plus NN functional reconstruction inaccuracies and robot disturbances, mean that the standard delta rule using backpropagation tuning does not suffice for closed - loop dynamic control. Novel online weight tuning algorithms, including correction terms to the delta rule plus an added robust signal, guarantee bounded tracking errors as well as bounded NN weights. Specific bounds are determined, and the tracking error bound can be made arbitrarily small by increasing a certain feedback gain. The correction terms involve a second - order forward - propagated wave in the backpropagation network. New NN properties including the notions of a passive NN, a dissipative NN, and a robust NN are introduced.

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