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Title: Discrete-time Lyapunov design for neuroadaptive control of elastic-joint robots

Abstract

A neural-network controller operating in discrete time is shown to result in stable trajectory tracking for rigid and elastic-joint robots. The technique assumes continuous-time state feedback. The proof of stability uses discrete-time Lyapunov functions. For the elastic-joint case, a discrete-time version of the adaptive backstepping technique is used. The result is that the neural network can be run at a very slow control rate, suitable for online calculations. The neural network used is referred to as the CMAC-RBF Associative Memory (CRAM), a modification of Albus's Cerebellar Model Arithmetic Computer (CMAC) algorithm using radial basis functions (RBFs). Simulation results are provided for a two-link planar elastic-joint robot and show that performance can be improved by using a larger network at a slower control rate.

Authors:
;
Publication Date:
Research Org.:
Univ. of Toronto , Ontario (CA)
OSTI Identifier:
20030408
Alternate Identifier(s):
OSTI ID: 20030408
Resource Type:
Journal Article
Journal Name:
International Journal of Robotics Research
Additional Journal Information:
Journal Volume: 19; Journal Issue: 5; Other Information: PBD: May 2000; Journal ID: ISSN 0278-3649
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; NEURAL NETWORKS; COMPUTERIZED CONTROL SYSTEMS; ROBOTS; FEEDBACK; LYAPUNOV METHOD

Citation Formats

Macnab, C.J.B., and D'Eleuterio, G.M.T. Discrete-time Lyapunov design for neuroadaptive control of elastic-joint robots. United States: N. p., 2000. Web. doi:10.1177/02783640022067003.
Macnab, C.J.B., & D'Eleuterio, G.M.T. Discrete-time Lyapunov design for neuroadaptive control of elastic-joint robots. United States. doi:10.1177/02783640022067003.
Macnab, C.J.B., and D'Eleuterio, G.M.T. Mon . "Discrete-time Lyapunov design for neuroadaptive control of elastic-joint robots". United States. doi:10.1177/02783640022067003.
@article{osti_20030408,
title = {Discrete-time Lyapunov design for neuroadaptive control of elastic-joint robots},
author = {Macnab, C.J.B. and D'Eleuterio, G.M.T.},
abstractNote = {A neural-network controller operating in discrete time is shown to result in stable trajectory tracking for rigid and elastic-joint robots. The technique assumes continuous-time state feedback. The proof of stability uses discrete-time Lyapunov functions. For the elastic-joint case, a discrete-time version of the adaptive backstepping technique is used. The result is that the neural network can be run at a very slow control rate, suitable for online calculations. The neural network used is referred to as the CMAC-RBF Associative Memory (CRAM), a modification of Albus's Cerebellar Model Arithmetic Computer (CMAC) algorithm using radial basis functions (RBFs). Simulation results are provided for a two-link planar elastic-joint robot and show that performance can be improved by using a larger network at a slower control rate.},
doi = {10.1177/02783640022067003},
journal = {International Journal of Robotics Research},
issn = {0278-3649},
number = 5,
volume = 19,
place = {United States},
year = {2000},
month = {5}
}