NDLI: Adaptive learning control using affine radial basis function network approximation

Content Provider	IEEE Xplore Digital Library
Author	Gorinevsky, D.M.
Copyright Year	1993
Description	Author affiliation: Robotics & Autom. Lab., Toronto Univ., Ont., Canada (Gorinevsky, D.M.)
Abstract	A new paradigm for learning control of a generic nonlinear system is presented. The method is based on a special radial basis function network architecture and is adaptive as the input/output properties of the system are estimated and the learning control gain is computed online. The controlled system is described with a mapping between the feedforward control and an array of sampled output values over a given time interval. The network approximates the system input/output mapping as a function of the task parameter vector comprising the initial and a desired final system state. The approximation is affine and linear in the control input. For given network weights, this allows inverting the network approximation and an optimal control program. The network weights are updated depending on the results of the learning trials. The application of the algorithm to the control of an arbitrary planar motion of a two link arm demonstrates fast learning and the high accuracy of the method.
Starting Page	505
Ending Page	510
File Size	692775
Page Count	6
File Format	PDF
ISBN	0780312066
ISSN	21589860
DOI	10.1109/ISIC.1993.397662
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1993-08-25
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Programmable control Adaptive control Control systems Nonlinear control systems Nonlinear systems Radial basis function networks Computer architecture Computer networks Vectors Optimal control
Content Type	Text
Resource Type	Article

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