A neural-network-based controller for a single-link flexiblemanipulator using the inverse dynamics approach
Zhihong Su
Khorasani, K.
Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, Que.;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Dec 2001
Volume: 48,
Issue: 6
On page(s): 1074-1086
ISSN: 0278-0046
References Cited: 11
CODEN: ITIED6
INSPEC Accession Number: 7122714
Digital Object Identifier: 10.1109/41.969386
Current Version Published: 2002-08-07
Abstract
This paper presents an intelligent-based control strategy for tip
position tracking control of a single-link flexible manipulator.
Motivated by the well-known inverse dynamics control strategy for
rigid-link manipulators, two feedforward neural networks (NNs) are
proposed to learn the nonlinearities of the flexible arm associated with
the inverse dynamics controller. The redefined output approach is used
by feeding back this output to guarantee the minimum phase behavior of
the resulting closed-loop system. No a priori knowledge about the
nonlinearities of the system is needed and the payload mass is also
assumed to be unknown. The network weights are adjusted using a modified
online error backpropagation algorithm that is based on the propagation
of output tracking error, derivative of that error and the tip
deflection of the manipulator. The real-time controller is implemented
on an experimental test bed. The results achieved by the proposed
NN-based controller are compared experimentally with conventional
proportional-plus-derivative-type and standard inverse dynamics controls
to substantiate and verify the advantages of our proposed scheme and its
promising potential in identification and control of nonlinear systems
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