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A new utilization of the Hamiltonian formalism in the adaptive control of mechanical systems under external perturbation

CímA new utilization of the Hamiltonian formalism in the adaptive control of mechanical systems under external perturbation
Közlemény típusaJournal Article
Kiadás éve1999
SzerzőkTar, J. K., I. J. Rudas, J. Bitó, and O. M. Kaynak
FolyóiratINTELLIGENT AUTOMATION AND SOFT COMPUTING
Évfolyam5
Kötet4
Oldalszám303 - 312
Kiadás dátuma1999
Kiadás nyelveeng
Összefoglalás

A novel approach toward the adaptive control of robots dynamicallyinteracting with an unmodeled environment and having only approximately
known dynamic parameters has been developed on the basis of the
principles of the Hamiltonian Mechanics. As the different means of
modem Soft Computing technology having a more or less uniform
architecture independent of the particular details of the problems to
be solved by them, the proposed method also has a uniform structure not
strictly tailored to the peculiar properties of the mechanical system
to be controlled. However, as special kinds of Artificial Neural
Networks (ANN) are fit to solve wide but typical classes of tasks, the
proposed method is invented for tackling problems related to the
control of mechanical devices in which the dominating non-linear
coupling originates from the laws of Classical Mechanics. As ANNs have
a plenty of free parameters (connection weights and threshold values)
the tuning of which means learning, this mechanical model also contains
tunable parameters so offering the possibility of learning. In this
paper the model's free parameters, possible constraints imposed on them
as well as different tuning strategies are compared to each other on
the basis of computer simulations. It is concluded that the method
based on the canonical formalism of classical mechanics is promising
for gaining different solutions to the problem. However, finding the
appropriate tuning rule is far not trivial and a wide area is open for
further research from this point of view. The simple tuning strategies
here investigated serve as basic paradigms open for further development
in the direction of more conventional and better understood methods as
Genetic Algorithms or other "Evolutionary Computation" approach.