Liu, Tengfei
; Ou, Yan.
(Ed.)
We design a regulation-triggered adaptive controller
for robot manipulators to efficiently estimate unknown
parameters and to achieve asymptotic stability in the presence of
coupled uncertainties. Robot manipulators are widely used in
telemanipulation systems where they are subject to model and
environmental uncertainties. Using conventional control
algorithms on such systems can cause not only poor control
performance, but also expensive computational costs and
catastrophic instabilities. Therefore, system uncertainties need to
be estimated through designing a computationally efficient
adaptive control law. We focus on robot manipulators as an
example of a highly nonlinear system. As a case study, a 2-DOF
manipulator subject to four parametric uncertainties is
investigated. First, the dynamic equations of the manipulator are
derived, and the corresponding regressor matrix is constructed
for the unknown parameters. For a general nonlinear system, a
theorem is presented to guarantee the asymptotic stability of the
system and the convergence of parameters’ estimations. Finally,
simulation results are discussed for a two-link manipulator, and
the performance of the proposed scheme is thoroughly evaluated.
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