  This work proposes a new adaptive-robust control (ARC) architecture for a
class of uncertain Euler-Lagrange (EL) systems where the upper bound of the
uncertainty satisfies linear in parameters (LIP) structure. Conventional ARC
strategies either require structural knowledge of the system or presume that
the overall uncertainties or its time derivative are norm bounded by a
constant. Due to unmodelled dynamics and modelling imperfection, true
structural knowledge of the system is not always available. Further, for the
class of systems under consideration, prior assumption regarding the
uncertainties (or its time derivative) being upper bounded by a constant, puts
a restriction on states beforehand. Conventional ARC laws invite
overestimation-underestimation problem of switching gain. Towards this front,
Adaptive Switching-gain based Robust Control (ASRC) is proposed which
alleviates the overestimation-underestimation problem of switching gain.
Moreover, ASRC avoids any presumption of constant upper bound on the overall
uncertainties and can negotiate uncertainties regardless of being linear or
nonlinear in parameters. Experimental results of ASRC using a wheeled mobile
robot notes improved control performance in comparison to adaptive sliding mode
control.
