  In this paper we consider trajectory tracking problem for robotic systems
affected by unknown external perturbations. Considering possible solutions, we
restrict our attention to composite adaptation, which, particularly, ensures
parametric error convergence being desirable to enhance overall stability and
robustness of a closed-loop system. At the same time, existing composite
approaches cannot simultaneously relax stringent persistence of excitation
requirement and guarantee convergence of parametric error to zero for a
perturbed scenario. So, a new composite adaptation scheme is proposed, which
successfully overcomes mentioned problems of known counterparts and has several
salient features. First, it includes a novel adaptive disturbance rejection
control law for a general n-DoF dynamical model in the Euler-Lagrange form,
which, without achievement of the parameter estimation goal, ensures global
stability via application of a high-gain external torque observer augmented
with some adaptation law. Secondly, such law is extended with a composite
summand derived via the recently proposed Instrumental Variables based Dynamic
Regressor Extension and Mixing procedure, which relaxes excitation conditions
and ensures asymptotic parameter estimation and reference tracking in the
presence of external torque under some non-restrictive assumptions. An
illustrative example shows the effectiveness and superiority of the proposed
approach in comparison with existing solutions.
