  This paper represents a novel online self-learning disturbance observer
(SLDO) by benefiting from the combination of a type-2 neuro-fuzzy structure
(T2NFS), feedback-error learning scheme and sliding mode control (SMC) theory.
The SLDO is developed within a framework of feedback-error learning scheme in
which a conventional estimation law and a T2NFS work in parallel. In this
scheme, the latter learns uncertainties and becomes the leading estimator
whereas the former provides the learning error to the T2NFS for learning system
dynamics. A learning algorithm established on SMC theory is derived for an
interval type-2 fuzzy logic system. In addition to the stability of the
learning algorithm, the stability of the SLDO and the stability of the overall
system are proven in the presence of time-varying disturbances. Thanks to
learning process by the T2NFS, the simulation results show that the SLDO is
able to estimate time-varying disturbances precisely as distinct from the basic
nonlinear disturbance observer (BNDO) so that the controller based on the SLDO
ensures robust control performance for systems with time-varying uncertainties,
and maintains nominal performance in the absence of uncertainties.
