  This paper focuses on a novel feedback linearization control (FLC) law based
on a self-learning disturbance observer (SLDO) to counteract mismatched
uncertainties. The FLC based on BNDO (FLC-BNDO) demonstrates robust control
performance only against mismatched time-invariant uncertainties while the FLC
based on SLDO (FLC-SLDO) demonstrates robust control performance against
mismatched time-invariant and -varying uncertainties, and both of them maintain
the nominal control performance in the absence of mismatched uncertainties. In
the estimation scheme for the SLDO, the BNDO is used to provide a conventional
estimation law, which is used as being the learning error for the type-2
neuro-fuzzy system (T2NFS), and T2NFS learns mismatched uncertainties. Thus,
the T2NFS takes the overall control of the estimation signal entirely in a very
short time and gives unbiased estimation results for the disturbance. A novel
learning algorithm established on sliding mode control theory is derived for an
interval type-2 fuzzy logic system. The stability of the overall system is
proven for a second-order nonlinear system with mismatched uncertainties. The
simulation results show that the FLC-SLDO demonstrates better control
performance than the traditional FLC, FLC with an integral action (FLC-I) and
FLC-BNDO.
