  This paper presents a novel Sliding Mode Control (SMC) algorithm to handle
mismatched uncertainties in systems via a novel Self-Learning Disturbance
Observer (SLDO). A computationally efficient SLDO is developed within a
framework of feedback-error learning scheme in which a conventional estimation
law and a Neuro-Fuzzy Structure (NFS) work in parallel. In this framework, the
NFS estimates the mismatched disturbances and becomes the leading disturbance
estimator while the former feeds the learning error to the NFS to learn system
behavior. The simulation results demonstrate that the proposed SMC based on
SLDO (SMC-SLDO) ensures the robust control performance in the presence of
mismatched time-varying uncertainties when compared to SMC, integral SMC (ISMC)
and SMC based on a Basic Nonlinear Disturbance Observer (SMC-BNDO), and also
remains the nominal control performance in the absence of mismatched
uncertainties. Additionally, the SMC-SLDO not only counteracts mismatched
time-varying uncertainties but also improve the transient response performance
in the presence of mismatched time-invariant uncertainties. Moreover, the
controller gain of the SMC-SLDO is required to be selected larger than the
upper bound of the disturbance estimation error rather than the upper bound of
the actual disturbance to guarantee the system stability which results in
eliminating the chattering effects on the control signal.
