  This paper addresses to Sliding Mode Learning Control (SMLC) of uncertain
nonlinear systems with Lyapunov stability analysis. In the control scheme, a
conventional control term is used to provide the system stability in compact
space while a Type-2 Neuro-Fuzzy Controller (T2NFC) learns system behavior so
that the T2NFC takes the overall control of the system completely in a very
short time period. The stability of the sliding mode learning algorithm was
proven in literature; however, it is so restrictive for systems without the
overall system stability. To address this shortcoming, a novel control
structure with a novel sliding surface is proposed in this paper and the
stability of the overall system is proven for nth-order uncertain nonlinear
systems. To investigate the capability and effectiveness of the proposed
learning and control algorithms, the simulation studies have been achieved
under noisy conditions. The simulation results confirm that the developed SMLC
algorithm can learn the system behavior in the absence of any mathematical
model knowledge and exhibit robust control performance against external
disturbances.
