A self-learning disturbance observer for nonlinear systems in feedback-error learning scheme

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. HighlightsAn online learning algorithm based on sliding mode control theory for type-2 neuro-fuzzy structure (T2NFS) is developed.The feedback-error learning approach is proposed for an observer design for the first-time.The overall system stability is proven considering the dynamics of the proposed self-learning disturbance observer (SLDO).The required computation time for the SLDO is significantly less than other methods.It is possible to estimate disturbances without the knowledge about the upper bound of disturbances and their derivatives.