Sliding mode control for systems with mismatched time-varying uncertainties via a self-learning disturbance observer

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 behaviour. The simulation results demonstrate that the proposed SMC based on SLDO (SMC-SLDO) ensures 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 system stability, which results in eliminating the chattering effects on the control signal.