Sliding Mode Control for Systems with Mismatched Time-Varying Uncertainties via a Self-Learning Disturbance Observer
Erkan Kayacan

TL;DR
This paper introduces a novel Sliding Mode Control algorithm utilizing a Self-Learning Disturbance Observer, which effectively manages mismatched time-varying uncertainties and enhances transient response while reducing chattering.
Contribution
A new SMC method with a computationally efficient SLDO and Neuro-Fuzzy Structure for improved robustness against mismatched uncertainties.
Findings
Ensures robust control with mismatched time-varying uncertainties.
Maintains nominal performance without uncertainties.
Reduces chattering effects in control signals.
Abstract
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…
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Taxonomy
MethodsSelf-Learning
