Agricultural and Biosystems Engineering Publications

Campus Units

Agricultural and Biosystems Engineering

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

6-2017

Journal or Book Title

Engineering Applications of Artificial Intelligence

Volume

62

First Page

276

Last Page

285

Research Focus Area(s)

Advanced Machinery Engineering and Manufacturing Systems

DOI

10.1016/j.engappai.2017.04.013

Abstract

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.

Comments

This is a manuscript of an article published as Kayacan, Erkan, Joshua M. Peschel, and Girish Chowdhary. "A self-learning disturbance observer for nonlinear systems in feedback-error learning scheme." Engineering Applications of Artificial Intelligence 62 (2017): 276-285. DOI: 10.1016/j.engappai.2017.04.013. Posted with permission.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

Elsevier Ltd.

Language

en

File Format

application/pdf

Available for download on Friday, June 01, 2018

Published Version

Share

COinS