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

Kayacan, Erkan; Peschel, Joshua M.; Chowdhary, Girish

doi:10.1016/j.engappai.2017.04.013

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

Abstract

Here 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.

Authors:

Kayacan, Erkan ^[1]; Peschel, Joshua M. ^[2]; Chowdhary, Girish ^[1]

University of Illinois at Urbana-Champaign, IL (United States)
Iowa State University, Ames, IA (United States)

Publication Date:: Wed Apr 26 00:00:00 EDT 2017

Research Org.:: Univ. of Illinois at Urbana-Champaign, IL (United States)

Sponsoring Org.:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

OSTI Identifier:: 1533758

Alternate Identifier(s):: OSTI ID: 1397657

Grant/Contract Number:: AR0000598

Resource Type:: Accepted Manuscript

Journal Name:: Engineering Applications of Artificial Intelligence

Additional Journal Information:: Journal Volume: 62; Journal Issue: C; Journal ID: ISSN 0952-1976

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 42 ENGINEERING; disturbance observer; neural networks; neuro-fuzzy structure; online learning algorithm; robustness; sliding mode control; type-2 fuzzy logic systems; uncertainty

Citation Formats


                    Kayacan, Erkan, Peschel, Joshua M., and Chowdhary, Girish. A self-learning disturbance observer for nonlinear systems in feedback-error learning scheme.  United States: N. p., 2017. 
Web.  doi:10.1016/j.engappai.2017.04.013.

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                    Kayacan, Erkan, Peschel, Joshua M., & Chowdhary, Girish. A self-learning disturbance observer for nonlinear systems in feedback-error learning scheme.  United States.  https://doi.org/10.1016/j.engappai.2017.04.013

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                    Kayacan, Erkan, Peschel, Joshua M., and Chowdhary, Girish. Wed .  
"A self-learning disturbance observer for nonlinear systems in feedback-error learning scheme".  United States.  https://doi.org/10.1016/j.engappai.2017.04.013.  https://www.osti.gov/servlets/purl/1533758.

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@article{osti_1533758,

  title        = {A self-learning disturbance observer for nonlinear systems in feedback-error learning scheme},

  author       = {Kayacan, Erkan and Peschel, Joshua M. and Chowdhary, Girish},

  abstractNote = {Here 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.},

  doi          = {10.1016/j.engappai.2017.04.013},

  journal      = {Engineering Applications of Artificial Intelligence},

  number       = C,

  volume       = 62,

  place        = {United States},

  year         = {Wed Apr 26 00:00:00 EDT 2017},

  month        = {Wed Apr 26 00:00:00 EDT 2017}

}

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Works referencing / citing this record:

Sliding Mode Control for Systems with Mismatched Time-Varying Uncertainties via a Self-Learning Disturbance Observer
text, January 2021

Kayacan, Erkan
arXiv
DOI: 10.48550/arxiv.2103.11277

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

Abstract

Citation Formats

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Sliding-Mode Control for Systems With Mismatched Uncertainties via a Disturbance Observer journal, January 2013

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Universal Approximation of a Class of Interval Type-2 Fuzzy Neural Networks in Nonlinear Identification journal, January 2013

Disturbance-Observer-Based Control and Related Methods—An Overview journal, February 2016

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The fusion of computationally intelligent methodologies and sliding-mode control-a survey journal, January 2001

A nonlinear disturbance observer for robotic manipulators journal, January 2000

Learning in Centralized Nonlinear Model Predictive Control: Application to an Autonomous Tractor-Trailer System journal, January 2015

Adaptive Sliding-Mode Control for Nonlinear Active Suspension Vehicle Systems Using T–S Fuzzy Approach journal, August 2013

T2FS-Based Adaptive Linguistic Assessment System for Semantic Analysis and Human Performance Evaluation on Game of Go journal, April 2015

Simplified Interval Type-2 Fuzzy Neural Networks journal, May 2014

State-space formulae for all stabilizing controllers that satisfy an H∞-norm bound and relations to relations to risk sensitivity journal, September 1988

Disturbance Observer Based Control for Nonlinear Systems journal, December 2004

Design of a nonlinear disturbance observer journal, April 2000

Nonlinear filters: beyond the Kalman filter journal, August 2005

Identification of Nonlinear Dynamic Systems Using Type-2 Fuzzy Neural Networks—A Novel Learning Algorithm and a Comparative Study journal, March 2015

Interval type-2 fuzzy logic systems: theory and design journal, January 2000

Design and Implementation of Integral Sliding-Mode Control on an Underactuated Two-Wheeled Mobile Robot journal, July 2014

Connection admission control in ATM networks using survey-based type-2 fuzzy logic systems journal, January 2000

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Design of interval type-2 fuzzy sliding-mode controller
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Adaptive Control of Hydraulic Actuators With LuGre Model-Based Friction Compensation
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Interval Type-2 Radial Basis Function Neural Network: A Modeling Framework
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Application of interval type-2 fuzzy neural networks in non-linear identification and time series prediction
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Self-repairing control of a helicopter with input time delay via adaptive global sliding mode control and quantum logic
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From Interval-Valued Data to General Type-2 Fuzzy Sets
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Sliding-Mode Control With Soft Computing: A Survey
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Sliding Mode Control for Mismatched Uncertain Systems Using an Extended Disturbance Observer
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Nonlinear Disturbance Observer-Enhanced Dynamic Inversion Control of Missiles
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Adaptive dynamic CMAC neural control of nonlinear chaotic systems with L2 tracking performance
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A linear matrix inequality approach to H_∞ control
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Saturated Adaptive Robust Control for Active Suspension Systems
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Sliding Mode Control of a Three Degrees of Freedom Anthropoid Robot by Driving the Controller Parameters to an Equivalent Regime
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Adaptive Control of a Flexible Crane System With the Boundary Output Constraint
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Disturbance-Observer-Based Control and Related Methods—An Overview
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