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Technical Brief

DYNAMIC NEURAL NETWORK-BASED OUTPUT FEEDBACK TRACKING CONTROL FOR UNCERTAIN NONLINEAR SYSTEMS

[+] Author and Article Information
Huyen Dinh

Department of Mechanical Engineering University of Transport and Communications Hanoi, Vietnam
huyentdinh@utc.edu.vn

Shubhendu Bhasin

Department of Electrical Engineering Indian Institute of Technology Delhi, India
sbhasin@ee.iitd.ac.in

Rushikesh Kamalapurkar

School of Mechanical and Aerospace Engineering Oklahoma State University Stillwater, OK, 74074
rushikesh.kamalapurkar@okstate.edu

Warren E. Dixon

Department of Mechanical and Aerospace Engineering University of Florida Gainesville, FL, 32611
wdixon@ufl.edu

1Corresponding author.

ASME doi:10.1115/1.4035871 History: Received May 03, 2016; Revised January 20, 2017

Abstract

A dynamic neural network (DNN) observer-based o utput feedback controller for uncertain nonlinear systems with bounded disturbances is developed. The DNN-based observer works in conjunction with a dynamic filter for state estimation using only output measurements during on-line operation. A sliding mode term is included in the DNN structure to robustly account for exogenous disturbances and reconstruction errors. Weight update laws for the DNN, based on estimation errors, tracking errors, and the filter output are developed which guarantee asymptotic regulation of the state estimation error. A combination of a DNN feedforward term, along with estimated state feedback and sliding mode terms yield an asymptotic tracking result. The developed output feedback method yields asymptotic tracking and asymptotic estimation of unmeasurable states for a class of uncertain nonlinear systems with bounded disturbances. A two-link robot manipulator is used to investigate the performance of the proposed control approach.

Copyright (c) 2017 by ASME
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