Event-Based Neural Network Approximation and Control of Uncertain Nonlinear Continuous-Time Systems


This paper presents a novel event-based adaptive control of uncertain nonlinear continuous-time systems. An adaptive model by using two linearly parameterized neural networks (NNs) is designed to approximate the unknown internal dynamics of the nonlinear system with event sampled state vector. The estimated state vector and the dynamics from the adaptive model are subsequently used to design the control law. Novel NN weight update laws are proposed in the context of event-based availability of state vector wherein the NN weights are updated once at every aperiodic sampling instant unlike the traditional periodically sampled adaptive NN based control. A positive lower bound on the inter-sample times is shown. The boundedness of the NN weight estimation errors and system state vector are demonstrated by representing the event sampled closed-loop system as a nonlinear impulsive dynamical system and by using an adaptive trigger condition. Finally, simulation results are included to show the performance of the proposed approach.

Meeting Name

2015 American Control Conference, ACC 2015 (2015: Jul. 1-3, Chicago, IL)


Electrical and Computer Engineering


This work was supported in part by NSF ECCS#1406533 and in part by Intelligent Systems Center, Missouri University of Science and Technology, Rolla, MO.

Keywords and Phrases

Adaptive control systems; Closed loop systems; Dynamical systems; Nonlinear systems; Uncertainty analysis; Vectors; Adaptive modeling; Impulsive dynamical system; Linearly parameterized neural networks; Neural network approximation; Nonlinear continuous-time systems; Sampling instants; Trigger conditions; Weight estimation; Continuous time systems

International Standard Book Number (ISBN)


International Standard Serial Number (ISSN)

0743-1619; 2378-5861

Document Type

Article - Conference proceedings

Document Version


File Type





© 2015 American Automatic Control Council, All rights reserved.

Publication Date

01 Jul 2015