Near Optimal Event-Triggered Control of Nonlinear Continuous-Time Systems using Input and Output Data


The optimal event-triggered control of nonlinear continuous-time systems by using input and output data is a challenging problem due to system uncertainties, non-availability of state vector and event-based sampled outputs between the plant and the controller. Therefore, a novel reinforcement learning-based approach is proposed to solve time-based near optimal event-triggered control of nonlinear continuous-time systems. First, by using measured input and output data, nonlinear continuous-time system is represented in the input-output form that is suitable for data-driven control. Then, an online neural network (NN) identifier is developed to estimate the control coefficient matrix from the input-output data which is subsequently utilized along with the critic NN to obtain a time-based near optimal event triggered control scheme in a forward-in-time manner. Novel apeiodic update laws are derived for NNs by using event trigger error while a novel event-trigger condition is designed to ensure the overall stability of proposed scheme. Eventually, Lyapunov analysis is utilized to demonstrate that all closed-loop signals and NN weights are ultimately bounded (UB).

Meeting Name

11th World Congress on Intelligent Control and Automation, WCICA 2014 (2014: Jun. 29-Jul. 4, Shenyang, China)


Electrical and Computer Engineering


This work is partially supported by NSF Grant #1128281 and Intelligent Systems Center at Missouri University of Science and Technology.

Keywords and Phrases

Event-trigger; Neural Network; Optimal Control

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


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© 2014 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jun 2014