Neural Network-Based Event-Triggered State Feedback Control of Nonlinear Continuous-Time Systems


This paper presents a novel approximation-based event-triggered control of multi-input multi-output uncertain nonlinear continuous-time systems in affine form. The controller is approximated using a linearly parameterized neural network (NN) in the context of event-based sampling. After revisiting the NN approximation property in the context of event-based sampling, an event-triggered condition is proposed using the Lyapunov technique to reduce the network resource utilization and to generate the required number of events for the NN approximation. In addition, a novel weight update law for aperiodic tuning of the NN weights at triggered instants is proposed to relax the knowledge of complete system dynamics and to reduce the computation when compared with the traditional NN-based control. Nonetheless, a nonzero positive lower bound for the inter-event times is guaranteed to avoid the accumulation of events or Zeno behavior. For analyzing the stability, the event-triggered system is modeled as a nonlinear impulsive dynamical system and the Lyapunov technique is used to show local ultimate boundedness of all signals. Furthermore, in order to overcome the unnecessary triggered events when the system states are inside the ultimate bound, a dead-zone operator is used to reset the event-trigger errors to zero. Finally, the analytical design is substantiated with numerical results.


Electrical and Computer Engineering

Research Center/Lab(s)

Intelligent Systems Center


This work was supported in part by the Intelligent Systems Center, Missouri University of Science and Technology, Rolla, MO, USA, and in part by the National Science Foundation under Grant ECCS 1406533.

Keywords and Phrases

Dynamical systems; Feedback; Feedback control; MIMO systems; Nonlinear feedback; Nonlinear systems; State feedback; Uncertainty analysis; Approximation properties; Event-triggered controls; Event-triggered system; Impulsive dynamical system; Linearly parameterized neural networks; Multi input multi output; Network resource utilization; Nonlinear continuous-time systems; Continuous time systems; Adaptive control; Approximation; Event-triggered control (ETC); Neural network (NN) control

International Standard Serial Number (ISSN)

2162-237X; 2162-2388

Document Type

Article - Journal

Document Version


File Type





© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Mar 2016