Near Optimal Event-Triggered Control of Nonlinear Discrete-Time Systems using Neurodynamic Programming


This paper presents an event-triggered near optimal control of uncertain nonlinear discrete-time systems. Event-driven neurodynamic programming (NDP) is utilized to design the control policy. A neural network (NN)-based identifier, with event-based state and input vectors, is utilized to learn the system dynamics. An actor-critic framework is used to learn the cost function and the optimal control input. The NN weights of the identifier, the critic, and the actor NNs are tuned aperiodically once every triggered instant. An adaptive event-trigger condition to decide the trigger instants is derived. Thus, a suitable number of events are generated to ensure a desired accuracy of approximation. A near optimal performance is achieved without using value and/or policy iterations. A detailed analysis of nontrivial inter-event times with an explicit formula to show the reduction in computation is also derived. The Lyapunov technique is used in conjunction with the event-trigger condition to guarantee the ultimate boundedness of the closed-loop system. The simulation results are included to verify the performance of the controller. The net result is the development of event-driven NDP.


Electrical and Computer Engineering

Research Center/Lab(s)

Intelligent Systems Center


This work was supported in part by 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

Closed loop systems; Cost functions; Digital control systems; Uncertainty analysis; Event-triggered controls (ETC); Lyapunov techniques; Near-optimal control; Near-optimal performance; Neural networks (NNs); Neuro-Dynamic Programming; Nonlinear discrete-time systems; Ultimate boundedness; Discrete time control systems; Hamilton-Jacobi-Bellman equation; Neurodynamic programming (NDP); Optimal 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 Sep 2016