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Near Optimal Event-Triggered Control of Nonlinear Discrete-Time Systems Using Neurodynamic Programming | IEEE Journals & Magazine | IEEE Xplore

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


Abstract:

This paper presents an event-triggered near optimal control of uncertain nonlinear discrete-time systems. Event-driven neurodynamic programming (NDP) is utilized to desig...Show More

Abstract:

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.
Published in: IEEE Transactions on Neural Networks and Learning Systems ( Volume: 27, Issue: 9, September 2016)
Page(s): 1801 - 1815
Date of Publication: 13 August 2015

ISSN Information:

PubMed ID: 26285220

Funding Agency:


I. Introduction

Event-triggered control (ETC) [1]–[6], which is evolved as an alternate control paradigm in the recent times, is found to be effective in terms of resource utilization. The ETC scheme uses events to sample the system state and execute the controller in an aperiodic manner. The aperiodic sampling and execution reduces the computational costs for the closed-loop system. In the case of a networked control system (NCS) [7], the ETC scheme saves network bandwidth due to the event-based aperiodic transmissions. The sampling and transmission instants, referred to as event-trigger instants, are decided using a state-dependent criterion. The threshold in the criterion is designed analytically via the Lyapunov stability technique. Thus, the event-triggered paradigm saves resources, and maintains both stability and closed-loop performance.

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