NDLI: Near optimal event-based control of nonlinear discrete time systems in affine form with measured input and output data

Content Provider	IEEE Xplore Digital Library
Author	Sahoo, A. Hao Xu Jagannathan, S.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Missouri Univ. of S&T, Rolla, MO, USA (Sahoo, A.; Jagannathan, S.) \|\| Dept. of Eng., Univ. of Tennessee at Martin, Martin, TN, USA (Hao Xu)
Abstract	In this paper, an event-based near optimal control of uncertain nonlinear discrete time systems is presented by using input-output data and approximate dynamic programming (ADP). The nonlinear system dynamics in affine form are transformed into an input-output form. Then, three neural networks (NN) with event sampled input-output vector are used, namely, the identifier NN to relax the knowledge of the system dynamics, a critic NN to approximate the value function which is the solution to the Hamilton-Jacobi Bellman (HJB) equation, and an actor NN to approximate the optimal control policy, in an online manner without utilizing value or policy iterations. In addition, the NN weights of all the three NNs are tuned only at event-triggered instants leading to a novel non-periodic update rule to reduce computation when compared to traditional NN based scheme. Further, an event-trigger condition to decide the trigger instants is derived. Finally, the Lyapunov technique is used in conjunction with the event-trigger condition to guarantee the uniform ultimate boundedness (UUB) of the closed-loop system. The analytical design is substantiated with numerical results via simulation.
Starting Page	3671
Ending Page	3676
File Size	3983589
Page Count	6
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889715
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Optimal control Approximation methods Equations Discrete-time systems Vectors Nonlinear dynamical systems Approximate dynamic programming event-triggered control Hamilton-Jacobi-Belltnan equation neural networks
Content Type	Text
Resource Type	Article

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