Approximate Optimal Distributed Control of Uncertain Nonlinear Interconnected Systems with Event-Sampled Feedback


In this paper, a novel approximate optimal distributed controller for a class of uncertain nonlinear interconnected system with event-sampled state vector is presented by using approximate dynamic programming (ADP). The event-sampled function approximation property of the neural network (NN) is utilized to generate a solution to the Hamilton-Jacobi-Bellman (HJB) equation and subsequently to obtain an optimal control policy of each subsystem in a forward-in-time manner. To relax the accurate knowledge of subsystem and interconnection dynamics, and input gain matrix, a novel NN identifier with event sampled state vector is designed at each subsystem. An adaptive event sampling condition and novel weight tuning rules for the NN identifier and NN controller using the Lyapunov stability theory are derived. To attain optimality faster, an iterative learning scheme is embedded within the inter-event part of the sampling interval along with time-driven learning at the event sampled instants. Further, the benefit of incorporating exploration in this event sampled framework to improve optimality is discussed along with the challenges involved. The state vector of the interconnected system and the weight estimation errors of the NN identifier and controller are demonstrated to be locally uniformly ultimately bounded (UUB). Finally, the analytical design is verified via simulation.

Meeting Name

2016 IEEE 55th Conference on Decision and Control, CDC (2016: Dec. 12-14, Las Vegas, NV)


Electrical and Computer Engineering

Research Center/Lab(s)

Intelligent Systems Center


This research supported in part by NSF grants ECCS #1128281 and #1406533 and Intelligent Systems Center, at the Missouri University of Science and Technology, Rolla.

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


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

Publication Date

01 Dec 2016