Paper Info
Title
Approximate Optimal Distributed Control Of Uncertain Nonlinear Interconnected Systems With Event-Sampled Feedback
Abstract
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.
Year
Venue
Field
2016
2016 IEEE 55TH CONFERENCE ON DECISION AND CONTROL (CDC)
Hamilton–Jacobi–Bellman equation,Dynamic programming,Mathematical optimization,State vector,Control theory,Optimal control,Computer science,Control theory,Lyapunov stability,Iterative learning control,Artificial neural network
DocType
ISSN
Citations 
Conference
0743-1546
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
vignesh narayanan1293.77
Sarangapani Jagannathan2113694.89