A duality framework for stochastic optimal control of complex systems

Journal Article · Fri Jan 01 00:00:00 EST 2016 · IEEE Transactions on Automatic Control

DOI:https://doi.org/10.1109/TAC.2015.2504518· OSTI ID:1224740

Malikopoulos, Andreas A. ^[1]

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1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

In this study, we address the problem of minimizing the long-run expected average cost of a complex system consisting of interactive subsystems. We formulate a multiobjective optimization problem of the one-stage expected costs of the subsystems and provide a duality framework to prove that the control policy yielding the Pareto optimal solution minimizes the average cost criterion of the system. We provide the conditions of existence and a geometric interpretation of the solution. For practical situations having constraints consistent with those studied here, our results imply that the Pareto control policy may be of value when we seek to derive online the optimal control policy in complex systems.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). National Transportation Research Center (NTRC)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1224740
• Journal Information: IEEE Transactions on Automatic Control, Vol. 1, Issue 1; ISSN 0018-9286
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
Complex Systems
Stochastic Optimal Control
Multiobjective Optimization
Pareto Efficiency
HEV optimization