Abstract
This paper addresses the optimality of stochastic control strategies based on the infinite horizon average cost criterion, subject to total variation distance ambiguity on the conditional distribution of the controlled process. This stochastic optimal control problem is formulated using minimax theory, in which the minimization is over the control strategies and the maximization is over the conditional distributions. Under the assumption that, for every stationary Markov control law the maximizing conditional distribution of the controlled process is irreducible, we derive a new dynamic programming recursion which minimizes the future ambiguity, and we propose a new policy iteration algorithm. The new dynamic programming recursion includes, in addition to the standard terms, the oscillator semi-norm of the cost-to-go. The maximizing conditional distribution is found via a water-filling algorithm. The implications of our results are demonstrated through an example.
Original language | English |
---|---|
Title of host publication | 2015 54th IEEE Conference on Decision and Control, CDC 2015 |
Publisher | IEEE |
Pages | 7171-7176 |
Number of pages | 6 |
Volume | 2016-February |
ISBN (Electronic) | 9781479978861 |
DOIs | |
Publication status | Published - 8 Feb 2016 |
MoE publication type | A4 Article in a conference publication |
Event | IEEE Conference on Decision and Control - Osaka, Japan Duration: 15 Dec 2015 → 18 Dec 2015 Conference number: 54 |
Conference
Conference | IEEE Conference on Decision and Control |
---|---|
Abbreviated title | CDC |
Country/Territory | Japan |
City | Osaka |
Period | 15/12/2015 → 18/12/2015 |
Keywords
- Aerospace electronics
- Dynamic programming
- Heuristic algorithms
- Markov processes
- Optimal control
- Process control