Sampling-based stochastic optimal control with metric interval temporal logic specifications

Montana, F.J., Liu, J. and DODD, Tony (2016) Sampling-based stochastic optimal control with metric interval temporal logic specifications. In: 2016 IEEE Conference on Control Applications (CCA), 19-22 September 2016. IEEE, Buenos Aires, Argentina. ISBN 978-1-5090-0755-4

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Abstract or description

This paper describes a method to find optimal policies for stochastic dynamic systems that maximise the probability of satisfying real-time properties. The method consists of two phases. In the first phase, a coarse abstraction of the original system is created. In each region of the abstraction, a sampling-based algorithm is utilised to compute local policies that allow the system to move between regions. Then, in the second phase, the selection of a policy in each region is obtained by solving a reachability problem on the Cartesian product between the abstraction and a timed automaton representing a real-time specification given as a metric interval temporal logic formula. In contrast to current methods that require a fine abstraction, the proposed method achieves computational tractability by modelling the coarse abstraction of the system as a bounded-parameter Markov decision process (BMDP). Moreover, once the BMDP is created, this can be reused for new specifications assuming the same stochastic system and workspace. The method is demonstrated with an autonomous driving example.

Item Type:	Book Chapter, Section or Conference Proceeding
Additional Information:	© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.
Uncontrolled Keywords:	Automata; Clocks; Stochastic systems; Real-time systems; Markov processes
Faculty:	School of Creative Arts and Engineering > Engineering
Depositing User:	Library STORE team
Date Deposited:	15 Jul 2020 15:00
Last Modified:	24 Feb 2023 13:58
URI:	https://eprints.staffs.ac.uk/id/eprint/6240

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