82 research outputs found
Aggregation and Control of Populations of Thermostatically Controlled Loads by Formal Abstractions
This work discusses a two-step procedure, based on formal abstractions, to
generate a finite-space stochastic dynamical model as an aggregation of the
continuous temperature dynamics of a homogeneous population of Thermostatically
Controlled Loads (TCL). The temperature of a single TCL is described by a
stochastic difference equation and the TCL status (ON, OFF) by a deterministic
switching mechanism. The procedure is formal as it allows the exact
quantification of the error introduced by the abstraction -- as such it builds
and improves on a known, earlier approximation technique in the literature.
Further, the contribution discusses the extension to the case of a
heterogeneous population of TCL by means of two approaches resulting in the
notion of approximate abstractions. It moreover investigates the problem of
global (population-level) regulation and load balancing for the case of TCL
that are dependent on a control input. The procedure is tested on a case study
and benchmarked against the mentioned alternative approach in the literature.Comment: 40 pages, 21 figures; the paper generalizes the result of conference
publication: S. Esmaeil Zadeh Soudjani and A. Abate, "Aggregation of
Thermostatically Controlled Loads by Formal Abstractions," Proceedings of the
European Control Conference 2013, pp. 4232-4237. version 2: added references
for section
Quantitative Approximation of the Probability Distribution of a Markov Process by Formal Abstractions
The goal of this work is to formally abstract a Markov process evolving in
discrete time over a general state space as a finite-state Markov chain, with
the objective of precisely approximating its state probability distribution in
time, which allows for its approximate, faster computation by that of the
Markov chain. The approach is based on formal abstractions and employs an
arbitrary finite partition of the state space of the Markov process, and the
computation of average transition probabilities between partition sets. The
abstraction technique is formal, in that it comes with guarantees on the
introduced approximation that depend on the diameters of the partitions: as
such, they can be tuned at will. Further in the case of Markov processes with
unbounded state spaces, a procedure for precisely truncating the state space
within a compact set is provided, together with an error bound that depends on
the asymptotic properties of the transition kernel of the original process. The
overall abstraction algorithm, which practically hinges on piecewise constant
approximations of the density functions of the Markov process, is extended to
higher-order function approximations: these can lead to improved error bounds
and associated lower computational requirements. The approach is practically
tested to compute probabilistic invariance of the Markov process under study,
and is compared to a known alternative approach from the literature.Comment: 29 pages, Journal of Logical Methods in Computer Scienc
On Decidability of Time-Bounded Reachability in CTMDPs
We consider the time-bounded reachability problem for continuous-time Markov
decision processes. We show that the problem is decidable subject to Schanuel's
conjecture. Our decision procedure relies on the structure of optimal policies
and the conditional decidability (under Schanuel's conjecture) of the theory of
reals extended with exponential and trigonometric functions over bounded
domains. We further show that any unconditional decidability result would imply
unconditional decidability of the bounded continuous Skolem problem, or
equivalently, the problem of checking if an exponential polynomial has a
non-tangential zero in a bounded interval. We note that the latter problems are
also decidable subject to Schanuel's conjecture but finding unconditional
decision procedures remain longstanding open problems
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