2,117 research outputs found
Incentivizing Truth-Telling in MPC-based Load Frequency Control
We present a mechanism for socially efficient implementation of model
predictive control (MPC) algorithms for load frequency control (LFC) in the
presence of self-interested power generators. Specifically, we consider a
situation in which the system operator seeks to implement an MPC-based LFC for
aggregated social cost minimization, but necessary information such as
individual generators' cost functions is privately owned. Without appropriate
monetary compensation mechanisms that incentivize truth-telling,
self-interested market participants may be inclined to misreport their private
parameters in an effort to maximize their own profits, which may result in a
loss of social welfare. The main challenge in our framework arises from the
fact that every participant's strategy at any time affects the future state of
other participants; the consequences of such dynamic coupling has not been
fully addressed in the literature on online mechanism design. We propose a
class of real-time monetary compensation schemes that incentivize market
participants to report their private parameters truthfully at every time step,
which enables the system operator to implement MPC-based LFC in a socially
optimal manner
Individual Fairness in Hindsight
Since many critical decisions impacting human lives are increasingly being
made by algorithms, it is important to ensure that the treatment of individuals
under such algorithms is demonstrably fair under reasonable notions of
fairness. One compelling notion proposed in the literature is that of
individual fairness (IF), which advocates that similar individuals should be
treated similarly (Dwork et al. 2012). Originally proposed for offline
decisions, this notion does not, however, account for temporal considerations
relevant for online decision-making. In this paper, we extend the notion of IF
to account for the time at which a decision is made, in settings where there
exists a notion of conduciveness of decisions as perceived by the affected
individuals. We introduce two definitions: (i) fairness-across-time (FT) and
(ii) fairness-in-hindsight (FH). FT is the simplest temporal extension of IF
where treatment of individuals is required to be individually fair relative to
the past as well as future, while in FH, we require a one-sided notion of
individual fairness that is defined relative to only the past decisions. We
show that these two definitions can have drastically different implications in
the setting where the principal needs to learn the utility model. Linear regret
relative to optimal individually fair decisions is inevitable under FT for
non-trivial examples. On the other hand, we design a new algorithm: Cautious
Fair Exploration (CaFE), which satisfies FH and achieves sub-linear regret
guarantees for a broad range of settings. We characterize lower bounds showing
that these guarantees are order-optimal in the worst case. FH can thus be
embedded as a primary safeguard against unfair discrimination in algorithmic
deployments, without hindering the ability to take good decisions in the
long-run
Sequence-based Anytime Control
We present two related anytime algorithms for control of nonlinear systems
when the processing resources available are time-varying. The basic idea is to
calculate tentative control input sequences for as many time steps into the
future as allowed by the available processing resources at every time step.
This serves to compensate for the time steps when the processor is not
available to perform any control calculations. Using a stochastic Lyapunov
function based approach, we analyze the stability of the resulting closed loop
system for the cases when the processor availability can be modeled as an
independent and identically distributed sequence and via an underlying Markov
chain. Numerical simulations indicate that the increase in performance due to
the proposed algorithms can be significant.Comment: 14 page
Passivity Degradation In Discrete Control Implementations: An Approximate Bisimulation Approach
In this paper, we present some preliminary results for compositional analysis
of heterogeneous systems containing both discrete state models and continuous
systems using consistent notions of dissipativity and passivity. We study the
following problem: given a physical plant model and a continuous feedback
controller designed using traditional control techniques, how is the
closed-loop passivity affected when the continuous controller is replaced by a
discrete (i.e., symbolic) implementation within this framework? Specifically,
we give quantitative results on performance degradation when the discrete
control implementation is approximately bisimilar to the continuous controller,
and based on them, we provide conditions that guarantee the boundedness
property of the closed-loop system.Comment: This is an extended version of our IEEE CDC 2015 paper to appear in
Japa
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