52,232 research outputs found
Synchronization with partial state coupling on SO(n)
This paper studies autonomous synchronization of k agents whose states evolve
on SO(n), but which are only coupled through the action of their states on one
"reference vector" in Rn for each link. Thus each link conveys only partial
state information at each time, and to reach synchronization agents must
combine this information over time or throughout the network. A natural
gradient coupling law for synchronization is proposed. Extensive convergence
analysis of the coupled agents is provided, both for fixed and time-varying
reference vectors. The case of SO(3) with fixed reference vectors is discussed
in more detail. For comparison, we also treat the equivalent setting in Rn,
i.e. with states in Rn and connected agents comparing scalar product of their
states with a reference vector.Comment: to be submitted to SIAM Journal on Control and Optimizatio
Backward Linear Control Systems on Time Scales
We show how a linear control systems theory for the backward nabla
differential operator on an arbitrary time scale can be obtained via Caputo's
duality. More precisely, we consider linear control systems with outputs
defined with respect to the backward jump operator. Kalman criteria of
controllability and observability, as well as realizability conditions, are
proved.Comment: Submitted November 11, 2009; Revised March 28, 2010; Accepted April
03, 2010; for publication in the International Journal of Control
Eminence Grise Coalitions: On the Shaping of Public Opinion
We consider a network of evolving opinions. It includes multiple individuals
with first-order opinion dynamics defined in continuous time and evolving based
on a general exogenously defined time-varying underlying graph. In such a
network, for an arbitrary fixed initial time, a subset of individuals forms an
eminence grise coalition, abbreviated as EGC, if the individuals in that subset
are capable of leading the entire network to agreeing on any desired opinion,
through a cooperative choice of their own initial opinions. In this endeavor,
the coalition members are assumed to have access to full profile of the
underlying graph of the network as well as the initial opinions of all other
individuals. While the complete coalition of individuals always qualifies as an
EGC, we establish the existence of a minimum size EGC for an arbitrary
time-varying network; also, we develop a non-trivial set of upper and lower
bounds on that size. As a result, we show that, even when the underlying graph
does not guarantee convergence to a global or multiple consensus, a generally
restricted coalition of agents can steer public opinion towards a desired
global consensus without affecting any of the predefined graph interactions,
provided they can cooperatively adjust their own initial opinions. Geometric
insights into the structure of EGC's are given. The results are also extended
to the discrete time case where the relation with Decomposition-Separation
Theorem is also made explicit.Comment: 35 page
Time-varying Projected Dynamical Systems with Applications to Feedback Optimization of Power Systems
This paper is concerned with the study of continuous-time, non-smooth
dynamical systems which arise in the context of time-varying non-convex
optimization problems, as for example the feedback-based optimization of power
systems. We generalize the notion of projected dynamical systems to
time-varying, possibly non-regular, domains and derive conditions for the
existence of so-called Krasovskii solutions. The key insight is that for
trajectories to exist, informally, the time-varying domain can only contract at
a bounded rate whereas it may expand discontinuously. This condition is met, in
particular, by feasible sets delimited via piecewise differentiable functions
under appropriate constraint qualifications. To illustrate the necessity and
usefulness of such a general framework, we consider a simple yet insightful
power system example, and we discuss the implications of the proposed
conditions for the design of feedback optimization schemes
Fault-tolerant control under controller-driven sampling using virtual actuator strategy
We present a new output feedback fault tolerant control strategy for
continuous-time linear systems. The strategy combines a digital nominal
controller under controller-driven (varying) sampling with virtual-actuator
(VA)-based controller reconfiguration to compensate for actuator faults. In the
proposed scheme, the controller controls both the plant and the sampling
period, and performs controller reconfiguration by engaging in the loop the VA
adapted to the diagnosed fault. The VA also operates under controller-driven
sampling. Two independent objectives are considered: (a) closed-loop stability
with setpoint tracking and (b) controller reconfiguration under faults. Our
main contribution is to extend an existing VA-based controller reconfiguration
strategy to systems under controller-driven sampling in such a way that if
objective (a) is possible under controller-driven sampling (without VA) and
objective (b) is possible under uniform sampling (without controller-driven
sampling), then closed-loop stability and setpoint tracking will be preserved
under both healthy and faulty operation for all possible sampling rate
evolutions that may be selected by the controller
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