4,135 research outputs found
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
Stochastic Stability of Event-triggered Anytime Control
We investigate control of a non-linear process when communication and
processing capabilities are limited. The sensor communicates with a controller
node through an erasure channel which introduces i.i.d. packet dropouts.
Processor availability for control is random and, at times, insufficient to
calculate plant inputs. To make efficient use of communication and processing
resources, the sensor only transmits when the plant state lies outside a
bounded target set. Control calculations are triggered by the received data. If
a plant state measurement is successfully received and while the processor is
available for control, the algorithm recursively calculates a sequence of
tentative plant inputs, which are stored in a buffer for potential future use.
This safeguards for time-steps when the processor is unavailable for control.
We derive sufficient conditions on system parameters for stochastic stability
of the closed loop and illustrate performance gains through numerical studies.Comment: IEEE Transactions on Automatic Control, under revie
Computation-Communication Trade-offs and Sensor Selection in Real-time Estimation for Processing Networks
Recent advances in electronics are enabling substantial processing to be
performed at each node (robots, sensors) of a networked system. Local
processing enables data compression and may mitigate measurement noise, but it
is still slower compared to a central computer (it entails a larger
computational delay). However, while nodes can process the data in parallel,
the centralized computational is sequential in nature. On the other hand, if a
node sends raw data to a central computer for processing, it incurs
communication delay. This leads to a fundamental communication-computation
trade-off, where each node has to decide on the optimal amount of preprocessing
in order to maximize the network performance. We consider a network in charge
of estimating the state of a dynamical system and provide three contributions.
First, we provide a rigorous problem formulation for optimal real-time
estimation in processing networks in the presence of delays. Second, we show
that, in the case of a homogeneous network (where all sensors have the same
computation) that monitors a continuous-time scalar linear system, the optimal
amount of local preprocessing maximizing the network estimation performance can
be computed analytically. Third, we consider the realistic case of a
heterogeneous network monitoring a discrete-time multi-variate linear system
and provide algorithms to decide on suitable preprocessing at each node, and to
select a sensor subset when computational constraints make using all sensors
suboptimal. Numerical simulations show that selecting the sensors is crucial.
Moreover, we show that if the nodes apply the preprocessing policy suggested by
our algorithms, they can largely improve the network estimation performance.Comment: 15 pages, 16 figures. Accepted journal versio
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Stability analysis of event-triggered anytime control with multiple control laws
To deal with time-varying processor availability and lossy communication
channels in embedded and networked control systems, one can employ an
event-triggered sequence-based anytime control (E-SAC) algorithm. The main idea
of E-SAC is, when computing resources and measurements are available, to
compute a sequence of tentative control inputs and store them in a buffer for
potential future use. State-dependent Random-time Drift (SRD) approach is often
used to analyse and establish stability properties of such E-SAC algorithms.
However, using SRD, the analysis quickly becomes combinatoric and hence
difficult to extend to more sophisticated E-SAC. In this technical note, we
develop a general model and a new stability analysis for E-SAC based on Markov
jump systems. Using the new stability analysis, stochastic stability conditions
of existing E-SAC are also recovered. In addition, the proposed technique
systematically extends to a more sophisticated E-SAC scheme for which, until
now, no analytical expression had been obtained.Comment: Accepted for publication in IEEE Transactions on Automatic Contro
Anytime Control using Input Sequences with Markovian Processor Availability
We study an anytime control algorithm for situations where the processing
resources available for control are time-varying in an a priori unknown
fashion. Thus, at times, processing resources are insufficient to calculate
control inputs. To address this issue, the algorithm calculates sequences of
tentative future control inputs whenever possible, which are then buffered for
possible future use. We assume that the processor availability is correlated so
that the number of control inputs calculated at any time step is described by a
Markov chain. Using a Lyapunov function based approach we derive sufficient
conditions for stochastic stability of the closed loop.Comment: IEEE Transactions on Automatic Control, to be publishe
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