16,941 research outputs found
Coordination of passive systems under quantized measurements
In this paper we investigate a passivity approach to collective coordination
and synchronization problems in the presence of quantized measurements and show
that coordination tasks can be achieved in a practical sense for a large class
of passive systems.Comment: 40 pages, 1 figure, submitted to journal, second round of revie
Estimation and control with limited information and unreliable feedback
Advancement in sensing technology is introducing new sensors that can provide information that was not
available before. This creates many opportunities for the development of new control systems. However,
the measurements provided by these sensors may not follow the classical assumptions from the control
literature. As a result, standard control tools fail to maximize the performance in control systems utilizing
these new sensors. In this work we formulate new assumptions on the measurements applicable to new
sensing capabilities, and develop and analyze control tools that perform better than the standard tools
under these assumptions. Specifically, we make the assumption that the measurements are quantized. This
assumption is applicable, for example, to low resolution sensors, remote sensing using limited bandwidth
communication links, and vision-based control. We also make the assumption that some of the measurements
may be faulty. This assumption is applicable to advanced sensors such as GPS and video surveillance, as
well as to remote sensing using unreliable communication links.
The first tool that we develop is a dynamic quantization scheme that makes a control system stable
to any bounded disturbance using the minimum number of quantization regions. Both full state feedback
and output feedback are considered, as well as nonlinear systems. We further show that our approach
remains stable under modeling errors and delays. The main analysis tool we use for proving these results
is the nonlinear input-to-state stability property. The second tool that we analyze is the Minimum Sum
of Distances estimator that is robust to faulty measurements. We prove that this robustness is maintained
when the measurements are also corrupted by noise, and that the estimate is stable with respect to such
noise. We also develop an algorithm to compute the maximum number of faulty measurements that this
estimator is robust to. The last tool we consider is motivated by vision-based control systems. We use a
nonlinear optimization that is taking place over both the model parameters and the state of the plant in
order to estimate these quantities. Using the example of an automatic landing controller, we demonstrate
the improvement in performance attainable with such a tool
Lazy global feedbacks for quantized nonlinear event systems
We consider nonlinear event systems with quantized state information and
design a globally stabilizing controller from which only the minimal required
number of control value changes along the feedback trajectory to a given
initial condition is transmitted to the plant. In addition, we present a
non-optimal heuristic approach which might reduce the number of control value
changes and requires a lower computational effort. The constructions are
illustrated by two numerical examples
Event-Driven Optimal Feedback Control for Multi-Antenna Beamforming
Transmit beamforming is a simple multi-antenna technique for increasing
throughput and the transmission range of a wireless communication system. The
required feedback of channel state information (CSI) can potentially result in
excessive overhead especially for high mobility or many antennas. This work
concerns efficient feedback for transmit beamforming and establishes a new
approach of controlling feedback for maximizing net throughput, defined as
throughput minus average feedback cost. The feedback controller using a
stationary policy turns CSI feedback on/off according to the system state that
comprises the channel state and transmit beamformer. Assuming channel isotropy
and Markovity, the controller's state reduces to two scalars. This allows the
optimal control policy to be efficiently computed using dynamic programming.
Consider the perfect feedback channel free of error, where each feedback
instant pays a fixed price. The corresponding optimal feedback control policy
is proved to be of the threshold type. This result holds regardless of whether
the controller's state space is discretized or continuous. Under the
threshold-type policy, feedback is performed whenever a state variable
indicating the accuracy of transmit CSI is below a threshold, which varies with
channel power. The practical finite-rate feedback channel is also considered.
The optimal policy for quantized feedback is proved to be also of the threshold
type. The effect of CSI quantization is shown to be equivalent to an increment
on the feedback price. Moreover, the increment is upper bounded by the expected
logarithm of one minus the quantization error. Finally, simulation shows that
feedback control increases net throughput of the conventional periodic feedback
by up to 0.5 bit/s/Hz without requiring additional bandwidth or antennas.Comment: 29 pages; submitted for publicatio
Stability of quantized time-delay nonlinear systems: A Lyapunov-Krasowskii-functional approach
Lyapunov-Krasowskii functionals are used to design quantized control laws for
nonlinear continuous-time systems in the presence of constant delays in the
input. The quantized control law is implemented via hysteresis to prevent
chattering. Under appropriate conditions, our analysis applies to stabilizable
nonlinear systems for any value of the quantization density. The resulting
quantized feedback is parametrized with respect to the quantization density.
Moreover, the maximal allowable delay tolerated by the system is characterized
as a function of the quantization density.Comment: 31 pages, 3 figures, to appear in Mathematics of Control, Signals,
and System
Recent advances on filtering and control for nonlinear stochastic complex systems with incomplete information: A survey
This Article is provided by the Brunel Open Access Publishing Fund - Copyright @ 2012 Hindawi PublishingSome recent advances on the filtering and control problems for nonlinear stochastic complex systems with incomplete information are surveyed. The incomplete information under consideration mainly includes missing measurements, randomly varying sensor delays, signal quantization, sensor saturations, and signal sampling. With such incomplete information, the developments on various filtering and control issues are reviewed in great detail. In particular, the addressed nonlinear stochastic complex systems are so comprehensive that they include conventional nonlinear stochastic systems, different kinds of complex networks, and a large class of sensor networks. The corresponding filtering and control technologies for such nonlinear stochastic complex systems are then discussed. Subsequently, some latest results on the filtering and control problems for the complex systems with incomplete information are given. Finally, conclusions are drawn and several possible future research directions are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61104125, 61028008, 61174136, 60974030, and 61074129, the Qing Lan Project of Jiangsu Province of China, the Project sponsored by SRF for ROCS of SEM of China, the Engineering and Physical Sciences Research Council EPSRC of the UK under Grant GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
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