7,702 research outputs found
Value of Information in Feedback Control
In this article, we investigate the impact of information on networked
control systems, and illustrate how to quantify a fundamental property of
stochastic processes that can enrich our understanding about such systems. To
that end, we develop a theoretical framework for the joint design of an event
trigger and a controller in optimal event-triggered control. We cover two
distinct information patterns: perfect information and imperfect information.
In both cases, observations are available at the event trigger instantly, but
are transmitted to the controller sporadically with one-step delay. For each
information pattern, we characterize the optimal triggering policy and optimal
control policy such that the corresponding policy profile represents a Nash
equilibrium. Accordingly, we quantify the value of information
as the variation in the cost-to-go of the system given
an observation at time . Finally, we provide an algorithm for approximation
of the value of information, and synthesize a closed-form suboptimal triggering
policy with a performance guarantee that can readily be implemented
Event-based State Estimation: An Emulation-based Approach
An event-based state estimation approach for reducing communication in a
networked control system is proposed. Multiple distributed sensor agents
observe a dynamic process and sporadically transmit their measurements to
estimator agents over a shared bus network. Local event-triggering protocols
ensure that data is transmitted only when necessary to meet a desired
estimation accuracy. The event-based design is shown to emulate the performance
of a centralised state observer design up to guaranteed bounds, but with
reduced communication. The stability results for state estimation are extended
to the distributed control system that results when the local estimates are
used for feedback control. Results from numerical simulations and hardware
experiments illustrate the effectiveness of the proposed approach in reducing
network communication.Comment: 21 pages, 8 figures, this article is based on the technical report
arXiv:1511.05223 and is accepted for publication in IET Control Theory &
Application
Resource-aware IoT Control: Saving Communication through Predictive Triggering
The Internet of Things (IoT) interconnects multiple physical devices in
large-scale networks. When the 'things' coordinate decisions and act
collectively on shared information, feedback is introduced between them.
Multiple feedback loops are thus closed over a shared, general-purpose network.
Traditional feedback control is unsuitable for design of IoT control because it
relies on high-rate periodic communication and is ignorant of the shared
network resource. Therefore, recent event-based estimation methods are applied
herein for resource-aware IoT control allowing agents to decide online whether
communication with other agents is needed, or not. While this can reduce
network traffic significantly, a severe limitation of typical event-based
approaches is the need for instantaneous triggering decisions that leave no
time to reallocate freed resources (e.g., communication slots), which hence
remain unused. To address this problem, novel predictive and self triggering
protocols are proposed herein. From a unified Bayesian decision framework, two
schemes are developed: self triggers that predict, at the current triggering
instant, the next one; and predictive triggers that check at every time step,
whether communication will be needed at a given prediction horizon. The
suitability of these triggers for feedback control is demonstrated in hardware
experiments on a cart-pole, and scalability is discussed with a multi-vehicle
simulation.Comment: 16 pages, 15 figures, accepted article to appear in IEEE Internet of
Things Journal. arXiv admin note: text overlap with arXiv:1609.0753
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