4,606 research outputs found
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
Distributed convex optimization via continuous-time coordination algorithms with discrete-time communication
This paper proposes a novel class of distributed continuous-time coordination
algorithms to solve network optimization problems whose cost function is a sum
of local cost functions associated to the individual agents. We establish the
exponential convergence of the proposed algorithm under (i) strongly connected
and weight-balanced digraph topologies when the local costs are strongly convex
with globally Lipschitz gradients, and (ii) connected graph topologies when the
local costs are strongly convex with locally Lipschitz gradients. When the
local cost functions are convex and the global cost function is strictly
convex, we establish asymptotic convergence under connected graph topologies.
We also characterize the algorithm's correctness under time-varying interaction
topologies and study its privacy preservation properties. Motivated by
practical considerations, we analyze the algorithm implementation with
discrete-time communication. We provide an upper bound on the stepsize that
guarantees exponential convergence over connected graphs for implementations
with periodic communication. Building on this result, we design a
provably-correct centralized event-triggered communication scheme that is free
of Zeno behavior. Finally, we develop a distributed, asynchronous
event-triggered communication scheme that is also free of Zeno with asymptotic
convergence guarantees. Several simulations illustrate our results.Comment: 12 page
How collective asperity detachments nucleate slip at frictional interfaces
Sliding at a quasi-statically loaded frictional interface can occur via
macroscopic slip events, which nucleate locally before propagating as rupture
fronts very similar to fracture. We introduce a novel microscopic model of a
frictional interface that includes asperity-level disorder, elastic interaction
between local slip events, and inertia. For a perfectly flat and homogeneously
loaded interface, we find that slip is nucleated by avalanches of asperity
detachments of extension larger than a critical radius governed by a
Griffith criterion. We find that after slip, the density of asperities at a
local distance to yielding presents a pseudo-gap , where is a non-universal exponent that depends on
the statistics of the disorder. This result makes a link between friction and
the plasticity of amorphous materials where a pseudo-gap is also present. For
friction, we find that a consequence is that stick-slip is an extremely slowly
decaying finite size effect, while the slip nucleation radius diverges as
a -dependent power law of the system size. We discuss how these
predictions can be tested experimentally
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
Networked and event-triggered control systems
In this thesis, control algorithms are studied that are tailored for platforms with limited computation and communication resources. The interest in such control algorithms is motivated by the fact that nowadays control algorithms are implemented on small and inexpensive embedded microprocessors and that the sensors, actuators and controllers are connected through multipurpose communication networks. To handle the fact that computation power is no longer abundant and that communication networks do not have in finite bandwidth, the control algorithms need to be either robust for the deficiencies induced by these constraints, or they need to optimally utilise the available computation and communication resources. In this thesis, methodologies for the design and analysis of control algorithms with such properties are developed. Networked Control Systems: In the first part of the thesis, so-called networked control systems (NCSs) are studied. The control algorithms studied in this part of the thesis can be seen as conventional sampled-data controllers that need to be robust against the artefacts introduced by using a finite bandwidth communication channel. The network-induced phenomena that are considered in this thesis are time-varying transmission intervals, time-varying delays, packet dropouts and communication constraints. The latter phenomenon causes that not all sensor and actuator data can be transmitted simultaneously and, therefore, a scheduling protocol is needed to orchestrate when to transmit what data over the network. To analyse the stability of the NCSs, a discrete-time modelling framework is presented and, in particular, two cases are considered: in the first case, the transmission intervals and delays are assumed to be upper and lower bounded, and in the second case, they are described by a random process, satisfying a continuous joint probability distribution. Both cases are relevant. The former case requires a less detailed description of the network behaviour than the latter case, while the latter results in a less conservative stability analysis than the former. This allows to make a tradeoff between modelling accuracy (of network-induced effects) and conservatism in the stability analysis. In both cases, linear plants and controllers are considered and the NCS is modelled as a discrete-time switched linear parameter-varying system. To assess the stability of this system, novel polytopic overapproximations are developed, which allows the stability of the NCS to be studied using a finite number of linear matrix inequalities. It will be shown that this approach reduces conservatism significantly with respect to existing results in the literature and allows for studying larger classes of controllers, including discrete-time dynamical output-based controllers. Hence, the main contribution of this part of the thesis is the development of a new and general framework to analyse the stability of NCSs subject to four network-induced phenomena in a hardly conservative manner. Event-Triggered Control Systems: In the second part of the thesis, socalled event-triggered control (ETC) systems are studied. ETC is a control strategy in which the control task is executed after the occurrence of an external event, rather than the elapse of a certain period of time as in conventional periodic control. In this way, ETC can be designed to only provide control updates when needed and, thereby, to optimally utilise the available computation and communication resources. This part of the thesis consists of three main contributions in this appealing area of research. The first contribution is the extension of the existing results on ETC towards dynamical output-based feedback controllers, instead of state-feedback control, as is common in the majority of the literature on ETC. Furthermore, extensions towards decentralised event triggering are presented. These extensions are important for practical implementations of ETC, as in many control applications the full state is hardly ever available for feedback, and sensors and actuators are often physically distributed, which prohibits the use of centralised event-triggering conditions. To study the stability and the L1-performance of this ETC system, a modelling framework based on impulsive systems is developed. Furthermore, for the novel output-based decentralised event-triggering conditions that are proposed, it is shown how nonzero lower bounds on the minimum inter-event times can be guaranteed and how they can be computed. The second contribution is the proposition of the new class of periodic event-triggered control (PETC) algorithms, where the objective is to combine the benefits that, on the one hand, periodic control and, on the other hand, ETC offer. In PETC, the event-triggering condition is monitored periodically and at each sampling instant it is decided whether or not to transmit the data and to use computation resources for the control task. Such an event-triggering condition has several benefits, including the inherent existence of a minimum inter-event time, which can be tuned directly. Furthermore, the fact that the event-triggering condition is only verified at the periodic sampling times, instead of continuously, makes it possible to implement this strategy in standard time-sliced embedded software architectures. To analyse the stability and the L2-performance for these PETC systems, methodologies based on piecewiselinear systems models and impulsive system models will be provided, leading to an effective analysis framework for PETC. Finally, a novel approach to solving the codesign problem of both the feedback control algorithm and the event-triggering condition is presented. In particular, a novel way to solve the minimum attention and anytime attention control problems is proposed. In minimum attention control, the `attention' that a control task requires is minimised, and in anytime attention control, the performance under the `attention' given by a scheduler is maximised. In this context, `attention' is interpreted as the inverse of the time elapsed between two consecutive executions of a control task. The two control problems are solved by formulating them as linear programs, which can be solved efficiently in an online fashion. This offers a new and elegant way to solve both the minimum attention control problem and the anytime attention control problem in one unifying framework. The contributions presented in this thesis can form a basis for future research explorations that can eventually lead to a mature system theory for both NCSs and ETC systems, which are indispensable for the deployment of NCSs and ETC systems in a large variety of practical control applications
Multivariate Hawkes Processes for Large-scale Inference
In this paper, we present a framework for fitting multivariate Hawkes
processes for large-scale problems both in the number of events in the observed
history and the number of event types (i.e. dimensions). The proposed
Low-Rank Hawkes Process (LRHP) framework introduces a low-rank approximation of
the kernel matrix that allows to perform the nonparametric learning of the
triggering kernels using at most operations, where is the
rank of the approximation (). This comes as a major improvement to
the existing state-of-the-art inference algorithms that are in .
Furthermore, the low-rank approximation allows LRHP to learn representative
patterns of interaction between event types, which may be valuable for the
analysis of such complex processes in real world datasets. The efficiency and
scalability of our approach is illustrated with numerical experiments on
simulated as well as real datasets.Comment: 16 pages, 5 figure
Isochronous Partitions for Region-Based Self-Triggered Control
In this work, we propose a region-based self-triggered control (STC) scheme
for nonlinear systems. The state space is partitioned into a finite number of
regions, each of which is associated to a uniform inter-event time. The
controller, at each sampling time instant, checks to which region does the
current state belong, and correspondingly decides the next sampling time
instant. To derive the regions along with their corresponding inter-event
times, we use approximations of isochronous manifolds, a notion firstly
introduced in [1]. This work addresses some theoretical issues of [1] and
proposes an effective computational approach that generates approximations of
isochronous manifolds, thus enabling the region-based STC scheme. The
efficiency of both our theoretical results and the proposed algorithm are
demonstrated through simulation examples
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