533,939 research outputs found
Scalable and efficient data processing in networked control systems
Network control systems (NCSs) are spatially distributed systems in which the communication between sensors,
actuators and controllers occurs through a shared band-limited digital communication network. However, the use of a
shared communication network, in contrast to using several dedicated independent connections, introduces new
challenges which are even more acute in large scale and dense networked control systems. In this paper we investigate a
recently introduced technique of gathering information from a dense sensor network to be used in networked control
applications. Obtaining efficiently an approximate interpolation of the sensed data is exploited as offering a good tradeoff
between accuracy in the measurement of the input signals and the delay to the actuation. These are important aspects
to take into account for the quality of control. We introduce a variation to the state-of-the-art algorithms which we
prove to perform relatively better because it takes into account the changes over time of the input signal within the
process of obtaining an approximate interpolation
D2NO: Efficient Handling of Heterogeneous Input Function Spaces with Distributed Deep Neural Operators
Neural operators have been applied in various scientific fields, such as
solving parametric partial differential equations, dynamical systems with
control, and inverse problems. However, challenges arise when dealing with
input functions that exhibit heterogeneous properties, requiring multiple
sensors to handle functions with minimal regularity. To address this issue,
discretization-invariant neural operators have been used, allowing the sampling
of diverse input functions with different sensor locations. However, existing
frameworks still require an equal number of sensors for all functions. In our
study, we propose a novel distributed approach to further relax the
discretization requirements and solve the heterogeneous dataset challenges. Our
method involves partitioning the input function space and processing individual
input functions using independent and separate neural networks. A centralized
neural network is used to handle shared information across all output
functions. This distributed methodology reduces the number of gradient descent
back-propagation steps, improving efficiency while maintaining accuracy. We
demonstrate that the corresponding neural network is a universal approximator
of continuous nonlinear operators and present four numerical examples to
validate its performance
Distributed Estimation and Control for LTI Systems under Finite-Time Agreement
This paper considers a strongly connected network of agents, each capable of
partially observing and controlling a discrete-time linear time-invariant (LTI)
system that is jointly observable and controllable. Additionally, agents
collaborate to achieve a shared estimated state, computed as the average of
their local state estimates. Recent studies suggest that increasing the number
of average consensus steps between state estimation updates allows agents to
choose from a wider range of state feedback controllers, thereby potentially
enhancing control performance. However, such approaches require that agents
know the input matrices of all other nodes, and the selection of control gains
is, in general, centralized. Motivated by the limitations of such approaches,
we propose a new technique where: (i) estimation and control gain design is
fully distributed and finite-time, and (ii) agent coordination involves a
finite-time exact average consensus algorithm, allowing arbitrary selection of
estimation convergence rate despite the estimator's distributed nature. We
verify our methodology's effectiveness using illustrative numerical
simulations
Plug-and-Play Fault Detection and control-reconfiguration for a class of nonlinear large-scale constrained systems
This paper deals with a novel Plug-and-Play (PnP) architecture for the control and monitoring of Large-Scale Systems (LSSs). The proposed approach integrates a distributed Model Predictive Control (MPC) strategy with a distributed Fault Detection (FD) architecture and methodology in a PnP framework. The basic concept is to use the FD scheme as an autonomous decision support system: once a fault is detected, the faulty subsystem can be unplugged to avoid the propagation of the fault in the interconnected LSS. Analogously, once the issue has been solved, the disconnected subsystem can be re-plugged-in. PnP design of local controllers and detectors allow these operations to be performed safely, i.e. without spoiling stability and constraint satisfaction for the whole LSS. The PnP distributed MPC is derived for a class of nonlinear LSSs and an integrated PnP distributed FD architecture is proposed. Simulation results in two paradigmatic examples show the effectiveness and the potential of the general methodology
Control versus Data Flow in Parallel Database Machines
The execution of a query in a parallel database machine can be controlled in either a control flow way, or in a data flow way. In the former case a single system node controls the entire query execution. In the latter case the processes that execute the query, although possibly running on different nodes of the system, trigger each other. Lately, many database research projects focus on data flow control since it should enhance response times and throughput. The authors study control versus data flow with regard to controlling the execution of database queries. An analytical model is used to compare control and data flow in order to gain insights into the question which mechanism is better under which circumstances. Also, some systems using data flow techniques are described, and the authors investigate to which degree they are really data flow. The results show that for particular types of queries data flow is very attractive, since it reduces the number of control messages and balances these messages over the node
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