13 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
Remote Estimation of Markov Processes over Costly Channels: On the Benefits of Implicit Information
In this paper, we study the remote estimation problem of a Markov process
over a channel with a cost. We formulate this problem as an infinite horizon
optimization problem with two players, i.e., a sensor and a monitor, that have
distinct information, and with a reward function that takes into account both
the communication cost and the estimation quality. We show that the main
challenge in solving this problem is associated with the consideration of
implicit information, i.e., information that the monitor can obtain about the
source when the sensor is silent. Our main objective is to develop a framework
for finding solutions to this problem without neglecting implicit information a
priori. To that end, we propose three different algorithms. The first one is an
alternating policy algorithm that converges to a Nash equilibrium. The second
one is an occupancy-state algorithm that is guaranteed to find a globally
optimal solution. The last one is a heuristic algorithm that is able to find a
near-optimal solution
Autonomous Construction with Compliant Building Material
In this paper, we develop an autonomous construction system in which a self-contained ground robot builds a protective barrier by means of compliant pockets (i.e., filled bags). We present a stochastic control algorithm based on two biological mechanisms (stigmergy and templates) that takes advantage of compliant pockets for autonomous construction. The control algorithm guides the robot to build the structure without relying on any external motion capture system or external computer. We propose a statistical model to represent the structures built with the compliant pockets, and we provide a set of criteria for assessing the performance of the proposed system. To demonstrate the feasibility of the proposed system, real-robot experiments were carried out. In each experiment, the robot successfully built the structure. The results show the viability of the proposed autonomous construction system
Distributed constrained connectivity control for proximity networks based on a receding horizon scheme
info:eu-repo/semantics/publishe
Semantic Communications in Networked Systems
We present our vision for a departure from the established way of
architecting and assessing communication networks, by incorporating the
semantics of information for communications and control in networked systems.
We define semantics of information, not as the meaning of the messages, but as
their significance, possibly within a real time constraint, relative to the
purpose of the data exchange. We argue that research efforts must focus on
laying the theoretical foundations of a redesign of the entire process of
information generation, transmission and usage in unison by developing:
advanced semantic metrics for communications and control systems; an optimal
sampling theory combining signal sparsity and semantics, for real-time
prediction, reconstruction and control under communication constraints and
delays; semantic compressed sensing techniques for decision making and
inference directly in the compressed domain; semantic-aware data generation,
channel coding, feedback, multiple and random access schemes that reduce the
volume of data and the energy consumption, increasing the number of supportable
devices.Comment: 9 pages, 6 figures, 1500 word
Scenario-based stochastic optimal operation of wind/PV/FC/CHP/boiler/tidal/energy storage system considering DR programs and uncertainties
Abstract Background Micro-grid (MG) can be described as a group of controllable loads and distributed energy resources that can be connected and disconnected from the main grid and utilized in grid-connected or islanded modes considering certain electrical constraints. Methods The objective of this article are as follows: (1) predict the uncertainties through the hybrid method of WT-ANN-ICA and (2) determine the optimal generation strategy of a MG containing wind farms (WFs), photovoltaic (PV), fuel cell (FC), combined heat and power (CHP) units, tidal steam turbine (TST), and also boiler and energy storage devices (ESDs). The uncertainties include wind speed, tidal steam speed, photovoltaic power generation (PVPG), market price, power, and thermal load demand. For modeling uncertainties, an effort has been made to predict uncertainties through the hybrid method of wavelet transform (WT) in order to reduce fluctuations in the historical input data. An improved artificial neural network (ANN) based on the nonlinear structure is applied for better training and learning. Furthermore, the imperialist competitive algorithm (ICA) is applied to find the best weights and biases for minimizing the mean square error of predictions. Result The scenario-based stochastic optimization problem is proposed to determine the optimal points for the energy resources generation and to maximize the expected profit considering demand response (DR) programs and uncertainties. Conclusions In this study, three cases are assessed to confirm the performance of the proposed method. In the first case study programming, MG is isolated from grid. In the second case study, which is grid-connected mode, the WT-ANN-ICA and WT-ANN uncertainty prediction methods are compared. In the third case, which is grid-connected mode, the effect of DR programs on the expected profit of energy resources is assessed
Bio-inspired construction with mobile robots and compliant pockets
In this paper, we develop an autonomous construction system in which self-contained ground robots build a protective barrier by means of compliant pockets. We present a stochastic control algorithm based on two biological mechanisms-stigmergy and templates-that takes advantage of compliant pockets for autonomous construction with single and multiple robots. The control algorithm guides the robot(s) to build the protective barrier without relying on a central planner, an external computer, or a motion capture system. We propose a statistical model to represent the structures built with the compliant pockets, and we provide a set of criteria for assessing the performance of the proposed system. To demonstrate the feasibility of the proposed system, real-robot and simulation experiments were carried out. The results show the viability of the proposed autonomous construction system. © 2015 Elsevier B.V.SCOPUS: cp.jinfo:eu-repo/semantics/publishe
Value of Information in Feedback Control: Global Optimality
The rate-regulation tradeoff, defined between two objective functions, one
penalizing the packet rate and one the regulation cost, can express the
fundamental performance bound of networked control systems. However, the
characterization of the set of globally optimal solutions in this tradeoff for
multi-dimensional Gauss-Markov processes has been an open problem. In the
present article, we characterize a policy profile that belongs to this set
without imposing any restrictions on the information structure or the policy
structure. We prove that such a policy profile consists of a symmetric
threshold triggering policy based on the value of information and a
certainty-equivalent control policy based on a non-Gaussian linear estimator.
These policies are deterministic and can be designed separately. Besides, we
provide a global optimality analysis for the value of information
, a semantic metric that emerges from the rate-regulation
tradeoff as the difference between the benefit and the cost of a data packet.
We prove that it is globally optimal that a data packet containing sensory
information at time be transmitted to the controller only if
becomes nonnegative. These results have important implications in the areas of
communication and control.Comment: arXiv admin note: text overlap with arXiv:1812.0753
Semantic Communications in Networked Systems: A Data Significance Perspective
We present our vision for a departure from the established way of architecting and assessing communication networks, by incorporating the semantics of information, defined not necessarily as the meaning of the messages, but as their significance, possibly within a real-time constraint, relative to the purpose of the data exchange. We argue that research efforts must focus on laying the theoretical foundations of a redesign of the entire process of information generation, transmission, and usage for networked systems in unison by developing advanced semantic metrics for communications and control systems; an optimal sampling theory combining signal sparsity and timeliness, for real-time prediction/reconstruction/control under communication constraints and delays; temporally effective compressed sensing techniques for decision making and inference directly in the compressed domain; and semantic-aware data generation, channel coding, packetization, feedback, and multiple and random access schemes that reduce the volume of data and the energy consumption, increasing the number of supportable devices. This paradigm shift targets jointly optimal information gathering, information dissemination, and decision-making policies in networked systems