Control and Communication Challenges in Networked Real-Time Systems

Baillieul, John; Antsaklis, Panos J.

doi:10.1109/jproc.2006.887290

Cited by 819 publications

(406 citation statements)

References 89 publications

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“…It can be seen from [1,24] that among the topics on optimal estimation and control for the system with packet loss, there are some closely related fundamental issues: the stability of the estimator [25,26], the distribution and convergence of the estimation error covariance [27,28], the estimation performance evaluation [29,30], and the stability of the closed-loop system [14,31]. These four issues have been fully investigated for TCP-like systems, but they are seldom studied for SS-UDP systems.…”

Section: A Background and Motivationsmentioning

confidence: 99%

Optimal Estimation and Control for Lossy Network: Stability, Convergence, and Performance

Lin

Shi

et al. 2017

IEEE Trans. Automat. Contr.

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Abstract-In this paper, we study the problems of optimal estimation and control, i.e., the linear quadratic Gaussian (LQG) control, for systems with packet losses but without acknowledgment. Such acknowledgment is a signal sent by the actuator to inform the estimator of the incidence of control packet losses. For such system, which is usually called as a User Datagram Protocol (UDP)-like system, the optimal estimation is nonlinear and its calculation is time-consuming, making its corresponding optimal LQG problem complicated. We first propose two conditions: 1) the sensor has some computation abilities; and 2) the control command, exerted to the plant, is known to the sensor. For a UDP-like system satisfying these two conditions, we derive the optimal estimation. By constructing the finite and infinite product probability measure spaces for the estimation error covariances (EEC), we give the stability condition for the expected EEC, and show the existence of a measurable function to which the EEC converges in distribution, and propose some practical methods to evaluate the estimation performance. Finally, the LQG controllers are derived, and the conditions for the mean square stability of the closed-loop system are established.

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Section: A Background and Motivationsmentioning

confidence: 99%

Optimal Estimation and Control for Lossy Network: Stability, Convergence, and Performance

Lin

Shi

et al. 2017

IEEE Trans. Automat. Contr.

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“…Theorem 1. Consider the NCSs in (1). Under Assumption 1, the sparse controller (11), (10) drives the state of the system (1) to the composite sliding surface (6) and maintains a sliding motion if…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

ℋ<inf>2</inf>-based optimal sparse sliding mode control for networked control systems

Argha

et al. 2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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This paper is devoted to the problem of designing a sparsely distributed sliding mode control for networked systems. Indeed, this note employs a distributed sliding mode control framework by exploiting (some of) other subsystems' information to improve the performance of each local controller so that it can widen the applicability region of the given scheme. To do so, different from the traditional schemes in the literature, a novel approach is proposed to design the sliding surface, in which the level of required control effort is taken into account during the sliding surface design based on the H 2 control. We then use this novel scheme to provide an innovative less-complex procedure that explores sparse control networks to satisfy the underlying control objective. Besides, the proposed scheme to design the sliding surface makes it possible to avoid unbounded growth of control effort during the sparsification of the control network structure. Illustrative examples are presented to show the effectiveness of the proposed approach. keywords Networked control systems, H 2 -based optimal sparse sliding mode control, distributed control systems, linear matrix inequality.

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“…Such situations could arise naturally; for instance, in (cooperative) distributed systems supported by wireless networks (e.g., coordinated motion control of autonomous mobile agents), in which individual agents attempt to take actions they believe to be most beneficial based on locally available information [4]. This requires that each agent identify a subset of other agents with which it wishes to coordinate actions or exchange information in order to optimize the overall system performance.…”

Section: Random Graph Model With Self-optimizing Nodes 529mentioning

confidence: 99%

A New Random Graph Model with Self-Optimizing Nodes: Connectivity and Diameter

Kabkab

2015

Internet Mathematics

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We introduce a new random graph model. In our model, n, n ≥ 2, vertices choose a subset of potential edges by considering the (estimated) benefits or utilities of the edges. More precisely, each vertex selects k, k ≥ 1, incident edges it wishes to set up, and an undirected edge between two vertices is present in the graph if and only if both of the end vertices choose the edge. First, we examine the scaling law of the smallest k needed for graph connectivity with increasing n and prove that it is (log(n)). Second, we study the diameter of the random graph and demonstrate that, under certain conditions on k, the diameter is close to log(n)/ log(log(n)) with high probability. In addition, as a byproduct of our findings, we show that, for all sufficiently large n, if k > β log(n), where β ≈ 2.4626, there exists a connected Erdös-Rényi random graph that is embedded in our random graph, with high probability.

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Control and Communication Challenges in Networked Real-Time Systems

Cited by 819 publications

References 89 publications

Optimal Estimation and Control for Lossy Network: Stability, Convergence, and Performance

Optimal Estimation and Control for Lossy Network: Stability, Convergence, and Performance

ℋ<inf>2</inf>-based optimal sparse sliding mode control for networked control systems

A New Random Graph Model with Self-Optimizing Nodes: Connectivity and Diameter

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