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Automatic Control, IEEE Transactions on

Issue 3 • Date March 2013

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Displaying Results 1 - 25 of 34
  • Table of Contents

    Publication Year: 2013 , Page(s): C1 - C4
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  • IEEE Transactions on Automatic Control publication information

    Publication Year: 2013 , Page(s): C2
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  • List of Reviewers for 2012

    Publication Year: 2013 , Page(s): 545 - 551
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  • Scanning the issue

    Publication Year: 2013 , Page(s): 552 - 553
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  • Stabilization of Networked Multi-Input Systems With Channel Resource Allocation

    Publication Year: 2013 , Page(s): 554 - 568
    Cited by:  Papers (15)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (3122 KB) |  | HTML iconHTML  

    In this paper, we study the problem of state feedback stabilization of a linear time-invariant (LTI) discrete-time multi-input system with imperfect input channels. Each input channel is modeled in three different ways. First it is modeled as an ideal transmission system together with an additive norm bounded uncertainty, introducing a multiplicative uncertainty to the plant. Then it is modeled as an ideal transmission system together with a feedback norm bounded uncertainty, introducing a relative uncertainty to the plant. Finally it is modeled as an additive white Gaussian noise channel. For each of these models, we properly define the capacity of each channel whose sum yields the total capacity of all input channels. We aim at finding the least total channel capacity for stabilization. Different from the single-input case that is available in the literature and boils down to a typical H or H2 optimal control problem, the multi-input case involves allocation of the total capacity among the input channels in addition to the design of the feedback controller. The overall process of channel resource allocation and the controller design can be considered as a case of channel-controller co-design which gives rise to modified nonconvex optimization problems. Surprisingly, the modified nonconvex optimization problems, though appear more complicated, can be solved analytically. The main results of this paper can be summarized into a universal theorem: The state feedback stabilization can be accomplished by the channel-controller co-design, if and only if the total input channel capacity is greater than the topological entropy of the open-loop system. View full abstract»

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  • A Nonlinear High-Gain Observer for Systems With Measurement Noise in a Feedback Control Framework

    Publication Year: 2013 , Page(s): 569 - 580
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3010 KB) |  | HTML iconHTML  

    We address the problem of state estimation for a class of nonlinear systems with measurement noise in the context of feedback control. It is well-known that high-gain observers are robust against model uncertainty and disturbances, but sensitive to measurement noise when implemented in a feedback loop. This work presents the benefits of a nonlinear-gain structure in the innovation process of the high-gain observer, in order to overcome the tradeoff between fast state reconstruction and measurement noise attenuation. The goal is to generate a larger observer gain during the transient response than in the steady-state response. Thus, by reducing the observer gain after achieving satisfactory state estimates, the effect of noise on the steady-state performance is reduced. Moreover, the nonlinear-gain observer presented in this paper is shown to surpass the system performance achieved when using comparable linear-gain observers. The proof argues boundedness and ultimate boundedness of the closed-loop system under the proposed output feedback. View full abstract»

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  • State Estimation Over Sensor Networks With Correlated Wireless Fading Channels

    Publication Year: 2013 , Page(s): 581 - 593
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3380 KB) |  | HTML iconHTML  

    Stochastic stability for centralized time-varying Kalman filtering over a wireless sensor network with correlated fading channels is studied. On their route to the gateway, sensor packets, possibly aggregated with measurements from several nodes, may be dropped because of fading links. To study this situation, we introduce a network state process, which describes a finite set of configurations of the radio environment. The network state characterizes the channel gain distributions of the links, which are allowed to be correlated between each other. Temporal correlations of channel gains are modeled by allowing the network state process to form a (semi-)Markov chain. We establish sufficient conditions that ensure the Kalman filter to be exponentially bounded. In the one-sensor case, this new stability condition is shown to include previous results obtained in the literature as special cases. The results also hold when using power and bit-rate control policies, where the transmission power and bit-rate of each node are nonlinear mapping of the network state and channel gains. View full abstract»

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  • Regularized Iterative Stochastic Approximation Methods for Stochastic Variational Inequality Problems

    Publication Year: 2013 , Page(s): 594 - 609
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4938 KB) |  | HTML iconHTML  

    We consider a Cartesian stochastic variational inequality problem with a monotone map. Monotone stochastic variational inequalities arise naturally, for instance, as the equilibrium conditions of monotone stochastic Nash games over continuous strategy sets or multiuser stochastic optimization problems. We introduce two classes of stochastic approximation methods, each of which requires exactly one projection step at every iteration, and provide convergence analysis for each of them. Of these, the first is a stochastic iterative Tikhonov regularization method which necessitates the update of the regularization parameter after every iteration. The second method is a stochastic iterative proximal-point method, where the centering term is updated after every iteration. The Cartesian structure lends itself to constructing distributed multi-agent extensions and conditions are provided for recovering global convergence in limited coordination variants where agents are allowed to choose their steplength sequences, regularization and centering parameters independently, while meeting a suitable coordination requirement. We apply the proposed class of techniques and their limited coordination versions to a stochastic networked rate allocation problem. View full abstract»

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  • Reaching an Optimal Consensus: Dynamical Systems That Compute Intersections of Convex Sets

    Publication Year: 2013 , Page(s): 610 - 622
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3865 KB) |  | HTML iconHTML  

    In this paper, multi-agent systems minimizing a sum of objective functions, where each component is only known to a particular node, is considered for continuous-time dynamics with time-varying interconnection topologies. Assuming that each node can observe a convex solution set of its optimization component, and the intersection of all such sets is nonempty, the considered optimization problem is converted to an intersection computation problem. By a simple distributed control rule, the considered multi-agent system with continuous-time dynamics achieves not only a consensus, but also an optimal agreement within the optimal solution set of the overall optimization objective. Directed and bidirectional communications are studied, respectively, and connectivity conditions are given to ensure a global optimal consensus. In this way, the corresponding intersection computation problem is solved by the proposed decentralized continuous-time algorithm. We establish several important properties of the distance functions with respect to the global optimal solution set and a class of invariant sets with the help of convex and non-smooth analysis. View full abstract»

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  • Cooperative Estimation of Averaged 3-D Moving Target Poses via Networked Visual Motion Observer

    Publication Year: 2013 , Page(s): 623 - 638
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5912 KB) |  | HTML iconHTML  

    This paper investigates cooperative estimation of averaged moving target object poses in three dimensions for visual sensor networks. In particular, we consider the situation where multiple vision cameras see a common target object but the poses consistent with visual measurements differ from camera to camera due to a variety of uncertainties. Under the situation, we try to estimate an average of the contaminated poses not only for static but also for moving target objects by using only local negotiations. For this purpose, we present a cooperative estimation mechanism called networked visual motion observer. We then derive an upper bound of the ultimate error between the actual average and the estimates produced by the present estimation mechanism for both static and moving target objects. Finally the effectiveness of the networked visual motion observer is demonstrated through simulation. View full abstract»

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  • Nash, Social and Centralized Solutions to Consensus Problems via Mean Field Control Theory

    Publication Year: 2013 , Page(s): 639 - 653
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4169 KB) |  | HTML iconHTML  

    The purpose of this paper is to synthesize initial mean consensus behavior of a set of agents from the fundamental optimization principles of i) stochastic dynamic games, and ii) optimal control. In the stochastic dynamic game model each agent seeks to minimize its individual quadratic discounted cost function involving the mean of the states of all other agents. In this formulation we derive the limiting infinite population mean field equation system and explicitly compute its unique solution. The resulting mean field (MF) control strategies drive each agent to track the overall population's initial state distribution mean, and by applying these decentralized strategies, any finite population system reaches mean consensus asymptotically as time goes to infinity. Furthermore, these control laws possess an εN-Nash equilibrium property where εN goes to zero as the population size N goes to infinity. Finally, the analysis is extended to the case of random mean field couplings. In the social cooperative formulation the basic objective is to minimize a social cost as the sum of the individual cost functions containing mean field coupling. In this formulation we show that for any individual agent the decentralized mean field social (MF Social) control strategy is the same as the mean field Nash (MF Nash) equilibrium strategy. Hence MF-Nash Controls UNashN=MF - Social Controls USocN. On the other hand, the solution to the centralized LQR optimal control formulation yields the Standard Consensus (SC) algorithm whenever the graph representing the corresponding topology of the network is Completely Connected (CC). Hence Cent. LQR Controls UCentN=SC-CC Controls USCN. Moreover, a system with centralized control laws reaches consensus on the initial state distribution mean as time and population size N go to infinity. Hence, asymptotically in time M- -Nash Controls UNashN=MF-Social Controls USocN = Cent. LQR Controls UCent = SC-CC Controls USC. Finally, the analysis is extended to the long time average (LTA) cost functions case. View full abstract»

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  • Adaptive Deployment of Mobile Robotic Networks

    Publication Year: 2013 , Page(s): 654 - 666
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3300 KB) |  | HTML iconHTML  

    This paper considers deployment problems where a mobile robotic network must optimize its configuration in a distributed way in order to minimize a steady-state cost function that depends on the spatial distribution of certain probabilistic events of interest. Moreover, it is assumed that the event location distribution is a priori unknown, and can only be progressively inferred from the observation of the actual event occurrences. Three classes of problems are discussed in detail: coverage control problems, spatial partitioning problems, and dynamic vehicle routing problems. In each case, distributed stochastic gradient algorithms optimizing the performance objective are presented. The stochastic gradient view simplifies and generalizes previously proposed solutions, and is applicable to new complex scenarios, such as adaptive coverage involving heterogeneous agents. Remarkably, these algorithms often take the form of simple distributed rules that could be implemented on resource-limited platforms. View full abstract»

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  • Distributed Matrix Scaling and Application to Average Consensus in Directed Graphs

    Publication Year: 2013 , Page(s): 667 - 681
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4622 KB) |  | HTML iconHTML  

    We propose a class of distributed iterative algorithms that enable the asymptotic scaling of a primitive column stochastic matrix, with a given sparsity structure, to a doubly stochastic form. We also demonstrate the application of these algorithms to the average consensus problem in networked multi-component systems. More specifically, we consider a setting where each node is in charge of assigning weights on its outgoing edges based on the weights on its incoming edges. We establish that, as long as the (generally directed) graph that describes the communication links between components is strongly connected, each of the proposed matrix scaling algorithms allows the system components to asymptotically assign, in a distributed fashion, weights that comprise a primitive doubly stochastic matrix. We also show that the nodes can asymptotically reach average consensus by executing a linear iteration that uses the time-varying weights (as they result at the end of each iteration of the chosen matrix scaling algorithm). View full abstract»

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  • Robust State Space Filtering Under Incremental Model Perturbations Subject to a Relative Entropy Tolerance

    Publication Year: 2013 , Page(s): 682 - 695
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2761 KB) |  | HTML iconHTML  

    This paper considers robust filtering for a nominal Gaussian state-space model, when a relative entropy tolerance is applied to each time increment of a dynamical model. The problem is formulated as a dynamic minimax game where the maximizer adopts a myopic strategy. This game is shown to admit a saddle point whose structure is characterized by applying and extending results presented earlier in “Robust least-squares estimation with a relative entropy constraint” (B. C. Levy and R. Nikoukhah, IEEE Trans. Inf. Theory, vol. 50, no. 1, 89-104, Jan. 2004) for static least-squares estimation. The resulting minimax filter takes the form of a risk-sensitive filter with a time varying risk sensitivity parameter, which depends on the tolerance bound applied to the model dynamics and observations at the corresponding time index. The least-favorable model is constructed and used to evaluate the performance of alternative filters. Simulations comparing the proposed risk-sensitive filter to a standard Kalman filter show a significant performance advantage when applied to the least-favorable model, and only a small performance loss for the nominal model. View full abstract»

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  • Optimization of Lyapunov Invariants in Verification of Software Systems

    Publication Year: 2013 , Page(s): 696 - 711
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3753 KB) |  | HTML iconHTML  

    The paper proposes a control-theoretic framework for verification of numerical software systems, and puts forward software verification as an important application of control and systems theory. The idea is to transfer Lyapunov functions and the associated computational techniques from control systems analysis and convex optimization to verification of various software safety and performance specifications. These include but are not limited to absence of overflow, absence of division-by-zero, termination in finite time, absence of dead-code, and certain user-specified assertions. Central to this framework are Lyapunov invariants. These are properly constructed functions of the program variables, and satisfy certain properties-analogous to those of Lyapunov functions-along the execution trace. The search for the invariants can be formulated as a convex optimization problem. If the associated optimization problem is feasible, the result is a certificate for the specification. View full abstract»

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  • Delay Robustness of Interconnected Passive Systems: An Integral Quadratic Constraint Approach

    Publication Year: 2013 , Page(s): 712 - 724
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3553 KB) |  | HTML iconHTML  

    We consider networks of passive systems with time delays in the interconnections, and present a stability analysis technique with the help of the integral quadratic constraint (IQC) framework. Unlike the classical passivity approach that fails to characterize delay robustness, and the small-gain approach that conservatively accounts for arbitrarily large delays, the new technique gives sharp stability estimates that depend on the duration of delay. Since the effect of delay depends on its duration relative to the time scales of the system, we make use of a “roll-off” IQC that captures magnitude roll-off at high frequencies, thus, providing the critical time-scale information. We then combine this roll-off IQC with an output strict passivity IQC that incorporates gain and phase information, and demonstrate the benefit of this combined IQC approach on a cyclic interconnection structure with delay. Finally, we develop a technique to verify these IQCs for classes of nonlinear state-space models and present an example from Internet congestion control. View full abstract»

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  • Receding Horizon Control With Numerical Solution for Nonlinear Parabolic Partial Differential Equations

    Publication Year: 2013 , Page(s): 725 - 730
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1345 KB) |  | HTML iconHTML  

    The optimal control of nonlinear partial differential equations (PDEs) is an open problem with applications that include fluid, thermal, biological, and chemical systems. Receding horizon control is a kind of optimal feedback control, and its performance index has a moving initial time and a moving terminal time. In this study, we develop a design method of receding horizon control for systems described by nonlinear parabolic PDEs. The objective of this study is to develop a novel algorithm for numerically solving the receding horizon control problem for nonlinear parabolic PDEs. The effectiveness of the proposed method is verified by numerical simulations. View full abstract»

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  • Robustified Anti-Windup via Switching Adaptation

    Publication Year: 2013 , Page(s): 731 - 737
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1696 KB) |  | HTML iconHTML  

    An adaptive approach is proposed to address the problem of robustification (and performance loss reduction) of anti-windup (AW) compensation in the presence of large uncertainties. The approach relies on the augmentation of the AW compensator with an adaptive robustifying filter; a constructive procedure is provided for designing such a filter. The use of adaptation allows to reduce the conservativeness of previous robust approaches to what is needed to preserve stability with respect to the uncertainty actually present on the plant during operation, as detected by input and output plant measurement. View full abstract»

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  • Formation Control of Mobile Agents Based on Distributed Position Estimation

    Publication Year: 2013 , Page(s): 737 - 742
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (803 KB) |  | HTML iconHTML  

    We propose a formation control strategy based on distributed position estimation for single-integrator modeled agents by using relative position measurements. Under the proposed strategy, the estimated and the actual formations globally exponentially converge to the actual and the desired formations, respectively, up to translation, if and only if the interaction graph for the agents has a spanning tree. A sufficient condition is provided for the case that the edge weights of the interaction graph are time-varying. Further, the proposed strategy is applied to formation control of unicycle-like agents. View full abstract»

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  • On a Control Algorithm for Time-Varying Processor Availability

    Publication Year: 2013 , Page(s): 743 - 748
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1517 KB) |  | HTML iconHTML  

    We propose an anytime algorithm for control when the processor resource availability is limited and time-varying. The basic idea behind the algorithm is to calculate the components of the control input vector sequentially to maximally utilize the processing resources available at every time step. For the LQR case, we present a Markovian jump linear system formulation that provides analytical performance and stability expressions. For more general cases, we present and analyze a receding horizon control based formulation. The improvement in performance is also illustrated through numerical simulations. View full abstract»

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  • A Constructive Approach to Linear Lyapunov Functions for Positive Switched Systems Using Collatz-Wielandt Sets

    Publication Year: 2013 , Page(s): 748 - 751
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (956 KB) |  | HTML iconHTML  

    We establish the link between linear Lyapunov functions for positive switched systems and corresponding Collatz-Wielandt sets. This leads to an algorithm to compute a linear Lyapunov function whenever a Lyapunov function exists. View full abstract»

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  • A Contraction Theory Approach to Singularly Perturbed Systems

    Publication Year: 2013 , Page(s): 752 - 757
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1649 KB) |  | HTML iconHTML  

    In this technical note, we revisit standard results on singular perturbations and multiple time-scales using convergence analysis tools based on nonlinear contraction theory. Specifically, assuming that the fast and slow subsystems are each partially contracting, we obtain explicit bounds on the convergence rate of the trajectories to the slow manifold and on the asymptotic error between the trajectories of the singularly perturbed system and those of the reduced system. As an application example, we illustrate the design of a biomolecular insulation device. View full abstract»

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  • Observer Design for Uniformly Observable Systems With Sampled Measurements

    Publication Year: 2013 , Page(s): 757 - 762
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1768 KB) |  | HTML iconHTML  

    This work considers the problem of observer design for continuous-time systems with sampled output measurements (continuous-discrete time systems). In classical literature and in many applications, the continuous-discrete time extended Kalman filter (EKF) is used in order to tackle this problem. In this work, using a normal form characterizing the class of nonlinear uniformly observable single output nonlinear systems, it is shown that a particular stationary solution of a continuous discrete time Lyapunov equation can be used in order to design a constant high gain observer. Explicit conditions are given to ensure global convergence of the observer. Finally, an illustration of this result is given using an example of a biological process. View full abstract»

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  • Modal Trajectory Estimation Using Maximum Gaussian Mixture

    Publication Year: 2013 , Page(s): 763 - 768
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1135 KB) |  | HTML iconHTML  

    This technical note deals with the estimation of the whole trajectory of a stochastic dynamic system with highest probability, conditionally upon the past observation process, using a maximum Gaussian mixture. We first recall the Gaussian sum technique applied to minimum variance filtering. It is then shown that the same concept of Gaussian mixture can be applied in that context, provided we replace the Sum operator by the Max operator. View full abstract»

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  • Guaranteed Cost Certification for Discrete-Time Linear Switched Systems With a Dwell Time

    Publication Year: 2013 , Page(s): 768 - 772
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1074 KB) |  | HTML iconHTML  

    This technical note studies the guaranteed cost of a quadratic criterion associated with a linear discrete-time switched system for all the set of admissible switching laws. The admissible switching laws are here the ones exhibiting a dwell time. The approach provided here is to design an upper bound and a parametrized family of lower bounds of the guaranteed cost as close as possible in order to obtain a certification of the guaranteed cost. The upper bound is determined via a switched Lyapunov function and the lower bounds are obtained via the numerical computation of the cost induced by particular periodic switching laws. The features of the proposed approach are illustrated by a numerical example. View full abstract»

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Aims & Scope

In the IEEE Transactions on Automatic Control, the IEEE Control Systems Society publishes high-quality papers on the theory, design, and applications of control engineering.  Two types of contributions are regularly considered

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Meet Our Editors

Editor-in-Chief
P. J. Antsaklis
Dept. Electrical Engineering
University of Notre Dame