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  • 1. Han, D.
    et al.
    You, K.
    Xie, L.
    Wu, Junfeng
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Shi, L.
    Stochastic packet scheduling for optimal parameter estimation2016In: Proceedings of the IEEE Conference on Decision and Control, 2016, p. 3057-3062Conference paper (Refereed)
    Abstract [en]

    In this paper we consider optimal parameter estimation with a constrained packet transmission rate. Due to the limited battery power and the traffic congestion over a large sensor network, each sensor is required to discard some packets and save transmission times. We propose a packet-driven sensor scheduling policy such that the sensor transmits only the important measurements to the estimator. Unlike the existing deterministic scheduler in [1], our stochastic packet scheduling is novelly designed to maintain the computational simplicity of the resulting maximum-likelihood estimator (MLE). This results in a nice feature that the MLE is still able to be recursively computed in a closed form, and the Cramér-Rao lower bound (CRLB) can be explicitly evaluated. Moreover, an optimization problem is formulated and solved to obtain the optimal parameters of the scheduling policy under which the estimation performance is comparable to the standard MLE (with full measurements) even with a moderate transmission rate. Numerical simulations are included to show the effectiveness.

  • 2. Han, Duo
    et al.
    Mo, Yilin
    Wu, Junfeng
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Automatic Control.
    Shi, Ling
    An Opportunistic Sensor Scheduling Solution to Remote State Estimation Over Multiple Channels2016In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 64, no 18, p. 4905-4917Article in journal (Refereed)
    Abstract [en]

    We consider a sensor scheduling problem where the sensors have multiple choices of communication channel to send their local measurements to a remote state estimator for state estimation. Specifically, the sensors can transmit high-precision data packets over an expensive channel or low-precision data packets, which are quantized in several bits, over some cheap channels. The expensive channel, though being able to deliver more accurate data which leads to good estimation quality at the remote estimator, can only be used scarcely due to its high cost (e.g., high energy consumption). On the other hand, the cheap channel, though having a small cost, delivers less accurate data which inevitably deteriorates the remote estimation quality. In this work we propose a new framework in which the sensors switch between the two channels to achieve a better tradeoff among the communication cost, the estimation performance and the computational complexity, where the two-channel case can be easily extended to a multiple-channel case. We propose an opportunistic sensor schedule which reduces the communication cost by randomly switching among the expensive and cheap channels, and in the meantime maintains low computational complexity while introducing data quantization into the estimation problem. We present a minimum mean square error (MMSE) estimator in a closed-form under the proposed opportunistic sensor schedule. We also formulate an optimization problem to search the best opportunistic schedule with a linear quantizer. Furthermore, we show that the MMSE estimator in the limiting case becomes the standard Kalman filter.

  • 3. Han, Duo
    et al.
    Wu, Junfeng
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Mo, Yilin
    Xie, Lihua
    Stochastic Sensor Scheduling for Multiple Dynamical Processes over a Shared Channel2016In: 2016 IEEE 55th Conference on Decision and Control, CDC 2016, IEEE, 2016, p. 6315-6320, article id 7799241Conference paper (Refereed)
    Abstract [en]

    We consider the problem of multiple sensor scheduling for remote state estimation over a shared link. A number of sensors monitor different dynamical processes simultaneously but only one sensor can access the shared channel at each time instant to transmit the data packet to the estimator. We propose a stochastic event-based sensor scheduling framework in which each sensor makes transmission decisions based on both the channel accessibility and the self event-triggering condition. The corresponding optimal estimator is explicitly given. By ultilizing the realtime information, the proposed schedule is shown to be a generalization of the time based ones and outperform the time-based ones in terms of the estimation quality. By formulating an Markov decision process (MDP) problem with average cost criterion, we can find the optimal parameters for the event-based schedule. For practical use, we also design a simple suboptimal schedule to mitigate the computational complexity of solving an MDP problem. We also propose a method to quantify the optimality gap for any suboptimal schedules.

  • 4. Han, Duo
    et al.
    Wu, Junfeng
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Zhang, Huanshui
    Shi, Ling
    Optimal sensor scheduling for multiple linear dynamical systems2017In: Automatica, ISSN 0005-1098, Vol. 75, p. 260-270Article in journal (Refereed)
    Abstract [en]

    We consider the design of an optimal collision-free sensor schedule fora number of sensors which monitor different linear dynamical systems correspondingly. At each time, only one of all the sensors can send its local estimate to the remote estimator. A preliminary work for the two-sensor scheduling case has been studied in the literature. The generalization into multiple-sensor scheduling case is shown to be nontrivial. We first find a necessary condition of the optimal solution which can significantly reduce the feasible optimal solution space without loss of performance. By modelling a finite-state Markov decision process (MDP) problem, we can numerically search an asymptotic periodic schedule which is proven to be optimal. Some simple but effective suboptimal schedules for any systems are proposed. We also find a lower bound of the optimal cost, which enables us to quantify the performance gap between any suboptimal schedule and an optimal one.

  • 5. Han, Duo
    et al.
    You, Keyou
    Xie, Lihua
    Wu, Junfeng
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Shi, Ling
    Optimal Parameter Estimation Under Controlled Communication Over Sensor Networks2015In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 63, no 24, p. 6473-6485Article in journal (Refereed)
    Abstract [en]

    This paper considers parameter estimation of linear systems under sensor-to-estimator communication constraint. Due to the limited battery power and the traffic congestion over a large sensor network, each sensor is required to reduce the rate of communication between the estimator and itself. We propose an observation-driven sensor scheduling policy such that the sensor transmits only the important measurements to the estimator. Unlike the existing deterministic scheduler, our stochastic scheduling is smartly designed to well compensate for the loss of the Gaussianity of the system. This results in a nice feature that the maximum-likelihood estimator (MLE) is still able to be recursively computed in a closed form, and the resulting estimation performance can be explicitly evaluated. Moreover, an optimization problem is formulated and solved to obtain the best parameters of the scheduling policy under which the estimation performance becomes comparable to the standard MLE with full measurements under a moderate transmission rate. Finally, simulations are included to validate the theoretical results.

  • 6. Han, YeDuo
    et al.
    Wu, Junfeng
    KTH, School of Electrical Engineering (EES), Automatic Control. Zhejiang University, China.
    Mo, Yilin
    Xie, Lihua
    On Stochastic Sensor Network Scheduling for Multiple Processes2017In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 62, no 12, p. 6633-6640Article in journal (Refereed)
    Abstract [en]

    We consider the problem of multiple sensor scheduling for remote state estimation of multiple process over a shared link. In this problem, a set of sensors monitor mutually independent dynamical systems in parallel but only one sensor can access the shared channel at each time to transmit the data packet to the estimator. We propose a stochastic event-based sensor scheduling in which each sensor makes transmission decisions based on both channel accessibility and distributed event-triggering conditions. The corresponding minimum mean squared error estimator is explicitly given. Considering information patterns accessed by sensor schedulers, time-based ones can be treated as a special case of the proposed one. By ultilizing real-time information, the proposed schedule outperforms the time-based ones in terms of the estimation quality. Resorting to solving a Markov decision process (MDP) problem with an average cost criterion, we can find optimal parameters for the proposed schedule. As for practical use, a greedy algorithm is devised for parameter design, which has rather low computational complexity. We also provide a method to quantify the performance gap between the schedule optimized via MDP and any other schedules.

  • 7. Kung, E.
    et al.
    Wu, Junfeng
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Shi, D.
    Shi, L.
    On the nonexistence of event-based triggers that preserve Gaussian state in presence of package-drop2017In: 2017 American Control Conference (ACC) 24-26 May 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1233-1237Conference paper (Refereed)
    Abstract [en]

    State estimation is a core objective in cyber-physical systems. In the state estimation problem over linear systems, the Kalman filter is the standard solution. The filter is the format on which the solutions to subsequent estimation problems are based. Among these problems are the estimation problem in the presence of packet drops and estimation problem involving event-based triggers. We study in this paper both phenomena simultaneously. In an attempt to find the Kalman-like filter, which proves the Gaussianity of the state and offers a set of update equations, our paper shows that no such filter exists. More precisely, one cannot find an event-based trigger such that under possible packet drops, the state variable remains a Gaussian variable. This conclusion can be reasonably extended to a more general setting.

  • 8. Ren, X.
    et al.
    Wu, Junfeng
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. Zhejiang University, China.
    Dey, S.
    Shi, L.
    Attack allocation on remote state estimation in multi-systems: Structural results and asymptotic solution2018In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 87, p. 184-194Article in journal (Refereed)
    Abstract [en]

    This paper considers optimal attack attention allocation on remote state estimation in multi-systems. Suppose there are M independent systems, each of which has a remote sensor monitoring the system and sending its local estimates to a fusion center over a packet-dropping channel. An attacker may generate noises to exacerbate the communication channels between sensors and the fusion center. Due to capacity limitation, at each time the attacker can exacerbate at most N of the M channels. The goal of the attacker side is to seek an optimal policy maximizing the estimation error at the fusion center. The problem is formulated as a Markov decision process (MDP) problem, and the existence of an optimal deterministic and stationary policy is proved. We further show that the optimal policy has a threshold structure, by which the computational complexity is reduced significantly. Based on the threshold structure, a myopic policy is proposed for homogeneous models and its optimality is established. To overcome the curse of dimensionality of MDP algorithms for general heterogeneous models, we further provide an asymptotically (as M and N go to infinity) optimal solution, which is easy to compute and implement. Numerical examples are given to illustrate the main results.

  • 9. Ren, Xiaoqiang
    et al.
    Wu, Junfeng
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Shi, Guodong
    Shi, Ling
    Infinite Horizon Optimal Transmission Power Control for Remote State Estimation Over Fading Channels2018In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 63, no 1, p. 85-100Article in journal (Refereed)
    Abstract [en]

    This paper studies the joint design over an infinite horizon of the transmission power controller and remote estimator for state estimation over fading channels. A sensor observes a dynamic process and sends its observations to a remote estimator over a wireless fading channel characterized by a time-homogeneous Markov chain. The successful transmission probability depends on both the channel gains and the transmission power used by the sensor. The transmission power control rule and the remote estimator should be jointly designed, aiming to minimize an infinite-horizon cost consisting of the power usage and the remote estimation error. We formulate the joint optimization problem as an average cost belief-state Markov decision process and prove that there exists an optimal deterministic and stationary policy. We then show that when the monitored dynamic process is scalar or the system matrix is orthogonal, the optimal remote estimates depend only on the most recently received sensor observation, and the optimal transmission power is symmetric and monotonically increasing with respect to the norm of the innovation error.

  • 10.
    Wei, Jieqiang
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Wu, Junfeng
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Molinari, Marco
    KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration.
    Cvetkovic, Vladimir
    KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering, Resources, Energy and Infrastructure.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    On the modeling of neural cognition for social network applications2017In: 2017 IEEE Conference on Control Technology and Applications (CCTA), Institute of Electrical and Electronics Engineers (IEEE), 2017Conference paper (Refereed)
    Abstract [en]

    In this paper, we study neural cognition in social network. A stochastic model is introduced and shown to incorporate two well-known models in Pavlovian conditioning and social networks as special case, namely Rescorla-Wagner model and Friedkin-Johnsen model. The interpretation and comparison of these model are discussed. We consider two cases when the disturbance is independent identically distributed for all time and when the distribution of the random variable evolves according to a Markov chain. We show that the systems for both cases are mean square stable and the expectation of the states converges to consensus.

  • 11.
    Wu, Junfeng
    et al.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Peak Covariance Stability of Kalman Filtering with Markovian Packet Losses2014In: Cyber-Physical Systems, Networks, and Applications (CPSNA), 2014 IEEE International Conference on, IEEE conference proceedings, 2014, p. 13-18Conference paper (Refereed)
    Abstract [en]

    In this paper, we consider state estimation using a Kalman filter of a linear time-invariant process over an unreliable network. The stability of Kalman filtering with random packet losses is studied, where the packet losses are modeled by the Gilbert-Elliott channel model and the stability is measured by the so-called peak covariance stability introduced in [1]. We give two sufficient conditions for the peak covariance stability: one combined with a numerical method provides an accurate criterion, and the other is in a simple form and easy to check, both of which are shown to be less conservative than existing works in practice. Numerical examples demonstrate the effectiveness of our result compared with relevant literature.

  • 12.
    Wu, Junfeng
    et al.
    Hong Kong University.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Shi, Ling
    Hong Kong University.
    An improved hybrid sensor schedule for remote state estimation under limited communication resources2012In: Decision and Control (CDC), 2012 IEEE 51st Annual Conference on, IEEE conference proceedings, 2012, p. 3305-3310Conference paper (Refereed)
    Abstract [en]

    In this paper, we consider remote state estimation. A sensor locally processes its measurement data and sends its local estimate to a remote estimator for further processing. Due to the limited communication resources, the sensor can only communicate with the estimator for a pre-specified number within a given horizon. We propose a hybrid sensor data schedule which introduces an event-triggering mechanism on top of an optimal offline sensor schedule. This hybrid schedule, having a small implementation cost, leads to a smaller estimation error at the remote estimator when compared with the optimal offline sensor schedule.

  • 13.
    Wu, Junfeng
    et al.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Meng, Z.
    Yang, Tao
    Shi, G.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Critical sampling rate for sampled-data consensus over random networks2016In: Proceedings of the IEEE Conference on Decision and Control, IEEE conference proceedings, 2016, p. 412-417Conference paper (Refereed)
    Abstract [en]

    In this paper, we consider the consensus problem for a network of nodes with random interactions and sampled-data control actions. Each node independently samples its neighbors in a random manner over a directed graph underlying the information exchange of different nodes. The relationship between the sampling rate and the achievement of consensus is studied. We first establish a sufficient condition, in terms of the inter-sampling interval, such that consensus in expectation, in mean square, and in almost sure sense are simultaneously achieved provided a mild connectivity assumption for the underlying graph. Necessary and sufficient conditions for mean-square consensus are derived in terms of the spectral radius of the corresponding state transition matrix. These conditions are then interpreted as the existence of a critical value on the inter-sampling interval, below which global mean-square consensus is achieved and above which the system diverges in mean-square sense for some initial states. Finally, we establish an upper bound of the inter-sampling interval, below which almost sure consensus is reached, and a lower bound, above which almost sure divergence is reached. An numerical example is given to validate the theoretical results.

  • 14.
    Wu, Junfeng
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Meng, Ziyang
    Yang, Tao
    Shi, Guodong
    Johansson, Karl H.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Sampled-Data Consensus Over Random Networks2016In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 64, no 17, p. 4479-4492Article in journal (Refereed)
    Abstract [en]

    This paper considers the consensus problem for a network of nodes with random interactions and sampled-data control actions. We first show that consensus in expectation, in mean square, and almost surely are equivalent for a general random network model when the inter-sampling interval and maximum node degree satisfy a simple relation. The three types of consensus are shown to be simultaneously achieved over an independent or a Markovian random network defined on an underlying graph with a directed spanning tree. For both independent and Markovian random network models, necessary and sufficient conditions for mean-square consensus are derived in terms of the spectral radius of the corresponding state transition matrix. These conditions are then interpreted as the existence of critical value on the intersampling interval, below which a global mean-square consensus is achieved and above which the system diverges in a mean-square sense for some initial states. Finally, we establish an upper bound on the intersampling interval below which almost sure consensus is reached, and a lower bound on the intersampling interval above which almost sure divergence is reached. Some numerical simulations are given to validate the theoretical results and some discussions on the critical value of the inter-sampling intervals for the mean-square consensus are provided.

  • 15.
    Wu, Junfeng
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. Hong Kong University of Science and Technology, China.
    Quevedo, Daniel E.
    Xiong, Junlin
    Ren, Zhu
    Cao, Xianghui
    Demirel, Burak
    Special Issue: Resource-efficient Control in Cyber-Physical Systems2017In: IET Control Theory & Applications, ISSN 1751-8644, E-ISSN 1751-8652, Vol. 11, no 11, p. 1663-1665Article in journal (Refereed)
  • 16.
    Wu, Junfeng
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. Hong Kong University of Science and Technology, Hong Kong.
    Ren, Xiaoqiang
    Han, Duo
    Shi, Dawei
    Shi, Ling
    Finite-horizon Gaussianity-preserving event-based sensor scheduling in Kalman filter applications2016In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 72, p. 100-107Article in journal (Refereed)
    Abstract [en]

    This paper considers a remote state estimation problem, where a sensor measures the state of a linear discrete-time system. The sensor has computational capability to implement a local Kalman filter. The sensor-to-estimator communications are scheduled intentionally over a finite time horizon to obtain a desirable tradeoff between the state estimation quality and the limited communication resources. Compared with the literature, we adopt a Gaussianity-preserving event-based sensor schedule bypassing the nonlinearity problem met in threshold event-based polices. We derive the closed-form of minimum mean-square error (MMSE) estimator and show that, if communication is triggered, the estimator cannot do better than the local Kalman filter, otherwise, the associated error covariance, is simply a sum of the estimation error of the local Kalman filter and the performance loss due to the absence of communication, We further design the scheduler's parameters by solving a dynamic programming (DP) problem. The computational overhead of the DP problem is less sensitive to the system dimension compared with that of existing algorithms in the literature.

  • 17.
    Wu, Junfeng
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Shi, G.
    Anderson, B. D. O.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Stability conditions and phase transition for Kalman filtering over Markovian channels2015In: 2015 34th Chinese Control Conference (CCC), IEEE Computer Society, 2015, p. 6721-6728Conference paper (Refereed)
    Abstract [en]

    This paper investigates the stability of Kalman filtering over Gilbert-Elliott channels where the random packet drop follows a time-homogeneous two-state Markov chain whose state transition is determined by a pair of failure and recovery rates. First, we establish a relaxed condition guaranteeing peak-covariance stability described by an inequality in terms of the spectral radius of the system matrix and transition probabilities of the Markov chain. We show that this condition can be rewritten as a linear matrix inequality feasibility problem. Next, we prove that the peak-covariance stability implies mean-square stability, if the system matrix has no defective eigenvalues on the unit circle. This implication holds for any random packet drop process, and is thus not restricted to Gilbert-Elliott channels. We prove that there exists a critical curve in the failure-recovery rate plane, below which the Kalman filter is mean-square stable and above is unstable for some initial values. Finally, a lower bound for this critical failure rate is obtained making use of the relationship we establish between the two stability criteria, based on an approximate relaxation of the system matrix.

  • 18.
    Wu, Junfeng
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Shi, Guodong
    Australian Natl Univ, Res Sch Engn, Canberra, ACT 0200, Australia..
    Anderson, Brian D. O.
    Australian Natl Univ, Res Sch Engn, Canberra, ACT 0200, Australia..
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Kalman Filtering Over Gilbert-Elliott Channels: Stability Conditions and Critical Curve2018In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 63, no 4, p. 1003-1017Article in journal (Refereed)
    Abstract [en]

    This paper investigates the stability of Kalman filtering over Gilbert-Elliott channels where random packet drops follow a time-homogeneous two-state Markov chain whose state transition is determined by a pair of failure and recovery rates. First of all, we establish a relaxed condition guaranteeing peak-covariance stability described by an inequality in terms of the spectral radius of the system matrix and transition probabilities of the Markov chain. We further show that the condition can be interpreted using a linear matrix inequality feasibility problem. Next, we prove that the peak-covariance stability implies mean-square stability, if the system matrix has no defective eigenvalues on the unit circle. This connection between the two stability notions holds for any random packet drop process. We prove that there exists a critical curve in the failure-recovery rate plane, below which the Kalman filter is mean-square stable and no longer mean-square stable above. Finally, a lower bound for this critical failure rate is obtained making use of the relationship we establish between the two stability criteria, based on an approximate relaxation of the system matrix.

  • 19.
    Wu, Junfeng
    et al.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Shi, Guodong
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Probabilistic Convergence of Kalman Filtering over Nonstationary Fading Channels2014In: Decision and Control (CDC), 2014 IEEE 53rd Annual Conference on, IEEE conference proceedings, 2014, , p. 6p. 3783-3788Conference paper (Refereed)
    Abstract [en]

    In this paper, we consider state estimation using a Kalman filter of a linear time-invariant process with nonstationary intermittent observations caused by packet losses. The packet loss process is modeled as a sequence of independent, but not necessarily identical Bernoulli random variables. Under this model, we show how the probabilistic convergence of the trace of the prediction error covariance matrices, which is denoted as Tr(Pk), depends on the statistical property of the nonstationary packet loss process. A series of sufficient and/or necessary conditions for the convergence of supk≥n Tr(Pk) and infk≥n Tr(Pk) are derived. In particular, for one-step observable linear system, a sufficient and necessary condition for the convergence of infk≥n Tr(Pk) is provided.

  • 20.
    Wu, Junfeng
    et al.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Shi, Guodong
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Probabilistic Convergence of Kalman Filtering with Nonstationary Intermittent Observations2014In: 2014 IEEE 53RD ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2014, p. 3783-3788Conference paper (Refereed)
    Abstract [en]

    In this paper, we consider state estimation using a Kalman filter of a linear time-invariant process with non-stationary intermittent observations caused by packet losses. The packet loss process is modeled as a sequence of independent, but not necessarily identical Bernoulli random variables. Under this model, we show how the probabilistic convergence of the trace of the prediction error covariance matrices, which is denoted as Tr(P-k), depends on the statistical property of the nonstationary packet loss process. A series of sufficient and/or necessary conditions for the convergence of sup(k >= n) Tr(P-k) and inf(k >= n) Tr(P-k) are derived. In particular, for one-step observable linear system, a sufficient and necessary condition for the convergence of inf(k >= n) Tr(P-k) is provided.

  • 21.
    Wu, Junfeng
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Shi, Ling
    Xie, Lihua
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    An improved stability condition for Kalman filtering with bounded Markovian packet losses2015In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 62, p. 32-38Article in journal (Refereed)
    Abstract [en]

    In this paper, we consider the peak-covariance-stability of Kalman filtering subject to packet losses. The length of consecutive packet losses is governed by a time-homogeneous finite-state Markov chain. We establish a sufficient condition for peak-covariance stability and show that this stability check can be recast as a linear matrix inequality (LMI) feasibility problem. Compared with the literature, the stability condition given in this paper is invariant with respect to similarity state transformations; moreover, our condition is proved to be less conservative than the existing results. Numerical examples are provided to demonstrate the effectiveness of our result.

  • 22.
    Wu, Junfeng
    et al.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Yang, Tao
    Univ North Texas, Dept Elect Engn, Denton, TX 75203 USA..
    Wu, Di
    Pacific Northwest Natl Lab, Richland, WA 99352 USA..
    Kalsi, Karanjit
    Pacific Northwest Natl Lab, Richland, WA 99352 USA..
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Distributed Optimal Dispatch of Distributed Energy Resources Over Lossy Communication Networks2017In: IEEE Transactions on Smart Grid, ISSN 1949-3053, E-ISSN 1949-3061, Vol. 8, no 6, p. 3125-3137Article in journal (Refereed)
    Abstract [en]

    Driven by smart grid technologies, a great effort has been made in developing distributed energy resources (DERs) in recent years for improving reliability and efficiency of distribution systems. Emerging DERs require effective and efficient control and coordination in order to harvest their potential benefits. In this paper, we consider optimal DER coordination problem, where the goal is to minimize the total generation cost while meeting total demand and satisfying individual generator output limit. This paper develops a distributed algorithm for solving the optimal DER coordination problem over lossy communication networks with packet-dropping communication links. Under the assumption that the underlying communication network is strongly connected with a positive probability and the packet drops are independent and identically distributed, we show that the proposed algorithm is able to solve the optimal DER coordination problem even in the presence of packet drops. Numerical simulation results are used to validate and illustrate the proposed algorithm.

  • 23. Wu, Y.
    et al.
    Iwaki, Takuya
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Wu, Junfeng
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Shi, L.
    Sensor selection and routing design for state estimation over wireless sensor networks2017In: 2017 36th Chinese Control Conference, CCC, IEEE Computer Society, 2017, p. 8008-8013, article id 8028623Conference paper (Refereed)
    Abstract [en]

    In this paper, we consider a wireless sensor network that consists of a group of sensor nodes estimating multiple independent LTI systems. Each point-to-point link between the sensor nodes is a slow frequency-flat fading channel and the states of the channel are described by a finite-state Markov channel (FSMC) model. We propose a transmission schedule of the sensors such that the overall estimation error at the remote estimator is minimized. Furthermore, we present an event-based routing scheme with respect to channel state, i.e., online routing among the selected sensors to ensure the zero outage-probability at a constant transmission rate with the least transmission energy. Using channel inversion, we separate the sensor scheduling and routing as two independent steps. Simulation results for a simple wireless sensor network is presented to compare the energy cost with and without event-based routing.

  • 24. Yang, Chao
    et al.
    Wu, Junfeng
    KTH, School of Electrical Engineering (EES).
    Ren, Xiaoqiang
    Yang, Wen
    Shi, Hongbo
    Shi, Ling
    Deterministic Sensor Selection for Centralized State Estimation Under Limited Communication Resource2015In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 63, no 9, p. 2336-2348Article in journal (Refereed)
    Abstract [en]

    This paper studies a sensor selection problem. A group of sensors measure the state of a process and send their measurements to a remote estimator. Due to communication constraints, only limited sensors are allowed to communicate with the estimator. The paper intends to answer which sensors should be chosen such that the estimation performance of the estimator is optimized. Both reliable and packet-dropping channels are considered. It is required to minimize the steady-state estimation error covariance for reliable channels and to minimize the upper bound of the expected estimation error covariance for packet-dropping channels. For both scenarios, the original optimization problems are transformed to problems which can be solved by convex optimization techniques.

  • 25. Yang, Tao
    et al.
    Lu, Jie
    Wu, Di
    Wu, Junfeng
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Shi, Guodong
    Meng, Ziyang
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    A Distributed Algorithm for Economic Dispatch Over Time-Varying Directed Networks With Delays2017In: IEEE transactions on industrial electronics (1982. Print), ISSN 0278-0046, E-ISSN 1557-9948, Vol. 64, no 6, p. 5095-5106Article in journal (Refereed)
    Abstract [en]

    In power system operation, the economic dispatch problem (EDP) aims to minimize the total generation cost while meeting the demand and satisfying generator capacity limits. This paper proposes an algorithm based on the gradient push-sum method to solve the EDP in a distributed manner over communication networks potentially with time-varying topologies and communication delays. This paper shows that the proposed algorithm is guaranteed to solve the EDP if the time-varying directed communication network is uniformly jointly strongly connected. Moreover, the proposed algorithm is also able to handle arbitrarily large but bounded time-varying delays on communication links. Numerical simulations are used to illustrate and validate the proposed algorithm.

  • 26.
    Yi, Xinlei
    et al.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Yang, Tao
    Wu, Junfeng
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl H.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Automatic Control.
    Distributed Event-Triggered Control for Global Consensus of Multi-Agent Systems with Input SaturationManuscript (preprint) (Other academic)
    Abstract [en]

    We consider the global consensus problem for multi-agent systems with input saturation over digraphs. Under a mild connectivity condition that the underlying digraph has a directed spanning tree, we use Lyapunov methods to show that the widely used distributed consensus protocol, which solves the consensus problem for the case without input saturation constraints, also solves the global consensus problem for the case with input saturation constraints. In order to reduce the overall need of communication and system updates, we then propose a distributed event-triggered control law. Global consensus is still realized and Zeno behavior is excluded. Numerical simulations are provided to illustrate the effectiveness of the theoretical results.

  • 27.
    Yi, Xinlei
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Yang, Tao
    Wu, Junfeng
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl H.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Event-Triggered Control for Multi-Agent Systems with Output Saturation2017In: 2017 36th Chinese Control Conference (CCC), IEEE Computer Society, 2017, p. 8431-8436, article id 8028693Conference paper (Refereed)
    Abstract [en]

    We propose distributed static and dynamic event-triggered control laws to solve the consensus problem for multiagent systems with output saturation. Under the condition that the underlying graph is undirected and connected, we show that consensus is achieved under both event-triggered control laws if and only if the average of the initial states is within the saturation level. Numerical simulations are provided to illustrate the effectiveness of the theoretical results and to show that the control laws lead to reduced need for inter-agent communications.

  • 28. Zhang, H.
    et al.
    Qi, Y.
    Wu, Junfeng
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Optimal jamming power allocation against remote state estimation2017In: American Control Conference (ACC), 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1660-1665, article id 7963191Conference paper (Refereed)
    Abstract [en]

    This paper investigates a remote state estimation problem, where a smart sensor observes the state of a physical plant, locally estimates the state with Kalman filter, and then sends its local estimation data to a remote estimator through a communication network. There is a jamming attacker in the communication network who intentionally blocks the network with the purpose of deteriorating estimation quality subject to the jamming energy constraint. From the viewpoint of jamming attacker, in order to maximize attack effect, we focus on the questions 'when to jam the communication network', and 'how much power the attacker should adopt to jam the network'. After theoretic analysis, we point out that any grouped together attack schedule is optimal for the given jamming power. We provide a close form of jamming power when a sufficient condition holds, and the corresponding jamming effect on remote state estimation is explicitly presented. For a general case, we design a jamming power allocation algorithm and show the computational complexity of the proposed algorithm is not worse than O(T), where T is the length of the time horizon considered.

  • 29.
    Zhang, Heng
    et al.
    Huaihai Inst Technol, Lianyungang, Peoples R China..
    Qi, Yifei
    Zhejiang Univ, State Key Lab Ind Control Technol, Hangzhou, Zhejiang, Peoples R China..
    Wu, Junfeng
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Fu, Lingkun
    Zhejiang Univ, State Key Lab Ind Control Technol, Hangzhou, Zhejiang, Peoples R China..
    He, Lidong
    Zhejiang Univ, State Key Lab Ind Control Technol, Hangzhou, Zhejiang, Peoples R China..
    DoS Attack Energy Management Against Remote State Estimation2018In: IEEE Transactions on Big Data, ISSN 2325-5870, E-ISSN 2168-6750, Vol. 5, no 1, p. 383-394Article in journal (Refereed)
    Abstract [en]

    This paper considers a remote state estimation problem, where a sensor measures the state of a linear discrete-time process and has computational capability to implement a local Kalman filter based on its own measurements. The sensor sends its local estimates to a remote estimator over a communication channel that is exposed to a Denial-of-Service (DoS) attacker. The DoS attacker, subject to limited energy budget, intentionally jams the communication channel by emitting interference noises with the purpose of deteriorating estimation performance. In order to maximize attack effect, following the existing answer to "when to attack the communication channel", in this paper we manage to solve the problem of "how much power the attacker should use to jam the channel in each time". For the static attack energy allocation problem, when the system matrix is normal, we derive a sufficient condition for when the maximum number of jamming operations should be used. The associated jamming power is explicitly provided. For a general system case, we propose an attack power allocation algorithm and show the computational complexity of the proposed algorithm is not worse than O(T), where T is the length of the time horizon considered. When the attack can receive the real-time ACK information, we formulate a dynamic attack energy allocation problem, and transform it to a Markov Decision Process to find the optimal solution.

1 - 29 of 29
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