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This article is concerned with the secure state estimation problem of a linear discrete-time Gaussian system in the presence of sparse integrity attacks. $\mathbf {m}$ sensors are deployed to monitor the state and $\mathbf {p}$ of them can potentially be compromised by an adversary, whose data can be arbitrarily manipulated by the attacker. We show that the optimal Kalman estimate can be decomposed as a weighted sum of local state estimates. Based on these local estimates, we propose a convex optimization based approach to generate a more secure state estimate. It is proved that our proposed estimator coincides with the Kalman estimator with a certain probability when all sensors are benign. Besides, we establish a sufficient condition under which the proposed estimator is stable against the $\mathbf {(p,m)}$ -sparse attack. A numerical example is provided to validate the secure state estimation scheme. [ABSTRACT FROM AUTHOR]

The goal of this note is to highlight that the reformulation of saturation as constraints, typical of the computation of the maximal output admissible sets used in the reference governor literature, is not without loss of generality and may introduce a relevant amount of conservativeness. The note also proposes a modified reference governor able to overcome this limitation. [ABSTRACT FROM AUTHOR]

The explicit reference governor (ERG) is a simple and systematic approach that provides constraint handling capabilities to prestabilized systems. The basic idea behind this approach is to translate state and input constraints into an upper-bound on the value of the Lyapunov function, which is then enforced by suitably manipulating the derivative of the applied reference. When designing the ERG, one of the main challenges is the determination of an upper-bound on the value of the Lyapunov function that ensures constraints satisfaction. This paper proposes a systematic approach for estimating online the optimal upper-bound for systems subject to the intersection of concave constraints. To do this, the Barrier function method is used. The effect of the estimation error caused by the time-varying nature of the auxiliary reference on the constraint satisfaction capability of the ERG is studied analytically. A procedure is proposed to modify the estimated upper-bound to avoid constraints violation in the presence of estimation errors. The effectiveness of the proposed scheme is demonstrated through a simulation study on an overhead gantry crane system. [ABSTRACT FROM AUTHOR]

The systematic design of control laws for nonlinear systems subject to state and input constraints is one of the major challenges in the control of realworld systems. Depending on the application, the desired solution must achieve a satisfactory tradeoff between multiple and often contradicting requirements such as optimality, computational efficiency, robustness, reliability, tuning simplicity, and generality. This article proposes a novel framework for the closed-form control of nonlinear systems subject to constraints on the state and input variables. The proposed strategy differs from most existing methods because it systematically handles both state and input constraints without making use of online optimization. [ABSTRACT FROM AUTHOR]

This technical note introduces a novel control law that dynamically modifies the reference of a pre-compensated nonlinear system to ensure the satisfaction of constraints. This is done by translating the state space constraints into an upper bound on the value of the Lyapunov function and by manipulating the velocity of the applied reference to enforce this bound. The main advantage of this approach is that it systematically provides a closed-form control law able to enforce constraints. The theory is introduced for general nonlinear systems subject to constraints and is then specialized to the large and highly relevant class of nonlinear systems admitting a Lyapunov function lower-bounded by a quadratic form and subject to linear constraints. Numerical simulations show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A novel distributed coordination strategy is presented for networked, locally regulated and possibly dynamically coupled, interconnected systems. Such systems are assumed to be connected via data links and subject to pointwise-in-time global constraints on some relevant variables of them, to be enforced as a coordination goal along the overall system evolutions. Such a coordination-by-constraint paradigm is accomplished by resorting to a novel distributed multi-agent Command Governor (CG) approach where each agent is in charge to locally modify, whenever necessary and on the basis of a reduced amount of data exchanged with the other agents, the prescribed set-points to the regulated subsystems so that the coordination constraints are always satisfied. The strategy is described and its main properties analyzed, especially for what it concerns the stability, feasibility and Pareto optimality of the solution. A liveness analysis concerning the possible presence of undesirable Nash equilibria and/or deadlock situations is presented and a discussion on the scalability of the solution with the problem dimension is reported as well. The constrained coordination of a network of interconnected water tanks is presented as a final example in order to show the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

In this technical note, we present a Receding Horizon Control (RHC) design method for linear parameter varying (LPV) systems subject to input and/or state constraints based on a class of nonlinearly parameterized Lyapunov functions recently introduced by Guerra and Vermeiren. As it will be made clear, their use gives rise to less conservative stabilizability conditions w.r.t. those arising from quadratic Lyapunov functions. A workable convex optimization procedure is first presented for control design purposes which allows the synthesis of stabilizing scheduling state-feedback control laws complying with the prescribed constraints. This control design method is then arranged into a receding horizon framework and its feasibility and stability properties are carefully analyzed. Numerical comparisons with existing RHC methods for LPV systems are reported in the final example. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) enable a wealth of new applications where remote estimation is essential. Individual sensors simultaneously sense a dynamic process and transmit measured information over a shared channel to a central fusion center. The fusion center computes an estimate of the process state by means of a Kalman filter. In this technical note we assume that the WSN admits a tree topology with one fusion center at the root. At each time step only a subset of sensors can be selected to transmit observations to the fusion center due to a limited energy budget. We propose a stochastic sensor selection algorithm that randomly selects a subset of sensors according to a certain probability distribution, which is opportunely designed to minimize the asymptotic expected covariance matrix of the estimation error. We show that the optimal stochastic sensor selection problem can be relaxed into a convex optimization problem and thus efficiently solved. We also provide a possible implementation of our algorithm which does not introduce any communication overhead. The technical note ends with some numerical examples that show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]