Safety monitoring under stealthy sensor injection attacks using reachable sets

Stealthy sensor injection attacks are serious threats for industrial plants as they can compromise the plant's integrity without being detected by traditional fault detectors. In this manuscript, we study the possibility of revealing the presence of such attacks by monitoring only the control input. This approach consists in computing an ellipsoidal bound of the input reachable set. When the control input does not belong to this set, this means that a stealthy sensor injection attack is driving the plant to critical states. The problem of finding this ellipsoidal bound is posed as a convex optimization problem (convex cost with Linear Matrix Inequalities constraints). Our monitoring approach is tested in simulation

Nonlinear force tracking control of electrohydrostatic actuators submitted to motion disturbances

In some industrial fields, such as aerospace, electro-hydrostatic actuators (EHAs) are increasingly used to replace conventional standard hydraulic actuators due to their better energy performance. Moreover, implementing different type or technology of actuators in redundant actuation systems working on the same moving part introduced some new challenges. This paper presents a force-tracking controller for an asymmetric electro-hydrostatic actuator that is submitted to an external motion generated by an external source. In this case, the rod displacement is considered as an external disturbance for the hydraulic cylinder, but it is assumed that this disturbance can be easily measured using sensors. The theoretical motivation of this work is discussed along and a variable gain state feedback control based on Linear Parameter Varying control (LPV) theory is proposed to achieve stability, disturbance rejection and tracking performance. The Linear Matrix Inequalities (LMI) framework is used to determine a control law including an augmented state feedback with an integral action that reduces trajectory-tracking errors. Simulation results of the control law are finally given to verify the global performance of this control design

Distributed Kalman filtering compared to Fourier domain preconditioned conjugate gradient for laser guide star tomography on extremely large telescopes

This paper discusses the performance and cost of two computationally efficient Fourier-based tomographic wavefront reconstruction algorithms for wide-field laser guide star (LGS) adaptive optics (AO). The first algorithm is the iterative Fourier domain preconditioned conjugate gradient (FDPCG) algorithm developed by Yang et al. [Appl. Opt. 45, 5281 (2006)], combined with pseudo-open-loop control (POLC). FDPCG’s computational cost is proportional to N log(N), where N denotes the dimensionality of the tomography problem. The second algorithm is the distributed Kalman filter (DKF) developed by Massioni et al. [J. Opt. Soc. Am. A 28, 2298 (2011)], which is a noniterative spatially invariant controller. When implemented in the Fourier domain, DKF’s cost is also proportional to N log(N). Both algorithms are capable of estimating spatial frequency components of the residual phase beyond the wavefront sensor (WFS) cutoff frequency thanks to regularization, thereby reducing WFS spatial aliasing at the expense of more computations. We present performance and cost analyses for the LGS multiconjugate AO system under design for the Thirty Meter Telescope, as well as DKF’s sensitivity to uncertainties in wind profile prior information. We found that, provided the wind profile is known to better than 10% wind speed accuracy and 20 deg wind direction accuracy, DKF, despite its spatial invariance assumptions, delivers a significantly reduced wavefront error compared to the static FDPCG minimum variance estimator combined with POLC. Due to its nonsequential nature and high degree of parallelism, DKF is particularly well suited for real-time implementation on inexpensive off-the-shelf graphics processing units

Distributed control for alpha-heterogeneous dynamically coupled systems

International audienceThis paper concerns the problem of distributed controller synthesis for a class of heterogeneous distributed systems composed of α (2 or more) different kinds of subsystems, interacting with one another according to a certain given graph topology. We will show that by employing Linear Matrix Inequalities (LMIs) tools, namely the full-block S-procedure, we can derive a control synthesis method based on L2 gain performance. This synthesis method guarantees stability and performance of a whole set of possible interconnection graphs, and its computational complexity does not depend on the number of subsystems involved but only on the number of different kinds of subsystems. The effectiveness of the new method is verified on a test case

Convex optimisation approach to constrained fuel optimal control of spacecraft in close relative motion

International audienceThis paper describes an interesting and powerful approach to the constrained fuel-optimal control of spacecraft in close relative motion. The proposed approach is well suited for problems under linear dynamic equations, therefore perfectly fitting to the case of spacecraft flying in close relative motion. If the solution of the optimisation is approximated as a polynomial with respect to the time variable, then the problem can be approached with a technique developed in the control engineering community, known as “Sum Of Squares” (SOS), and the constraints can be reduced to bounds on the polynomials. Such a technique allows rewriting polynomial bounding problems in the form of convex optimisation problems, at the cost of a certain amount of conservatism. The principles of the techniques are explained and some application related to spacecraft flying in close relative motion are shown

Guaranteed systematic simulation of discrete-time systems defined by polynomial expressions via convex relaxations

International audienceThis paper concerns the simulation of a class of nonlinear discrete-time systems under a set of initial conditions described by a bounding ellipsoid. We derive a procedure allowing the propagation of such ellipsoids through time, which makes it possible to set a guaranteed hard bound on the evolution of the state of the system for all the possible initial conditions. Two versions of this procedure are given, the second of which is slightly less general but less computationally demanding. At the end of the paper, we first show an application of the method in the domain of aerospace engineering; subsequently, three academic examples of applications are presented, two of which come from the theory of fractals. Copyright

Fuel-optimal convex trajectory optimization of rendezvous on elliptical orbits

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Robust simulation of rational discrete-time systems via sum of squares relaxations

International audienceThis paper concerns the simulation of a class of nonlinear discrete-time systems under a set of initial conditions described by an ellipsoid. We derive a procedure allowing the propagation of such ellipsoids through time, which makes it possible to set a guaranteed hard bound on the evolution of the state of the system for all the possible initial conditions. At the end of the paper, we show an application of the method through three academics examples, two of which are taken from the theory of fractals

Fuel-optimal convex trajectory optimization of rendezvous on elliptical orbits

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