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We present a general emulation-based framework to address the distributed control of multi-agent systems over packet-based networks. We consider the setup where information is only transmitted at (non-uniform) sampling times and where packets are received with unknown delays. We design local dynamic periodic event-triggering mechanisms to generate the transmissions. The triggering mechanisms can run on non-synchronized digital platforms, i.e., we ensure that the conditions must only be verified at asynchronous sampling times, which may differ for each platform. Different stability and performance characteristics can be considered as we follow a general dissipativity-based approach. Moreover, Zeno-free properties are guaranteed by design. The results are illustrated on a consensus problem., To appear in the proceedings of NecSys2022

The voltage received by each customer connected to a power distribution line with local controllers (inverters) is regulated to be within a desired margin through a class of slope-restricted controllers, known conventionally as \emph{droop} controllers. We adapt the design of the droop controllers according to the known bounds of the net power consumption of each customer in each observation time window. A sufficient condition for voltage regulation is provided for each time window, which guides the design of the droop controllers, depending on the properties of the distribution line (line impedances) and the upper bound of all the customers' power consumption during each time window. The resulting adaptive scheme is verified on a benchmark model of a European low-voltage network by the CIGRE task force., This work has been accepted to IFAC World Congress 2020 for publication under a Creative Commons Licence CC-BY-NC-ND

Motivated by the environmental and economical benefits of using renewable energy, we consider the problem of regulating the voltage of a power distribution network in a line configuration where each customer is equipped with an inverter. The substation at the head of the line determines the nominal voltage level which is communicated to each customer in the distribution line. The voltage level of each customer is regulated by an inverter which generates reactive power according to our class of droop controllers satisfying the slope-restriction property. This paper provides a sufficient condition for regulating the customers’ voltage level within a desired band, which depends on the properties of the distribution line (line impedances) and the droop controller employed. This is achieved when only the upper bound of all the customers’ net power usage is known, thereby preserving the privacy of each customer. Simulation studies are performed on a benchmark model for a distribution system with renewable sources.

We address the problem of attack detection and attack correction for multi-input multi-output discrete-time linear time-invariant systems under sensor attacks. More specifically, we consider the situation that a system with known input is corrupted by additive adversarial attack signals on some of the system’s outputs. In this paper, we use system representation in a behavioural approach, which allows for natural and compact statements regarding linear system security. We extend our earlier results for systems with zero inputs to systems with non-zero inputs. We assume that these non-zero inputs are known.

In this paper, we propose a state and fault estimation scheme for multi-agent systems. The proposed estimator is based on an ℓ 1 -norm optimization problem, which is inspired by sparse signal recovery in the field of compressive sampling. Moreover, we provide a necessary and sufficient condition such that state and fault signals are correctly estimated. The result presents a fundamental limitation of the algorithm, which shows how many faulty nodes are allowed to ensure a correct estimation. An illustrative example of a vehicle platoon is given to validate the effectiveness of the proposed approach. QC 20201124

We introduce a scalable observer architecture, which can efficiently estimate the states of a discrete-time linear-time-invariant system whose sensors are manipulated by an attacker, and is robust to measurement noise. Given an upper bound on the number of attacked sensors, we build on previous results on necessary and sufficient conditions for state estimation, and propose a novel Multi-Modal Luenberger (MML) observer based on efficient Satisfiability Modulo Theory (SMT) solving. We present two techniques to reduce the complexity of the estimation problem. As a first strategy, instead of a bank of distinct observers, we use a family of filters sharing a single dynamical equation for the states, but different output equations, to generate estimates corresponding to different subsets of sensors. Such an architecture can reduce the memory usage of the observer from an exponential to a linear function of the number of sensors. We then develop an efficient SMT-based decision procedure that is able to reason about the estimates of the MML observer to detect at runtime which sets of sensors are attack-free, and use them to obtain a correct state estimate. Finally, we discuss two optimization-based algorithms that can efficiently select the observer parameters with the goal of minimizing the sensitivity of the estimates with respect to sensor noise. We provide proofs of convergence for our estimation algorithm and report simulation results to compare its runtime performance with alternative techniques. We show that our algorithm scales well for large systems (including up to 5,000 sensors) for which many previously proposed algorithms are not implementable due to excessive memory and time requirements. Finally, we illustrate the effectiveness of our approach, both in terms of resiliency to attacks and robustness to noise, on the design of large-scale power distribution networks.

International audience; A robust circle criterion observer is designed and applied to neural mass models. At present, no existing circle criterion observers apply to the considered models, i.e. the required linear matrix inequality is infeasible. Therefore, we generalise available results to derive a suitable estimation algorithm. Additionally, the design also takes into account input uncertainty and measurement noise. We show how to apply the observer to estimate the mean membrane potential of neuronal populations of a popular single cortical column model from the literature.

We present an estimator design to reconstruct the mean membrane potential of individual neuronal populations from a single channel simulated electroencephalographic signal based on a model of the hippocampus. The robustness of the estimator against variations in the synaptic gains of the neuronal populations and disturbances in the input and measurement is studied. Our results are further illustrated in simulations.

International audience; We present a multi-observer approach for the parameter and state estimation of continuous-time nonlinear systems. For nominal parameter values in the known parameter set, state observers are designed with a robustness property. At any time instant, one observer is selected by a given criterion to provide its state estimate and its corresponding nominal parameter value. Provided that a persistency of excitation condition holds, we guarantee the convergence of state and parameter estimates up to a given margin of error which can be reduced by increasing the number of observers. The potential computational burden of the scheme is eased by introducing a dynamic parameter re-sampling technique, where the nominal parameter values are iteratively updated using a zoom-in procedure on the parameter set. We illustrate the efficacy of the algorithm on a model of neural dynamics.

Deterministic and stochastic methods for online state and parameter estimation for neural mass models are presented and applied to synthetic and real seizure electrocorticographic signals in order to determine underlying brain changes that cannot easily be measured. The first ever online estimation of neural mass model parameters from real seizure data is presented. It is shown that parameter changes occur that are consistent with expected brain changes underlying seizures, such as increases in postsynaptic potential amplitudes, increases in the inhibitory postsynaptic time-constant and decreases in the firing threshold at seizure onset, as well as increases in the firing threshold as the seizure progresses towards termination. In addition, the deterministic and stochastic estimation methods are compared and contrasted. This work represents an important foundation for the development of biologically-inspired methods to image underlying brain changes and to develop improved methods for neurological monitoring, control and treatment.

International audience; We present an adaptive observer which asymptotically reconstructs the parameters and states of a model of interconnected cortical columns. Our study is motivated by the fact that the considered model is able to realistically reproduce patterns seen on (intracranial) electroencephalograms (EEG) by varying its parameters. Therefore, by estimating its parameters and states, we could gain a better understanding of the mechanisms underlying neurological phenomena such as seizures, which might lead to the prediction of the onsets of epileptic seizures. Simulations are performed to illustrate our results.

International audience; We present a model-based estimation method to reconstruct the unmeasured membrane potential of neuronal populations from a single-channel electroencephalographic (EEG) measurement. We consider a class of neural mass models that share a general structure, specifically the models by Stam et al (1999 Clin. Neurophysiol. 110 1801-13), Jansen and Rit (1995 Biol. Cybern. 73 357-66) and Wendling et al (2005 J. Clin. Neurophysiol. 22 343). Under idealized assumptions, we prove the global exponential convergence of our filter. Then, under more realistic assumptions, we investigate the robustness of our filter against model uncertainties and disturbances. Analytic proofs are provided for all results and our analyses are further illustrated via simulations.