Publications
85 results found
NIU Z, Haolin Z, Scarciotti G, 2024, Full-Information Output Regulation of Linear Systems with Non-Periodic Non-Smooth Exogenous Signals, 2024 European Control Conference (ECC)
Zhao Z, Mao J, Scarciotti G, 2024, Strategies to Alleviate the Impact of Noise in Data-Driven Model Order Reduction by Moment Matching, European Control Conference
Mao J, Scarciotti G, 2024, Model Reduction by Moment Matching under Explicit Filters: A Swapped Interconnection Perspective, European Control Conference
Mao J, Scarciotti G, 2024, Data-Driven Model Reduction by Two-Sided Moment Matching, Automatica, ISSN: 0005-1098
Bai H, Scarciotti G, 2024, Two-sided Interconnection-Based Model Reduction for Quadratic-Bilinear Systems, 7th IFAC Conference on Analysis and Control of Nonlinear Dynamics and Chaos
Shakib MF, Scarciotti G, Astolfi A, 2024, A Parameterised family of neuralODEs optimally fitting steady-state data, 2024 American Control Conference
Scarciotti G, Astolfi A, 2024, Interconnection-based model order reduction - a survey, European Journal of Control, Vol: 75, ISSN: 0947-3580
In this survey we present in an organic, complete, and accessible style the interconnection framework for model order reduction. While this framework originally started as a revisitation of the moment matching method, in the last 15 years it has expanded to provide solutions to the model order reduction problem for general classes of systems, such as nonlinear, time-delay, stochastic, and hybrid, and to give insight on topics as diverse as circuit analysis and optimal control. The main theme of the paper is the characterization of “interconnection behaviors” as primary properties to be maintained by the reduction process. The survey is enriched by historical notes on the development of the interconnection framework.
Mao J, Scarciotti G, 2023, Model reduction by matching zero-order moments for 2-D discrete systems, 2023 62nd IEEE Conference on Decision and Control (CDC), Publisher: IEEE
Ahstract-In this paper, the problem of model reduction for two-dimensional (2-D) systems in the Fornasini-Marchesini local state-space form is addressed by matching zero-order moments. Two characterizations of zero-order moments are proposed: the first based on the notion of interpolation of complex points and the second based on the concept of steady state. A parameterized family of reduced-order models that achieves moment matching while preserving the 2-D structure of the original system is presented. The developed theory is illustrated by means of a 2-D low-pass filter reduction problem.
Niu Z, Scarciotti G, 2023, Modelling and control of COVID-19 in small environments with two-group population, IFAC World Congress 2023, Publisher: Elsevier, Pages: 5071-5076, ISSN: 2405-8963
How to maximise socioeconomic activities while retaining the effect of COVID-19 endemic under control is an essential topic when many countries have decided to live with COVID. In this paper, we show how optimal control can be used to classify the importance of non-pharmaceutical interventions (NPIs) to counter an epidemic under different expectations. We focus on the modelling of small environments, such as schools, universities, and care homes, where the population can be separated into two distinctive subgroups. We propose a model for such a scenario, we compute the associated basic reproduction number and we provide conditions for stability. Finally, we formulate an optimal control problem and, by means of simulations, we show that the relative importance of the interventions naturally emerges from the optimal policy.
Shakib MF, Scarciotti G, Pogromsky AY, et al., 2023, Model reduction by moment matching with preservation of global stability for a class of nonlinear models, Automatica, Vol: 157, ISSN: 0005-1098
Model reduction by time-domain moment matching naturally extends to nonlinear models, where the notion of moments has a local nature stemming from the center manifold theorem. In this paper, the notion of moments of nonlinear models is extended to the global case and is, subsequently, utilized for model order reduction of convergent Lur’e-type nonlinear models. This model order reduction approach preserves the Lur’e-type model structure, inherits the frequency-response function interpretation of moment matching, preserves the convergence property, and allows formulating a posteriori error bound. By the grace of thepreservation of the convergence property, the reduced-order Lur’e-type model can be reliably used for generalized excitation signals without exhibiting instability issues. In a case study, the reduced-order model accurately matches the moment of the full-order Lur’e-type model and accurately describes the steady-state model response under input variations.
Shakib MF, Scarciotti G, Pogromsky AY, et al., 2023, Time-domain moment matching for multiple-input multiple-output linear time-invariant models, Automatica, Vol: 152, Pages: 1-8, ISSN: 0005-1098
Model reduction by moment matching for linear time-invariant (LTI) models is a reduction technique that has a clear interpretation in the Laplace domain. In particular, for the multiple-input multiple-output (MIMO) LTI case, Krylov subspace methods aim at matching the transfer-function matrix (and possibly its derivatives) of the reduced-order model to the transfer-function matrix of the full-order model along so-called tangential directions at desired interpolation points. A straightforward application of time-domain moment matching to MIMO LTI models does not result in such a match in the transfer-function matrix. In this paper, we derive a relation between the MIMO transfer-function matrices of the full- and the reduced-order models that follows from the application of time-domain moment matching on MIMO LTI models. This is subsequently exploited to formulate conditions on the parameters of time-domain moment matching under which the transfer-function matrix is matched along tangential directions, thus ensuring consistency with classical Krylov subspace methods.
Mellone A, Scarciotti G, 2023, Stochastic relative degree and path-wise control of nonlinear stochastic systems, IEEE Transactions on Automatic Control, Vol: 68, Pages: 1970-1985, ISSN: 0018-9286
We address the path-wise control of systems described by a set of nonlinear stochastic differential equations. For this class of systems, we introduce a notion of stochastic relative degree and a change of coordinates which transforms the dynamics to a stochastic normal form. The normal form is instrumental for the design of a state-feedback control which linearises and makes the dynamics deterministic. We observe that this control is idealistic, i.e. it is not practically implementable because it employs a feedback of the Brownian motion (which is never available) to cancel the noise. Using the idealistic control as a starting point, we introduce a hybrid control architecture which achieves practical path-wise control. This hybrid controller uses measurements of the state to perform periodic compensations for the noise contribution to the dynamics. We prove that the hybrid controller retrieves the idealistic performances in the limit as the compensating period approaches zero. We address the problem of asymptotic output tracking, solving it in the idealistic and in the practical framework. We finally validate the theory by means of a numerical example.
Gong Z, Scarciotti G, 2023, A hybrid observer for practical observability of linear stochastic systems, 61st IEEE Conference on Decision and Control, Publisher: IEEE
This paper focuses on the problem of observability and observer design for linear stochastic systems. To introduce our idea, we first construct an idealistic observer. This idealistic observer is not causal as it requires perfect knowledge of the Brownian motion. However, after introducing an a posteriori method to reconstruct the variations of the Brownian motion in discrete time, we propose a realistic hybrid observer which approximates the idealistic observer. The performance of this hybrid observer can be made arbitrarily close to that of the idealistic observer. Numerical simulations illustrate the results of the paper.
Mellone A, Scarciotti G, 2023, Stochastic Relative Degree and Path-Wise Control of Nonlinear Stochastic Systems., IEEE Trans. Autom. Control., Vol: 68, Pages: 1970-1985
Bai H, Mao J, Scarciotti G, 2023, Model reduction for quadratic-bilinear time-delay systems using nonlinear moments, 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS), Publisher: Elsevier, Pages: 91-95, ISSN: 2405-8963
In this paper, we address the problem of model reduction by moment matching for quadratic-bilinear time-delay systems. The moment of such systems is characterized by a power series representation, which can be computed by solving an infinite-dimensional system of Sylvester-like equations. A family of parameterized reduced-order models that preserves the quadratic-bilinear time-delay structure is derived through matching an approximate moment defined by truncating the power series. The developed theory is demonstrated on a numerical example.
Mao J, Scarciotti G, 2022, Data-driven model reduction by moment matching for linear systems through a swapped interconnection, European Control Conference 2022, Publisher: IEEE, Pages: 1690-1695
In this work, we propose a time-domain data-driven technique for model reduction by moment matching oflinear systems. We propose an algorithm, based on the so-calledswapped interconnection, that (asymptotically) approximates anarbitrary number of moments of the system from a singletime-domain sample. A family of reduced-order models thatmatch the estimated moments is derived. Finally, the use of theproposed algorithm is demonstrated on the problem of modelreduction of a building model
Bai H, Mylvaganam T, Scarciotti G, 2022, Model reduction for quadratic-bilinear systems using nonlinear moments, European Control Conference 2022, Publisher: IEEE, Pages: 1702-1707
We propose a steady-state based moment matchingmethod for model reduction of quadratic-bilinear systems.Considering a large-scale quadratic-bilinear system possessing astable equilibrium at the origin, the goal of this paper is to designa reduced order model which maintains certain properties of theoriginal system. More precisely, it is required that the reducedorder model also possesses a stable equilibrium at the originand, further, it may be desirable that its relative degree matchesthat of the original system. We use the notion of nonlinearmoments and exploit a formal power expansion to solve thisproblem. Two different families of reduced order models areprovided in this paper and their use is demonstrated on anillustrative numerical example.
Niu Z, Scarciotti G, 2022, Ranking the effectiveness of non-pharmaceutical interventions to counter COVID-19 in UK universities with vaccinated population, Scientific Reports, Vol: 12, ISSN: 2045-2322
Several universities around the world have resumed in-person teaching after successful vaccination campaigns have covered 70/80% of the population. In this study, we combine a new compartmental model with an optimal control formulation to discover, among different non-pharmaceutical interventions, the best prevention strategy to maximize on-campus activities while keeping spread under control. Composed of two interconnected Susceptible-Exposed-Infected-Quarantined-Recovered (SEIQR) structures, the model enables staff-to-staff infections, student-to-staff cross infections, student-to-student infections, and environment-to-individual infections. Then, we model input variables representing the implementation of different non-pharmaceutical interventions and formulate and solve optimal controlproblems for four desired scenarios: minimum number of cases, minimum intervention, minimum non-quarantine intervention, and minimum quarantine intervention. Our results reveal the particular significance of mask wearing and social distancing in universities with vaccinated population (with proportions according to UK data). The study also reveals that quarantining infected students has a higher importance than quarantining staff. In contrast, other measures such as environmental disinfection seems to be less important.
Mellone A, Scarciotti G, 2022, Output regulation of linear stochastic systems, IEEE Transactions on Automatic Control, Vol: 67, Pages: 1728-1743, ISSN: 0018-9286
We address the output regulation problem for a general class of linear stochastic systems. Specifically, we formulate and solve the ideal full-information and output-feedback problems, obtaining perfect, but non-causal, asymptotic regulation. A characterisation of the problem solvability is deduced. We point out that the ideal problems cannot be solved in practice because they unrealistically require that the Brownian motion affecting the system is available for feedback. Drawing from the ideal solution, we formulate and solve approximate versions of the full-information and output-feedback problems, which do not yield perfect asymptotic tracking but can be solved in a realistic scenario. These solutions rely on two key ideas: first we introduce a discrete-time a-posteriori estimator of the variations of the Brownian motion obtained causally by sampling the system state or output; second we introduce a hybrid state observer and a hybrid regulator scheme which employ the estimated Brownian variations. The approximate solution tends to the ideal as the sampling period tends to zero. The proposed theory is validated by the regulation of a circuit subject to electromagnetic noise.
Scarciotti G, Teel AR, 2022, On moment matching for stochastic systems, IEEE Transactions on Automatic Control, Vol: 67, Pages: 541-556, ISSN: 0018-9286
In this paper we study the problem of model reduction by moment matching for stochastic systems. We characterize the mathematical object which generalizes the notion of moment to stochastic differential equations and we find a class of models which achieve moment matching. However, differently from the deterministic case, these reduced order models cannot be considered “simpler” because of the high computational cost paid to determine the moment. To overcome this difficulty, we relax the moment matching problem in two different ways and we present two classes of reduced order models which, approximately matching the stochastic moment, are computationally tractable.
Mellone A, Scarciotti G, 2022, Output Regulation of Linear Stochastic Systems., IEEE Trans. Autom. Control., Vol: 67, Pages: 1728-1743
Scarciotti G, Teel AR, 2022, On Moment Matching for Stochastic Systems., IEEE Trans. Autom. Control., Vol: 67, Pages: 541-556
Mellone A, Scarciotti G, 2022, Stochastic Relative Degree and Path-wise Control of Nonlinear Stochastic Systems., CoRR, Vol: abs/2205.02759
Mao J, Scarciotti G, 2022, Data-Driven Model Reduction by Two-Sided Moment Matching., CoRR, Vol: abs/2212.08589
Shakib MF, Scarciotti G, Jungers M, et al., 2021, Optimal H∞ LMI-based model reduction by moment matching for linear time-invariant models, 2021 60th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 6914-6919
This paper proposes an approach to model order reduction of stable linear time-invariant (LTI) models. The proposed approach extends time-domain moment matching by the minimization of the H∞ norm of the error dynamics characterizing the difference between the full-order and reduced-order models given fixed interpolation points. The optimal H∞ moment matching problem is a constrained optimization problem with bilinear constraints. Introducing a novel numerical procedure, we minimize the approximation error, while respecting the constraints and, thereby, find a suboptimal H∞ reduced-order model. The effectiveness of the approach is illustrated in a numerical example.
Mellone A, Gong Z, Scarciotti G, 2021, Modelling, prediction and design of national COVID-19 lockdowns by stringency and duration, Scientific Reports, Vol: 11, Pages: 1-13, ISSN: 2045-2322
The implementation of lockdowns has been a key policy to curb the spread of COVID-19 and to keep under control the number of infections. However, quantitatively predicting in advance the effects of lockdowns based on their stringency and duration is a complex task, in turn making it difficult for governments to design effective strategies to stop the disease. Leveraging a novel mathematical “hybrid” approach, we propose a new epidemic model that is able to predict the future number of active cases and deaths when lockdowns with different stringency levels or durations are enforced. The key observation is that lockdown-induced modifications of social habits may not be captured by traditional mean-field compartmental models because these models assume uniformity of social interactions among the population, which fails during lockdown. Our model is able to capture the abrupt social habit changes caused by lockdowns. The results are validated on the data of Israel and Germany by predicting past lockdowns and providing predictions in alternative lockdown scenarios (different stringency and duration). The findings show that our model can effectively support the design of lockdown strategies by stringency and duration, and quantitatively forecast the course of the epidemic during lockdown.
Faedo N, Scarciotti G, Astolfi A, et al., 2021, On the approximation of moments for nonlinear systems, IEEE Transactions on Automatic Control, Vol: 66, Pages: 5538-5545, ISSN: 0018-9286
Model reduction by moment-matching relies upon the availability of the so-called moment. If the system is nonlinear, the computation of moments depends on an underlying specific invariance equation, which can be difficult or impossible to solve. This note presents four technical contributions related to the theory of moment matching: first, we identify a connection between moment-based theory and weighted residual methods. Second, we exploit this relation to provide an approximation technique for the computation of nonlinear moments. Third, we extend the definition of nonlinear moment to the case in which the generator is described in explicit form. Finally, we provide an approximation technique to compute the moments in this scenario. The results are illustrated by means of two examples.
Faedo N, Scarciotti G, Astolfi A, et al., 2021, Nonlinear energy-maximising optimal control of wave energy systems: A moment-based approach, IEEE Transactions on Control Systems Technology, Vol: 29, Pages: 2533-2547, ISSN: 1063-6536
Linear dynamics are virtually always assumed when designing optimal controllers for wave energy converters (WECs), motivated by both their simplicity and computational convenience. Nevertheless, unlike traditional tracking control applications, the assumptions under which the linearization of WEC models is performed are challenged by the energy-maximizing controller itself, which intrinsically enhances device motion to maximize power extraction from incoming ocean waves. \GSIn this article, we present a moment-based energy-maximizing control strategy for WECs subject to nonlinear dynamics. We develop a framework under which the objective function (and system variables) can be mapped to a finite-dimensional tractable nonlinear program, which can be efficiently solved using state-of-the-art nonlinear programming solvers. Moreover, we show that the objective function belongs to a class of generalized convex functions when mapped to the moment domain, guaranteeing the existence of a global energy-maximizing solution and giving explicit conditions for when a local solution is, effectively, a global maximizer. The performance of the strategy is demonstrated through a case study, where we consider (state and input-constrained) energy maximization for a state-of-the-art CorPower-like WEC, subject to different hydrodynamic nonlinearities.
Faedo N, Scarciotti G, Astolfi A, et al., 2021, Energy-maximising moment-based constrained optimal control of ocean wave energy farms, IET Renewable Power Generation
Shakib MF, Scarciotti G, Pogromsky AY, et al., 2021, Model reduction by moment matching for convergent Lur'e-type models, American Control Conference (ACC), Publisher: IEEE, Pages: 4459-4464, ISSN: 0743-1619
This paper proposes an approach to model order reduction of convergent Lur'e-type models, which consist of a linear time-invariant (LTI) block and a static nonlinear block that is placed in feedback with the LTI block. In the proposed approach, we match a finite number of moments of the LTI block and keep the static nonlinear block to approximate the moments of the Lur'e-type model. The benefits of this approach are that the Lur'e-type structure is preserved after reduction, that the reduction method has an interpretation in terms of the frequency response function of the LTI block and that global exponential stability properties of the full-order model are preserved. The effectiveness of the approach is illustrated in a numerical example.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.