Imperial College London

DrGiordanoScarciotti

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 6268g.scarciotti Website

 
 
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Location

 

1115Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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63 results found

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.

Journal article

Faedo N, Scarciotti G, Astolfi A, Ringwood JVet 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.

Journal article

Faedo N, Scarciotti G, Astolfi A, Ringwood JVet al., 2021, Energy-maximising moment-based constrained optimal control of ocean wave energy farms, IET Renewable Power Generation

Journal article

Mellone A, Scarciotti G, 2021, Approximate feedback linearisation and stabilisation of nonlinear stochastic systems, 21st IFAC World Congress, Publisher: IFAC Secretariat, Pages: 4985-4990, ISSN: 2405-8963

This paper addresses the design of a practically sound control architecture to solvethe problem of feedback linearisation and stabilisation of single-input single-output nonlinearstochastic systems. We first present a causal method to obtain, from measurements of the state,a-posterioriestimates of the variations of the Brownian motion which affected the system. Thenwe employ these estimates to design a control law that approximately compensates for thediffusive dynamics of the system. We address the local stabilisation problem and we prove thatthe control law which performs the proposed stochastic compensations stabilises a broader classof systems with respect to feedback laws without compensation. We finally validate the theorythrough a numerical example.

Conference paper

Mellone A, Scarciotti G, 2021, The zero dynamics of nonlinear stochastic systems: stabilization and output tracking in the ideal case, 21st IFAC World Congress, Publisher: IFAC Secretariat, Pages: 4979-4984, ISSN: 2405-8963

This paper introduces the notion of zero dynamics and presents results of localstabilisation and output tracking for single-input single-output nonlinear stochastic systemsdescribed by stochastic differential equations. For this class of systems we define the zero dynamicswhen the stochastic relative degree is strictly smaller than the order of the system. We show that,under suitable conditions on the zero dynamics, the equilibrium at the origin can be stabilisedvia a coordinate change and a nonlinear state feedback. In an analogous way, we show that it ispossible to achieve local asymptotic output tracking of a reference signal. We validate the theorythrough a numerical example.

Conference paper

Mellone A, Scarciotti G, 2021, Output regulation of linear stochastic systems, IEEE Transactions on Automatic Control, 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.

Journal article

Faedo N, Scarciotti G, Astolfi A, Ringwood JVet al., 2021, On the approximation of moments for nonlinear systems, IEEE Transactions on Automatic Control, 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.

Journal article

Scarciotti G, Teel AR, 2021, On moment matching for stochastic systems, IEEE Transactions on Automatic Control, 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.

Journal article

Shakib MF, Scarciotti G, Pogromsky AY, Pavlov A, van de Wouw Net 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

Conference paper

Mellone A, Scarciotti G, 2020, Error-feedback output regulation of linear stochastic systems: a hybrid nonlinear approach, 11th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2019, jointly with MECHATRONICS 2019), Publisher: IFAC Secretariat, Pages: 520-525, ISSN: 2405-8963

The problem of output regulation for linear stochastic systems is addressed. Wefirst define and solve the ideal problem of output regulation via error feedback. We note thatits solution is not implementable in practice because the Brownian motion is not available formeasure. Therefore, we define an approximate problem for which we provide a practical solution.The implemented controller is hybrid, in that a continuous-time, deterministic control law issupplemented by a discrete-time, stochastic correction. This correction is performed using ana-posterioriapproximation of the variations of the Brownian motion provided by a nonlinearestimator. The resulting hybrid closed-loop system is nonlinear, as the scalars approximatingthe increments of the Brownian motion depend nonlinearly on the states and the inputs. Theerror between the solution of the approximate problem and the solution of the ideal problem ischaracterised. We show that the ideal solution is retrieved as the sampling time tends to zero.We illustrate the results by means of a numerical example.

Conference paper

Astolfi A, Scarciotti G, Simard J, Faedo N, Ringwood JVet al., 2020, Model Reduction by Moment Matching: Beyond Linearity A Review of the Last 10 Years, 2020 59th IEEE Conference on Decision and Control (CDC), Publisher: IEEE

Conference paper

Mellone A, Scarciotti G, 2020, A note on the Ito and Stratonovich stochastic relative degree and normal form, 2020 IEEE Conference on Decision and Control (CDC), Publisher: IEEE

In this note we compare two notions of stochastic relative degree. Specifically, we consider nonlinear stochastic systems defined by the same stochastic differential equation and interpreted in either Ito’s or Stratonovich’s sense. We then recall the Ito stochastic relative degree and we introduce the concept of Stratonovich stochastic relative degree and Stratonovich normal form. We show, by means of examples, that the stochastic relative degrees arising from the two different interpretations of the same stochastic differential equations are, in general, different. We finally point out that this discrepancy can be eliminated through conversion formulas between Ito and Stratonovich integrals.

Conference paper

Di Francor P, Scarciotti G, Astolfi A, 2020, Stability of nonlinear differential-algebraic systems via additive identity, IEEE/CAA Journal of Automatica Sinica, Vol: 7, Pages: 929-941, ISSN: 2329-9266

The stability analysis for nonlinear differential-algebraic systems is addressed using tools from classical control theory. Sufficient stability conditions relying on matrix inequalities are established via Lyapunov Direct Method. In addition, a novel interpretation of differential-algebraic systems as feedback interconnection of a purely differential system and an algebraic system allows reducing the stability analysis to a small-gain-like condition. The study of stability properties for constrained mechanical systems, for a class of Lipschitz differential-algebraic systems and for an academic example is used to illustrate the theory.

Journal article

Di Franco P, Scarciotti G, Astolfi A, 2020, A globally stable algorithm for the integration of high-index differential-algebraic systems, IEEE Transactions on Automatic Control, Vol: 65, Pages: 2107-2122, ISSN: 0018-9286

The problem of constraint stabilization and numerical integration for differential-algebraic systems is addressed using Lyapunov theory. It is observed that the application of stabilization methods which rely on a linear feedback mechanism to nonlinear systems may result in trajectories with finite escape time. To overcome this problem we propose a method based on a nonlinear stabilization mechanism which guarantees the global existence and convergence of the solutions. Discretization schemes, which preserve the properties of the method, are also presented. The results are illustrated by means of the numerical integration of a slider-crank mechanism.

Journal article

Scarciotti G, Jiang Z-P, Astolfi A, 2020, Data-driven constrained optimal model reduction, European Journal of Control, Vol: 53, Pages: 68-78, ISSN: 0947-3580

Model reduction by moment matching can be interpreted as the problem of finding a reduced-order model which possesses the same steady-state output response of a given full-order system for a prescribed class of input signals. Little information regarding the transient behavior of the system is systematically preserved, limiting the use of reduced-order models in control applications. In this paper we formulate and solve the problem of constrained optimal model reduction. Using a data-driven approach we determine an estimate of the moments and of the transient response of a possibly unknown system. Consequently we determine a reduced-order model which matches the estimated moments at the prescribed interpolation signals and, simultaneously, possesses the estimated transient. We show that the resulting system is a solution of the constrained optimal model reduction problem. Detailed results are obtained when the optimality criterion is formulated with the time-domain ℓ1, ℓ2, ℓ∞ norms and with the frequency-domain norm. The paper is illustrated by two examples: the reduction of the model of the vibrations of a building and the reduction of the Eady model (an atmospheric storm track model).

Journal article

Mellone A, Scarciotti G, 2020, Normal form and exact feedback linearisation of nonlinear stochastic systems: the ideal case, 2019 IEEE Conference on Decision and Control (CDC), Publisher: IEEE

This paper introduces the concepts of stochastic relative degree, normal form and exact feedback linearisation for single-input single-output nonlinear stochastic systems. The systems are defined by stochastic differential equations in which both the drift and the diffusion terms are nonlinear functions of the states and the control input. First, we define new differential operators and the concept of stochastic relative degree. Then we introduce a suitable coordinate change and we show that the dynamics of the transformed state has a simplified structure, which we name normal form. Finally, we show that a condition on the stochastic relative degree of the system is sufficient for it to be rendered linear via a coordinate change and a nonlinear feedback. We provide an analytical example to illustrate the theory.

Conference paper

Astolfi A, Scarciotti G, Simard JD, Faedo N, Ringwood JVet al., 2020, Model Reduction by Moment Matching: Beyond Linearity A Review of the Last 10 Years., Publisher: IEEE, Pages: 1-16

Conference paper

Faedo N, Garcia-Violini D, Scarciotti G, Astolfi A, Ringwood JVet al., 2019, Robust Moment-Based Energy-Maximising Optimal Control of Wave Energy Converters, 2019 IEEE 58th Conference on Decision and Control (CDC), Publisher: IEEE

Conference paper

Scarciotti G, Mellone A, 2019, ε-approximate output regulation of linear stochastic systems: a hybrid approach, 2019 European Control Conference, Publisher: IEEE, Pages: 287-292

The problem of output regulation for linear stochastic systems is addressed. The controlled system belongs to a general class of linear systems, namely the state, the control input and the exogenous input appear in both the drift and diffusion terms of the differential equations. Building upon the solution of the ideal, non-causal, stochastic regulator problem,we define an approximate full-information problem. By means of measurements of the state vector, we provide a way to compute a sequence of scalars approximating a posteriori the variations of the Brownian motion. Then, we propose a hybrid control architecture which solves the approximate problem.The continuous-time part of the controller is deterministic,whereas the discrete-time part has the function of “correcting”the control action by means of the approximate discrete-time Brownian motion. The solution of the ideal stochastic regulator problem is recovered as the sampling time tends to zero. We illustrate the results by means of a numerical example and conclude the paper with some final remarks:the proposed control architecture is the first causal solution of the full-information output regulation problem and is an essential intermediate step for the solution of the error-feedback problem.

Conference paper

Breschi V, Formentin S, Scarciotti G, Astolfi Aet al., 2019, Simulation-driven fixed-order controller tuning via moment matching, 2019 European Control Conference, Publisher: IEEE, Pages: 2307-2312

We propose a controller tuning method basedon the data-driven model reduction by moment matchingtheory. By selecting a reference closed-loop transfer function,a moment matching data-driven model reduction algorithm isused to synthesize a fixed-order controller, via the identificationof a model of the inverse transfer function of the controller,i.e.the transfer function from the controlled input of thesystem to the mismatch error signal. The controller is finallyobtained by inverting this transfer function. The fixed-ordercontroller is guaranteed to match the steady-state behaviorof the ideal controller at certain pre-selected frequencies. Theeffectiveness of the resulting design method is assessed on acontrol problem for the48-th order model of the Los AngelesUniversity Hospital building.

Conference paper

Faedo N, Scarciotti G, Astolfi A, Ringwood JVet al., 2019, Moment-based constrained optimal control of an array of wave energy converters, 2019 American Control Conference, Publisher: IEEE, Pages: 4797-4802, ISSN: 2378-5861

The roadmap to a successful commercialisation ofwave energy inherently incorporates the concept of anarrayorfarmof Wave Energy Converters (WECs). These interactinghydrodynamic structures require an optimised process that canensure the maximum extraction of time-averaged energy fromocean waves, while respecting the physical limitations of eachdevice and actuator characteristics. Recently, a novel optimalcontrol framework based on the concept ofmoment, for asingle WEC device, has been introduced in [1]. Such a strategyoffers an energy-maximising computationally efficient solutionthat can systematically incorporate state and input constraints.This paper presents the mathematical extension of the optimalcontrol framework of [1] to the case where an array of WECsis considered, providing an efficient solution that exploits thehydrodynamic interaction between devices to maximise the totalabsorbed energy.

Conference paper

Mellone A, Scarciotti G, 2019, ϵ-approximate output regulation of linear stochastic systems: A hybrid approach, Pages: 287-292

The problem of output regulation for linear stochastic systems is addressed. The controlled system belongs to a general class of linear systems, namely the state, the control input and the exogenous input appear in both the drift and diffusion terms of the differential equations. Building upon the solution of the ideal, non-causal, stochastic regulator problem, we define an approximate full-information problem. By means of measurements of the state vector, we provide a way to compute a sequence of scalars approximating a posteriori the variations of the Brownian motion. Then, we propose a hybrid control architecture which solves the approximate problem. The continuous-time part of the controller is deterministic, whereas the discrete-time part has the function of 'correcting' the control action by means of the approximate discrete-time Brownian motion. The solution of the ideal stochastic regulator problem is recovered as the sampling time tends to zero. We illustrate the results by means of a numerical example and conclude the paper with some final remarks: the proposed control architecture is the first causal solution of the full-information output regulation problem and is an essential intermediate step for the solution of the error-feedback problem.

Conference paper

Scarciotti G, Mylvaganam T, 2019, Approximate infinite-horizon optimal control for stochastic systems, 57th IEEE Conference on Decision and Control (CDC), Publisher: IEEE

The policy of an optimal control problem fornonlinear stochastic systems can be characterized by a second-order partial differential equation for which solutions are notreadily available. In this paper we provide a systematic methodfor obtaining approximate solutions for the infinite-horizonoptimal control problem in the stochastic framework. Themethod is demonstrated on an illustrative numerical examplein which the control effort is not weighted, showing that thetechnique is able to deal with one of the most striking featuresof stochastic optimal control.

Conference paper

Di Franco P, Scarciotti G, Astolfi A, 2019, A disturbance attenuation approach for the control of differential-algebraic systems, 2018 IEEE Conference on Decision and Control (CDC), Publisher: IEEE

Inthispapertheproblemofcontrollingdifferential-algebraic systems with index-1 is addressed. Theproposed technique is based on the interpretation of differential-algebraic systems as the feedback interconnection of a differ-ential system and an algebraic system. In this framework, thealgebraic variable can be treated as an external disturbanceacting on the differential system. A direct consequence of thisapproach is that the control problem reduces to a classicaldisturbance attenuation problem with internal stability. We alsoshow that the application of the proposed theory to the linearcase yields classical results. Finally, an example inspired by anair suspension system in a truck illustrates the technique.

Conference paper

Di Franco P, Scarciotti G, Astolfi A, 2019, Stabilization of differential-algebraic systems with Lipschitz nonlinearities via feedback decomposition, 18th European Control Conference (ECC), Publisher: IEEE, Pages: 1154-1158

Conference paper

Faedo N, Scarciotti G, Astolfi A, Ringwood JVet al., 2018, Energy-maximising control of wave energy converters using a moment-domain representation, Control Engineering Practice, Vol: 81, Pages: 85-96, ISSN: 0967-0661

Wave Energy Converters (WECs) have to be controlled to ensure maximum energy extraction from waves while considering, at the same time, physical constraints on the motion of the real device and actuator characteristics. Since the control objective for WECs deviates significantly from the traditional reference “tracking” problem in classical control, the specification of an optimal control law, that optimises energy absorption under different sea-states, is non-trivial. Different approaches based on optimal control methodologies have been proposed for this energy-maximising objective, with considerable diversity on the optimisation formulation. Recently, a novel mathematical tool to compute the steady-state response of a system has been proposed: the moment-based phasor transform. This mathematical framework is inspired by the theory of model reduction by moment-matching and considers both continuous and discontinuous inputs, depicting an efficient and closed-form method to compute such a steady-state behaviour. This study approaches the design of an energy-maximising optimal controller for a single WEC device by employing the moment-based phasor transform, describing a pioneering application of this novel moment-matching mathematical scheme to an optimal control problem. Under this framework, the energy-maximising optimal control formulation is shown to be a strictly concave quadratic program, allowing the application of well-known efficient real-time algorithms.

Journal article

Scarciotti G, 2018, Output regulation of linear stochastic systems: the full-information case, 2018 European Control Conference, Publisher: IEEE

The full information output regulation problemfor linear stochastic systems is addressed. A general class oflinear systems is considered, namely systems in which thestate, control variable and exogenous variable may appearsimultaneously in the drift term and in the diffusion termof the differential equation. Similarly, we consider a stochas-tic signal generator, thus allowing tracking and/or rejectingBrownian motions in addition to deterministic trajectories. Inthe paper we first characterize the steady-state response of theinterconnection of the system with the signal generator and thenwe solve the full information output regulation problem. Theresults of the paper are illustrated by means of two examples.Finally a short discussion of the error feedback regulatorproblem concludes the paper.

Conference paper

Di Franco P, Scarciotti G, Astolfi A, 2018, Discretization schemes for constraint stabilization in nonlinear differential-algebraic systems, 2018 European Control Conference, Publisher: IEEE

In this paper the problem of simulation ofdifferential-algebraic systems is addressed. In modelling me-chanical systems the use of redundant coordinates and con-straints results in differential-algebraic equations, the integra-tion of which can lead to numerical instabilities, such as theso-called drift phenomenon. In [1] the authors have proposeda globally convergent conceptual continuous-time algorithmfor the integration of constrained mechanical systems whichensures the existence of solutions and global attractivity of thesolution manifold. The objective of this paper is to study thenumerical implementation of the algorithm presented in [1]. Inaddition, the stability properties of the constrained system inthe manifold are studied in both the continuous and discretetime cases. The proposed technique is illustrated by means ofa simple example.

Conference paper

Di Franco P, Scarciotti G, Astolfi A, 2018, On the stability of constrained mechanical systems, 56th IEEE Conference on Decision and Control, Publisher: IEEE

The problem of the stability analysis for constrained mechanical systems is addressed using tools from classical geometric control theory, such as the notion of zero dynamics. For the special case of linear constrained mechanical systems we show that stability is equivalent to a detectability property. The proposed techniques are illustrated by means of simple examples.

Conference paper

Di Franco P, Scarciotti G, Astolfi A, 2018, A globally stable convergent algorithm for the integration of constrained mechanical systems, 2018 American Control Conference, Publisher: IEEE

In this paper the problem of simulation of con-strained mechanical systems is addressed. In modeling multi-body mechanical systems, the Lagrange formulation producesa redundant set of differential-algebraic equations, the integra-tion of which can lead to several difficulties, for example thedrift of the “constraint violation”. One of the most popularapproaches to alleviate this issue is the so-called Baumgarte’smethod that relies on a linear feedback mechanism. Thismethod can however lead to numerical instabilities whenapplied to nonlinear (mechanical) systems. The objective ofthis study is to propose a new method that ensures existenceof solutions and makes the constraint manifold asymptoticallyattractive. The proposed technique is illustrated by means of asimple example.

Conference paper

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