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  • Journal article
    Kiviluoma J, Heinen S, Qazi H, Madsen H, Strbac G, Kang C, Zhang N, Patteeuw D, Naegler Tet al., 2017,

    Harnessing Flexibility from Hot and Cold

    , IEEE POWER & ENERGY MAGAZINE, Vol: 15, Pages: 25-33, ISSN: 1540-7977
  • Journal article
    Nduka O, Pal BC, 2017,

    Harmonic domain modelling of PV system for the assessment of grid integration impact

    , IEEE Transactions on Sustainable Energy, Vol: 8, Pages: 1154-1165, ISSN: 1949-3037

    In this paper, a comprehensive harmonic domainreference frame (HDRF) model of a voltage source converter(VSC) grid interactive photovoltaic (PV) system is presented.The model is useful for assessing the harmonic coupling betweenthe PV system and the network. Different components of the PVsystem such as inverter, LCL filter and interconnecting trans-former have been incorporated in the model. Using this model,harmonic currents from PV system connected to both distortedand undistorted networks have been quantified. Also, the modelhas been deployed in investigating resonance occurrence in amedium-voltage distribution network (MVDN) where the resultsprovide interesting technical insight and understanding.
  • Conference paper
    Picciau A, Inggs G, Wickerson J, Kerrigan E, Constantinides GAet al., 2017,

    Balancing locality and concurrency: solving sparse triangular systems on GPUs

    , 23rd IEEE International Conference on High Peformance Computing, Data, and Analytics (HiPC), Publisher: IEEE, Pages: 183-192

    Many numerical optimisation problems rely onfast algorithms for solving sparse triangular systems of linearequations (STLs). To accelerate the solution of such equations,two types of approaches have been used: on GPUs, concurrencyhas been prioritised to the disadvantage of data locality, whileon multi-core CPUs, data locality has been prioritised to thedisadvantage of concurrency.In this paper, we discuss the interaction between data localityand concurrency in the solution of STLs on GPUs, and we presenta new algorithm that balances both. We demonstrate empiricallythat, subject to there being enough concurrency available in theinput matrix, our algorithm outperforms Nvidia’s concurrencyprioritisingCUSPARSE algorithm for GPUs. Experimental resultsshow a maximum speedup of 5.8-fold.Our solution algorithm, which we have implemented inOpenCL, requires a pre-processing phase that partitions thegraph associated with the input matrix into sub-graphs, whosedata can be stored in low-latency local memories. This preliminaryanalysis phase is expensive, but because it depends onlyon the input matrix, its cost can be amortised when solving formany different right-hand sides.
  • Conference paper
    Aunedi M, Pudjianto D, Strbac G, 2017,

    Calculating system integration costs of low-carbon generation technologies in future GB electricity system

    , 5th IET International Conference on Renewable Power Generation (RPG) 2016, Publisher: Institution of Engineering and Technology

    System integration costs (SIC) of generation technologies, also referred to as system externalities, include various categories of additional costs that are incurred in the system in addition to the cost of building and operating the generation capacity that is added to the system. SIC may include increased balancing cost, cost of additional backup capacity, cost of reinforcing network infrastructure and the cost of maintaining system carbon emissions. In this paper we present a whole-system approach to quantifying the SIC and explore different approaches to calculating the relative SIC of a generation technology when compared to another technology. The results show that the SIC of low-carbon generation technologies will significantly depend on the composition of the generation mix, with higher penetrations of variable renewables giving rise to a higher SIC. Also, SIC will significantly depend on the deployment level of flexible options such as more flexible generation technologies, energy storage, demand side response or interconnection. The additional system cost driven by low-carbon technologies can provide a very useful input to inform the energy policy and support the selection of the low-carbon portfolio with the lowest total system cost.
  • Journal article
    Teng F, Strbac G, 2017,

    Full stochastic scheduling for low-carbon electricity systems

    , IEEE Transactions on Automation Science and Engineering, Vol: 14, Pages: 451-470, ISSN: 1558-3783

    High penetration of renewable generation will increase the requirement for both operating reserve and frequency response, due to its variability, uncertainty and limited inertia capability. Although the importance of optimal scheduling of operating reserve has been widely studied, the scheduling of frequency response has not yet been fully investigated. In this context, this paper proposes a computationally-efficient mixed integer linear programming formulation for a full stochastic scheduling model that simultaneously optimizes energy production, operating reserve, frequency response and under-frequency load shedding. By using value of lost load as the single security measure, the model optimally balances the cost associated with the provision of various ancillary services against the benefit of reduced cost of load curtailment. The proposed model is applied in a 2030 GB system to demonstrate its effectiveness. Impact of installed capacity of wind generation and setting of value of lost load are also analysed. Note to Practitioners— One of the obstacles for large scale deployment of wind generation is the challenges it imposes on the efficient operation of the electricity system. This paper presents a full stochastic scheduling model. The long-term uncertainty driven by wind forecasting errors and short-term uncertainty driven by generation outages are modelled by using scenario tree and capacity outage probability table, respectively. The model leads to significant operation cost saving within reasonable computational time. The proposed model could be applied in real large-scale power systems to support the cost-effective integration of wind generation.
  • Journal article
    Parisini T, Pin G, Chen B, 2017,

    Robust finite-time estimation of biased sinusoidal signals: a volterra operators approach

    , Automatica, Vol: 77, Pages: 120-132, ISSN: 0005-1098

    A novel finite-time convergent estimation technique is proposed for identifying the amplitude, frequency and phase of abiasedsinusoidal signal. Resorting to Volterra integral operators with suitably designed kernels, the measured signal is processedyielding a set of auxiliary signals in which the influence of the unknown initial conditions is removed. A second-order slidingmode-based adaptation law – fed by the aforementioned auxiliary signals – is designed for finite-time estimation of the frequency,amplitude, and phase. The worst case behavior of the proposed algorithm in presence of the bounded additive disturbancesis fully characterized by Input-to-State Stability arguments. The effectiveness of the estimation technique is evaluated andcompared with other existing tools via extensive numericalsimulations.
  • Journal article
    Manfredi S, Angeli D, 2017,

    Necessary and sufficient conditions for consensus in nonlinear monotone networks with unilateral interactions

    , Automatica, Vol: 77, Pages: 51-60, ISSN: 0005-1098

    This paper deals with an extended framework of the distributed asymptotic agreement problem by allowing the presence of unilateral interactions (optimistic or pessimistic) in place of bilateral ones, for a large class of nonlinear monotone time-varying networks. In this original setup we firstly introduce notions of unilateral optimistic and/or pessimistic interaction, of associated bicolored edge in the interaction graph and a suitable graph-theoretical connectedness property. Secondly, we formulate a new assumption of integral connectivity and show that it is sufficient to guarantee exponential convergence towards the agreement subspace. Finally, we show that the proposed conditions are also necessary for consensuability and discuss how the new notions of bicolored graph and related connectivity concepts encompass the usual criteria in the standard case of bilateral interactions. Theoretical advances are emphasized through illustrative examples given both to support the discussion and to highlight how the proposed framework extends all existing conditions for consensus of monotone networks.
  • Conference paper
    Al-Radhawi MA, Angeli D, 2017,

    Construction of robust Lyapunov functions for reaction networks

    , European Control Conference (ECC), Publisher: IEEE, Pages: 928-935

    Although Chemical Reaction Networks (CRNs) form a rich class of nonlinear systems that can exhibit wide range of nonlinear behaviours, many common examples are observed to be asymptotically stable regardless of the kinetics. This paper presents the recently uncovered class of Graphically Stable Networks (GSNs) which is characterized by the existence of a robust Lyapunov function defined in the reaction coordinates. Subject to mild conditions, the existence of these functions guarantees asymptotic stability of a network regardless of the specific form of kinetics. Construction methods for these functions are provided and illustrated by examples.
  • Conference paper
    Zhou Y, Boem F, Fischione C, Parisini Tet al., 2017,

    Distributed Fault Detection with Sensor Networks using Pareto-Optimal Dynamic Estimation Method

    , 2016 European Control Conference, Publisher: IEEE

    In this paper, a distributed method for faultdetection using sensor networks is proposed. Each sensorcommunicates only with neighboring nodes to compute locallyan estimate of the state of the system to monitor. A residualis defined and suitable stochastic thresholds are designed,allowing to set the parameters so to guarantee a maximumfalse alarms probability. The main novelty and challenge ofthe proposed approach consists in addressing the individualcorrelations between the state, the measurements, and thenoise components, thus significantly generalising the estimationmethodology compared to previous results. No assumptions onthe probability distribution family are needed for the noisevariables. Simulation results show the effectiveness of theproposed method, including an extensive sensitivity analysiswith respect to fault magnitude and measurement noise.
  • Conference paper
    Khusainov B, Kerrigan EC, Constantinides GA, 2017,

    Multi-objective Co-design for Model Predictive Control with an FPGA

    , European Control Conference 16, Publisher: IEEE, Pages: 110-115

    In order to achieve the best possible performanceof a model predictive controller (MPC) for a given set ofresources, the software algorithm and computational platformhave to be designed simultaneously. Moreover, in practicalapplications the controller design problem has a multi-objectivenature: performance is traded off against computational hardwareresource usage, namely time, energy and space. Thispaper proposes formulating an MPC design problem as a multiobjectiveoptimization (MOO) problem in order to explore thedesign trade-offs in a systematic way.Since the design objectives in the resulting MOO problem areexpensive to evaluate, i.e. evaluation requires time consumingsimulations, most of the classical and evolutionary MOOalgorithms cannot be employed for this class of design problems.For this reason a practical MOO algorithm that can deal withexpensive-to-evaluate functions is presented. The algorithm isbased on Kriging and the hypervolume criterion that wasrecently proposed in the expensive optimization literature. Anumerical example for a fast gradient-based controller designshows that the proposed approach can efficiently exploreoptimal performance-resource trade-offs.
  • Journal article
    Qadrdan M, Ameli H, Strbac G, Jenkins Net al., 2017,

    Efficacy of options to address balancing challenges: integrated gas and electricity perspectives

    , Applied Energy, Vol: 190, Pages: 181-190, ISSN: 1872-9118

    Integration of a large capacity of wind generation in the Great Britain(GB)electricity network is expected to pose a number of operational challenges. The variable nature of wind generation necessitates introduction of technologies that can provide flexibility to generation portfolios and therefore compensate for intermittency of wind generation. In this paper, the efficacy of three options to address electricity balancing challenges was evaluated: flexible gas-fired plants, electricity storage andPower-to-Gas system. The combined gas and electricity network model (CGEN) was enhanced and through adopting a rolling optimisation approach the model aims at minimising the operational cost of an integrated gas and electricity networks that represents a GBsystem in 2030. The potential impacts of employing each of the flexibility options on the operation of the integrated electricity and gas networks were investigated. The analysis showed that amongst all the flexibility options, the deployment of grid-scale electricity storage will achieve the highest reduction in the operational cost of the integrated system (£12 million reduction in a typical winter week, and £3 million reduction in a typical summerweek). The results of this study provide insights on the system-wide benefits offered by each of the flexibility options and role of the gas network in the energy system with large capacity of wind generation.
  • Journal article
    Boem F, Ferrari RMG, Keliris C, Parisini T, Polycarpou MMet al., 2017,

    A distributed networked approach for fault detection of large-scale systems

    , IEEE Transactions on Automatic Control, Vol: 62, Pages: 18-33, ISSN: 0018-9286

    Networked systems present some key new challenges in the development of fault diagnosis architectures. This paper proposes a novel distributed networked fault detection methodology for large-scale interconnected systems. The proposed formulation incorporates a synchronization methodology with a filtering approach in order to reduce the effect of measurement noise and time delays on the fault detection performance. The proposed approach allows the monitoring of multi-rate systems, where asynchronous and delayed measurements are available. This is achieved through the development of a virtual sensor scheme with a model-based re-synchronization algorithm and a delay compensation strategy for distributed fault diagnostic units. The monitoring architecture exploits an adaptive approximator with learning capabilities for handling uncertainties in the interconnection dynamics. A consensus-based estimator with timevarying weights is introduced, for improving fault detectability in the case of variables shared among more than one subsystem. Furthermore, time-varying threshold functions are designed to prevent false-positive alarms. Analytical fault detectability sufficient conditions are derived and extensive simulation results are presented to illustrate the effectiveness of the distributed fault detection technique.
  • Journal article
    Wang XM, Hui SYR, 2017,

    Graphical modelling of pinched hysteresis loops of memristors

    , IET Science Measurement and Technology, Vol: 11, Pages: 86-96, ISSN: 1751-8822

    In this study, a graphical modelling approach of the pinched hysteresis loops exhibited by memristors is presented. This method provides a tool to emulate the hysteresis loop pinched at the origin, with the lobe area varying with the excitation frequency. The direction of the pinched hysteresis loop can be controlled. This graphical modelling method provides an alternative to describe the behaviour of memristors without deriving the coupled non-linear differential equations typically required for physical memristors. The method has been successfully applied to model the Hewlett–Packard memristor device.
  • Journal article
    Green RJ, Pudjianto D, Staffell I, Strbac Get al., 2016,

    Market Design for Long-Distance Trade in Renewable Electricity

    , Energy Journal, Vol: 37, Pages: 5-22, ISSN: 0195-6574

    While the 2009 EU Renewables Directive allows countries to purchase some of their obligation fromanother member state, no country has yet done so, preferring to invest locally even where load factors arevery low. If countries specialised in renewables most suited to their own endowments and expandedinternational trade, we estimate that system costs in 2030 could be reduced by 5%, or €15 billion a year,after allowing for the costs of extra transmission capacity, peaking generation and balancing operationsneeded to maintain electrical feasibility.Significant barriers must be overcome to unlock these savings. Countries that produce more renewablepower should be compensated for the extra cost through tradable certificates, while those that buy fromabroad will want to know that the power can be imported when needed. Financial Transmission Rightscould offer companies investing abroad confidence that the power can be delivered to their consumers.They would hedge short-term fluctuations in prices and operate much more flexibly than the existingsystem of physical point-to-point rights on interconnectors. Using FTRs to generate revenue fortransmission expansion could produce perverse incentives to under-invest and raise their prices, sorevenues from FTRs should instead be offset against payments under the existing ENTSO-Ecompensation scheme for transit flows. FTRs could also facilitate cross-border participation in capacitymarkets, which are likely to be needed to reduce risks for the extra peaking plants required.
  • Conference paper
    Thammawichai M, Kerrigan EC, 2016,

    Feedback Scheduling for Energy-Efficient Real-Time Homogeneous Multiprocessor Systems

    , 55th IEEE Conference on Decision and Control, Publisher: IEEE

    Real-time scheduling algorithms proposed in theliterature are often based on worst-case estimates of taskparameters and the performance of an open-loop scheme cantherefore be poor. To improve on such a situation, one caninstead apply a closed-loop scheme, where feedback is exploitedto dynamically adjust the system parameters at run-time. Wepropose an optimal control framework that takes advantageof feeding back information of finished tasks to solve a realtimemultiprocessor scheduling problem with uncertainty intask execution times, with the objective of minimizing thetotal energy consumption. Specifically, we propose a linearprogramming-based algorithm to solve a workload partitioningproblem and adopt McNaughton’s wrap around algorithmto find the task execution order. Simulation results for aPowerPC 405LP and an XScale processor illustrate that ourfeedback scheduling algorithm can result in an energy savingof approximately 40% compared to an open-loop method.
  • Conference paper
    Raimondo DM, Boem F, Gallo A, Parisini Tet al., 2016,

    A decentralized fault-tolerant control scheme based on Active Fault Diagnosis

    , 2016 IEEE 55th Conference on Decision and Control, Publisher: IEEE

    This paper deals with a decentralized fault-tolerant control methodology based on an Active Fault Diagnosis approach. The proposed technique addresses the important problem of monitoring interconnected Large-Scale Systems (LSS). The fault diagnosis approach is made of a passive set-based fault detection method and an active fault isolation technique, able to guarantee isolability subject to local input and state constraints. The proposed scheme can be implemented locally in a decentralized way. A significant feature is the decentralized design constructed on tube-based Model Predictive Control to possibly allow the disconnection of faulty subsystems or the reconfiguration of local controllers. The Active Fault Diagnosis tool is designed to support the decision-making process for the control and monitoring of the LSS.
  • Conference paper
    Boem F, Carli R, Farina M, Ferrari-Trecate G, Parisini Tet al., 2016,

    Scalable monitoring of interconnected stochastic systems

    , 2016 IEEE 55th Conference on Decision and Control, Publisher: IEEE

    In this paper, we propose a novel distributed faultdetection method to monitor the state of a linear system, par-titioned into interconnected subsystems. The approach hingeson the definition of a partition-based distributed Luenbergerestimator, based on the local model of the subsystems andthat takes into account the dynamic coupling terms betweenthe subsystems. The proposed methodology computes –in adistributed way– a bound on the variance of a properly definedresidual signal, considering the uncertainty related to thestate estimates performed by the neighboring subsystems. Thisbound allows the computation of suitable local thresholds withguaranteed maximum false-alarms rate. The implementationof the proposed estimation and fault detection method isscalable, allowingPlug & Playoperations and the possibilityto disconnect the faulty subsystem after fault detection. Theo-retical conditions guaranteeing the convergence of the estimatesand of the bounds are provided. Simulation results show theeffectiveness of the proposed method.
  • Conference paper
    Mylvaganam T, Astolfi A, 2016,

    Dynamic Algorithms for Solving Coupled Algebraic Riccati Equations Arising in Mixed H2/H∞ Control for Scalar Linear Systems

    , IEEE Conference on Decision & Control, Publisher: IEEE, ISSN: 0743-1546

    The problem of mixed H2/H∞ control canbe formulated as a two-player nonzero-sum differentialgame as done by Limebeer et al. in the 1990s. For linearsystems the problem is characterised by two coupled algebraicRiccati equations. Solutions for such algebraic Riccatiequations are not straight-forward to obtain, particularly forinfinite-horizon problems. In this paper two algorithms forobtaining solutions for the coupled algebraic Riccati equationsassociated with the mixed H2/H∞ control problemfor scalar, linear systems is provided along with illustrativenumerical examples.
  • Conference paper
    Padoan A, Scarciotti G, Astolfi A, 2016,

    A geometric characterisation of the persistence of excitation condition for sequences generated by discrete-time autonomous systems

    , IEEE 55th Annual Conference on Decision and Control (CDC), Publisher: IEEE

    The persistence of excitation condition for sequencesgenerated by time-invariant, discrete-time, autonomouslinear and nonlinear systems is studied. A rank conditionis shown to be equivalent to the persistence of excitationof sequences generated by the class of systems considered,consistently with the results established by the authors for thecontinuous-time case. The condition is geometric in nature andcan be checked a priori for a Poisson stable system, that is,without knowing explicitly the state trajectories of the system.The significance of the ideas and tools presented is illustratedby means of simple examples.
  • Conference paper
    De Paola A, Angeli D, Strbac G, 2016,

    Decentralized coordination of large populations of flexible electrical appliances through demand saturation

    , 2016 IEEE 55th Conference on Decision and Control, CDC 2016, Publisher: Institute of Electrical and Electronics Engineers (IEEE), Pages: 4937-4943

    This paper presents a novel decentralized control strategy for integration of price-responsive loads in the electricity market. Previous work has shown that, by approximating the devices population as a continuum, it is possible to provide necessary and sufficient conditions for the existence of a Nash equilibrium (no device has unilateral interest in changing its scheduling when considering the resulting profile of aggregate demand). These results are now extended by introducing a time varying proportional constraint on the maximum power consumption of the appliances. This allows to saturate the flexible demand and obtain a Nash equilibrium for a much wider range of scenarios. The performance of the proposed control technique, which also minimizes the task time of all appliances, is tested in simulation.
  • Conference paper
    Forni P, Angeli D, 2016,

    Cascades of iISS and Strong iISS systems with multiple invariant sets

    , 2016 IEEE 55th Conference on Decision and Control, CDC 2016, Publisher: Institute of Electrical and Electronics Engineers (IEEE), Pages: 459-464

    In recent papers, the notions of Input-to-State Stability (ISS) and Integral ISS (iISS) have been generalized for systems evolving on manifolds and having multiple invariant sets, i.e. multistable systems. The well-known property of conservation of ISS under cascade interconnection has also been proven true for multistable systems in different scenarios [3]. Unfortunately, multistability hampers a straightforward extension of analogous conservation properties for integral ISS systems. By means of counterexamples, this work highlights the necessity of the additional assumptions which yield the conservation of the iISS and Strong iISS properties in cascades of multistable systems. In particular, a characterization of the invariant set of the cascade is provided in terms of its finest possible decomposition.
  • Conference paper
    Manfredi S, Angeli D, 2016,

    Consensus for nonlinear monotone networks with unilateral interactions

    , 2016 IEEE 55th Conference on Decision and Control, CDC 2016, Publisher: Institute of Electrical and Electronics Engineers (IEEE), Pages: 2609-2614

    This paper deals with an extended framework of the distributed asymptotic agreement problem by allowing the presence of unilateral interactions (optimistic or pessimistic) in place of bilateral ones, for a large class of nonlinear monotone time-varying networks. In this original setup we firstly introduce notions of unilateral optimistic and/or pessimistic interaction, of associated bicolored edge in the interaction graph and a suitable graph-theoretical connectedness property. Secondly, we formulate a new assumption of integral connectivity and show that it is sufficient to guarantee exponential convergence towards the agreement subspace. Finally, we remark that the proposed conditions are also necessary for consensuability. Theoretical advances are emphasized through illustrative examples given both to support the discussion and to highlight how the proposed framework extends all existing conditions for consensus of monotone networks.
  • Conference paper
    Forni P, Angeli D, 2016,

    Output-to-State Stability for systems on manifolds with multiple invariant sets

    , 2016 IEEE 55th Conference on Decision and Control, CDC 2016, Publisher: Institute of Electrical and Electronics Engineers (IEEE), Pages: 453-458

    Output-to-State Stability (OSS) is a notion of detectability for nonlinear systems that is formulated in the ISS framework. We generalize the notion of OSS for systems evolving on manifolds and having multiple invariant sets. Building upon a recent extension of the Input-to-State Stability (ISS) theory for this very class of systems [1], the paper provides equivalent characterizations of the OSS property in terms of asymptotic estimates of the state trajectories and, in particular, in terms of existence of Lyapunov-like functions.
  • Conference paper
    Scarciotti G, Astolfi A, Jiang Z-P, 2016,

    Constrained optimal reduced-order models from input/output data

    , IEEE 55th Annual Conference on Decision and Control (CDC), Publisher: IEEE

    Model reduction by moment matching does notpreserve, in a systematic way, the transient response of thesystem to be reduced, thus limiting the use of this modelreduction technique in control problems. With the final goalof designing reduced-order models which can effectively beused (not just for analysis but also) for control purposes, wedetermine, using a data-driven approach, an estimate of themoments and of the transient response of an unknown system.We compute the unique, up to a change of coordinates, reducedordermodel which possesses the estimated transient and,simultaneously, achieves moment matching at the prescribedinterpolation points. The error between the output of the systemand the output of the reduced-order model is minimized andwe show that the resulting system is a constrained optimal (ina sense to be specified) reduced-order model. The results of thepaper are illustrated by means of a simple numerical example.
  • Journal article
    Strbac G, Kirschen D, Moreno R, 2016,

    Reliability Standards for the Operation and Planning of Future Electricity Networks

    , Foundations and Trends® in Electric Energy Systems, Vol: 1, Pages: 143-219, ISSN: 2332-6557

    Electricity networks, designed and operated in accordance with the historic deterministic standards, have broadly delivered secure and reliable supplies to customers. A key issue regarding their evolution is how the operation and planning standards should evolve to make efficient use of the existing assets while taking advantage of emerging, non-network (or non-wires) technologies. Deployment of the smart grid will require fundamental changes in the historical principles used for network security in order to ensure that integration of low-carbon generation is undertaken as efficiently as possible through the use of new information and communication technology (ICT), and new flexible network technologies that can maximize utilization of existing electricity infrastructure. These new technologies could reduce network redundancy in providing security of supply by enabling the application of a range of advanced, technically effective, and economically efficient corrective (or post-fault) actions that can release latent network capacity of the existing system. In this context, this paper demonstrates that historical deterministic practices and standards, mostly developed in the 1950s, should be reviewed in order to take full advantage of new emerging technologies and facilitate transition to a smart grid paradigm. This paper also demonstrates that a probabilistic approach to developing future efficient operating and design strategies enabled by new technologies, will appropriately balance network investment against non-network solutions while truly recognizing effects of adverse weather, common-mode failures, high-impact low-probability events, changing market prices for pre- and post-contingency actions, equipment malfunctioning, etc. This clearly requires explicit consideration of the likelihood of various outages (beyond those considered in deterministic studies) and quantification of their impacts on alternative network operation and investment decisions, which canno
  • Conference paper
    Scarciotti G, 2016,

    Moment matching for nonlinear differential-algebraic equations

    , IEEE 55th Annual Conference on Decision and Control (CDC), Publisher: IEEE

    The problem of model reduction by momentmatching for nonlinear singular systems is considered. Thenotion of moment is extended to this class of systems bymeans of the center manifold theory. The characterization ofthe moments at infinity and of the moments for nonlinearparametric singular systems is discussed. The problem of modelreduction for singular perturbation systems is analyzed. Afamily of singular reduced order models achieving momentmatching is presented. Throughout the paper the results areillustrated by means of examples.
  • Conference paper
    Ascencio P, Astolfi A, Parisini T, 2016,

    Backstepping PDE-based adaptive observer for a single particle model of lithium-ion batteries

    , 55th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 5623-5628, ISSN: 0743-1546

    This article deals with the observer design problem for the simultaneous estimation of the solid Lithium concentration and of the diffusion parameter for a Single Particle Model of Lithium-Ion Batteries. The design is based on the Backstepping PDE methodology, including a modified Volterra transformation to compensate for the diffusivity uncertainty. The resulting coupled/uncoupled Kernel-PDE and Ordinary Differential Equation (ODE) are recast, via a Sum-of-Squares decomposition, in terms of a convex optimization problem and solved by semidefinite programming, allowing, at each fixed time, an efficient computation of the state and parameter observer gains. In addition, based on the Moment approach, a novel scheme of inversion of the nonlinear output mapping of the Single Particle Model is presented. The effectiveness of this approach is illustrated by numerical simulations.
  • Conference paper
    Ge M, Kerrigan EC, 2016,

    Relations between Full Information and Kalman-Based Estimation

    , 55th IEEE Conference on Decision and Control, Publisher: IEEE

    For nonlinear state space systems with additivenoises, sometimes the number of process noise signals couldbe less than the dimension of the state space. In order toimprove the accuracy and stability of nonlinear state estimation,this paper provides for the first time the derivation of thefull information estimator (FIE) for such nonlinear systems.We verify our derivation of the FIE by firstly proving theunbiasedness and minimum-variance of the FIE for linear timevarying (LTV) systems, then showing the equivalence betweenthe Kalman filter/smoother and the FIE for LTV systems.Finally, we prove that the FIE will provide more accurate stateestimates than the extended Kalman filter (EKF) and smoother(EKS) for nonlinear systems.
  • Conference paper
    Padoan A, Scarciotti G, Astolfi A, 2016,

    A geometric characterisation of persistently exciting signals generated byautonomous systems

    , 10th IFAC Symposium on Nonlinear Control Systems, Publisher: Elsevier, Pages: 826-831, ISSN: 1474-6670

    The persistence of excitation of signals generated by time-invariant, continuous-time,autonomous linear and nonlinear systems is studied. The notion of persistence of excitation ischaracterised as a rank condition which is reminiscent of a geometric condition used to study thecontrollability properties of a control system. The notions and tools introduced are illustratedby means of simple examples and of an application in system identification.
  • Conference paper
    Scarciotti G, Astolfi A, 2016,

    Model reduction for hybrid systems with state-dependent jumps

    , 10th IFAC Symposium on Nonlinear Control Systems, Publisher: Elsevier, Pages: 850-855, ISSN: 1474-6670

    In this paper we present a model reduction technique based on moment matchingfor a class of hybrid systems with state-dependent jumps. The problem of characterizing thesteady-state for this class of systems is studied and a result which allows to described the steadystateresponse of hybrid systems through the use of a hybrid mapping is given. Then a familyof hybrid reduced order models which achieve moment matching and are easily parameterizableis provided. The special case of periodic input signals is analyzed and conditions for applyingthe technique are given for this class. A numerical simulation illustrates the results.

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