Imperial College London

DrPaolaFalugi

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Research Associate
 
 
 
//

Contact

 

p.falugi

 
 
//

Location

 

1107Electrical EngineeringSouth Kensington Campus

//

Summary

 

Publications

Publication Type
Year
to

55 results found

Falugi P, 2021, Predictive Control Co-Design for Enhancing Flexibility in Residential Housing with Battery Degradation, 7th IFAC Conference on Nonlinear Model Predictive Control

Conference paper

Falugi P, O'Dwyer E, Kerrigan EC, Atam E, Zagorowska M, Strbac G, Shah Net al., 2021, Predictive Control Co-Design for Enhancing Flexibility in Residential Housing with Battery Degradation, 7th IFAC Conference on Nonlinear Model Predictive Control

Conference paper

Huyghues-Beaufond N, Tindemans S, Falugi P, Sun M, Strbac Get al., 2020, Robust and automatic data cleansing method for short-term load forecasting of distribution feeders, Applied Energy, Vol: 261, Pages: 1-17, ISSN: 0306-2619

Distribution networks are undergoing fundamental changes at medium voltage level. To support growing planning and control decision-making, the need for large numbers of short-term load forecasts has emerged. Data-driven modelling of medium voltage feeders can be affected by (1) data quality issues, namely, large gross errors and missing observations (2) the presence of structural breaks in the data due to occasional network reconfiguration and load transfers. The present work investigates and reports on the effects of advanced data cleansing techniques on forecast accuracy. A hybrid framework to detect and remove outliers in large datasets is proposed; this automatic procedure combines the Tukey labelling rule and the binary segmentation algorithm to cleanse data more efficiently, it is fast and easy to implement. Various approaches for missing value imputation are investigated, including unconditional mean, Hot Deck via k-nearest neighbour and Kalman smoothing. A combination of the automatic detection/removal of outliers and the imputation methods mentioned above are implemented to cleanse time series of 342 medium-voltage feeders. A nested rolling-origin-validation technique is used to evaluate the feed-forward deep neural network models. The proposed data cleansing framework efficiently removes outliers from the data, and the accuracy of forecasts is improved. It is found that Hot Deck (k-NN) imputation performs best in balancing the bias-variance trade-off for short-term forecasting.

Journal article

Narbondo L, Falugi P, Strbac G, 2020, Application of energy storage in systems with high penetration of intermittent renewables, IEEE PES Transmission and Distribution Conference and Exhibition - Latin America (T and D LA), Publisher: IEEE, ISSN: 2381-3571

Conference paper

Mayne DQ, Falugi P, 2019, Stabilizing conditions for model predictive control, International Journal of Robust and Nonlinear Control, Vol: 29, Pages: 894-903, ISSN: 1049-8923

Existing stabilizing conditions that use a terminal cost and constraint that, if satisfied, ensure stability and recursive feasibility for deterministic, robust, and stochastic model predictive control are briefly reviewed and analyzed. It is pointed out that these conditions do not cover all situations. Proposals are made to cover a wider range of desired applications.

Journal article

Falugi P, Konstantelos I, Strbac G, 2018, Planning with multiple transmission and storage investment options under uncertainty: a nested decomposition approach, IEEE Transactions on Power Systems, Vol: 33, Pages: 3559-3572, ISSN: 0885-8950

Achieving the ambitious climate change mitigation objectives set by governments worldwide is bound to lead to unprecedented amounts of network investment to accommodate low-carbon sources of energy. Beyond investing in conventional transmission lines, new technologies such as energy storage can improve operational flexibility and assist with the cost-effective integration of renewables. Given the long lifetime of these network assets and their substantial capital cost, it is imperative to decide on their deployment on a long-term cost-benefit basis. However, such an analysis can result in large-scale Mixed Integer Linear Programming (MILP) problems which contain many thousands of continuous and binary variables. Complexity is severely exacerbated by the need to accommodate multiple candidate assets and consider a wide range of exogenous system development scenarios that may occur. In this manuscript we propose a novel, efficient and highly-generalizable framework for solving large-scale planning problems under uncertainty by using a temporal decomposition scheme based on the principles of Nested Benders. The challenges that arise due to the presence of non-sequential investment state equations and sub-problem non-convexity are highlighted and tackled. The substantial computational gains of the proposed method are demonstrated via a case study on the IEEE 118 bus test system that involve planning of multiple transmission and storage assets under long-term uncertainty.

Journal article

falugi P, Konstantelos I, strbac G, 2016, Application of novel Nested decomposition techniques to long-term planning problems, Power Systems Computation Conference, Publisher: IEEE

Cost effective, long term planning under uncertainty constitutes a significant challenge since a meaningful description of the planning problem is given by large Mixed Integer Linear Programming (MILP) models which may contain thousands of binary variables and millions of continuous variables. In this paper, a novel multistage decomposition scheme, based on Nested Benders decomposition is applied to the transmission planning problem. The difficulties in using temporal decomposition schemes in the context of planning problems due to the presence of non-sequential investment state equations are highlighted. An efficient and highly-generalizable framework for recasting the temporal constraints of such problems in a structure amenable to nested decomposition methods is presented. The proposed scheme's solution validity and substantial computational benefits are clearly demonstrated through the aid of case studies on the IEEE24-bus test system.

Conference paper

Mayne D, Falugi P, 2015, Generalised stabilizing conditions for model predictive control, Journal of Optimization Theory and Applications, Vol: 169, Pages: 719-734, ISSN: 1573-2878

This note addresses the tracking problem for model predictive control. It presents simple procedures for both linear and nonlinear constrained model predictive control when the desired equilibrium state is any point in a specified set. The resultant region of attraction is the union of the regions of attraction for each equilibrium state in the specified set and is therefore larger than that obtained when conventional model predictive control is employed.

Journal article

Falugi P, 2014, Model predictive control for tracking randomly varying references, International Journal of Control, Vol: 88, Pages: 745-753, ISSN: 1366-5820

This paper proposes a model predictive control scheme for tracking a-priori unknown references varying in a wide range and analyses its performance. It is usual to assume that the reference eventually converges to a constant in which case convergence to zero of the tracking error can be established. In this note we remove this simplifying assumption and characterise the set to which the tracking error converges and the associated region of convergence.

Journal article

Falugi P, 2014, Model predictive Control: a passive scheme, 19th IFAC World Congress, Publisher: International Federation of Automatic Control, Pages: 1017-1022

This note studies the formulation of model predictive control exploiting passivity properties. The introduction of passive constraints in model predictive control schemes is particularly appealing since robustness against model uncertainty is inherently guaranteed. The potential of the discussed control scheme is shown on the regulation problem of a robot manipulator.

Conference paper

Falugi P, Mayne DQ, 2014, Getting Robustness Against Unstructured Uncertainty: A Tube-Based MPC Approach, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, Vol: 59, Pages: 1290-1295, ISSN: 0018-9286

Journal article

Falugi P, Mayne DQ, 2013, Tracking a periodic reference using nonlinear model predictive control, IEEE Conference on decision and Control, Pages: 5096-6000, ISSN: 0743-1546

Conference paper

Falugi P, Mayne DQ, 2013, Model predictive control for tracking random references, European Control Conference (ECC), Publisher: IEEE, Pages: 518-523

Conference paper

Boccia A, Falugi P, Maurer H, Vinter RBet al., 2013, Free time optimal control problems with time delays, 52nd IEEE Annual Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 520-525, ISSN: 0743-1546

Conference paper

P Falugi, D Q Mayne, 2012, Tracking performance of model predictive control, Proc. 51st IEEE Conference on Decision and Control, Pages: 2631-2636

Conference paper

Falugi P, Kountouriotis P-A, Vinter RB, 2012, Differential Games Controllers That Confine a System to a Safe Region in the State Space, With Applications to Surge Tank Control, IEEE Transactions on Automatic Control, Vol: 57, Pages: 2778-2788, ISSN: 1558-2523

Surge tanks are units employed in chemical processing to regulate the flow of fluids between reactors. A notable feature of surge tank control is the need to constrain the magnitude of the Maximum Rate of Change (MROC) of the surge tank outflow, since excessive fluctuations in the rate of change of outflow can adversely affect down-stream processing (through disturbance of sediments, initiation of turbulence, etc.). Proportional + Integral controllers, traditionally employed in surge tank control, do not take direct account of the MROC. It is therefore of interest to explore alternative approaches. We show that the surge tank controller design problem naturally fits a differential games framework, proposed by Dupuis and McEneaney, for controlling a system to confine the state to a safe region of the state space. We show furthermore that the differential game arising in this way can be solved by decomposing it into a collection of (one player) optimal control problems. We discuss the implications of this decomposition technique, for the solution of other controller design problems possessing some features of the surge tank controller design problem.

Journal article

Falugi P, Mayne DQ, 2011, Tube-based model predictive control for nonlinear systems with unstructured uncertainty, 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011), Publisher: IEEE

Conference paper

Mayne DQ, Kerrigan EC, van Wyk EJ, Falugi Pet al., 2011, Tube-based robust nonlinear model predictive control, International Journal of Robust and Nonlinear Control, Vol: 21, Pages: 1341-1353, ISSN: 1099-1239

This paper extends tube-based model predictive control of linear systems to achieve robust control of nonlinear systems subject to additive disturbances. A central or reference trajectory is determined by solving a nominal optimal control problem. The local linear controller, employed in tube-based robust control of linear systems, is replaced by an ancillary model predictive controller that forces the trajectories of the disturbed system to lie in a tube whose center is the reference trajectory thereby enabling robust control of uncertain nonlinear systems to be achieved.

Journal article

Mayne DQ, Kerrigan EC, Falugi P, 2011, Robust model predictive control: Advantages and disadvantages of tube-based methods, Pages: 191-196, ISSN: 1474-6670

An important reason for the success of model predictive control is the fact that, for deterministic systems, feedback and open-loop control are equivalent so that 'optimal' (but implicit) feedback may be obtained by solving an open-loop optimal control problem at each state encountered. This equivalence is not maintained in the presence of uncertainty complicating the development of robust model predictive control. The advantages and limitations of tubebased model predictive control for dealing with uncertainty of various forms are discussed, firstly in the context of constrained linear systems, and an extension to deal with robustness against unstructured uncertainty is briefly described. Tube-based control requires the determination both of a nominal or reference trajectory and an ancillary controller that constrains deviations of the state of the uncertain systems from the nominal trajectory and is difficult to extend to nonlinear systems. A novel ancillary controller for tube-based control of constrained nonlinear systems with additive disturbances that overcomes some disadvantages of a previous version and is relatively simple to implement is described and assessed; the addition of a terminal equality constraint to the nominal optimal control problem and its removal from the ancillary optimal control problem simplifies the analysis, removes some restrictive assumptions, and enables stronger results to be obtained. © 2011 IFAC.

Conference paper

Giarré L, Falugi P, Badalamenti R, 2011, Hybrid LPV Modeling and Identification, Linear Parameter-Varying System Identification New Developments and Trends, Publisher: World Scientific, Pages: 11-39, ISBN: 9789814355452

This review volume reports the state-of-the-art in Linear Parameter Varying (LPV) system identification.

Book chapter

E J Van Wyk, P Falugi, E C Kerrigan, 2010, ICLOCS

Solves nonlinear optimal control problems subject to constraints.

Software

Falugi P, Olaru S, Dumur D, 2010, Robust Multi-model Predictive Control Using LMIs, INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, Vol: 8, Pages: 169-175, ISSN: 1598-6446

Journal article

P Falugi, S Olaru and D Dumur, 2010, Multi-model predictive control based on LMI: from the adaptation of the state-space model to the analytic description of the control law, Vol: 83, Pages: 1548-1563

Journal article

Falugi P, Giarre L, 2009, Identification and validation of quasispecies models for biological systems, SYSTEMS & CONTROL LETTERS, Vol: 58, Pages: 529-539, ISSN: 0167-6911

Journal article

Falugi P, Olaru S, Dumur D, 2008, Robust multi-model predictive control using LMIs, ISSN: 1474-6670

This paper proposes a novel robust predictive control synthesis technique for constrained nonlinear systems based on linear matrix inequalities (LMIs) formalism. Local discrete-time polytopic models have been exploited for prediction of the system behavior. This design strategy can be applied to a wide class of nonlinear systems provided a suitable embedding is available. The devised procedure guarantees constraint satisfaction and asymptotic stability. The proposed result extends previous works by allowing different local descriptions of nonlinearity and uncertainty and by handling less conservative input constraints. The multimodel prediction shows significant improvements in terms of closed-loop performance and estimation of the feasibility domain. Copyright © 2007 International Federation of Automatic Control All Rights Reserved.

Conference paper

Falugi P, Olaru S, Dumur D, 2008, Explicit robust multi-model predictive control, Pages: 1868-1873

The paper deals with robust predictive control based on a LMI approach. With respect to the well established case of linear models, with a global polytopic uncertainty, in the present approach the conservativeness reduction is assured by allowing different local descriptions of the uncertainty. The prediction model can thus be interpreted as a multi-model description of the plant to be controlled. The techniques is effective for a large class of nonlinear systems embedded into polytopic models. For the practical implementation the construction of suitable (explicit) descriptions of the control law are described upon concrete algorithms. © 2008 IEEE.

Conference paper

Falugi P, Giarre L, 2008, Application of model quality evaluation to systems biology, 3rd IEEE International Symposium on Control, Communications and Signal Processing (ISCCSP 2008), Publisher: IEEE, Pages: 135-+

Conference paper

Falugi P, Olaru S, Dumur D, 2008, Explicit robust multi-model predictive control, 16th Mediterranean Conference on Control and Automation, Publisher: IEEE, Pages: 808-813, ISSN: 2325-369X

Conference paper

Falugi P, Olaru S, Dumur D, 2008, Robust multi-model predictive control using LMIs, IFAC Proceedings Volumes, Vol: 41, Pages: 8809-8814, ISSN: 1474-6670

Journal article

Bianchini G, Falugi P, Tesi A, Vicino Aet al., 2007, A convex lower bound for the real l(2) parametric stability margin of linear control systems with restricted complexity controllers, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, Vol: 52, Pages: 514-520, ISSN: 0018-9286

Journal article

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.

Request URL: http://wlsprd.imperial.ac.uk:80/respub/WEB-INF/jsp/search-html.jsp Request URI: /respub/WEB-INF/jsp/search-html.jsp Query String: respub-action=search.html&id=00586949&limit=30&person=true