60 results found
O'Dwyer E, Kerrigan E, Falugi P, et al., 2023, Data-driven predictive control with improved performance using segmented trajectories, IEEE Transactions on Control Systems Technology, Vol: 31, Pages: 1355-1365, ISSN: 1063-6536
A class of data-driven control methods has recently emerged based on Willems' fundamental lemma. Such methods can ease the modelling burden in control design but can be sensitive to disturbances acting on the system under control. In this paper, we extend these methods to incorporate segmented prediction trajectories. The proposed segmentation enables longer prediction horizons to be used in the presence of unmeasured disturbance. Furthermore, a computation time reduction can be achieved through segmentation by exploiting the problem structure, with computation time scaling linearly with increasing horizon length. The performance characteristics are illustrated in a set-point tracking case study in which the segmented formulation enables more consistent performance over a wide range of prediction horizons. The computation time for the segmented formulation is approximately half that of an unsegmented formulation for a horizon of 100 samples. The method is then applied to a building energy management problem, using a detailed simulation environment, in which we seek to minimise the discomfort and energy of a 6-room apartment. With the segmented formulation, a 72% reduction in discomfort and 5% financial cost reduction is achieved, compared to an unsegmented formulation using a one-day-ahead prediction horizon.
Nita L, Vila EMG, Zagorowska M, et al., 2022, Fast and accurate method for computing non-smooth solutions to constrained control problems, 2022 European Control Conference (ECC), Publisher: IEEE, Pages: 1049-1054
Introducing flexibility in the time-discretisation mesh can improve convergence and computational time when solving differential equations numerically, particularly when the solutions are discontinuous, as commonly found in control problems with constraints. State-of-the-art methods use fixed mesh schemes, which cannot achieve superlinear convergence in the presence of non-smooth solutions. In this paper, we propose using a flexible mesh in an integrated residual method. The locations of the mesh nodes are introduced as decision variables, and constraints are added to set upper and lower bounds on the size of the mesh intervals. We compare our approach to a uniform fixed mesh on a real-world satellite reorientation example. This example demonstrates that the flexible mesh enables the solver to automatically locate the discontinuities in the solution, has superlinear convergence and faster solve times, while achieving the same accuracy as a fixed mesh.
O'Dwyer E, Falugi P, Shah N, et al., 2022, Automating the data-driven predictive control design process for building thermal management, ECOS 2022 35th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems
Falugi P, O’Dwyer E, Zagorowska MA, et al., 2022, MPC and optimal design of residential buildings with seasonal storage: a case study, Active Building Energy Systems, Editors: Doyle, Publisher: Springer International Publishing, Pages: 129-160, ISBN: 9783030797416
Residential buildings account for about a quarter of the global energy use. As such, residential buildings can play a vital role in achieving net-zero carbon emissions through efficient use of energy and balance of intermittent renewable generation. This chapter presents a co-design framework for simultaneous optimisation of the design and operation of residential buildings using Model Predictive Control (MPC). The adopted optimality criterion maximises cost savings under time-varying electricity prices. By formulating the co-design problem using model predictive control, we then show a way to exploit the use of seasonal storage elements operating on a yearly timescale. A case study illustrates the potential of co-design in enhancing flexibility and self-sufficiency of a system operating on multiple timescales. In particular, numerical results from a low-fidelity model report approximately doubled bill savings and carbon emission reduction compared to the a priori sizing approach.
Giannelos S, Jain A, Borozan S, et al., 2021, Long-term expansion planning of the transmission network in India under multi-dimensional uncertainty, Energies, Vol: 14, Pages: 7813-7813, ISSN: 1996-1073
Considerable investment in India’s electricity system may be required in the coming decades in order to help accommodate the expected increase of renewables capacity as part of the country’s commitment to decarbonize its energy sector. In addition, electricity demand is geared to significantly increase due to the ongoing electrification of the transport sector, the growing population, and the improving economy. However, the multi-dimensional uncertainty surrounding these aspects gives rise to the prospect of stranded investments and underutilized network assets, rendering investment decision making challenging for network planners. In this work, a stochastic optimization model is applied to the transmission network in India to identify the optimal expansion strategy in the period from 2020 until 2060, considering conventional network reinforcements as well as energy storage investments. An advanced Nested Benders decomposition algorithm was used to overcome the complexity of the multistage stochastic optimization problem. The model additionally considers the uncertainty around the future investment cost of energy storage. The case study shows that deployment of energy storage is expected on a wide scale across India as it provides a range of benefits, including strategic investment flexibility and increased output from renewables, thereby reducing total expected system costs; this economic benefit of planning with energy storage under uncertainty is quantified as Option Value and is found to be in excess of GBP 12.9 bn. The key message of this work is that under potential high integration of wind and solar in India, there is significant economic benefit associated with the wide-scale deployment of storage in the system.
Falugi P, O'Dwyer E, Kerrigan EC, et al., 2021, Predictive control co-design for enhancing flexibility in residential housing with battery degradation, 7th IFAC Conference on Nonlinear Model Predictive Control, Publisher: Elsevier, Pages: 8-13, ISSN: 2405-8963
Buildings are responsible for about a quarter of global energy-related CO2 emissions. Consequently, the decarbonisation of the housing stock is essential in achieving net-zero carbon emissions. Global decarbonisation targets can be achieved through increased efficiency in using energy generated by intermittent resources. The paper presents a co-design framework for simultaneous optimal design and operation of residential buildings using Model Predictive Control (MPC). The framework is capable of explicitly taking into account operational constraints and pushing the system to its efficiency and performance limits in an integrated fashion. The optimality criterion minimises system cost considering time-varying electricity prices and battery degradation. A case study illustrates the potential of co-design in enhancing flexibility and self-sufficiency of a system operating under different conditions. Specifically, numerical results from a low-fidelity model show substantial carbon emission reduction and bill savings compared to an a-priori sizing approach.
O’Dwyer E, Atam E, Falugi P, et al., 2021, A modelling workflow for predictive control in residential buildings, Active Building Energy Systems, Editors: Doyle, Publisher: Springer International Publishing, Pages: 99-128, ISBN: 9783030797416
Despite a large body of research, the widespread application of Model Predictive Control (MPC) to residential buildings has yet to be realised. The modelling challenge is often cited as a significant obstacle. This chapter establishes a systematic workflow, from detailed simulation model development to control-oriented model generation to act as a guide for practitioners in the residential sector. The workflow begins with physics-based modelling methods for analysis and evaluation. Following this, model-based and data-driven techniques for developing low-complexity, control-oriented models are outlined. Through sections detailing these different stages, a case study is constructed, concluding with a final section in which MPC strategies based on the proposed methods are evaluated, with a price-aware formulation producing a reduction in operational space-heating cost of 11%. The combination of simulation model development, control design and analysis in a single workflow can encourage a more rapid uptake of MPC in the sector.
Narbondo L, Falugi P, Strbac G, 2021, 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, Pages: 1-6, ISSN: 2381-3571
Nowadays, in Uruguay, a considerable amount of energy produced by renewable resources is curtailed inducing frequent substantial reductions in the spot market prices. This paper analyses the incorporation of energy storage into the Uruguayan network, taking the different perspectives of a private investor and a central planner. From the investor point of view, we investigate the option of doing energy arbitrage in the wholesale market, taking advantage of the spot price fluctuations. From the national perspective, we develop an optimal power flow planning model to perform a cost-benefit analysis of batteries’ integration in reducing thermal generation. We conclude that, from a private investor perspective, fluctuations in the spot prices are not enough to make investments in batteries profitable with current prices. On the other hand, from a national perspective, results are more promising, obtaining very high revenues in some case studies.
Huyghues-Beaufond N, Tindemans S, Falugi P, et 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.
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.
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.
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.
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.
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.
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.
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
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
Boccia A, Falugi P, Maurer H, et 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
Falugi P, Mayne DQ, 2013, Model predictive control for tracking random references, European Control Conference (ECC), Publisher: IEEE, Pages: 518-523
P Falugi, D Q Mayne, 2012, Tracking performance of model predictive control, Proc. 51st IEEE Conference on Decision and Control, Pages: 2631-2636
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.
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
Mayne DQ, Kerrigan EC, van Wyk EJ, et 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.
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.
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.
E J Van Wyk, P Falugi, E C Kerrigan, 2010, ICLOCS
Solves nonlinear optimal control problems subject to constraints.
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
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
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
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.
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