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  • Conference paper
    Ascencio P, Astolfi A, Parisini T, 2016,

    An Adaptive Observer for a class of Parabolic PDEs based on a Convex Optimization Approach for Backstepping PDE Design

    , American Control Conference (ACC), Publisher: IEEE, Pages: 3429-3434, ISSN: 0743-1619
  • Conference paper
    Sassano M, Astolfi A, 2016,

    Approximate Dynamic Tracking and Feedback Linearization

    , 55th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 5688-5693, ISSN: 0743-1546
  • Conference paper
    Yates DC, Aldhaher S, Mitcheson PD, 2016,

    Design of 3 MHz DC/AC Inverter with Resonant Gate Drive for a 3.3 kW EV WPT System

    , 2nd IEEE Annual Southern Power Electronics Conference (SPEC), Publisher: IEEE
  • Conference paper
    Sanz IM, Chaudhuri B, Strbac G, 2016,

    Inertial Response From Offshore Wind Farms Connected Through DC Grids

    , IEEE-Power-and-Energy-Society General Meeting (PESGM), Publisher: IEEE, ISSN: 1944-9925
  • Conference paper
    Prieto-Araujo E, Junyent-Ferre A, Lavernia-Ferrer D, Gomis-Bellmunt Oet al., 2016,

    Decentralized Control of a Nine-Phase Permanent Magnet Generator for Offshore Wind Turbines

    , IEEE-Power-and-Energy-Society General Meeting (PESGM), Publisher: IEEE, ISSN: 1944-9925
  • Conference paper
    Ye Y, Papadaskalopoulos D, Strbac G, 2016,

    Factoring Flexible Demand Non-Convexities in Electricity Markets

    , IEEE-Power-and-Energy-Society General Meeting (PESGM), Publisher: IEEE, ISSN: 1944-9925
  • Conference paper
    Papadaskalopoulos D, Strbac G, 2016,

    Nonlinear and Randomized Pricing for Distributed Management of Flexible Loads

    , IEEE-Power-and-Energy-Society General Meeting (PESGM), Publisher: IEEE, ISSN: 1944-9925
  • Conference paper
    Teng F, Trovato V, Strbac G, 2016,

    Stochastic Scheduling with Inertia-dependent Fast Frequency Response Requirements

    , IEEE-Power-and-Energy-Society General Meeting (PESGM), Publisher: IEEE, ISSN: 1944-9925
  • Conference paper
    Chen Y, Teng F, Moreno R, Strbac Get al., 2016,

    Impact of Dynamic Line Rating with Forecast Error on the Scheduling of Reserve Service

    , IEEE-Power-and-Energy-Society General Meeting (PESGM), Publisher: IEEE, ISSN: 1944-9925
  • Conference paper
    Huang Y, Xiong S, Tan S-C, Hui S-YRet al., 2016,

    Compact Modular Switched-Capacitor DC/DC Converters with Exponential Voltage Gain

    , 31st Annual IEEE Applied Power Electronics Specialists Conference and Exposition (APEC), Publisher: IEEE, Pages: 1894-1899, ISSN: 1048-2334
  • Conference paper
    Ho GKY, Pong BMH, Hui RSY, 2016,

    LLC Resonant Converter Design for Bendable Power Converter

    , 31st Annual IEEE Applied Power Electronics Specialists Conference and Exposition (APEC), Publisher: IEEE, Pages: 2328-2333, ISSN: 1048-2334
  • Conference paper
    Xiong S, Huang Y, Tan S-C, Hui S-YRet al., 2016,

    Morphing Switched-Capacitor Step-Down DC-DC Converters with Variable Conversion Ratio

    , 31st Annual IEEE Applied Power Electronics Specialists Conference and Exposition (APEC), Publisher: IEEE, Pages: 1888-1893, ISSN: 1048-2334
  • Conference paper
    Karimi H, Papadaskalopoulos D, Strbac G, 2016,

    Integrating Customers' Differentiated Supply Valuation in Distribution Network Planning and Charging

    , 13th International Conference on the European Energy Market (EEM), Publisher: IEEE, ISSN: 2165-4077
  • Conference paper
    Fan Y, Papadaskalopoulos D, Strbac G, 2016,

    A Game Theoretic Modeling Framework for Decentralized Transmission Planning

    , 19th Power Systems Computation Conference (PSCC), Publisher: IEEE
  • Conference paper
    Zhang C, Tang N, Zhong W, Lee CK, Hui RSYet al., 2016,

    A New Energy Harvesting and Wireless Power Transfer System for Smart Grid

    , 7th IEEE International Symposium On Power Electronics for Distributed Generation Systems (PEDG), Publisher: IEEE, ISSN: 2329-5759
  • Conference paper
    Lee CK, Liu H, Zhang G, Yan S, Waffenschmidt E, Hui RSYet al., 2016,

    A Unified Converter Topology for Electric Spring

    , 7th IEEE International Symposium On Power Electronics for Distributed Generation Systems (PEDG), Publisher: IEEE, ISSN: 2329-5759
  • Conference paper
    Liu H, Lee CK, Hui RSY, Waffenschmidt Eet al., 2016,

    Capability Analysis and Design Considerations of Electric Springs

    , 7th IEEE International Symposium On Power Electronics for Distributed Generation Systems (PEDG), Publisher: IEEE, ISSN: 2329-5759
  • Conference paper
    Chen B, Parisini T, Polycarpou MM, 2016,

    A Deadbeat Estimator-Based Fault Isolation Scheme for Nonlinear Systems

    , European Control Conference (ECC), Publisher: IEEE, Pages: 734-739
  • Journal article
    Cao W, Wu J, Jenkins N, Wang C, Green Tet al., 2015,

    Benefits analysis of Soft Open Points for electrical distribution network operation

    , Applied Energy, Vol: 165, Pages: 36-47, ISSN: 1872-9118

    Soft Open Points (SOPs) are power electronic devices installed in place of normally-open points in electrical power distribution networks. They are able to provide active power flow control, reactive power compensation and voltage regulation under normal network operating conditions, as well as fast fault isolation and supply restoration under abnormal conditions. A steady state analysis framework was developed to quantify the operational benefits of a distribution network with SOPs under normal network operating conditions. A generic power injection model was developed and used to determine the optimal SOP operation using an improved Powell’s Direct Set method. Physical limits and power losses of the SOP device (based on back to back voltage-source converters) were considered in the model. Distribution network reconfiguration algorithms, with and without SOPs, were developed and used to identify the benefits of using SOPs. Test results on a 33-bus distribution network compared the benefits of using SOPs, traditional network reconfiguration and the combination of both. The results showed that using only one SOP achieved a similar improvement in network operation compared to the case of using network reconfiguration with all branches equipped with remotely controlled switches. A combination of SOP control and network reconfiguration provided the optimal network operation.
  • Journal article
    Suardi A, Longo S, Kerrigan EC, Constantinides GAet al., 2015,

    Explicit MPC: hard constraint satisfaction under low precision arithmetic

    , Control Engineering Practice, Vol: 47, Pages: 60-69, ISSN: 1873-6939

    MPC is becoming increasingly implemented on embedded systems, where low precision computation is preferred either to reduce costs, speedup execution or reduce power consumption. However, in a low precision implementation, constraint satisfaction cannot be guaranteed. To enforce constraint satisfaction under numerical errors, we adopt tools from forward error analysis to compute an error bound on the output of the embedded controller. We treat this error as a state disturbance and use it to inform the design of a constraint-tightening robust controller. The technique is validated via a practical implementation on an FPGA evaluation board.
  • Journal article
    Ramirez PJ, Papadaskalopoulos D, Strbac G, 2015,

    Co-Optimization of Generation Expansion Planning and Electric Vehicles Flexibility

    , IEEE Transactions on Smart Grid, Vol: 7, Pages: 1609-1619, ISSN: 1949-3061

    The envisaged de-carbonization of power systems poses unprecedented challenges enhancing the potential of flexible demand. However, the incorporation of the latter in system planning has yet to be comprehensively investigated. This paper proposes a novel planning model that allows co-optimizing the investment and operating costs of conventional generation assets and demand flexibility, in the form of smart-charging/discharging electric vehicles (EV). The model includes a detailed representation of EV operational constraints along with the generation technical characteristics, and accounts for the costs required to enable demand flexibility. Computational tractability is achieved through clustering generation units and EV, which allows massively reducing the number of decision variables and constraints, and avoiding non-linearities. Case studies in the context of the U.K. demonstrate the economic value of EV flexibility in reducing peak demand levels and absorbing wind generation variability, and the dependence of this value on the required enabling cost and users' traveling patterns.
  • Journal article
    Cao W, Wu J, Jenkins N, Wang C, Green Tet al., 2015,

    Operating principle of Soft Open Points for electrical distribution network operation

    , Applied Energy, Vol: 164, Pages: 245-257, ISSN: 1872-9118

    Soft Open Points (SOPs) are power electronic devices installed in place of normally-open points in electrical power distribution networks. They are able to provide active power flow control, reactive power compensation and voltage regulation under normal network operating conditions, as well as fast fault isolation and supply restoration under abnormal conditions. Two control modes were developed for the operation of an SOP, using back-to-back voltage-source converters (VSCs). A power flow control mode with current control provides independent control of real and reactive power. A supply restoration mode with a voltage controller enables power supply to isolated loads due to network faults. The operating principle of the back-to-back VSCs based SOP was investigated under both normal and abnormal network operating conditions. Studies on a two-feeder medium-voltage distribution network showed the performance of the SOP under different network-operating conditions: normal, during a fault and post-fault supply restoration. During the change of network operating conditions, a mode switch method based on the phase locked loop controller was used to achieve the transitions between the two control modes. Hard transitions by a direct mode switching were noticed unfavourable, but seamless transitions were obtained by deploying a soft cold load pickup and voltage synchronization process.
  • Conference paper
    Kerrigan EC, Constantinides GA, Suardi A, Picciau A, Khusainov Bet al., 2015,

    Computer Architectures to Close the Loop in Real-time Optimization

    , 54th IEEE Conference on Decision and Control, Publisher: IEEE, Pages: 4597-4611

    Many modern control, automation, signal processing and machine learning applications rely on solving a sequence of optimization problems, which are updated with measurements of a real system that evolves in time. The solutions of each of these optimization problems are then used to make decisions, which may be followed by changing some parameters of the physical system, thereby resulting in a feedback loop between the computing and the physical system. Real-time optimization is not the same as `fast' optimization, due to the fact that the computation is affected by an uncertain system that evolves in time. The suitability of a design should therefore not be judged from the optimality of a single optimization problem, but based on the evolution of the entire cyber-physical system. The algorithms and hardware used for solving a single optimization problem in the office might therefore be far from ideal when solving a sequence of real-time optimization problems. Instead of there being a single, optimal design, one has to trade-off a number of objectives, including performance, robustness, energy usage, size and cost. We therefore provide here a tutorial introduction to some of the questions and implementation issues that arise in real-time optimization applications. We will concentrate on some of the decisions that have to be made when designing the computing architecture and algorithm and argue that the choice of one informs the other.
  • Conference paper
    Liu C, Jaimoukha I, 2015,

    The Computation of Full-complexity Polytopic Robust Control Invariant Sets

    , 54th Conference on Decision and Control, Publisher: IEEE, Pages: 6233-6238

    This paper considers the problem of evaluating robust control invariant (RCI) sets for linear discrete-time systems subject to state and input constraints as well as additive disturbances. An RCI set has the property that if the system state is inside the set at any one time, then it is guaranteed to remain in the set for all future times using a pre-defined state feedback control law. This problem is important in many control applications. We present a numerically efficient algorithm for the computation of full-complexity polytopic RCI sets. Farkas' Theorem is first used to derive necessary and sufficient conditions for the existence of an admissible polytopic RCI set in the form of nonlinear matrix inequalities. An Elimination Lemma is then used to derive sufficient conditions, in the form of linear matrix inequalities, for the existence of the solution. An optimization algorithm to approximate maximal RCI sets is also proposed. Numerical examples are given to illustrate the effectiveness of the proposed algorithm.
  • Conference paper
    Padoan A, Astolfi A, 2015,

    Towards deterministic subspace identification for autonomous nonlinear systems

    , 54th IEEE Conference on Decision and Control, Publisher: IEEE, Pages: 127-132

    The problem of identifying deterministic autonomous linear and nonlinear systems is studied. A specific version of the theory of deterministic subspace identification for discrete-time autonomous linear systems is developed in continuous time. By combining the subspace approach to linear identification and the differential-geometric approach to nonlinear control systems, a novel identification framework for continuous-time autonomous nonlinear systems is developed.
  • Conference paper
    Padoan A, Astolfi A, 2015,

    Dimension estimation for autonomous nonlinear systems

    , 54th IEEE Conference on Decision and Control, Publisher: IEEE, Pages: 103-108

    The problem of estimating the dimension of the state-space of an autonomous nonlinear system is considered. Assuming that sampled measurements of the output and finitely many of its time derivatives are available, an exhaustive search algorithm able to retrieve the dimension of the minimal state-space realization is proposed. The performance of the algorithm are evaluated on specific nonlinear systems.
  • Journal article
    Jiang J, Astolfi A, 2015,

    State and output-feedback shared-control for a class of linear constrained systems

    , IEEE Transactions on Automatic Control, Vol: 61, Pages: 3209-3214, ISSN: 0018-9286

    This paper presents state and output feedback sharedcontrol algorithms for a class of linear systems in the presence of constraints on the output described by means of linear inequalities. The properties of the closed-loop shared-control systems are studied using Lyapunov arguments. Simulation results demonstrate the effectiveness of the algorithm.
  • Journal article
    Yin J, Lin D, Parisini T, Ron Hui SYet al., 2015,

    Front-End Monitoring of the Mutual Inductance and Load Resistance in a Series-Series Compensated Wireless Power Transfer System

    , IEEE Transactions on Power Electronics, Vol: 31, Pages: 7339-7352, ISSN: 1941-0107

    In this paper, a new method to estimate the mutual inductance and load resistance in a series-series compensated wireless power transfer system is presented. Reasonably accurate estimations can be obtained from measurements of the input voltage and current obtained at one operating frequency only. The proposal can be used to dynamically monitor both the coupling relationship between the transmitter and receiver coils and the load conditions without any direct measurement on the receiver side. It can also be used as a simple method to measure the mutual inductance of any pair of coupled coils. A novel impedance spectrum analysis method is further presented to show that series-series compensation has special characteristics in its input impedance spectrum. Experimental results with acceptable tolerance are included to show the effectiveness of the proposed method.
  • Conference paper
    Palma V, Suardi A, Kerrigan EC, 2015,

    Sensitivity-based multistep MPC for embedded systems

    , 5th IFAC Conference on Nonlinear Model Predictive Control 2015 (NMPC'15), Publisher: Elsevier, Pages: 360-365, ISSN: 1474-6670

    In model predictive control (MPC), an optimization problem is solved every sampling instant to determine an optimal control for a physical system. We aim to accelerate this procedure for fast systems applications and address the challenge of implementing the resulting MPC scheme on an embedded system with limited computing power. We present the sensitivity-based multistep MPC, a strategy which considerably reduces the computing requirements in terms of floating point operations (FLOPs), compared to a standard MPC formulation, while fulfilling closed- loop performance expectations. We illustrate by applying the method to a DC-DC converter model and show how a designer can optimally trade off closed-loop performance considerations with computing requirements in order to fit the controller into a resource-constrained embedded system.
  • Conference paper
    Kerrigan EC, 2015,

    Feedback and time are essential for the optimal control of computing systems

    , 5th IFAC Conference on Nonlinear Model Predictive Control, Publisher: Elsevier, Pages: 380-387, ISSN: 1474-6670

    The performance, reliability, cost, size and energy usage of computing systems can be improved by one or more orders of magnitude by the systematic use of modern control and optimization methods. Computing systems rely on the use of feedback algorithms to schedule tasks, data and resources, but the models that are used to design these algorithms are validated using open-loop metrics. By using closed-loop metrics instead, such as the gap metric developed in the control community, it should be possible to develop improved scheduling algorithms and computing systems that have not been over-engineered. Furthermore, scheduling problems are most naturally formulated as constraint satisfaction or mathematical optimization problems, but these are seldom implemented using state of the art numerical methods, nor do they explicitly take into account the fact that the scheduling problem itself takes time to solve. This paper makes the case that recent results in real-time model predictive control, where optimization problems are solved in order to control a process that evolves in time, are likely to form the basis of scheduling algorithms of the future. We therefore outline some of the research problems and opportunities that could arise by explicitly considering feedback and time when designing optimal scheduling algorithms for computing systems.

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