# DrEricKerrigan

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Reader in Control Engineering and Optimization

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### Contact

+44 (0)20 7594 6343e.kerrigan

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### Assistant

Miss Michelle Hammond +44 (0)20 7594 6281

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### Location

1108cElectrical EngineeringSouth Kensington Campus

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## Publications

Publication Type
Year
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127 results found

Thammawichai M, Kerrigan EC, 2018, Energy-efficient real-time scheduling for two-type heterogeneous multiprocessors, Real-Time Systems, Vol: 54, Pages: 132-165, ISSN: 0922-6443

JOURNAL ARTICLE

Bachtiar V, Manzie C, Kerrigan EC, 2017, Nonlinear Model-Predictive Integrated Missile Control and Its Multiobjective Tuning, Journal of Guidance, Control, and Dynamics, Vol: 40, Pages: 2961-2970, ISSN: 0731-5090

JOURNAL ARTICLE

Cantoni M, Farokhi F, Kerrigan E, Shames Iet al., 2017, Structured computation of optimal controls for constrained cascade systems, International Journal of Control, Pages: 1-10, ISSN: 0020-7179

JOURNAL ARTICLE

Faqir OJ, Kerrigan EC, Gunduz D, 2017, Joint optimization of transmission and propulsion in aerial communication networks, IEEE 56th Annual Conference on Decision and Control (CDC), Publisher: IEEE, ISSN: 0743-1546

CONFERENCE PAPER

Ge M, Kerrigan EC, 2017, Noise covariance identification for time-varying and nonlinear systems, International Journal of Control, Vol: 90, Pages: 1903-1915, ISSN: 0020-7179

JOURNAL ARTICLE

Khusainov B, Kerrigan EC, Suardi A, Constantinides GAet al., 2017, Nonlinear predictive control on a heterogeneous computing platform, 20th World Congress of the International-Federation-of-Automatic-Control (IFAC), Publisher: ELSEVIER SCIENCE BV, Pages: 11877-11882, ISSN: 2405-8963

CONFERENCE PAPER

Shukla HA, Khusainov B, Kerrigan EC, Jones CNet al., 2017, Software and Hardware Code Generation for Predictive Control Using Splitting Methods, 20th World Congress of the International-Federation-of-Automatic-Control (IFAC), Publisher: ELSEVIER SCIENCE BV, Pages: 14386-14391, ISSN: 2405-8963

CONFERENCE PAPER

Bachtiar V, Kerrigan EC, Moase WH, Manzie Cet al., 2016, Continuity and monotonicity of the MPC value function with respect to sampling time and prediction horizon, Automatica, Vol: 63, Pages: 330-337, ISSN: 0005-1098

JOURNAL ARTICLE

Bachtiar V, Manzie C, Moase WH, Kerrigan ECet al., 2016, Analytical results for the multi-objective design of model-predictive control, Control Engineering Practice, Vol: 56, Pages: 1-12, ISSN: 0967-0661

JOURNAL ARTICLE

Ge M, Kerrigan EC, 2016, Short-term ocean wave forecasting using an autoregressive moving average model, Control 2016 - 11th International Conference on Control, Publisher: IEEE

In order to predict future observations of a noisedrivensystem, we have to find a model that exactly or atleast approximately describes the behavior of the system sothat the current system state can be recovered from pastobservations. However, sometimes it is very difficult to modela system accurately, such as real ocean waves. It is thereforeparticularly interesting to analyze ocean wave properties inthe time-domain using autoregressive moving average (ARMA)models. Two ARMA/AR based models and their equivalent statespace representations will be used for predicting future oceanwave elevations, where unknown parameters will be determinedusing linear least squares and auto-covariance least squaresalgorithms. Compared to existing wave prediction methods, inthis paper (i) an ARMA model is used to enhance the predictionperformance, (ii) noise covariances in the ARMA/AR model arecomputed rather than guessed and (iii) we show that, in practice,low pass filtering of historical wave data does not improve theforecasting results.

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

Khusainov B, Kerrigan EC, Constantinides GA, 2016, Multi-objective Co-design for Model Predictive Control with an FPGA, European Control Conference (ECC), Publisher: IEEE, Pages: 110-115

CONFERENCE PAPER

Lee KW, Moase W, Ooi A, Manzie C, Kerrigan ECet al., 2016, Optimization framework for codesign of controlled aerodynamic systems, AIAA Journal, Vol: 54, Pages: 3149-3159, ISSN: 0001-1452

© 2016 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. Optimization studies of dynamic systems using high-fidelity numerical models necessitate a tradeoff between fidelity and the total computational time required during design.Agradient-based optimization framework is proposed for the aerodynamic shape and controller design of aerodynamic systems using computationally intensive high-fidelitymodels. Subject to some general properties, the framework offers flexibility in the types of simulation models used and provides guarantees regarding closeness to an optimal design. A nested optimization loop that allows for the partitioning of controller and plant architecture is implemented. The proposed framework exploits time-scale properties of the dynamic system model, closeness properties of partially converged iterative solutions of computational fluid dynamics models, and the continuous adjoint method. It is shown that combining these methods can improve the total computational time relative to finitedifferencing.Anexample of optimizing the aerodynamic body and control gains of a tail-fin controlled supersonic missile is presented.

JOURNAL ARTICLE

Ng BF, Palacios R, Kerrigan EC, Graham JMR, Hesse Het al., 2016, Aerodynamic load control in horizontal axis wind turbines with combined aeroelastic tailoring and trailing-edge flaps, WIND ENERGY, Vol: 19, Pages: 243-263, ISSN: 1095-4244

JOURNAL ARTICLE

Picciau A, Inggs GE, Wickerson J, Kerrigan EC, Constantinides GAet al., 2016, Balancing locality and concurrency: solving sparse triangular systems on GPUs, 23rd IEEE International Conference on High Performance Computing (HiPC), Publisher: IEEE, Pages: 183-192, ISSN: 1094-7256

CONFERENCE PAPER

Suardi A, Longo S, Kerrigan EC, Constantinides GAet al., 2016, Explicit MPC: Hard constraint satisfaction under low precision arithmetic, CONTROL ENGINEERING PRACTICE, Vol: 47, Pages: 60-69, ISSN: 0967-0661

JOURNAL ARTICLE

Thammawichai M, Kerrigan EC, 2016, Feedback Scheduling for Energy-Efficient Real-Time Homogeneous Multiprocessor Systems, 55th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 1643-1648, ISSN: 0743-1546

CONFERENCE PAPER

Abraham E, Kerrigan EC, 2015, Lower-Order <formula formulatype="inline"><tex Notation="TeX">$H_{\infty}$</tex></formula> Filter Design for Bilinear Systems With Bounded Inputs, IEEE Transactions on Signal Processing, Vol: 63, Pages: 895-906, ISSN: 1053-587X

JOURNAL ARTICLE

Feng Z, Kerrigan EC, 2015, Latching-Declutching Control of Wave Energy Converters Using Derivative-Free Optimization, IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, Vol: 6, Pages: 773-780, ISSN: 1949-3029

JOURNAL ARTICLE

Jerez JL, Constantinides GA, Kerrigan EC, 2015, A Low Complexity Scaling Method for the Lanczos Kernel in Fixed-Point Arithmetic, IEEE TRANSACTIONS ON COMPUTERS, Vol: 64, Pages: 303-315, ISSN: 0018-9340

JOURNAL ARTICLE

Jones BL, Heins PH, Kerrigan EC, Morrison JF, Sharma ASet al., 2015, Modelling for robust feedback control of fluid flows, JOURNAL OF FLUID MECHANICS, Vol: 769, Pages: 687-722, ISSN: 0022-1120

JOURNAL ARTICLE

Jones BL, Kerrigan EC, Morrison JF, 2015, A modeling and filtering framework for the semi-discretised Navier-Stokes equations, Pages: 1215-1220

© 2009 EUCA. Spatial discretisation of fluid mechanical systems typically leads to descriptor systems consisting of large numbers of differential algebraic equations (DAEs). In an effort to apply standard control theory to such systems, physical insight is often used to analytically reformulate the DAE as an ordinary differential equation (ODE). In general, this is a difficult process that typically requires expert insight into specific systems, and so in this work we consider a more flexible numerical method that is straightforward to implement on any regular DAE. The numerical procedure is outlined and a new method for computing one of the steps is presented.With respect to Kalman filtering of descriptor systems, it is known in general that 'process noise' can sometimes not be added to all states owing to a violation of causality. In this paper we present a new method for computing the subspace of causal disturbances, suitable for large DAEs. Finally, the techniques developed in this paper are applied to the specific case of plane Poiseuille flow and it is shown how a standard state-space system is easily obtained that possesses a similar spectrum and pseudospectrum to the Orr-Somerfeld-Squire system.

CONFERENCE PAPER

Kerrigan EC, 2015, Feedback and Time are Essential for the Optimal Control of Computing Systems, Pages: 380-387, ISSN: 2405-8963

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 (CDC), Publisher: IEEE, Pages: 4597-4611

CONFERENCE PAPER

Liu Y, Van Schijndel J, Longo S, Kerrigan ECet al., 2015, UAV Energy Extraction With Incomplete Atmospheric Data Using MPC, IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, Vol: 51, Pages: 1203-1215, ISSN: 0018-9251

JOURNAL ARTICLE

Ng BF, Hesse H, Palacios R, Graham JMR, Kerrigan ECet al., 2015, Aeroservoelastic state-space vortex lattice modeling and load alleviation of wind turbine blades, WIND ENERGY, Vol: 18, Pages: 1317-1331, ISSN: 1095-4244

JOURNAL ARTICLE

Palma VG, Suardi A, Kerrigan EC, 2015, Sensitivity-based multistep MPC for embedded systems, Pages: 360-365, ISSN: 2405-8963

CONFERENCE PAPER

Suardi A, Kerrigan EC, Constantinides GA, 2015, Fast FPGA prototyping toolbox for embedded optimization, European Control Conference (ECC), Publisher: IEEE, Pages: 2589-2594

CONFERENCE PAPER

Ahmed S, Kerrigan EC, 2014, Suboptimal Predictive Control for Satellite Detumbling, JOURNAL OF GUIDANCE CONTROL AND DYNAMICS, Vol: 37, Pages: 850-859, ISSN: 0731-5090

JOURNAL ARTICLE

Feng Z, Kerrigan EC, 2014, Declutching control of wave energy converters using derivative-free optimization, Pages: 7647-7652, ISSN: 1474-6670

© IFAC. We propose a novel formulation for declutching control of wave energy converters with the power takeoff time as the only decision variable. The optimal control problem is modeled as a single-variable optimization problem, thereby making real-time implementation a possibility. We present a derivate-free optimization algorithm that exploits the quantization of the decision variable in order to reduce the number of function evaluations needed to compute a solution. We propose two receding horizon closed-loop strategies: the first one uses past wave information and can increase the energy generation by 42% compared to the uncontrolled case, while the second formulation uses predictions of future waves and results in a further 40% increase in energy generation. For irregular waves with peak periods longer than 6 s, one can generate at least four times more energy when co-designing the physical system and controller, compared to a controlled system that was optimized without a controller in the feedback loop.

CONFERENCE PAPER

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