# 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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125 results found

Bachtiar V, Kerrigan EC, Moase WH, Manzie Cet al., Smoothness Properties of the MPC Value Function with respect to Sampling Time and Prediction Horizon, 10th Asian Control Conference (ASCC)

CONFERENCE PAPER

Cantoni M, Farokhi F, Kerrigan EC, Shames Iet al., A scalable QP solver for optimal control of cascades with constraints, Australian Control Conference

CONFERENCE PAPER

Ge M, Kerrigan E, Noise Covariance Estimation for Time-varying and Nonlinear Systems, The 19th World Congress of the International Federation of Automatic Control

CONFERENCE PAPER

Ge M, Kerrigan EC, Ocean Wave Forecasting Using an Autoregressive Moving Average Model, Control 2016 - 11th International Conference on Control

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, 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

Ge M, Kerrigan EC, Noise Covariance Identification for Nonlinear Systems using Expectation Maximization and Moving Horizon Estimation, Automatica, ISSN: 0005-1098

In order to estimate states from a noise-driven state space system, the state estimator requires a priori knowledge of bothprocess and output noise covariances. Unfortunately, noise statistics are usually unknown and have to be determined fromoutput measurements. Current expectation maximization (EM) based algorithms for estimating noise covariances for nonlinearsystems assume the number of additive process and output noise signals are the same as the number of states and outputs,respectively. However, in some applications, the number of additive process noises could be less than the number of states. Inthis paper, a more general nonlinear system is considered by allowing the number of process and output noises to be smaller orequal to the number of states and outputs, respectively. In order to estimate noise covariances, a semi-definite programmingsolver is applied, since an analytical solution is no longer easy to obtain. The expectation step in current EM algorithms relyon state estimates from the extended Kalman filter (EKF) or smoother. However, the instability and divergence problems ofthe EKF could cause the EM algorithm to converge to a local optimum that is far away from true values. We use movinghorizon estimation instead of the EKF/smoother so that the accuracy of the covariance estimation in nonlinear systems canbe significantly improved.

JOURNAL ARTICLE

Kerrigan EC, Khusainov B, Constantinides GA, What Is Different about Embedded Optimization?, European Control Conference (ECC), Publisher: IEEE, Pages: 600-600

CONFERENCE PAPER

Khusainov B, Kerrigan EC, Suardi A, Constantinides GAet al., Nonlinear predictive control on a heterogeneous computing platform, IFAC World Congress 2017, Publisher: IFAC

Nonlinear Model Predictive Control (NMPC) is an advanced control technique that often relieson computationally demanding optimization and integration algorithms. This paper proposesand investigates a heterogeneous hardware implementation of an NMPC controller based onan interior point algorithm. The proposed implementation provides flexibility of splitting theworkload between a general-purpose CPU with a fixed architecture and a field-programmablegate array (FPGA) to trade off contradicting design objectives, namely performance andcomputational resource usage. A new way of exploiting the structure of the Karush-Kuhn-Tucker(KKT) matrix yields significant memory savings, which is crucial for reconfigurable hardware.For the considered case study, a 10x memory savings compared to existing approaches anda 10x speedup over a software implementation are reported. The proposed implementationcan be tested from Matlab using a new release of the Protoip software tool, which isanother contribution of the paper. Protoip abstracts many low-level details of heterogeneoushardware programming and allows quick prototyping and processor-in-the-loop verification ofheterogeneous hardware implementations.

CONFERENCE PAPER

Shukla H, Khusainov B, Kerrigan EC, Jones CNet al., Software and hardware code generation for predictive control using splitting methods, IFAC World Congress 2017, Publisher: IFAC

This paper presents SPLIT, a C code generation tool for Model Predictive Control (MPC)based on operator splitting methods. In contrast to existing code generation packages, SPLIT iscapable of generating both software and hardware-oriented C code to allow quick prototyping ofoptimization algorithms on conventional CPUs and field-programmable gate arrays (FPGAs). AMatlab interface is provided for compatibility with existing commercial and open-source softwarepackages. A numerical study compares software, hardware and heterogeneous implementationsof splitting methods and investigates MPC design trade-offs. For the considered testcases thereported speedup of hardware implementations over software realizations is 3x to 11x.

CONFERENCE PAPER

Bachtiar V, Manzie C, Kerrigan EC, 2017, Nonlinear Model-Predictive Integrated Missile Control and Its Multiobjective Tuning, Journal of Guidance, Control, and Dynamics, Pages: 1-10, 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

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

Picciau A, Inggs GE, Wickerson J, Kerrigan EC, Constantinides GAet al., 2017, Balancing Locality and Concurrency: Solving Sparse Triangular Systems on GPUs, Pages: 183-192

© 2016 IEEE. Many numerical optimisation problems rely on fast algorithms for solving sparse triangular systems of linear equations (STLs). To accelerate the solution of such equations, two types of approaches have been used: on GPUs, concurrency has been prioritised to the disadvantage of data locality, while on multi-core CPUs, data locality has been prioritised to the disadvantage of concurrency. In this paper, we discuss the interaction between data locality and concurrency in the solution of STLs on GPUs, and we present a new algorithm that balances both. We demonstrate empirically that, subject to there being enough concurrency available in the input matrix, our algorithm outperforms Nvidia's concurrency-prioritising CUSPARSE algorithm for GPUs. Experimental results show a maximum speedup of 5.8-fold. Our solution algorithm, which we have implemented in OpenCL, requires a pre-processing phase that partitions the graph associated with the input matrix into sub-graphs, whose data can be stored in low-latency local memories. This preliminary analysis phase is expensive, but because it depends only on the input matrix, its cost can be amortised when solving for many different right-hand sides.

CONFERENCE PAPER

Thammawichai M, Kerrigan EC, 2017, Energy-Efficient Real-Time Scheduling for Two-Type Heterogeneous Multiprocessors, Real-Time Systems, ISSN: 0922-6443

JOURNAL ARTICLE

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

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

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

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