Summary
I specialise in Model Predictive Control (MPC) and its applications. MPC is a popular advanced control method used in various industries, because it handles constraints, nonlinearities, and uncertainties in a systematic and optimal way. In MPC, we need to solve a sequence of challenging optimal control and estimation challenges in real-time, which demands significantly more computational power compared to other control methods. My focus is on developing innovative numerical optimization techniques and computer architectures that can efficiently solve the relevant nonlinear optimal control and estimation problems with uncertainties in real-time. This opens the door to solving previously unattainable engineering problems.
I'm also interested in developing new multi-objective optimization methods for co-designing entire closed-loop systems. By considering both computer and physical system parameters as design variables, we can achieve significantly improved performance compared to solely concentrating on controller design. Mathematical optimization allows us to systematically handle the co-design process, reducing design time and costs. For this purpose, we are developing novel multi-objective optimization techniques that help us balance the trade-offs between optimization-based control algorithms, computer architectures, and physical designs to meet performance specifications.
My appointment spans across two departments: Electrical & Electronic Engineering and Aeronautics. This dual role drives my research, as it covers a wide range of problems in aerospace, renewable energy, and information systems. Practical applications include optimizing computation and communication schedules in aerial robotic networks, reducing aerodynamic drag, mitigating gusts, and managing loads in wind turbine blades, and enhancing the performance of space launch and re-entry vehicles.
See my Google Scholar page for my most recent publications and preprints.
PHD STUDENTSHIPS AVAILABLE
The department and College have a number of open studentships, which can be tailored to areas of mutual interest, for start dates in 2024. If you are interested in doing a PhD under my supervision in the development of novel numerical methods for predictive control and dynamic optimization, please contact me with your CV, transcript of your academic record and a personal statement. Unfortunately, because of the high number of emails that I receive, I will only reply to those who I think will stand a good chance of securing a place.
Software
ICLOCS: Solves nonlinear optimal control problems subject to constraints.
SELECTED Media
My inaugural lecture on "Making Decisions with Known Unknowns".
My talk on co-design of optimization-based controllers, given at MATLAB EXPO 2015.
Background
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2006-present: Department of Aeronautics and Department of Electrical and Electronic Engineering, Imperial College London
- 2014: Sabbatical Visitor, Department of Electrical and Electronic Engineering, University of Melbourne
- 2002-2007: Royal Academy of Engineering Research Fellow, University of Cambridge and Imperial College London
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2001-2005: Research Fellow, Wolfson College and Department of Engineering, University of Cambridge
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2001-2002: Research Associate, Department of Engineering, University of Cambridge
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1997-2001: PhD in Control Engineering, St John's College and Department of Engineering, University of Cambridge
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1997: Electromechanical Engineer, Council for Scientific and Industrial Research (CSIR), South Africa
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1993-1996: BSc(Eng) in Electrical Engineering, University of Cape Town
Selected Publications
Journal Articles
Nie Y, Kerrigan E, 2020, Efficient and more accurate representation of solution trajectories in numerical optimal control, Ieee Control Systems Letters, Vol:4, ISSN:2475-1456, Pages:61-66
Khusainov B, Kerrigan EC, Suardi A, et al., 2018, Nonlinear predictive control on a heterogeneous computing platform, Control Engineering Practice, Vol:78, ISSN:0967-0661, Pages:105-115
Khusainov B, Kerrigan EC, Constantinides G, 2018, Automatic software and computing hardware co-design for predictive control, IEEE Transactions on Control Systems Technology, Vol:27, ISSN:1063-6536, Pages:2295-2304
Thammawichai M, Baliyarasimhuni SP, Kerrigan EC, et al., 2018, Optimizing communication and computation for multi-UAV information gathering applications, IEEE Transactions on Aerospace and Electronic Systems, Vol:54, ISSN:0018-9251, Pages:601-615
Thammawichai M, Kerrigan EC, 2017, Energy-efficient real-time scheduling for two-type heterogeneous multiprocessors, Real-Time Systems, Vol:54, ISSN:0922-6443, Pages:132-165
Cantoni M, Farokhi F, Kerrigan EC, et al., 2020, Structured computation of optimal controls for constrained cascade systems, International Journal of Control, Vol:93, ISSN:0020-7179, Pages:30-39
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, ISSN:1533-3884, Pages:2961-2970
Lee KW, Moase W, Ooi A, et al., 2016, Optimization Framework for Codesign of Controlled Aerodynamic Systems, AIAA Journal, Vol:54, ISSN:1533-385X, Pages:3149-3159
Ge M, Kerrigan EC, 2016, Noise Covariance Identification for Time-varying and Nonlinear Systems, International Journal of Control, Vol:90, ISSN:1366-5820, Pages:1903-1915
Jones BL, Heins PH, Kerrigan EC, et al., 2015, Modelling for robust feedback control of fluid flows, Journal of Fluid Mechanics, Vol:769, ISSN:0022-1120, Pages:687-722
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, ISSN:0018-9340, Pages:303-315
Jerez JL, Goulart PJ, Richter S, et al., 2014, Embedded Online Optimization for Model Predictive Control at Megahertz Rates, IEEE Transactions on Automatic Control, Vol:59, ISSN:0018-9286, Pages:3238-3251
Hartley EN, Jerez JL, Suardi A, et al., 2014, Predictive control using an FPGA with application to aircraft control, IEEE Transactions on Control Systems Technology, Vol:22, ISSN:1558-0865, Pages:1006-1017
Ahmed S, Kerrigan EC, 2014, Suboptimal predictive control for satellite detumbling, Journal of Guidance Control and Dynamics, Vol:37, ISSN:1533-3884, Pages:850-859
Longo S, Kerrigan EC, Constantinides GA, 2014, Constrained LQR for low-precision data representation, Automatica, Vol:50, ISSN:0005-1098, Pages:162-168
Shahzad A, Kerrigan EC, Constantinides GA, 2012, A Stable and Efficient Method for Solving a Convex Quadratic Program with Application to Optimal Control, SIAM Journal on Optimization, Vol:22, ISSN:1052-6234, Pages:1369-1393
Jones BL, Kerrigan EC, 2010, When is the discretization of a spatially distributed system good enough for control?, Automatica, Vol:46, ISSN:0005-1098, Pages:1462-1468
Goulart PJ, Kerrigan EC, Ralph D, 2007, Efficient Robust Optimization for Robust Control with Constraints., Mathematical Programming, Vol:114, ISSN:1436-4646, Pages:115-147
Goulart, P.J., Kerrigan, E.C., Maciejowski, J.M., 2006, Optimization over state feedback policies for robust control with constraints, Automatica, Vol:42, ISSN:0005-1098, Pages:523-533
Conference
Kerrigan E, Nie Y, Faqir O, et al., 2021, Direct transcription for dynamic optimization: a tutorial with a case study on dual-patient ventilation during the COVID-19 pandemic, 59th IEEE Conference on Decision and Control 2020, IEEE, Pages:2597-2614
Neuenhofen MP, Kerrigan E, 2021, An integral penalty-barrier direct transcription method for optimal control, 59th IEEE Conference on Decision and Control 2020, IEEE, Pages:456-463
Kerrigan EC, Constantinides GA, Suardi A, et al., 2015, Computer Architectures to Close the Loop in Real-time Optimization, 54th IEEE Conference on Decision and Control, IEEE, Pages:4597-4611
Kerrigan EC, 2015, Feedback and time are essential for the optimal control of computing systems, 5th IFAC Conference on Nonlinear Model Predictive Control, Elsevier, Pages:380-387, ISSN:1474-6670