My main area of research is the development of theory and methods for model predictive control (MPC) to handle nonlinearities and uncertainties in a systematic fashion. MPC is the most widely implemented advanced control technique in industry, because it deals with constraints, nonlinearities and uncertainties in a systematic and optimal manner. In MPC, a sequence of optimal control and estimation problems need to be solved in real-time – this requires orders of magnitude more computational resources than most other control methods. My interest is in the development of novel structure-exploiting numerical optimization methods and computer architectures for solving nonlinear optimal control and estimation problems with uncertainties in real-time. This allows engineers to solve new problems that are beyond the reach of current methods and computers.
I am also interested in developing new multi-objective optimization methods for the co-design of the overall closed-loop system. If one includes the parameters of the computer and physical system as part of the design variables, then it is possible to achieve significantly better performance than by just concentrating on the controller design. Mathematical optimization allows one to take a systematic approach to co-design in order to reduce design time and cost. I am therefore also interested in the development of novel multi-objective optimization methods for exploring how the parameters of an optimization-based control algorithm, computer architecture and physical design need to be traded off to satisfy performance specifications.
I have a joint appointment in the Department of Electrical & Electronic Engineering and the Department of Aeronautics. My theoretical research is therefore motivated by a wide variety of problems in the design of aerospace, renewable energy and information systems. Applications include scheduling of computation and communication in aerial and mobile robotic networks, aerodynamic drag reduction over aerofoils, gust and load alleviation in wind turbine blades and space launch and re-entry vehicles.
See my Google Scholar page for my most recent publications and preprints.
PHD STUDENTSHIPS AVAILABLE
If you are interested in doing a PhD under my supervision in the development of novel numerical methods and computer architectures for model predictive control and dynamic optimization, please contact me with your CV, transcript of your academic record and a personal statement. We will have a number of open studentships, which can be tailored to areas of mutual interest, available for applications submitted from October 2020 for start dates in 2021.
ICLOCS: Solves nonlinear optimal control problems subject to constraints.
SPLIT: C code generation for Model Predictive Control based on operator splitting methods. SPLIT is capable of generating both software and hardware-oriented C code to allow quick prototyping of optimization algorithms on conventional CPUs and field-programmable gate arrays (FPGAs). See our paper for more details.
protoip: Quickly prototype C-based IP in FPGA hardware. This tool abstracts many specific low-level FPGA design details and shifts the main focus to algorithm coding and boosts productivity.
ECC 2016 Tutorial Session on Embedded Optimization: Presentations can be downloaded here.
My talk on co-design of optimization-based controllers, given at MATLAB EXPO 2015, is now available.
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
2001-2005: Research Fellow, Wolfson College and Department of Engineering, University of Cambridge
2001-2002: Research Associate, Department of Engineering, University of Cambridge
1997-2001: PhD in Control Engineering, St John's College and Department of Engineering, University of Cambridge
1997: Electromechanical Engineer, Council for Scientific and Industrial Research (CSIR), South Africa
1993-1996: BSc(Eng) in Electrical Engineering, University of Cape Town
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
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
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
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
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
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
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
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
et al., 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
Neuenhofen MP, Kerrigan E, An integral penalty-barrier direct transcription method for optimal control, 59th IEEE Conference on Decision and Control 2020, IEEE
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