My particular area of expertise in the design of efficient numerical methods and embedded computing architectures for solving advanced optimal control and estimation problems in real-time. Central to my work is the development of theory and methods for model predictive control (MPC) to handle nonlinearities and uncertainties in a systematic fashion. I am also interested in developing new multi-objective optimization methods for the design of cyber-physical systems, where physical systems affect computations and vice versa in a closed loop; by incorporating computational components into a system, one can engineer functionalities that are impossible by physical design alone. I am particularly interested in exploring how the complexities of the algorithms, computing architecture and physical realization need to be traded off to satisfy given system-wide 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, skin friction drag reduction over aerofoils, gust and load alleviation in wind turbine blades and control of satellites.
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 general area of model predictive control and optimization, please contact me with your CV, transcript of academic records and a personal statement. We have a number of open studentships available that can be tailored to areas of mutual interest. I am particularly looking for an outstanding student interested in a project that is in collaboration with Mathworks, the creators of Matlab and Simulink.
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
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, Embedded online optimization for model predictive control at megahertz rates, IEEE Transactions on Automatic Control, Vol:59, ISSN:0018-9286, Pages:3238-3251
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
Longo S, Kerrigan EC, Constantinides GA, 2014, Constrained LQR for low-precision data representation, Automatica, Vol:50, ISSN:0005-1098, Pages:162-168
et al., 2013, Predictive control using an FPGA with applicationto aircraft control, IEEE Transactions on Control Systems Technology, Vol:22, ISSN:1558-0865, Pages:1006-1017
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, 2008, Efficient robust optimization for robust control with constraints, Mathematical Programming, Vol:114, ISSN:0025-5610, 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
Kerrigan EC, 2015, Feedback and Time are Essential for the Optimal Control of Computing Systems, Pages:380-387, ISSN:2405-8963
et al., 2015, Computer Architectures to Close the Loop in Real-time Optimization, 54th IEEE Conference on Decision and Control (CDC), IEEE, Pages:4597-4611, ISSN:0743-1546
Kerrigan EC, 2014, Co-design of Hardware and Algorithms for Real-time Optimization, 2014 European Control Conference (ECC), IEEE, Pages:2484-2489