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. 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 classical control methods. My particular area of expertise is in the design of efficient numerical methods and computer architectures for solving these optimal control and estimation problems in real-time.
I am also interested in developing new multi-objective optimization methods for the co-design of the overall closed-loop system. These methods can be used to explore how the complexities of the algorithms, computer 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, aerodynamic drag reduction over aerofoils, gust and load alleviation in wind turbine blades and control of small 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 development of novel numerical methods and 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 after October 2019 for start dates in 2020.
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
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
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, 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., 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
Kerrigan EC, 2014, Co-design of Hardware and Algorithms for Real-time Optimization, 2014 European Control Conference (ECC), IEEE, Pages:2484-2489