Imperial College London


Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Reader in Control Engineering and Optimization



+44 (0)20 7594 6343e.kerrigan Website




Miss Michelle Hammond +44 (0)20 7594 6281




1108cElectrical EngineeringSouth Kensington Campus






Model predictive control; Co-design of cyber-physical systems; Efficient numerical methods for solving dynamic optimization, control and estimation problems; Embedded and distributed computing architectures for solving optimization, control and estimation problems; Control and estimation of infinite-dimensional systems, such as distributed parameter and time delay systems.


Control of aerial and mobile robotic communication networks; Control of real-time computing systems; Gust and load alleviation in wind turbine blades; Skin friction drag reduction over aerofoils; 


ICLOCS: Solves nonlinear optimal control problems subject to constraints.

SPLITC 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.

Current interests

Optimal Co-design and Control of Cyber-Physical Systems

Cyber-physical systems (CPS) are composed of physical systems that affect computations, and vice versa, in a closed loop. By tightly integrating computing with physical systems one can design CPS that are smarter, cheaper, more reliable, efficient and environmentally friendly than systems based on physical design alone. Examples include modern automobiles, aircraft and trains, power systems, medical devices and manufacturing processes. CPS co-design

The key questions for CPS design are what, where, when and how accurate to measure, compute, communicate and store? My team is providing answers to these questions by developing control systems theory and mathematical optimization methods to automatically design the algorithms, computer architecture and physical system at the same time. This co-design process results in a better overall system compared to iterative methods, where sub-systems are independently designed and optimized.

The main technical challenge in CPS co-design is to merge abstractions from physics with computer science: the study of physical systems is based on differential equations, continuous mathematics and analogue data, whereas the study of computing systems is based on logical operations, discrete mathematics and digital data. Furthermore, while a computation is being carried out, time is ticking and the system continues to evolve according to the laws of physics. A designer therefore has to trade off system performance, robustness and physical resources against the timing and accuracy of measurements, communications, computations and model fidelity.

We are therefore developing methods to: (i) understand and exploit the hybrid and real-time nature of CPS, (ii) model and solve the non-smooth and uncertain mathematical optimization problems that result during the co-design process, and (iii) solve constrained, nonlinear optimization algorithms in real-time on embedded and distributed computing systems.

My talk on cyber-physical systems, hosted by the IET, is now available (audio starts at 1:05). See also the companion talk by Andy Chang from National Instruments with a further Q&A from both of us.

REAL-tIME Optimization and CONTROL of Computing Systems

Computing systems may be composed of reliable and efficient components, but reliability of the overall system comes at the cost of large inefficiencies due to over-engineering. Information and Communication Technologies (ICT) are responsible for more than 10% of electricty consumption worldwide, a figure that is expected to grow to more than 14% by 2020. Though the GeSI SMARTer 2020 report claims that ICT-enabled solutions has the potential to reduce greenhouse gas emissions by 16.5% in 2020, there is clearly a need for computing systems to reduce their own contribution to these emissions. 

Every computing system today employs feedback in some form or other to guarantee a certain level of performance and reliability in the presence of uncertainty, such as unpredictable work-loads, delays, data losses, cyber attacks and component failures. Data, tasks and resources (such as processors, memory, storage and communication networks) need to be managed to balance loads, achieve a certain quality of service, guarantee that computations are correct and ensure that tasks are completed before deadlines. Feedback is also used to minimize power consumption by dynamic voltage and frequency scaling and smart scheduling of jobs. What is lacking, however, is a complete theory that allows computer engineers to understand how components interact with each other and what effect this has on overall system behavior. Feedback in Computing Systems

The aim of our research is therefore to develop new control theory and mathematical optimization methods for designing computing systems that are at least one order of magnitude more energy efficient, cheaper, faster, smaller and more reliable than today. Our work is currently focused on achieving this by developing: (i) efficient optimization-based algorithms for hard and soft real-time scheduling problems; (ii) scalable distributed scheduling algorithms based on recent methods in cooperative control theory; (iii) methods for modeling computing systems that capture the dynamics essential for feedback design using closed-loop, rather than open-loop metrics.

For an overview of some of the research questions that we are currently working on, see my paper on why Feedback and Time are Essential for the Optimal Control of Computing Systems.



Dr George Constantinides, Imperial College London

Mike Graham, Department of Aeronautics, Imperial College London

Rafael Palacios, Department of Aeronautics

Jonathan Morrison, Department of Aeronautics, Imperial College London

Keck-Voon Ling, NTU Singapore

Jan Maciejowski, University of Cambridge

BASF, ETH, IPCOS, KU Leuven, LMS International, National Instruments, Otto-von-Guericke-Universitaet Magdeburg, TU Dortmund, University Politehnica Bucharest, Universitaet Heidelberg, EC FP7, EMBOCON, 2010 - 2013

Guest Lectures

Plenary: Feedback and Time for Computer Design, Control and Optimisation UK, Oxford, 2016

Semi-plenary: Feedback and Time Are Essential for the Optimal Control of Computing Systems, 5th IFAC Conference on Nonlinear Model Predictive Control 2015 (NMPC'15), Seville, Spain, 2015

Number Representations for Embedding Optimization Algorithms in Cyber-Physical Systems, Invited talk, Workshop on the Control of Cyber-Physical Systems, UK, 2012

Faster, Easier, Cheaper, Safer, Main talk, International Workshop on Assessment and Future Directions of Nonlinear Model Predictive Control, Italy, 2008

Number representation in predictive control, Semi-plenary, IFAC Conference on Nonlinear Model Predictive Control 2012 (NMPC'12), The Netherlands, 2012

Research Staff


Research Student Supervision

Zhang,L, Controlling big data in a smart grid

Picciau,A, Accelerator Technologies for Structured Mixed-Integer Programming

Tammawichai,M, Real-time scheduling of computing systems using control theory

Vemuri,H, Feedback control of Tollmien-Schlichting waves

Ge,M, Estimation and prediction of ocean waves

Feng,Z, Efficient optimization methods for the control of wave energy converters

Ng,BF, Active aeroelastic design of large wind turbine blades for an increased fatigue life

Lopes,AR, Accelerating iterative methods for solving systems of linear equations

Ahmed,S, Satellite Attitude Estimation and Control using the Earth Magnetic Field

Abraham,E, Control of wave energy systems

Hasan,A, Control Theoretic Analysis and Design of Numerical Algorithms

Shahzad,A, New optimization methods for predictive control

Jerez,J, MPC for Deeply Pipelined FPGA Implementation: Algorithms and Circuitry

Jones,B, Control of fluid flows and other systems governed by partial differential-algebraic equations

Couchman,I, Optimal control of fluid mixing

Frederick,M, Load reduction using rapidly deployed trailing-edge flaps

Goulart,P, Affine feedback policies for robust control with constraints