CONTROL THEORY AND OPTIMIZATION
My main interest is the development of novel methods for (i) the design of optimization-based controllers and (ii) the optimization-based design of the overall closed-loop system.
- Model predictive control (MPC): MPC is the most widely implemented advanced control technique in industry, because it allows one to deal with constraints and nonlinearities 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 unique expertise is the development of novel structure-exploiting numerical optimization methods and computer architectures for solving constrained and nonlinear optimal control and estimation problems in real-time.
- Optimal cyber-physical co-design of controlled systems: 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 only considering the design of the controller. Mathematical optimization allows one to take a systematic approach to co-design in order to reduce design time and cost. My focus is on 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.
My theory and methods are supported by cross-disciplinary applications. The aim is to not only use control and optimization to develop new fundamental insights and open up possibilities in these application areas, but also to use these applications to motivate the development of novel control and optimization methods.
- Information systems: Many fundamental problems in the scheduling of multi-processor computer and communication networks can be formulated as constrained, nonlinear optimal control problems. One can use this insight to develop control methods to solve problems that cannot be solved using existing approaches in the computing or communication literature. My current interest is the development of novel optimal control methods for jointly optimizing the computation, communication and propulsion of aerial robotic information-gathering networks.
- Aerospace systems: The applications that I am interested in include the reduction of aerodynamic drag, gust load alleviation in wind turbine blades, small satellites and energy-harvesting autonomous gliders. These applications are used to motivate (i) efficient structure-exploiting optimization methods for MPC, (ii) the development of low-order physics-based and data-augmented models for control to reduce computational requirements during design and implementation and (iii) optimization methods for the co-design of the controller and physical parameters, such as aerofoil shape and the sizing and placement of energy sources, sensors and actuators.
More on Optimal Co-design and Control
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.
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.
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.
- The Active Building Centre Research Programme, EPSRC/ISCF, 2020-2022.
- Automatic methods to trade off performance and computing resources for controlled systems, Royal Society, 2018-2019
- Marie Curie Initial Training Network in Embedded Predictive Control and Optimization (TEMPO), European Commission FP7, 2014-2018.
- Accellerator Technologies for Structured Mixed-Integer Programming Problems, Siemens Corporate Technology, 2013-2016.
- Embedded Controller Prototype, EPSRC, 2013-2014.
- Feedback control of Tollmien-Schlichting waves, EADS Innovation Works, 2012-2015.
- Embedded Optimization for Resource Constrained Platforms (EMBOCON), European Commission FP7, 2010-2013.
- Real-time Numerical Optimization in Reconfigurable Hardware with Application to Model-Predictive Control, EPSRC, 2009-2012.
- Nonlinear Robust Model Predictive Control, EPSRC, 2008-2011.
- Robust Control of Constrained Dynamic Systems. Royal Academy of Engineering Research Fellowship, 2002-2007.
- EPSRC Doctoral Training Account Awards for PhD studentships.
- Xilinx, inc. Project support.
- The Mathworks. Project support
- European Space Agency. Project support.
- National Instruments. Project support.
- LMS International. Project support
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
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
Dr George Constantinides, Imperial College London
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
Number representation in predictive control, Semi-plenary, IFAC Conference on Nonlinear Model Predictive Control 2012 (NMPC'12), The Netherlands, 2012
Faster, Easier, Cheaper, Safer, Main talk, International Workshop on Assessment and Future Directions of Nonlinear Model Predictive Control, Italy, 2008
Research Student Supervision
Abraham,E, Control of wave energy systems
Ahmed,S, Satellite Attitude Estimation and Control using the Earth Magnetic Field
Couchman,I, Optimal control of fluid mixing
Faqir,O, Optimal design and control of networked cyber-physical systems
Feng,Z, Efficient optimization methods for the control of wave energy converters
Frederick,M, Load reduction using rapidly deployed trailing-edge flaps
Ge,M, Estimation and prediction of ocean waves
Goulart,P, Affine feedback policies for robust control with constraints
Hasan,A, Control Theoretic Analysis and Design of Numerical Algorithms
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
Khusainov,B, Co-design of FPGA implementations for model predictive control
Lopes,AR, Accelerating iterative methods for solving systems of linear equations
McInerney,I, Computer architectures for model predictive control
Ng,BF, Active aeroelastic design of large wind turbine blades for an increased fatigue life
Nie,Y, Model predictive control with applications to aircraft
Picciau,A, Concurrency and data locality for sparse linear algebra on modern processors
Shahzad,A, New optimization methods for predictive control
Tammawichai,M, Real-time scheduling of computing systems using control theory
Vemuri,H, Feedback control of Tollmien-Schlichting waves
Zhang,L, Optimal scheduling in sensor networks