Dr Paul Trodden

School of Electrical and Electronic Engineering

Senior Lecturer

Theme Lead for Control

p.trodden@sheffield.ac.uk
+44 114 222 5679

Amy Johnson Building

Full contact details

Dr Paul Trodden
School of Electrical and Electronic Engineering
Amy Johnson Building
Portobello Street
Sheffield
S1 3JD

Profile: I studied Engineering Science as an undergraduate at the University of Oxford, graduating in 2003. After a short spell in industry, working as a Systems Engineer for BAE Systems, I studied for a PhD in Aerospace Engineering at the University of Bristol, graduating in 2009. The title of my PhD thesis was Robust distributed control for constrained linear systems.

After Bristol, I joined the School of Mathematics at the University of Edinburgh, where I worked on problems related to optimization in electrical power systems.

Since 2012 I have been with the University of Sheffield, first as a Lecturer (2012–2021) and then as a Senior Lecturer (2022–).

My research interests lie in analysing and designing controllers for dynamic systems that are subject to constraints; I have particular interest and expertise in Model Predictive Control (MPC). I am interested in applications of control and optimization to problems in aerospace and power systems.

Research interests: Model predictive control
My research aims to develop novel theory and algorithms for model predictive control (MPC). MPC is an advanced control technique that uses online optimization to provide optimal but safe control inputs for a system whose variables (inputs, states and outputs) are subject to physical or operational limits. I am interested in several challenging aspects and instances of this problem, including distributed MPC for large-scale systems, adaptive and robust MPC for uncertain and time-varying systems, and networked MPC for systems that communicate with their controllers over imperfect channels. My interest is in developing new theory and algorithms for MPC, with focus on developing controllers that are implementable (in terms of design simplicity and computational tractability) yet achieve desirable guarantees (e.g. of fundamental properties such as stability and constraint satisfaction).

Invariant set theory and computation
My research in this area involves developing theory and algorithms for the characterization and computation of invariant sets for uncertain systems. Invariant sets describes regions of a systems state space where safe and stable operation is guaranteed. These sets play an important and fundamental role in the analysis and design of advanced control systems.

Applications of control and optimization
Power systems and smart grids
My current research in this area applies predictive control theory and efficient optimization algorithms to control and planning problems in smart grids and power networks. I have published research on problems including optimal power flow (OPF), economic dispatch, load-frequency control, and controlled islanding for the prevention of blackouts.

Aerospace
I have a long standing interest in control and optimization for spacecraft, e.g. trajectory optimization, rendezvous and docking. I provide control research leadership to the Rolls-Royce University Technology Centre in Control, Monitoring and Systems Engineering, where our current research involves developing advanced control laws for the next generation of large civil gas turbine engines, and hybrid electric power plant.