The CSPC research in control spans a wide range of theoretical and applications work.
Our work on fundamental theory, includes behavioural approaches to system theory; system identification particularly using structured low-rank approximations; multidimensional systems theory; robust nonlinear control; iterative learning control; adaptive control and flow control. We use a variety of techniques from linear algebra, functional analysis, partial differential equations and commutative algebra.
On the applications side we have a strong interest in the application of control to stroke rehabilitation systems (using a combination of robotic support and functional electrical stimulation) and in the benchmarking of iterative learning control systems on a variety of electro-mechanical platforms. We have current applied research interests in the control of atomic force microscopes; in sustainable policy design for climate change mitigation; and in the application of system identification in metrology.
In adaptive control, our work seeks to address long-standing open questions concerning the robustness and performance of such schemes, both for classical designs and for multiple model approaches. We have been active if the associated areas of nonlinear robust stability; particularly in developing nonlinear input-output approaches suited to applications in adaptive control.
In behavioural system theory ongoing work concerns the application of dissipativity ideas in identification and control, switched systems, and wireless sensor networks.
In system identification, we also use the behavioral approach, i.e., the models are viewed as sets of outcomes and are not a priori bound to particular representations. This formulation gives freedom in the choice of the most suitable representation for a particular purpose. Unless necessary, there is no a priori distinction between input and output variables and the approximation error is not assumed to be a stochastic process.
In sustainable policy design for climate change mitigation, we use concepts from modern systems control theory to develop algorithms for determining the optimal policy that both achieves sustainable levels of emissions of CO2 (and other greenhouse gases) and minimises the impact on the economy, but also explicitly addresses the high levels of uncertainty associated with predictions of future emissions and climate change. Including uncertainty within the design quantifies the risk associated with the emissions policy, which allows policy makers and emitters of CO2 to incorporate risk within their strategic plans.
The academic staff on the research team are: Dr. Bing Chu, Dr. Mark French, Dr. Ivan Markovsky, Dr. Paolo Rapisarda and Prof. Eric Rogers. Prof. David Owens is a visiting Professor. We currently supervise about ten PhD students and several post-docs. Our research is funded by a variety of sources, including EPSRC, the Royal Society, European Research Council, British Council, and the Great Britain Sasakawa Foundation. You'll find our publications listed under the team’s students and academic staff. We have a weekly seminar series.
We are always interested to hear from able prospective PhD students.
