Ladies Night for Women in Engineering Sciences

In the last two decades the design of nonlinear model predictive control (NMPC) schemes has received widespread attention by theoreticians and practitioners in the field of systems and control. The main reasons for this interest are (a) that NMPC allows considering input and state constraints in a structured manner, and (b) that NMPC allows handling nonlinear systems with multiple inputs. NMPC is built upon the repeated solution of an optimal control problem and the partial application of optimal input trajectories. Often, stability of NMPC schemes is enforced by means of computationally intensive terminal constraints and end penalties. In this talk, we discuss the design of NMPC schemes based on turnpike properties. We show that these properties enable avoiding terminal constraints. We begin the talk with a formal introduction of turnpike properties of optimal control problems. It is worth to be mentioned that the concept of turnpike properties has received widespread attention in optimal control approaches to economic dynamic systems. However, it is surprising that turnpike properties have received only limited attention in the context of NMPC. In this presentation, we present results attempting to partially bridge this gap. We show that exact turnpikes allow establishing stability of NMPC controlled systems as well as recursive feasibility without any terminal constraints. We draw upon examples from different areas such as process control and biology to illustrate our results. [more]

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Model Predictive Control (MPC) has become one of the most popular control techniques in the process industry mainly because of its ability to deal with multiple-input-multiple-output plants and with constraints. However, its performance can deteriorate in the presence of model uncertainties and disturbances. In the last years, the development of robust MPC techniques has been widely discussed, but these were rarely, if at all, applied in practice due to their conservativeness or their computational complexity. This talk presents multi-stage nonlinear model predictive control (multi-stage NMPC) as a promising non-conservative robust NMPC control scheme, which is applicable in real-time. The approach is based on the representation of the evolution of the uncertainty by a scenario tree. It leads to non-conservative robust control of the plant because it takes into account explicitly that new information (usually present in the form of measurements) will become available at future time steps and that the future control inputs can be adapted accordingly, acting as recourse variables. The approach is illustrated using a challenging industrial case-study, which shows that multi-stage NMPC is a promising strategy for the optimizing control of uncertain nonlinear systems subject to hard constraints. It is also shown that multi-stage NMPC performs better than standard NMPC or other robust NMPC approaches presented in the literature while still being implementable in real-time despite of the main challenge of the method: The size of the resulting optimization problem. Finally, it is illustrated that thanks to the flexibility of the presented approach, it is possible to integrate it with other existing methods to enhance its capabilities and performance. [more]

Reduction of oxides of nitrogen (NOx) emitted from diesel exhaust systems is a current problem due to increased stringency in worldwide emissions legislation. One of the most successful approaches to reduce tailpipe NOx is to reduce NOx by ammonia over a catalyst, known as Selective Catalytic Reduction (SCR). Control of the ammonia injection in such systems is typically a map-based approach, often augmented by feedback from NOx sensors to account for mechanical variation and ageing. We show that a predictive control approach to this system yields several compelling improvements over such industry standard controllers during a representative test cycle. These include better NOx conversion performance whilst simultaneously minimising the quantity of ammonia released to the environment, along with reduced design effort. Short CV Dina Shona Laila received her PhD degree in Control Engineering from the University of Melbourne in 2003. After finishing her PhD, she joined the Control and Power Group (EE CAP), Electrical and Electronic Engineering Dept., Imperial College London (2003-2006) working mainly in the nonlinear control research area. Between 2006 and 2007, she was with the Institute for Design and Control of Mechatronic Systems, JKU Linz, Austria where she was working on identification and control of mechatronics systems, particularly for combustion engine test bench. She returned to Imperial College in 2007, and expanding her research to the area to nonlinear control for an unmanned aerial vehicle (UAV) and to the measurement and control for power systems, and power system signal analysis. Dina was with Kingston University, London (2009-2011). She has since 2011 joined the University of Southampton, where she combined research and teaching in control engineering and automotive, and their applications. Dina Shona Laila is a Senior Member of the IEEE and Fellow of the UK Higher Education Academy. She actively serves as an associate editor for the European Journal of Control, the IEEE CSS Conference and the European Control Conference, besides serving as a reviewer for a number of other main journals and conferences in Control Engineering field. [more]