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]

[more]

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]