Computation
In order to deal with problems arising in control and optimization we develop efficient computational tools to design controllers and solve optimization problems, both offline and in embedded settings.
Embedded optimization
Predictive control is computationally intensive, while many embedded control platforms are limited in terms of their computational capabilities. With the goal of extending the scope of applications that can benefit from online optimization-based control and estimation, the group develops new optimization methods for convex and mixed-integer programming, as well as design automation tools and efficient software and hardware implementations for optimization solvers. More
Stochastic optimization
Stochastic optimization arising from uncertain process data and forecasts presents fundamental research challenges. In presence of limited statistical information, or merely of empirical data, we propose novel randomized convex optimization approaches to compute probabilistically feasible solutions. More
Multi-Parametric toolbox
The Multi-Parametric Toolbox is a collection of algorithms for modeling, control, analysis, and deployment of constrained optimal controllers developed under Matlab. The main focus of the toolbox is to provide code for high-speed hardware implementation of optimal controllers by means of explicit model predictive control. The toolbox features a powerful geometric library that extends the application of the toolbox beyond optimal control to various problems arising in computational geometry. external pageMore