341 citations · 835 across the 6 of their papers we have counts for
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Robust MPC for Large-scale Linear Systems
Georg Schildbach
State-of-the-art approaches of Robust Model Predictive Control (MPC) are restricted to linear systems of relatively small scale, i.e., with no more than about 5 states. The main re…
Deadbeat Robust Model Predictive Control for Linear Parameter-Varying Systems
Georg Schildbach, Hossam S. Abbas
The concept of Deadbeat Robust Model Predictive Control (DRMPC) is to completely extinguish the effect of external disturbances within the first few steps of the prediction horizon…
On the Sample Size of Random Convex Programs with Structured Dependence on the Uncertainty (Extended Version)
Xiaojing Zhang, Sergio Grammatico, Georg Schildbach +2
The "scenario approach" provides an intuitive method to address chance constrained problems arising in control design for uncertain systems. It addresses these problems by replacin…
Linear Controller Design for Chance Constrained Systems
Georg Schildbach, Paul Goulart, Manfred Morari
This paper is concerned with the design of a linear control law for linear systems with stationary additive disturbances. The objective is to find a state feedback gain that minimi…
The Scenario Approach for Stochastic Model Predictive Control with Bounds on Closed-Loop Constraint Violations
Georg Schildbach, Lorenzo Fagiano, Christoph Frei +1
Many practical applications of control require that constraints on the inputs and states of the system be respected, while optimizing some performance criterion. In the presence of…
Randomized Solutions to Convex Programs with Multiple Chance Constraints
Georg Schildbach, Lorenzo Fagiano, Manfred Morari
The scenario-based optimization approach (`scenario approach') provides an intuitive way of approximating the solution to chance-constrained optimization programs, based on finding…