Application of Monte Carlo Method for Probabilistic Robust Control Performance Assessment

When designing control loops for nonlinear systems, the selection of a suitable control strategy and the appropriate parametrization is a key challenge. If, in addition to the nominal control performance, the robustness with respect to parameter uncertainties has to be taken into account, this task becomes even more challenging and analytical approaches often get infeasible. Therefore, this paper shows the application of randomized algorithms to enable a statistical assessment of the closed-loop control performance. This approach provides probabilistic bounds for the worst-case behavior and is capable of quantifying the expected violation of given performance limits. Two use cases, namely a highway lane change scenario and an electro-hydraulic valve, are discussed to demonstrate the interpretation of these numerical analyses and how to use these results for choosing the preferable control strategy among two candidates.