Combined Robust and Stochastic Model Predictive Control for Models of Different Granularity. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Brüdigam, Tim; Teutsch, Johannes; Wollherr, Dirk; Leibold, Marion
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 7123
• Page End: 7129
• Publication Date: 2020
• Subjects: model predictive control -- model granularity -- robust mpc -- stochastic mpc -- chance constraint; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.515
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Long prediction horizons in Model Predictive Control (MPC) often prove to be efficient, however, this comes with increased computational cost. Recently, a Robust Model Predictive Control (RMPC) method has been proposed which exploits models of different granularity. The prediction over the control horizon is split into short-term predictions with a detailed model using MPC and long-term predictions with a coarse model using RMPC. In many applications robustness is required for the short-term future, but in the long-term future, subject to major uncertainty and potential modeling difficulties, robust planning can lead to highly conservative solutions. We therefore propose combining RMPC on a detailed model for short-term predictions and Stochastic MPC (SMPC), with chance constraints, on a simplified model for long-term predictions. This yields decreased computational effort due to a simple model for long-term predictions, and less conservative solutions, as robustness is only required for short-term predictions. The effectiveness of the method is shown in a mobile robot collision avoidance simulation.